Computational and Mathematical Methods in Medicine
Volume 2019 (2019), Article ID 9029351, 14 pages
https://doi.org/10.1155/2019/9029351

Research Article

Portrait of Tissue-Specific Coexpression Networks of Noncoding RNAs (miRNA and lncRNA) and mRNAs in Normal Tissues

Claudia Cava, Gloria Bertoli, and Isabella Castiglioni

Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate (Mi), Italy

Correspondence should be addressed to Claudia Cava; claudia.cava@ibfm.cnr.it

Received 10 January 2019; Revised 1 August 2019; Accepted 10 August 2019; Published 3 September 2019

Academic Editor: Nadia A. Chuzhanova

Copyright © 2019 Claudia Cava et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Genes that encode proteins playing a role in more than one biological process are frequently dependent on their tissue context, and human diseases result from the altered interplay of tissue- and cell-specific processes. In this work, we performed a computational approach that identifies tissue-specific co-expression networks by integrating miRNAs, long-non-coding RNAs, and mRNAs in more than eight thousands of human samples from thirty normal tissue types. Our analysis (1) shows that long-non coding RNAs and miRNAs have a high specificity, (2) confirms several known tissue-specific RNAs, and (3) identifies new tissue-specific co-expressed RNAs that are currently still not described in the literature. Some of these RNAs interact with known tissue-specific RNAs or are crucial in key cancer functions, suggesting that they are implicated in tissue specification or cell differentiation.

1. Introduction

The portion of the genome encoding proteins constitutes only 1.5% of the human genome, while the transcribed RNAs not yielding coding proteins, named non-coding RNAs (ncRNAs) [1], are the major part.

ncRNAs are capable of controlling the expression of many genes, thus can simultaneously control multiple cellular functions regulating a variety of physiological and pathological processes [2, 3].

The family of ncRNAs includes small ncRNAs such as miRNAs, piRNAs, and siRNAs and long ncRNAs (lncRNAs).

miRNAs, approximately 18–25 nucleotides in length, are highly conserved and involved in post-transcriptional gene silencing by binding complementary sequences in the 3′ untranslated region (3′ UTR) of messenger RNAs (mRNAs) [4]. Significant evidences in the last few years showed a fundamental role of miRNAs in determining cell fate, in pattern formation in embryonic development, and in controlling cell proliferation, cell differentiation, and cell death [4]. It has also been shown that aberrations in miRNA expression levels contribute to the onset and progression of many types of cancer [5–7].

Published studies have identified some tissue-specific (TS) or developmental-stage-specific miRNAs, suggesting their potential role in maintaining tissue identity and function [8–10]. The first comprehensive analyses of miRNAs across different human tissues have been reported in 2007, by Landgraf et al. [11] analyzing 340 distinct mature miRNAs in 26 human tissues and by Liang et al. [12] analyzing 345 distinct mature miRNAs in 40 human tissues. More recently, in 2016, Ludwig et al. profiled 1997 different mature miRNAs in 61 human tissues [13]. These studies showed that few miRNAs are expressed with a high degree of tissue specificity [11–13].

lncRNAs are transcribed RNA molecules bigger than 200 nucleotides in length, poorly conserved. They can regulate gene expression by different mechanisms that are not yet fully understood [3, 4]. Scientific evidences show they have roles in DNA replication, chromosomal dynamics, telomere biology, and subcellular structural organization [14–16].

Some works have found that lncRNAs tend to show even more TS expressions than mRNAs [14, 15]. For example, many lncRNAs are expressed only in limited developmental contexts or many of them are expressed during embryonic stem cell differentiation and in the brain (in a mouse model) [9]. However, few TS lncRNAs have been well characterized to date.

Although all human tissues carry out common processes, each tissue is characterized by specific gene expression patterns that are essential for the conservation and survival of the tissue environment in physiological conditions. According to the Human Protein Atlas [17], approximately 40% of human mRNAs show a higher gene level expression in one single tissue. However, these studies did not focus on TS-miRNAs, TS-lncRNAs, and TS-mRNAs interactions.

The common and TS processes are ultimately controlled by gene regulatory networks that can integrate the complex interactions between mRNAs, miRNAs, and lncRNAs. Since different tissues have different regulatory networks, mapping regulatory networks among TS-RNAs could represent a significant step towards the knowledge, on molecular basis, of tissues and on how tissue specificity is determined.

The most widely used network model is based on gene co-expression network [18, 19]. Co-expression networks of biological elements (TS-mRNAs, TS-miRNAs, and TS-lncRNAs) are interesting since co-expressed elements are regulated by the same, functionally related transcriptional program or by members of the same pathway or protein complex. However, most of the few published co-expression studies between mRNAs, miRNAs, and lncRNAs [11, 15, 20] are focused on a single tissue or on a small number of different tissues.

Furthermore, recent studies showed that those ncRNAs, proposed as cancer biomarkers, were not tissue specific or were not expressed in the tissues where the disease is supposed to originate [21, 22]. Thus, it is unexpected to think about these ncRNAs as cancer biomarkers specific for a tissue. Indeed, a robust cancer biomarker of a specific tissue should show enrichment in that tissue in which the tumor develops. These findings highlight the need of developing new computational models to identify disease-related miRNAs/lncRNAs based on the tissue-specific co-expression networks. For example, it is not sufficient to predict the association of some miRNAs to a disease only based on the similarity expression, but it is also necessary to include the tissue specificity of noncoding RNAs. Adding this analysis into several existing tools [23, 24] could lead to a more comprehensive knowledge and an improved precision in the prediction of the association between non-coding RNA and disease.

In our study, focusing on the co-expression network between TS-mRNAs, TS-miRNAs, and TS-lncRNAs, we generated a large overview of TS-distribution of RNAs by using thirty different human normal tissues and more than eight thousand human samples.

Our portrait may help to understand how genes, whose functions depend on their tissue context, are expressed and regulated in a normal condition.

2. Materials and Methods

2.1. Data

GTEx data [25] (2016 release) provided the expression levels of RNAs in thirty different human normal tissues: adipose tissue, adrenal gland, bladder, blood, blood vessel, brain, breast, cervix uteri, colon, esophagus, fallopian tube, heart, kidney, liver, lung, muscle, nerve, ovary, pancreas, pituitary, prostate, salivary gland, skin, small intestine, spleen, stomach, testis, thyroid, uterus, and vagina, specifically 1368 miRNAs, 10167 lncRNAs (as reported from lncRNome database [26]), and 30441 mRNAs (as reported from HUGO database [27]). The subjects enrolled for the study range from 6 to 1259 for a total of 8553 samples based on the examined tissue (Table 1). For each tissue, and for each sample, we filtered mRNA profiles by removing pseudogenes, and we calculated median values of RNA expression levels. We thus created an m × N expression matrix, being m the number of RNAs and N the number of tissues (1–30). As RNA expression levels, we considered the median values over all samples for that tissue. We removed RNAs with median expression values = 0.

Table 1: Number of samples for each normal tissue from GTEX.

Tissue
# of normal samples

(1) Adipose tissue
577

(2) Adrenal gland
145

(3) Bladder
11

(4) Blood
511

(5) Blood vessel
689

(6) Brain
1259

(7) Breast
214

(8) Cervix uteri
11

(9) Colon
345

(10) Esophagus
686

(11) Fallopian tube
6

(12) Heart
412

(13) Kidney
32

(14) Liver
119

(15) Lung
320

(16) Muscle
430

(17) Nerve
304

(18) Ovary
97

(19) Pancreas
171

(20) Pituitary
103

(21) Prostate
106

(22) Salivary gland
57

(23) Skin
890

(24) Small intestine
88

(25) Spleen
104

(26) Stomach
192

(27) Testis
172

(28) Thyroid
323

(29) Uterus
83

(30) Vagina
96

Total
8553

2.2. Gini Index

The Gini index (GI) [28] was used to identify TS-RNAs (miRNAs, lncRNAs, and mRNAs). Being m the number of RNAs and N the number of tissues (1–30), GI is defined as

where xij is the median expression of the RNA i in tissue j and xi is a vector in the nondescending order (i = 1, … , m; j = 1, … , N).

GI measures the specificity of expression of an RNA with respect to different tissues and ranges from 0 to 1 indicating RNAs expressed in many tissues (housekeepers and constitutive) for scores of GI close to 0, and RNAs expressed in only one specific tissue for scores of GI close to 1 [28].

2.3. Gini Index Selection of TS RNAs

We considered a TS-RNA i if GIi ≥ 0.85 and a nonspecific tissue RNA i if GIi ≤ 0.15 [13, 28]. For RNA i with GIi ≥ 0.85, we choose the TS where RNA expression profile has the highest value. Thus, an RNA can be TS only for one tissue but a tissue can have more TS RNAs.

2.4. Tissue-Specific miRNA-lncRNA-mRNA Regulatory Co-Expression Network

For each tissue, we generated a co-expression network: two RNAs are linked if their expression levels are correlated. We considered a co-expression interaction between two mRNAs if the Pearson correlation index was >0.7 or < −0.7. The correlation was calculated between expression levels of TS-miRNAs and TS-lncRNAs of the samples for each tissue. The same procedure was performed between TS-miRNAs and TS-mRNAs and between TS-mRNAs and TS-lncRNAs.

3. Results

3.1. Tissue-Specific RNAs

We present the results obtained from the Gini index used to measure tissue specificity.

RNA selection leaded to 240 miRNAs, 9297 lncRNAs, and 19028 mRNAs. GI was calculated from these selected miRNas, lncRNAs, and mRNAs (Figure 1). Only 4 miRNAs (miR-302b, miR-611, miR-1282, and miR-4721) (1.6%) were ubiquitously expressed with a GI < 0.15. Ninety-five miRNAs (39.7%) showed a TS expression with GI > 0.85 (Figure 1). The largest group of all miRNAs (58.7%) have 0.15 < GI < 0.85. Table 2 shows the partition of 95 miRNAs in the tissues.

Figure 1: The flow chart of the whole data selection and results.

Table 2: Number of tissue-specific (TS) miRNAs for each tissue as obtained from the Gini index.

Tissue
TS-miRNAs

(1) Adipose tissue
—

(2) Adrenal gland
miR-4263, miR-1224, miR-4709, miR-33b

(3) Bladder
miR-3972, miR-5091, miR-93, miR-4469

(4) Blood
miR-144, miR-3909, miR-223

(5) Blood vessel
—

(6) Brain
miR-3682, miR-4477b, miR-3665, miR-628

(7) Breast
—

(8) Cervix uteri
miR-320b2, miR-4269, miR-548i1, miR-593

(9) Colon
—

(10) Esophagus
—

(11) Fallopian tube
miR-3671, miR-3125, miR-3622a, miR-23b, miR-3613, miR-369, miR-1225

(12) Heart
—

(13) Kidney
miR-1227

(14) Liver
miR-1295a, miR-4783, miR-135a1, miR-1236, miR-4482-1, miR-4751

(15) Lung
miR-4635, miR-4668

(16) Muscle
miR-133bhg, miR-133b, miR-133a2

(17) Nerve
miR-378h, miR-4500hg, miR-5093, miR-657

(18) Ovary
miR-548I2, miR-3960, miR-433, miR-1257

(19) Pancreas
—

(20) Pituitary
miR-137hg, miR-339, miR-770, miR-1179, miR-212, miR-7-3hg, miR-7-3, miR-659

(21) Prostate
miR-224

(22) Salivary gland
miR-642a

(23) Skin
miR-205hg, miR-567, miR-936, miR-203

(24) Small intestine
miR-559, miR-3944, miR-192, miR-194-2, miR-147b

(25) Spleen
miR-589, miR-378i, miR-1587

(26) Stomach
—

(27) Testis
miR-1302-11, miR-4258, miR-4436a, miR-1302-3, miR-663b, miR-219-1, miR-3943, miR-5707, miR-3150b, miR-1302-2, miR-let7dhg, miR-3689b, miR-202, miR-26a2, miR-5188, miR-4313, miR-3180-3, miR-632, miR-3615, miR-4526, miR-181c, miR-639, miR-519a2, miR-371b, miR-663a

(28) Thyroid
miR-3907, miR-135a2

(29) Uterus
—

(30) Vagina
—

Total
95

No TS-miRNA was found for the adipose tissue, blood vessel, breast, colon, esophagus, heart, pancreas, stomach, uterus, and vagina. Testis contains the highest number of TS-miRNAs.

Only ∼1% lncRNAs were ubiquitously expressed with a GI < 0.15. The largest group (51%), including 4687 lncRNAs (Figure 1), showed a TS expression with GI > 0.85. However, 48.2% of all lncRNAs have 0.15 < GI < 0.85.

Table 3 shows the distribution of the 4687 lncRNAs for each tissue. TS-lncRNAs range from 1 in the esophagus to 3264 in the testis, representing the largest group. Supplementary Table 1 shows the list of TS-lncRNAs in detail.

Table 3: Number of tissue-specific (TS) lncRNAs for different tissues.

Tissue
# TS-lncRNAs

(1) Adipose tissue
16

(2) Adrenal gland
42

(3) Bladder
29

(4) Blood
31

(5) Blood vessel
23

(6) Brain
172

(7) Breast
8

(8) Cervix uteri
14

(9) Colon
10

(10) Esophagus
1

(11) Fallopian tube
39

(12) Heart
46

(13) Kidney
116

(14) Liver
108

(15) Lung
34

(16) Muscle
35

(17) Nerve
48

(18) Ovary
32

(19) Pancreas
52

(20) Pituitary
159

(21) Prostate
54

(22) Salivary gland
56

(23) Skin
44

(24) Small intestine
61

(25) Spleen
94

(26) Stomach
14

(27) Testis
3264

(28) Thyroid
53

(29) Uterus
11

(30) Vagina
21

Total
4687

About 9% mRNAs were ubiquitously expressed with GI < 0.15. A TS expression with GI > 0.85 was shown in 3668 mRNAs (Figure 1), and 72% of all mRNAs have 0.15 < GI < 0.85, representing the largest group.

Since the TS-mRNAs obtained from our analyses are only 19.3% of the total mRNAs, it appears that the regulation of the transcription by miRNAs and lncRNAs seems to be fundamental for the differentiation of each tissue.

Table 4 shows 3668 mRNAs for each tissue. TS-mRNAs range from 2 in the esophagus to 1563 in the testis, representing the largest group.

Table 4: Number of tissue-specific (TS) mRNAs for different tissues.

Tissue
# TS-mRNAs

(1) Adipose tissue
16

(2) Adrenal gland
41

(3) Bladder
28

(4) Blood
101

(5) Blood vessel
4

(6) Brain
260

(7) Breast
4

(8) Cervix uteri
9

(9) Colon
11

(10) Esophagus
2

(11) Fallopian tube
29

(12) Heart
48

(13) Kidney
87

(14) Liver
237

(15) Lung
34

(16) Muscle
127

(17) Nerve
27

(18) Ovary
20

(19) Pancreas
83

(20) Pituitary
155

(21) Prostate
33

(22) Salivary gland
72

(23) Skin
166

(24) Small intestine
144

(25) Spleen
219

(26) Stomach
40

(27) Testis
1563

(28) Thyroid
35

(29) Uterus
6

(30) Vagina
67

Total
3668

3.2. Tissue-Specific miRNA-LNC-mRNA Regulatory Networks

For each tissue type considered in this work, we analyzed the number of co-expression interactions between TS-miRNAs, TS-lncRNAs, and TS-mRNAs, and we present the results in Table 5.

Table 5: Number of co-expression interactions between tissue-specific (TS)-miRNAs, TS- lncRNAs, and TS-mRNAs for different tissues.

Tissue
# of interactions in co-expressed RNAs

(1) Adipose tissue
5

(2) Adrenal gland
6

(3) Bladder
199

(4) Blood
96

(5) Blood vessel
—

(6) Brain
1512

(7) Breast
2

(8) Cervix uteri
5

(9) Colon
6

(10) Esophagus
—

(11) Fallopian tube
189

(12) Heart
33

(13) Kidney
385

(14) Liver
476

(15) Lung
13

(16) Muscle
81

(17) Nerve
5

(18) Ovary
1

(19) Pancreas
3

(20) Pituitary
137

(21) Prostate
51

(22) Salivary gland
86

(23) Skin
711

(24) Small intestine
1197

(25) Spleen
136

(26) Stomach
37

(27) Testis
184041

(28) Thyroid
6

(29) Uterus
—

(30) Vagina
107

Total
189530

The most numerous co-expression networks involve the testis, brain, small intestine, skin, and liver with 184041, 1512, 1197, 711, and 476 interactions, respectively. No TS-gene regulatory network was found in the blood vessel, esophagus, and uterus. In total, we found 189530 interactions and 3751 TS-RNAs (Figure 1).

The flow chart of the whole data selection and results is shown in Figure 1, for better synthesis of methodology.

4. Discussion

In the past decades, functional studies have focused on the analysis of protein-coding transcripts (mRNA expression) to characterize biological processes such as metabolism, cell differentiation, immune response, and tumorigenesis. The recent growth of next-generation sequencing technology has brought the discovery and characterization of a new class of non-coding RNA transcripts, such as miRNAs and lncRNAs. Evidences from high-throughput genomic platforms suggest the importance of ncRNAs in the regulation of multiple major biological processes such as cell development, differentiation, and metabolism [2].

However, despite successful studies highlighting important roles of miRNAs and lncRNAs in different tissues and diseases, no comprehensive portrait across various tissue types has been reported.

To assess the relation between mRNAs, miRNAs, and lncRNAs in several normal tissues, by using a computational approach, we investigated tissue specificity of miRNAs, lncRNAs, and mRNAs across 30 normal tissue types, obtaining 95 TS-miRNAs, 4687 TS-lncRNAs, and 3668 TS-mRNAs.

Our analysis on mRNAs suggests that they are not the determinants of tissue phenotype since only 19.3% of the total mRNAs is tissue specific. On the contrary, our study confirms the high specificity of lncRNAs in various tissues, consistently with previous studies [15, 29]. Indeed, we found that 51% of lncRNAs show high tissue specificity, highlighting the role of lncRNAs as regulators of gene expression in specific cellular phenotype. Thus, the identification and characterization of human lncRNAs with TS expression become crucial in order to determine their relevant functions.

Similarly to what is observed with lncRNAs, we found that 39.7% of miRNAs is tissue specific, also in this case proving the high tissue specificity of miRNAs and their critical role as regulators of mRNA expression, and thus, tissue phenotype.

We speculated that lncRNAs could (1) be controlled by miRNAs, reducing lncRNA stability and altering the cellular response in physiologic and pathologic processes; (2) act as endogenous sponges, harbouring similar microRNA target sequences and sequestering microRNAs; and (3) be precursors for the generation of miRNAs to silence target mRNAs, thus competing with endogenous miRNAs [30].

For these reasons, the exploration of the regulatory relationships between miRNAs, lncRNAs, and mRNAs, specifically expressed in the same tissue, could offer useful information to shed light on how miRNAs, lncRNAs, and mRNAs play a role in tissue specification or cell differentiation [31].

However, the limit of our work is that we focused our analysis on a single dataset for each considered normal tissue. Future studies should validate our results on an independent dataset. Furthermore, we did not assess the influence on gene expression of some risk factors, such as age, smoking status, gender, and body mass index; thus, further studies on datasets including phenotype risk data could improve our methodology and results.

Despite these limitations and although several studies found that non-coding RNAs have important roles in diverse physiology and disease processes, we proposed a unique study that comprehensively compared the expression levels of tissue-specific non-coding RNAs with those of tissue-specific mRNAs.

Since a great number of TS-mRNAs are also key biomarkers in specific cancer diseases, the identification of TS co-expression network could be useful to deepen TS function of RNAs at molecular basis of cancer.

4.1. Adipose Tissue

TS-co-expression gene regulatory network in adipose tissue involves 5 mRNAs (ADIPOQ, CIDEA, CIDEC, PLIN1, and TUSC5) and 1 lncRNA (ADIPOQ-AS1), sharing 5 interactions. Although all 5 mRNAs in previous studies have showed a principal role in adipose tissue, no study has revealed an interaction between them.

Adiponectin, C1Q and collagen domain containing (ADIPOQ) is expressed exclusively in adipose tissue and is involved in the control of fat metabolism and insulin sensitivity [32].

Cell death-inducing DFFA-like effector A (CIDEA), along with CIDEB and CIDEC, constitutes the CIDE family of proteins. They are important modulators of several lipid metabolic pathways, including lipolysis, fatty acid oxidation, VLDL lipidation, and lipid droplet growth in adipocytes and hepatocytes [33]. Perilipin 1 (PLIN1) is a modulator of adipocyte lipid metabolism, lipolysis, and triglyceride levels. It plays a role in lipid formation by regulating CIDEC. The CIDEC-PLIN1 interaction promotes lipid droplet proteins acting on fat metabolism in adipocytes [34].

Tumor suppressor candidate 5 (TUSC5) was found as an adipose TS protein that is abundantly expressed in white [35] and brown adipose tissue [36], and TUSC5 mRNA expression increases during adipogenesis [36].

The interactions between them involve a lncRNA, ADIPOQ-AS1, that shows a positive correlation with other mRNAs. ADIPOQ-AS1 is located within the genomic location of ADIPOQ.

4.2. Adrenal Gland

TS-co-expression gene regulatory network in the adrenal gland involves 6 mRNAs (CYP11A1, CYP11B1, FAM166B, MRAP, NR5A1, and VWA5B2) 2 lncRNAs (AP000266.7 and RP11-320G24.1) and 1 miRNA (miR-1224), sharing 6 interactions.

Cytochrome P450 family 11 subfamily A member 1 (CYP11A1), subfamily B member 1 (CYP11B1), and family with sequence similarity 166 member B (FAM166B) showed a high expression in the adrenal gland but no study demonstrated a particular role in the adrenal gland to date [17, 37].

Melanocortin 2 receptor accessory protein (MRAP) encodes a protein that controls protein trafficking and the functions of its receptor in the adrenal gland [38]. It also associated with adrenal disease and was detected in human adrenal and brain tissue [39].

Nuclear receptor subfamily 5 group A member 1 (NR5A1) is a nuclear receptor involved in gonadal and adrenal development. Mutations in this gene are a reason of XY sex reversal without a direct link with adrenal malfunctions [40].

von Willebrand factor A domain containing 5B2 (VWA5B2) was found expressed more than five fold in the human adrenal gland compared with other normal tissue types [41] but no study to date demonstrated a particular role in the adrenal gland. VWA5B2 showed a positive correlation with miR-1124.

AP000266.7 showed a positive correlation with MRAP and CYP11A1. AP000266.7 and MRAP are neighbor genes localized in chromosome 21.

RP11-320G24.1 showed a positive correlation with FAM166B, CYP11B1, and NR5A1. No relationship seems to be between them in literature.

4.3. Bladder

TS-co-expression gene regulatory network in the bladder involves 22 mRNAs (CLEC3A, CYP4F8, DHRS2, EVX1-AS, OR10H1, OR5M11, OR5P2, PADI3, PLA2G2F, S100P, SPINT4, TACR3, TMPRSS4, TRBV6-9, TRPA1, UGT1A1, UGT2B28, UPK1A, UPK1B, UPK2, UPK3A, and VGLL1), 25 lncRNAs (AC004543.2, AC009478.1, AC012307.2, AC017048.2, AC017060.1, AC019117.1, AC027119.1, AC079610.1, AC092669.3, AC097499.1, AC114812.8, AC114877.3, GS1-120K12.4, POU6F2-AS2, RP11-1078H9.2, RP11-109D24.1, RP11-113O24.3, RP11-128L5.1, RP11-272K23.3, RP11-463P17.1, RP11-753H16.4, RP11-875H7.5, RP11-91I8.1, RP3-405J10.4, and UPK1A-AS1), and 1 miRNA (miR-3972), sharing 199 interactions.

C-type lectin domain family 3 member A (CLEC3A) was originally identified as a cartilage protein from shark and bovine [42], but recently it was found for the first time to be associated to the urinary bladder in [43] along with dehydrogenase/reductase 2 (DHRS2) and peptidyl arginine deiminase 3 (PADI3).

Several of identified genes have been associated with the progression of bladder cancer, such as S100 calcium-binding protein P (S100P), belonging to a family of calcium-binding proteins, [44], tachykinin receptor 3 (TACR3) [45], and UDP glucuronosyltransferase family 1 member A1 (UGT1A1) [46].

Transient receptor potential cation channel subfamily A member 1 (TRPA1) is expressed in the urinary bladder [47] as well as uroplakins (UPK1A, UPK1B, UPK2, and UPK3A) with a role in regulating the membrane permeability [48].

Among lncRNAs, we found POU6F2-AS2, which showed a positive correlation with PADI3, UPK1B, S100P, TACR3, TRPA1, OR5P2, and TMPRSS4. Although POU6F2-AS2 has not yet been reported in the bladder, previous studies showed its role in the initiation and progression of esophageal squamous cell carcinoma [49]. No information has been reported in the literature about other lncRNAs.

4.4. Blood

TS-co-expression gene regulatory network in blood involves 27 mRNAs (APOBEC3A, AQP9, CSF3R, CST7, CXCR1, CXCR2, FCAR, FCGR3B, FFAR2, FPR1, FPR2, GYPA, KLF1, LILRA5, MMP9, NFE2, PADI4, PROK2, S100A9, S100A12, SELL, TRAV23DV6, TRAV9-2, TREM1, TREML2, VNN2, and VNN3), 11 lncRNAs (AC008984.2, AOAH-IT1, RP11-166N6.2, RP11-321E2.4, RP11-342D11.3, RP11-356C4.3, RP11-405F3.4, RP11-465L10.10, RP11-561P12.5, RP1-55C23.7, and TRBV11-2), and 2 miRNAs (miR-144 and miR-223), sharing 96 interactions.

Apolipoprotein B MRNA editing enzyme catalytic subunit 3A (APOBEC3A) induces RNA editing in monocytes and macrophages [50]. Aquaporin 9 (AQP9) was found expressed in endothelial cells of neo-vessels [51] and along with S100 calcium-binding protein (S100A) they were identified as potential biomarkers of infective endocarditis.

For several found genes, a role has been reported in blood such as (1) colony-stimulating factor 3 receptor (CSF3R) that encodes a receptor protein for colony-stimulating factor 3, a cytokine that regulates the assembly, differentiation, and role of granulocytes [52], (2) cystatin F (CST7) known to be expressed by CD8+ T-cells [53], and (3) Fc fragment of IgA receptor (FCAR) that is a member of the immunoglobulin gene superfamily localized on the surface of, e.g., neutrophils, monocytes, macrophages, and eosinophils, with a role in immunologic responses to pathogens [54]. In particular, using the Reactome pathways, 12 of 27 mRNAs are involved in neutrophil degranulation (i.e., matrix metallopeptidase 9 MMP9, C-X-C motif chemokine receptor 1 CXCR1, formyl peptide receptor 1 FPR1, Selectin L SELL, and leukocyte immunoglobulin-like receptor A5 LILRA5) [55].

Blood lncRNAs have not yet been reported in the literature.

miR-144, which correlates positively with Kruppel-like factor 1 (KLF1) and glycophorin A (MNS blood group) (GYPA), regulates hematopoiesis and vascular development by inhibiting expression of MEIS1 [56].

miR-223, which correlates positively with 16 mRNAs (APOBEC3A, AQP9, CSF3R, CST7, CXCR1, CXCR2, FFAR2, FPR1, FPR2, LILRA5, NFE2, S100A12, S100A9, SELL, TREML2, and VNN2) and 2 lncRNAs (AOAH-IT1 and AC008984.2), is a highly conserved miRNA [57] linked to granulopoiesis [58].

4.5. Brain

TS-co-expression gene regulatory network in the brain constitutes one of the biggest networks with 195 mRNAs, 102 lncRNAs, and 1 miRNA (miR-3665) sharing 1512 interactions.

Using the Reactome pathways, 38 of 195 mRNAs are involved in neuronal system (i.e., gamma-aminobutyric acid-type A receptor gamma 1 subunit GABRG1 and 2, glutamate decarboxylase 2, protein kinase C gamma, and G protein subunit gamma 3), 29 of 195 mRNAs in transmission across chemical synapses, and 21 of 195 mRNAs in neurotransmitter receptors and postsynaptic signal.

miR-3665 is positively correlated with 3 mRNAs (contactin 2, myelin oligodendrocyte glycoprotein, and myelin-associated glycoprotein) and 1 lncRNA (RP11-116N8.1).

4.6. Breast

TS-co-expression gene regulatory network in the breast involves 1 mRNA (ANKRD30A) and 2 lncRNAs (RP11-20F24.2 and CTD-3032H12.1), sharing 2 interactions.

Ankyrin repeat domain 30A (ANKRD30A) encodes a transcription factor that is exclusively expressed in the breast and the testis. Deregulated expression levels have been correlated with breast cancer progression [59].

The expression of the locus containing the lncRNA RP11-20F24.2 has been already described in the breast, being altered in triple-negative breast cancer [60], while the lncRNA CTD-3032H12.1 has a role of a proliferation controller in in vitro experiment on breast cancer cell lines [61].

4.7. Cervix Uteri

TS-co-expression gene regulatory network in the cervix uteri involves 3 mRNAs (HOXA13, MAS1, and TGM6), 3 lncRNAs (RP11-246A10.1, XX-C2158C12.2 and RP11-893F2.15), and 1 miRNA (miR-4269), sharing 5 interactions.

Homeobox A13 (HOXA13) is highly expressed in the uterus, and its variation can alter development of the female reproductive tract [62].

For MAS1 proto-oncogene, G protein-coupled receptor (MAS1), transglutaminase 6 (TGM6), RP11-246A10.1, XX-C2158C12.2, RP11-893F2.15, and miR-4269 has not yet been reported a specific role in the cervix uteri.

4.8. Colon

TS-co-expression gene regulatory network in the colon involves 6 mRNAs (CHRM2, HAND1, MAB21L2, PHOX2A, PHOX2B, and PIRT) and 3 lncRNAs (CTB-158E9.1, RP11-227F19.1 and RP11-1336O20.2), sharing 6 interactions. Cholinergic receptor muscarinic 2 (CHRM2), a muscarinic cholinergic receptors belonging to a larger family of G protein-coupled receptors, and mab-21 like 2 (MAB21L2) have not yet reported in colon tissue.

Paired-like homeobox 2A and B (PHOX2A and PHOX2B) are involved in the intestinal neuronal differentiation playing a direct role in the pathogenesis of enteric disorders [63]. The heart and neural crest derivatives expressed 1 (HAND1), belonging to the basic helix-loop-helix family of transcription factors, is showed to be a tumor suppressor gene in colorectal cancer [64]. Phosphoinositide interacting regulator of transient receptor potential channels (PIRT) was already described in various regions of the gastrointestinal tract of the adult mouse [65]. Regarding the lncRNAs, RP11-1336O20.2 is the only lncRNA that has been described in colon cancer tissue [66].

4.9. Fallopian Tube

TS-co-expression gene regulatory network in the fallopian tube involves 25 mRNAs, 39 lncRNAs, and 7 miRNAs (miR-23b, miR-369, miR-1225, miR-3125, miR-3613, miR-3622a, and miR-3671), sharing 189 interactions.

According to the Reactome pathway, among 25 mRNAs, several are involved in GPCR downstream signaling such as bombesin receptor subtype 3 (BRS3), phosphoinositide-3-kinase regulatory subunit 2 (PIK3R2), and olfactory receptor family 51 subfamily I member 2 (OR51I2), but there are not any mRNAs with a known role in the fallopian tube to date.

Previous studies have shown the role of miR-23b in cervical stem cells and ovarian cancer but no study has reported a role in the fallopian tube for miR-369, miR-1225, miR-3125, miR-3613, miR-3622a, and miR-3671, as well as for the 39 lncRNAs [67].

4.10. Heart

TS-co-expression gene regulatory network in the heart involves 20 mRNAs (CKMT2, DHRS7C, FBXO40, HRC, METTL11B, MYBPC3, MYH6, MYL3, MYL4, MYL7, MYOM2, MYOZ2, PXDNL, RD3L, SBK2, SMCO1, SMPX, SPHKAP, TBX5, and TNNI3), 11 lncRNAs (AC009878.2, CTD-2194D22.3, KB-173C10.1, RP11-19D2.1, RP11-19O2.1, RP11-213G6.2, RP11-401H2.1, RP11-481J2.2, RP11-532N4.2, RP11-700N1.1, and RP11-774O3.1), sharing 33 interactions.

Creatine kinase, mitochondrial 2 (CKMT2) and dehydrogenase/reductase 7C (DHRS7C) were found highly expressed in the heart [68, 69]. MYH6, MYBPC3, MYL3, MYL4, MYL7, MYOM2, and MYOZ2 encode proteins involved in cardiac function and they may be linked to modulation of cardiac contraction [70, 71].

Peroxidasin-like (PXDNL) was found as a potential peroxidase homologue in the human heart [72]. SH3 domain-binding kinase family member 2 (SBK2) was found expressed in the atrium [37]. Some evidences have shown a role of small muscle protein, X-linked (SMPX), SPHK1 interactor, AKAP domain containing (SPHKAP), T-box 5 (TBX5), and troponin I3, cardiac type (TNNI3) in the heart [73–76].

Retinal degeneration 3-like (RD3L) and single-pass membrane protein with coiled-coil domains 1 (SMCO1) have not yet been reported to have a role in the heart. No publication is present on the role of the 11 lncRNAs in heart tissue.

4.11. Kidney

TS-co-expression gene regulatory network in the kidney involves 64 mRNAs, 68 lncRNAs, and 1 miRNA (miR-1227), sharing 385 interactions.

According to the Reactome pathway, 24 of 64 mRNAs are involved in transport of small molecules, 17/64 mRNAs in SLC-mediated transmembrane transport, and 9/64 mRNAs in transport of glucose, bile salts and organic acids.

Among mRNAs of the first group, there are solute carrier family, ATPase H + transporting (ATP6V0A4, ATP6V0D2, and ATP6V1G3), and FXYD domain-containing ion transport. No publication is present on the role of miR-1227 in the kidney.

4.12. Liver

TS-co-expression gene regulatory network in the liver involves 133 mRNAs, 44 lncRNAs, and 1 miRNA (miR-135a1), sharing 476 interactions.

According to the Reactome pathway, 32 of 133 mRNAs are involved in biological oxidation, 77/133 mRNAs in metabolism, and 29/133 mRNAs in hemostasis.

miR-135a has an important role in proliferation and metastatic growth of hepatocellular cells and is associated with malignant thrombosis [77].

4.13. Lung

TS-co-expression gene regulatory network in the lung involves 11 mRNAs (ALPP, MS4A15, NAPSA, ROS1, SCGB1A1, SFTA2, SFTPA1, SFTPA2, SFTPB, SFTPC, and SFTPD) and 7 lncRNAs (AC068134.8, CTD-2531D15.4, RP11-312J18.6, RP11-476D10.1, RP11-62I21.1, RP11-95I16.2, and U82670.4), sharing 13 interactions.

Napsin A aspartic peptidase (NAPSA) encodes a member of the peptidase A1 family of aspartic proteases, and it is involved in the processing of pulmonary surfactant protein B in the lung [78]. ROS proto-oncogene 1, receptor tyrosine kinase (ROS1), encodes an orphan receptor tyrosine kinase and is vulnerable to intrachromosomal or interchromosomal rearrangements in several tumor types including non-small-cell-lung cancer [79].

Secretoglobin family 1A member 1 (SCGB1A1) is involved in the regeneration of alveolar epithelia in response to severe pulmonary damage in mice [80]. Surfactant family genes (SFTA2, SFTPA1, SFTPA2, SFTPB, SFTPC, and SFTPD) encode pulmonary surfactant-associated proteins [81]. Alkaline phosphatase, placental (ALPP) and membrane spanning 4-domains A15 (MS4A15) have not yet been reported with a specific role in the lung. None of the 7 lncRNAs has been described in lung tissue before.

4.14. Muscle

TS-co-expression gene regulatory network in the muscle involves 38 mRNAs, 12 lncRNAs (CTD-2003C8.2, CTD-2201G16.1, RP11-22P6.3, RP11-293P20.2, RP11-317J10.2, RP11-388M20.9, RP11-567E21.3, RP11-6I2.4, RP11-755O11.2, RP11-882G5.1, RP1-46F2.2 and RP4-597A16.2), and 1 miRNA (miR-133a-2), sharing 81 interactions.

Using the Reactome pathway, we found that 20/38 mRNAs (i.e., actinin alpha 2 (ACTN2), myosin heavy chain 13 (MYH13), troponin i1, slow skeletal type (tnni1), calcium voltage-gated channel subunit alpha1 S (CACNA1S), and calsequestrin 1 (CASQ1)) are involved in muscle contraction and 15/38 in striated muscle contraction (i.e., nebulin (NEB) and titin-cap (TCAP)).

miR-133a-2 along with miR-133a-1 is muscle-specific miRNAs that is modulated during muscle development by the serum response factor [82]. Although lncRNA RP11-6I2.4 has been recently associated to atherosclerosis [83], no publication is currently available for the identified lncRNAs in the muscle.

4.15. Nerve

TS-co-expression gene regulatory network in the nerves involves 4 mRNAs (HCN1, IL9, S100B, and SLC25A48) and 3 lncRNAs (GS1-39E22.2, LINC00462, and PEX5L-AS1), sharing 5 interactions.

Hyperpolarization-activated cyclic nucleotide-gated potassium channel 1 (HCN1) has been found in the central and peripheral nervous system, where they are associated with synaptic integration, neuronal excitability, and formation of resting membrane potentials [84]. Interleukin 9 (IL9), S100 calcium-binding protein B (S100B), and solute carrier family 25 member 48 (SLC25A48) have not yet been reported to have a specific role in the nerves.

4.16. Ovary

TS-co-expression gene regulatory network in the ovary involves only 1 mRNA (WFIKKN2) and 1 lncRNA (RP11-506D12.5), sharing only 1 interaction.

WAP, follistatin/kazal, immunoglobulin, kunitz and netrin domain containing 2 (WFIKKN2) has not yet been reported to have a specific role in the ovary.

RP11-506D12.5 along with other 12 lncRNAs was proposed as gene signature to identify breast cancer patients at high risk of tumor recurrence [85].

4.17. Pancreas

TS-co-expression gene regulatory network in the pancreas involves 3 mRNAs (IL22RA1, RNF186, and PRSS3) and 3 lncRNAs (RP11-133O22.6, RP11-91K11.2, and RP4-799P18.2), sharing 3 interactions. Interleukin 22 receptor subunit alpha 1 (IL22RA1) and protease serine 3 (PRSS3) were associated with chronic pancreatitis in mouse models [86] and with progression, metastasis, and prognosis of human pancreatic cancer [87].

Ring finger protein 186 (RNF186) has not yet been reported in the pancreas. No publication is found on the role of the 3 identified lncRNAs in the pancreas function.

4.18. Pituitary

TS-co-expression gene regulatory network in the pituitary involves 47 mRNAs, 34 lncRNAs, and 4 miRNAs (miR-7-3, miR-7-3hg, miR-137hg, and miR-770), sharing 137 interactions. According to the Reactome pathway, 4 of 47 mRNAs are involved in peptide hormone biosynthesis (i.e., glycoprotein hormones, alpha polypeptide (CGA), proopiomelanocortin (POMC), and luteinizing hormone beta polypeptide (LHB)), 3/47 glycoprotein hormones, and 4/47 in class A/1 (rhodopsin-like receptors) (i.e., arginine vasopressin receptor 1B (AVPR1B), and dopamine receptor D2 (DRD2)).

miR-7 was found to be enriched in the pituitary [88], miR-137hg was associated as schizophrenia-associated locus [89], and miR-770 have been linked to pituitary adenoma [90].

4.19. Prostate

TS-co-expression gene regulatory network in the prostate involves 18 mRNAs (ACPP, CHRM1, CHRNA2, HOXB13, KLK2, KLK3, KLK4, LMAN1L, MMP26, NKX3-1, PCAT4, PRAC1, PRAC2, SPDEF, TGM4, TRGJ1, TRGJP, and TRPM8) and 16 lncRNAs (AC005538.5, AC016730.1, AC018804.3, AC092635.1, CTD-2311B13.7, KB-1930G5.4, LSAMP-AS1, PCGEM1, RP11-118K6.2, RP11-366M4.3, RP11-452C8.1, RP11-493K23.4, RP11-536C10.7, RP11-597A11.2, RP11-91I20.3, and RP1-288H2.2), sharing 51 interactions.

Acid phosphatase, prostate (ACPP) is regulated by androgen and is produced by the epithelial cells of the prostate gland [91]. Cholinergic receptors (CHRM1 and CHRNA2), homeobox B13 (HOXB13), matrix metallopeptidase 26 (MMP26), NK3 homeobox 1 (NKX3-1), prostate cancer susceptibility candidate (PRAC1 and PRAC2), SAM pointed domain containing ETS transcription factor (SPDEF), transglutaminase 4 (TGM4), and transient receptor potential cation channel subfamily M member 8 (TRPM8) are associated with prostate cancer [92–99].

Kallikrein related peptidase family (KLK2, KLK3, and KLK4) encode a serine proteases mainly expressed in prostatic tissue. They are highly expressed in prostate tumor cells and may be a prognostic maker for prostate cancer risk [100].

Lectin, mannose-binding 1 like (LMAN1L), and T cell receptor gamma joining family (TRGJ1 and TRGJP) have not yet been associated with the prostate. No publication is currently available on the role of the 16 identified lncRNAs in prostate tissue.

4.20. Salivary Gland

TS-co-expression gene regulatory network in the salivary gland involves 28 mRNAs and 23 lncRNAs, sharing 86 interactions.

Polymeric immunoglobulin receptor (PIGR), also known as the membrane secretory component, plays a central role in this network. It has a crucial function in intestinal immunity, but since the oral cavity is protected by the mucosal immune system, PIGR seems to have a role also in the salivary gland [101].

4.21. Skin

TS-co-expression gene regulatory network in the skin involves 121 mRNAs, 23 lncRNAs, and 1 miRNA (miR-205hg), sharing 711 interactions.

According to the Reactome pathway, 51 of 121 mRNAs are involved in keratinization (i.e., keratin family genes (KRT), desmoglein (DSG), and desmocollin (DSC)), and formation of the cornified envelope (i.e., late cornified envelope 6A and plakophilin).

miR-205hg was associated with cutaneous squamous cell carcinoma [102] and controls neonatal expansion of skin stem cells by regulating the PI(3)K pathway [103].

4.22. Small Intestine

TS-co-expression gene regulatory network in the small intestine involves 117 mRNAs, 30 lncRNAs, and 4 miRNAs (miR-147b, miR-192, miR-194-2, and miR-559), sharing 1197 interactions.

According to the Reactome pathway, 8/117 mRNAs are involved in antimicrobial peptides (i.e., intelectin 2 and regenerating family member 3 alpha and 4), 7/117 mRNAs in O-linked glycosylation of mucins (i.e., mucin 2, 3A, and 17; cell surface-associated and UDP-GlcNAc : betagal beta-1, 3-N-Acetylglucosaminyltransferase 6), 7/117 in MHC class II antigen presentation (i.e., tubulin alpha-like 3 and sucrase-isomaltase), and 5/117 in alpha-defensins (i.e., defensin alpha 5 and 6).

miR-147b was found as a biomarker for colorectal tumor, miR-192 seems to play a vital role in the differentiation and function of the intestinal epithelium, and miR-194 is regulated by HNF-1 alpha and seems to have important functions in intestinal epithelium maturation [104–106]. To date, miR-559 has not yet been stated to be associated with the intestine in the literature.

4.23. Spleen

TS-co-expression gene regulatory network in the spleen involves 70 mRNAs, 17 lncRNAs, and 1 miRNA (miR-1587), sharing 136 interactions.

According to the Reactome pathway, 42/70 mRNAs are involved in FCGR activation (i.e., immunoglobulin lambda constant family and Fc Receptor), 42/70 mRNAs in the role of phospholipids in phagocytosis, 42/70 in Fc gamma receptor- (FCGR-) dependent phagocytosis, and 47/70 in the innate immune system (i.e., integrin subunit alpha D).

miR-1587 has not yet been reported in the spleen. No publication is currently available on the role of the 17 identified lncRNAs in the spleen.

4.24. Stomach

TS-co-expression gene regulatory network in the stomach involves 22 mRNAs (ANXA10, ATP4A, ATP4B, BARX1, CA9, CCKBR, CHIA, CLDN18, CTSE, DPCR1, GIF, GKN2, KCNE2, LHX5, MUC5AC, NCOA7-AS1, PGA4, SLC26A9, SLC9A4, TFF1, TRIM50, and VSIG1) and 10 lncRNAs (AP000320.6, CTD-2331C18.5, HCG21, HNF1A-AS1, RP11-231K24.2, RP11-567G11.1, RP11-708H21.4, RP11-82C23.2, RP4-681L3.2, and RP5-1125M8.2), sharing 37 interactions.

Annexin A10 (ANXA10) is a nuclear protein particularly expressed in the gastric mucosa, and it is a novel marker of gastric differentiation [107].

ATPase H+/K+ transporting subunit family (ATP4A and ATP4B) encodes a proton pump that catalyzes the hydrolysis of ATP. It is also responsible for gastric acid secretion [108].

BARX homeobox 1 (BARX1) regulates robust smooth muscle growth in the stomach by increasing proliferation of myogenic progenitors [109].

Carbonic anhydrase 9 (CA9) plays a role in the metabolism of carbon and has also been associated with hypoxia and proliferation in different cancers such as gastric cancer [110].

Cholecystokinin B receptor (CCKBR), chitinase acidic (CHIA), claudin 18 (CLDN18), and cathepsin E (CTSE) are found mainly in the gastrointestinal tract with high levels in human stomach tissue [111–114].

Gastric intrinsic factor (GIF) and gastrokine 2 (GKN2) encode a glycoprotein secreted by the cells of the gastric mucosa [115, 116]. GIF is necessary for the correct absorption of vitamin B12 [115].

Potassium voltage-gated channel subfamily E regulatory subunit 2 (KCNE2), secreted mucin 5AC, oligomeric mucus/gel-forming (MUC5AC), pepsinogen 4, group I (Pepsinogen A), and trefoil factor 1 (TFF1) are essential for gastric acid secretion and for normal gastrointestinal function [117–120]. Some of them are the main components of the gastrointestinal mucose layer [117–120].

Tripartite motif containing 50 (TRIM50) checks vesicular trafficking for acid secretion in gastric cells [121]. V-set and immunoglobulin domain containing 1 (VSIG1) is stomach-specific for the accurate differentiation of gastric epithelia [122].

Diffuse panbronchiolitis critical region 1 (DPCR1), LIM homeobox 5 (LHX5), and solute carrier family (SLC26A9 and SLC9A4) have not been reported with a specific role in the stomach. No publication is currently available on the role of the 10 identified lncRNAs in the stomach.

4.25. Testis

TS-co-expression gene regulatory network in the testis involves 849 mRNAs, 1318 lncRNAs, and 13 miRNAs (miR-let7dhg, miR-181c, miR-202, miR-371b, miR-519a2, miR-663a, miR-1302-11, miR-3150b, miR-3180-3, miR-3615, miR-3943, miR-4313, and miR-5188), sharing 184041 interactions.

According to the Reactome pathway, the major part of genes is involved in reproduction, fertilization, sperm motility, and meiosis.

miR-202 was found in Sertoli cells, and its expression varies between fertile and non-fertile men, suggesting it as a TS biomarker [123].

The other miRNAs have not yet been reported to have a comprehensive role in the testis.

4.26. Thyroid

TS-co-expression gene regulatory network in the thyroid involves 4 mRNAs (SLC26A4, KCNJ16, SFTA3, and TCERG1L) and 6 lncRNAs (RP11-7O14.1, AC002539.1, RMST, RP11-896J10.3, RP11-100G15.10, and RP11-462G8.3), sharing 6 interactions.

Solute carrier family 26 member 4 (SLC26A4) has not yet been reported with a clear role in the thyroid, but it is associated with thyroid dysfunction (Pendred’s syndrome) [124].

Surfactant-associated 3 (SFTA3), potassium voltage-gated channel subfamily J member 16 (KCNJ16), and transcription elongation regulator 1 like (TCERG1L) have not been associated with the thyroid. Alteration of expression of RMST lncRNA has been described in thyroid cancer tissue [125], while no publication is currently available for the other 5 lncRNAs.

4.27. Vagina

TS-co-expression gene regulatory network in the vagina involves 44 mRNAs and 9 lncRNAs, sharing 107 interactions.

According to the Reactome pathway, 25/44 mRNAs are involved in keratinization, formation of the cornified envelope, and developmental biology. No publication is currently available for the 9 lncRNAs.

5. Conclusions

Since tissue and cell-type specificity are at the core of human physiology and disease, the comprehension of the genetic underpinnings of complex tissues is fundamental for developing improved diagnostics and therapeutics. Overall, our analysis provides a comprehensive portrait of TS regulatory co-expression networks from thirty different human tissues. This result may be of help in understanding how RNA networks are different in each tissue and ultimately lead us to better understand how miRNAs and lncRNAs can regulate protein coding RNA and TS biological processes.

This work suggests that TS RNA networks need to be analyzed in each tissue in order to understand disease, cellular processes, and potential side effects of drugs inside target tissue.

Data Availability

The data used to support the findings of this study are included within the article and are derived from the Genotype-Tissue Expression (GTEx) project available at https://gtexportal.org/home/.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

The authors would like to thank the INTEROMICS flagship project (http://www.interomics.eu/it/home), the National Research Council CUP Grant B91J12000190001, and the project grant SysBioNet, Italian Roadmap Research Infrastructures 2012.

Supplementary Materials

Supplementary Table 1: the list of TS-lncRNAs. (Supplementary Materials)

References

T. Derrien, R. Johnson, G. Bussotti et al., “The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression,” Genome Research, vol. 22, no. 9, pp. 1775–1789, 2012.

O. Wapinski and H. Y. Chang, “Long noncoding RNAs and human disease,” Trends in Cell Biology, vol. 21, no. 6, pp. 354–361, 2011.

K. C. Wang and H. Y. Chang, “Molecular mechanisms of long noncoding RNAs,” Molecular Cell, vol. 43, no. 6, pp. 904–914, 2011.

G. Bertoli, C. Cava, and I. Castiglioni, “The potential of miRNAs for diagnosis, treatment and monitoring of breast cancer,” Scandinavian Journal of Clinical and Laboratory Investigation, vol. 76, no. S245, pp. S34–S39, 2016.

I. Alvarez-Garcia and E. A. Miska, “MicroRNA functions in animal development and human disease,” Development, vol. 132, no. 21, pp. 4653–4662, 2005.

C. Cava, G. Bertoli, and I. Castiglioni, “Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential,” BMC Systems Biology, vol. 9, no. 1, p. 62, 2015.

A. Colaprico, C. Cava, G. Bertoli, G. Bontempi, and I. Castiglioni, “Integrative analysis with Monte Carlo cross-validation reveals miRNAs regulating pathways cross-talk in aggressive breast cancer,” BioMed Research International, vol. 2015, Article ID 831314, 17 pages, 2015.

C. Cava, A. Colaprico, G. Bertoli, G. Bontempi, G. Mauri, and I. Castiglioni, “How interacting pathways are regulated by miRNAs in breast cancer subtypes,” BMC Bioinformatics, vol. 17, no. S12, p. 348, 2016.

P. P. Amaral and J. S. Mattick, “Noncoding RNA in development,” Mammalian Genome, vol. 19, no. 7-8, pp. 454–492, 2008.

T. R. Mercer, M. E. Dinger, and J. S. Mattick, “Long non-coding RNAs: insights into functions,” Nature Reviews Genetics, vol. 10, no. 3, pp. 155–159, 2009.

P. Landgraf, M. Rusu, R. Sheridan et al., “A mammalian microRNA expression atlas based on small RNA library sequencing,” Cell, vol. 129, no. 7, pp. 1401–1414, 2007.

Y. Liang, D. Ridzon, L. Wong, and C. Chen, “Characterization of microRNA expression profiles in normal human tissues,” BMC Genomics, vol. 8, no. 1, p. 166, 2007.

N. Ludwig, P. Leidinger, K. Becker et al., “Distribution of miRNA expression across human tissues,” Nucleic Acids Research, vol. 44, no. 8, pp. 3865–3877, 2016.

A. L. Brunner, A. H. Beck, B. Edris et al., “Transcriptional profiling of long non-coding RNAs and novel transcribed regions across a diverse panel of archived human cancers,” Genome Biology, vol. 13, no. 8, p. R75, 2012.

M. N. Cabili, C. Trapnell, L. Goff et al., “Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses,” Genes & Development, vol. 25, no. 18, pp. 1915–1927, 2011.

X. Chen, C. C. Yan, X. Zhang, and Z. H. You, “Long non-coding RNAs and complex diseases: from experimental results to computational models,” Briefings in Bioinformatics, vol. 18, no. 4, pp. 558–576, 2017.

M. Uhlen, L. Fagerberg, B. M. Hallstrom et al., “Tissue-based map of the human proteome,” Science, vol. 347, no. 6220, Article ID 1260419, 2015.

C. Cava, A. Colaprico, G. Bertoli et al., “SpidermiR: an R/bioconductor package for integrative analysis with miRNA data,” International Journal of Molecular Sciences, vol. 18, no. 2, p. 272, 2017.

J. M. Vaquerizas, S. K. Kummerfeld, S. A. Teichmann, and N. M. Luscombe, “A census of human transcription factors: function, expression and evolution,” Nature Reviews Genetics, vol. 10, no. 4, pp. 252–263, 2009.

X. Guo, L. Gao, Q. Liao et al., “Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks,” Nucleic Acids Research, vol. 41, no. 2, p. e35, 2013.

B. A. Haider, A. S. Baras, M. N. McCall, J. A. Hertel, T. C. Cornish, and M. K. Halushka, “A critical evaluation of microRNA biomarkers in non-neoplastic disease,” PLoS One, vol. 9, no. 2, Article ID e89565, 2014.

C. C. Pritchard, E. Kroh, B. Wood et al., “Blood cell origin of circulating microRNAs: a cautionary note for cancer biomarker studies,” Cancer Prevention Research, vol. 5, no. 3, pp. 492–497, 2012.

X. Chen and L. Huang, “LRSSLMDA: laplacian regularized sparse subspace learning for MiRNA-disease association prediction,” PLoS Computational Biology, vol. 13, no. 12, Article ID e1005912, 2017.

X. Chen and G.-Y. Yan, “Novel human lncRNA-disease association inference based on lncRNA expression profiles,” Bioinformatics, vol. 29, no. 20, pp. 2617–2624, 2013.

GTEx Consortium, “Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans,” Science, vol. 348, no. 6235, pp. 648–660, 2015.

D. Bhartiya, K. Pal, S. Ghosh et al., “lncRNome: a comprehensive knowledgebase of human long noncoding RNAs,” Database (Oxford), vol. 2013, p. bat034, 2013.

T. A. Eyre, F. Ducluzeau, T. P. Sneddon, S. Povey, E. A. Bruford, and M. J. Lush, “The HUGO gene nomenclature database, 2006 updates,” Nucleic Acids Research, vol. 34, no. 90001, pp. D319–D321, 2006.

J. D. Zhang, K. Hatje, G. Sturm et al., “Detect tissue heterogeneity in gene expression data with BioQC,” BMC Genomics, vol. 18, no. 1, p. 277, 2017.

Z. Guo, M. Maki, R. Ding, Y. Yang, B. Zhang, and L. Xiong, “Genome-wide survey of tissue-specific microRNA and transcription factor regulatory networks in 12 tissues,” Scientific Reports, vol. 4, no. 1, p. 5150, 2014.

I. M. Dykes and C. Emanueli, “Transcriptional and post-transcriptional gene regulation by long non-coding RNA,” Genomics, Proteomics and Bioinformatics, vol. 15, no. 3, pp. 177–186, 2017.

I. Yanai, H. Benjamin, M. Shmoish et al., “Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification,” Bioinformatics, vol. 21, no. 5, pp. 650–659, 2005.

J. Lessard, M. Pelletier, L. Biertho et al., “Characterization of dedifferentiating human mature adipocytes from the visceral and subcutaneous fat compartments: fibroblast-activation protein alpha and dipeptidyl peptidase 4 as major components of matrix remodeling,” PLoS One, vol. 10, no. 3, Article ID e0122065, 2015.

L. Xu, L. Zhou, and P. Li, “CIDE proteins and lipid metabolism,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 32, no. 5, pp. 1094–1098, 2012.

J. M. Moreno-Navarrete, F. Ortega, M. Serrano et al., “CIDEC/FSP27 and PLIN1 gene expression run in parallel to mitochondrial genes in human adipose tissue, both increasing after weight loss,” International Journal of Obesity, vol. 38, no. 6, pp. 865–872, 2014.

T. Shibata, H. Koide, R. Hayashi et al., “Molecular cloning and characterization of rat brain endothelial cell derived gene-1 (tumor suppressor candidate 5) expressing abundantly in adipose tissues,” Molecular and Cellular Endocrinology, vol. 263, no. 1-2, pp. 38–45, 2007.

H. Koide, T. Shibata, N. Yamada et al., “Tumor suppressor candidate 5 (TUSC5) is expressed in brown adipocytes,” Biochemical and Biophysical Research Communications, vol. 360, no. 1, pp. 139–145, 2007.

C. Lindskog, J. Linné, L. Fagerberg et al., “The human cardiac and skeletal muscle proteomes defined by transcriptomics and antibody-based profiling,” BMC Genomics, vol. 16, no. 1, p. 475, 2015.

J. S. Babischkin, G. W. Aberdeen, G. J. Pepe, and E. D. Albrecht, “Estrogen suppresses interaction of melanocortin 2 receptor and its accessory protein in the primate fetal adrenal cortex,” Endocrinology, vol. 157, no. 12, pp. 4588–4601, 2016.

L. F. Chan, T. R. Webb, T.-T. Chung et al., “MRAP and MRAP2 are bidirectional regulators of the melanocortin receptor family,” Proceedings of the National Academy of Sciences, vol. 106, no. 15, pp. 6146–6151, 2009.

A. Hattori, H. Zukeran, M. Igarashi et al., “A novel C-terminal truncating NR5A1 mutation in dizygotic twins,” Human Genome Variation, vol. 4, p. 17008, 2017.

J. Bergman, J. Botling, L. Fagerberg et al., “The human adrenal gland proteome defined by transcriptomics and antibody-based profiling,” Endocrinology, vol. 158, no. 2, pp. 239–251, 2017.

P. J. Neame, C. N. Young, and J. T. Treep, “Primary structure of a protein isolated from reef shark (Carcharhinus springeri) cartilage that is similar to the mammalian C-type lectin homolog, tetranectin,” Protein Science, vol. 1, no. 1, pp. 161–168, 1992.

M. Habuka, L. Fagerberg, B. M. Hallström, F. Pontén, T. Yamamoto, and M. Uhlen, “The urinary bladder transcriptome and proteome defined by transcriptomics and antibody-based profiling,” PLoS One, vol. 10, no. 12, Article ID e0145301, 2015.

R. Yao, A. Lopez-Beltran, G. T. Maclennan, R. Montironi, J. N. Eble, and L. Cheng, “Expression of S100 protein family members in the pathogenesis of bladder tumors,” Anticancer Research, vol. 27, no. 5A, pp. 3051–3058, 2007.

A. Zaravinos, G. I. Lambrou, I. Boulalas, D. Delakas, and D. A. Spandidos, “Identification of common differentially expressed genes in urinary bladder cancer,” PLoS One, vol. 6, no. 4, Article ID e18135, 2011.

K. Iida, J. Mimura, K. Itoh et al., “Suppression of AhR signaling pathway is associated with the down-regulation of UDP-glucuronosyltransferases during BBN-induced urinary bladder carcinogenesis in mice,” The Journal of Biochemistry, vol. 147, no. 3, pp. 353–360, 2010.

K.-E. Andersson, C. Gratzke, and P. Hedlund, “The role of the transient receptor potential (TRP) superfamily of cation-selective channels in the management of the overactive bladder,” BJU International, vol. 106, no. 8, pp. 1114–1127, 2010.

R. Desalle, J. U. Chicote, T.-T. Sun, and A. Garcia-España, “Generation of divergent uroplakin tetraspanins and their partners during vertebrate evolution: identification of novel uroplakins,” BMC Evolutionary Biology, vol. 14, no. 1, p. 13, 2014.

J. Liu, X. Sun, H. Zhu, Q. Qin, X. Yang, and X. Sun, “Long noncoding RNA POU6F2-AS2 is associated with oesophageal squamous cell carcinoma,” Journal of Biochemistry, vol. 160, no. 4, pp. 195–204, 2016.

S. Sharma, S. K. Patnaik, R. T. Taggart et al., “APOBEC3A cytidine deaminase induces RNA editing in monocytes and macrophages,” Nature Communications, vol. 6, no. 1, p. 6881, 2015.

M. Benoit, F. Thuny, Y. Le Priol et al., “The transcriptional programme of human heart valves reveals the natural history of infective endocarditis,” PLoS One, vol. 5, no. 1, p. e8939, 2010.

S. Vinzens, J. Zindel, M. Zweifel, T. Rau, B. Gloor, and A. Wochner, “Granulocyte colony-stimulating factor producing anaplastic carcinoma of the pancreas: case report and review of the literature,” Anticancer Research, vol. 37, no. 1, pp. 223–228, 2017.

C. Palmer, M. Diehn, A. A. Alizadeh, and P. O. Brown, “Cell-type specific gene expression profiles of leukocytes in human peripheral blood,” BMC Genomics, vol. 7, no. 1, p. 115, 2006.

S. Toyabe, Y. Kuwano, K. Takeda, M. Uchiyama, and T. Abo, “IgA nephropathy-specific expression of the IgA Fc receptors (CD89) on blood phagocytic cells,” Clinical & Experimental Immunology, vol. 110, no. 2, pp. 226–232, 2007.

A. Fabregat, K. Sidiropoulos, P. Garapati et al., “The reactome pathway knowledgebase,” Nucleic Acids Research, vol. 44, no. D1, pp. D481–D487, 2016.

Z. Su, W. Si, L. Li et al., “MiR-144 regulates hematopoiesis and vascular development by targeting meis1 during zebrafish development,” The International Journal of Biochemistry & Cell Biology, vol. 49, pp. 53–63, 2014.

C.-Z. Chen, L. Li, H. F. Lodish, and D. P. Bartel, “MicroRNAs modulate hematopoietic lineage differentiation,” Science, vol. 303, no. 5654, pp. 83–86, 2004.

F. Fazi, A. Rosa, A. Fatica et al., “A minicircuitry comprised of MicroRNA-223 and transcription factors NFI-a and C/EBPα regulates human granulopoiesis,” Cell, vol. 123, no. 5, pp. 819–831, 2005.

M. Safran, I. Solomon, O. Shmueli et al., “GeneCards 2002: an evolving human gene compendium,” in Proceedings of the IEEE Computer Science Bioinformatics Conference CSB2002, p. 339, Palo Alto, CA, USA, August 2002.

C. Chen, Z. Li, Y. Yang, T. Xiang, W. Song, and S. Liu, “Microarray expression profiling of dysregulated long non-coding RNAs in triple-negative breast cancer,” Cancer Biology & Therapy, vol. 16, no. 6, pp. 856–865, 2015.

L. Wu, W. Shi, J. Long et al., “A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer,” Nature Genetics, vol. 50, no. 7, pp. 968–978, 2018.

F. F. Migone, Y. Ren, R. G. Cowan, R. M. Harman, A. Y. Nikitin, and S. M. Quirk, “Dominant activation of the hedgehog signaling pathway alters development of the female reproductive tract,” Genesis, vol. 50, no. 1, pp. 28–40, 2012.

S. Borghini, M. Di Duca, G. Santamaria et al., “Transcriptional regulation of TLX2 and impaired intestinal innervation: possible role of the PHOX2A and PHOX2B genes,” European Journal of Human Genetics, vol. 15, no. 8, pp. 848–855, 2007.

J. Tan, X. Yang, X. Jiang et al., “Integrative epigenome analysis identifies a Polycomb-targeted differentiation program as a tumor-suppressor event epigenetically inactivated in colorectal cancer,” Cell Death & Disease, vol. 5, no. 7, p. e1324, 2014.

W. Guo, Q.-Q. Sui, X.-F. Gao et al., “Co-localization of Pirt protein and P2X2 receptors in the mouse enteric nervous system,” Purinergic Signalling, vol. 12, no. 3, pp. 489–496, 2016.

Y. Xing, Z. Zhao, Y. Zhu, L. Zhao, A. Zhu, and D. Piao, “Comprehensive analysis of differential expression profiles of mRNAs and lncRNAs and identification of a 14-lncRNA prognostic signature for patients with colon adenocarcinoma,” Oncology Reports, vol. 39, no. 5, pp. 2365–2375, 2018.

W. Wang, Y. Li, N. Liu, Y. Gao, and L. Li, “MiR-23b controls ALDH1A1 expression in cervical cancer stem cells,” BMC Cancer, vol. 17, no. 1, p. 292, 2017.

S. Bak, I. R. León, O. N. Jensen, and K. Højlund, “Tissue specific phosphorylation of mitochondrial proteins isolated from rat liver, heart muscle, and skeletal muscle,” Journal of Proteome Research, vol. 12, no. 10, pp. 4327–4339, 2013.

B. Lu, W. Tigchelaar, W. P. T. Ruifrok, W. H. van Gilst, R. A. de Boer, and H. H. W. Silljé, “DHRS7c, a novel cardiomyocyte-expressed gene that is down-regulated by adrenergic stimulation and in heart failure,” European Journal of Heart Failure, vol. 14, no. 1, pp. 5–13, 2012.

A. Weissler-Snir, W. Hindieh, C. Gruner et al., “Lack of phenotypic differences by cardiovascular magnetic resonance imaging in MYH7 (β-myosin heavy chain)-versus MYBPC3 (myosin-binding protein C)-related hypertrophic cardiomyopathy,” Circulation: Cardiovascular Imaging, vol. 10, no. 2, 2017.

A. Tomita-Mitchell, K. D. Stamm, D. K. Mahnke et al., “Impact of MYH6 variants in hypoplastic left heart syndrome,” Physiological Genomics, vol. 48, no. 12, pp. 912–921, 2016.

Z. Péterfi, Z. E. Tóth, H. A. Kovács et al., “Peroxidasin-like protein: a novel peroxidase homologue in the human heart,” Cardiovascular Research, vol. 101, no. 3, pp. 393–399, 2014.

D. Patzak, O. Zhuchenko, C.-C. Lee, and M. Wehnert, “Identification, mapping, and genomic structure of a novel X-chromosomal human gene (SMPX) encoding a small muscular protein,” Human Genetics, vol. 105, no. 5, pp. 506–512, 1999.

T.-T. Aye, S. Soni, T. A. B. van Veen et al., “Reorganized PKA-AKAP associations in the failing human heart,” Journal of Molecular and Cellular Cardiology, vol. 52, no. 2, pp. 511–518, 2012.

A. Khalil, R. Tanos, N. El-Hachem et al., “A HAND to TBX5 explains the link between thalidomide and cardiac diseases,” Scientific Reports, vol. 7, no. 1, p. 1416, 201.

D.-W. Sohn, H.-K. Kim, Y.-J. Kim, S. Oh, M.-W. Seong, and S.-S. Park, “Cardiomyopathies with mixed and inapparent morphological features in cardiac Troponin I3 mutation,” Korean Circulation Journal, vol. 47, no. 3, pp. 413–417, 2017.

J. von Felden, D. Heim, K. Schulze et al., “High expression of micro RNA-135A in hepatocellular carcinoma is associated with recurrence within 12 months after resection,” BMC Cancer, vol. 17, no. 1, p. 60, 2017.

M. Nishino, M. P. Hoang, P. Della Pelle et al., “Napsin A/p40 antibody cocktail for subtyping non-small cell lung carcinoma on cytology and small biopsy specimens,” Cancer Cytopathology, vol. 124, no. 7, pp. 472–484, 2016.

B. Solomon, “Validating ROS1 rearrangements as a therapeutic target in non-small-cell lung cancer,” Journal of Clinical Oncology, vol. 33, no. 9, pp. 972–974, 2015.

D. Zheng, G. V. Limmon, L. Yin et al., “Regeneration of alveolar type I and II cells from Scgb1a1-expressing cells following severe pulmonary damage induced by bleomycin and influenza,” PLoS One, vol. 7, no. 10, Article ID e48451, 2012.

C. D. Smallwood, P. Boloori-Zadeh, M. R. Silva, and A. Gouldstone, “High oxygen concentrations adversely affect the performance of pulmonary surfactant,” Respiratory Care, vol. 62, no. 8, pp. 1085–1090, 2017.

N. Liu, S. Bezprozvannaya, A. H. Williams et al., “microRNA-133a regulates cardiomyocyte proliferation and suppresses smooth muscle gene expression in the heart,” Genes & Development, vol. 22, no. 23, pp. 3242–3254, 2008.

C. H. Wang, H. H. Shi, L. H. Chen, X. L. Li, G. L. Cao, and X. F. Hu, “Identification of key lncRNAs associated with atherosclerosis progression based on public datasets,” Frontiers in Genetics, vol. 10, p. 123, 2019.

H. Liu, J. Zhou, L. Gu, and Y. Zuo, “The change of HCN1/HCN2 mRNA expression in peripheral nerve after chronic constriction injury induced neuropathy followed by pulsed electromagnetic field therapy,” Oncotarget, vol. 8, no. 1, pp. 1110–1116, 2017.

M. Zhou, L. Zhong, W. Xu et al., “Discovery of potential prognostic long non-coding RNA biomarkers for predicting the risk of tumor recurrence of breast cancer patients,” Scientific Reports, vol. 6, no. 1, p. 31038, 2016.

J. Xue, Q. Zhao, V. Sharma et al., “Aryl hydrocarbon receptor ligands in cigarette smoke induce production of interleukin-22 to promote pancreatic fibrosis in models of chronic pancreatitis,” Gastroenterology, vol. 151, no. 6, pp. 1206–1217, 2016.

G. Jiang, F. Cao, G. Ren et al., “PRSS3 promotes tumour growth and metastasis of human pancreatic cancer,” Gut, vol. 59, no. 11, pp. 1535–1544, 2010.

K. K.-H. Farh, A. Grimson, C. Jan et al., “The widespread impact of mammalian MicroRNAs on mRNA repression and evolution,” Science, vol. 310, no. 5755, pp. 1817–1821, 2005.

O. Gianfrancesco, A. Warburton, D. A. Collier, V. J. Bubb, and J. P. Quinn, “Novel brain expressed RNA identified at the MIR137 schizophrenia-associated locus,” Schizophrenia Research, vol. 184, pp. 109–115, 2017.

H. Zhang, C. Xu, N. Sun et al., “Gene expression profiling analysis of MENX-associated rat pituitary adenomas contributes to understand molecular mechanisms of human pituitary adenomas,” Oncology Letters, vol. 11, no. 1, pp. 125–133, 2016.

R. Ahmad, C. D. Nicora, A. K. Shukla, R. D. Smith, W.-J. Qian, and A. Y. Liu, “An efficient method for native protein purification in the selected range from prostate cancer tissue digests,” Chinese Clinical Oncology, vol. 5, no. 6, p. 78, 2016.

A. Mannan Baig, N. A. Khan, V. Effendi, Z. Rana, H. R. Ahmad, and F. Abbas, “Differential receptor dependencies,” Anti-Cancer Drugs, vol. 28, no. 1, pp. 75–87, 2017.

L. Larnaudie, E. Compérat, P. Conort, and J. Varinot, “HOXB13 a useful marker in pleomorphic giant cell adenocarcinoma of the prostate: a case report and review of the literature,” Virchows Archiv, vol. 471, no. 1, pp. 133–136, 2017.

S. Lee, K. K. Desai, K. A. Iczkowski et al., “Coordinated peak expression of MMP-26 and TIMP-4 in preinvasive human prostate tumor,” Cell Research, vol. 16, no. 9, pp. 750–758, 2006.

J. C. Yang and C. P. Evans, “Adjunct screening of NKX3.1 expression supports 5α-reductase inhibition intervention in prostate cancer active surveillance,” European Urology, vol. 72, no. 4, pp. 507-508, 2017.

A. Barnicle, C. Seoighe, J. M. Greally, A. Golden, and L. J. Egan, “Inflammation-associated DNA methylation patterns in epithelium of ulcerative colitis,” Epigenetics, vol. 12, no. 8, pp. 591–606, 2017.

J. J. Steffan, S. Koul, R. B. Meacham, and H. K. Koul, “The transcription factor SPDEF suppresses prostate tumor metastasis,” Journal of Biological Chemistry, vol. 291, no. 39, p. 20826, 2016.

T. Sequeiros, M. Rigau, C. Chiva et al., “Targeted proteomics in urinary extracellular vesicles identifies biomarkers for diagnosis and prognosis of prostate cancer,” Oncotarget, vol. 8, no. 3, pp. 4960–4976, 2017.

S. Asuthkar, L. Demirkhanyan, S. R. Mueting, A. Cohen, and E. Zakharian, “High-throughput proteome analysis reveals targeted TRPM8 degradation in prostate cancer,” Oncotarget, vol. 8, no. 8, pp. 12877–12890, 2017.

N. J. Nasser, J. Thoms, A. Soosaipillai et al., “Human tissue Kallikreins: blood levels and response to radiotherapy in intermediate risk prostate cancer,” Radiotherapy and Oncology, vol. 124, no. 3, pp. 427–432, 2017.

Y. Yamamoto, M. To, T. Hayashi et al., “Intake of indigestible carbohydrates influences IgA response and polymeric Ig receptor expression in the rat submandibular gland,” British Journal of Nutrition, vol. 113, no. 12, pp. 1895–1902, 2015.

J. Cañueto, E. Cardeñoso-Álvarez, J. L. García-Hernández et al., “MicroRNA (miR)-203 and miR-205 expression patterns identify subgroups of prognosis in cutaneous squamous cell carcinoma,” British Journal of Dermatology, vol. 177, no. 1, pp. 168–178, 2017.

D. Wang, Z. Zhang, E. O’Loughlin et al., “MicroRNA-205 controls neonatal expansion of skin stem cells by modulating the PI(3)K pathway,” Nature Cell Biology, vol. 15, no. 10, pp. 1153–1163, 2013.

I. Omrane, N. Kourda, N. Stambouli et al., “MicroRNAs 146a and 147b biomarkers for colorectal tumor’s localization,” BioMed Research International, vol. 2014, pp. 1–9, 2014.

L. B. McKenna, J. Schug, A. Vourekas et al., “MicroRNAs control intestinal epithelial differentiation, architecture, and barrier function,” Gastroenterology, vol. 139, no. 5, pp. 1654.e1–1664.e1, 2010.

K. Hino, K. Tsuchiya, T. Fukao et al., “Inducible expression of microRNA-194 is regulated by HNF-1 during intestinal epithelial cell differentiation,” RNA, vol. 14, no. 7, pp. 1433–1442, 2008.

S.-H. Lu, Y.-L. Chen, C.-T. Shun et al., “Expression and prognostic significance of gastric-specific annexin A10 in diffuse- and intestinal-type gastric carcinoma,” Journal of Gastroenterology and Hepatology, vol. 26, no. 1, pp. 90–97, 2011.

J. Wang, D. Barbuskaite, M. Tozzi, A. Giannuzzo, C. E. Sørensen, and I. Novak, “Proton pump inhibitors inhibit pancreatic secretion: role of gastric and non-gastric H⁺/K⁺-ATPases,” PLoS One, vol. 10, no. 5, Article ID e0126432, 2015.

C. D. Jayewickreme and R. A. Shivdasani, “Control of stomach smooth muscle development and intestinal rotation by transcription factor BARX1,” Developmental Biology, vol. 405, no. 1, pp. 21–32, 2015.

J. Chen, C. Röcken, J. Hoffmann et al., “Expression of carbonic anhydrase 9 at the invasion front of gastric cancers,” Gut, vol. 54, no. 7, pp. 920–927, 2005.

J. Roy, K. S. Putt, D. Coppola et al., “Assessment of cholecystokinin 2 receptor (CCK2R) in neoplastic tissue,” Oncotarget, vol. 7, no. 12, pp. 14605–14615, 2016.

R. Krykbaev, L. J. Fitz, P. S. Reddy et al., “Evolutionary and biochemical differences between human and monkey acidic mammalian chitinases,” Gene, vol. 452, no. 2, pp. 63–71, 2010.

T. Niimi, K. Nagashima, J. M. Ward et al., “claudin-18, a novel downstream target gene for the T/EBP/NKX2.1 homeodomain transcription factor, encodes lung- and stomach-specific isoforms through alternative splicing,” Molecular and Cellular Biology, vol. 21, no. 21, pp. 7380–7390, 2001.

M. Konno-Shimizu, N. Yamamichi, K. Inada et al., “Cathepsin E is a marker of gastric differentiation and signet-ring cell carcinoma of stomach: a novel suggestion on gastric tumorigenesis,” PLoS One, vol. 8, no. 2, Article ID e56766, 2013.

L. Mottram, A. O. Speak, R. M. Selek et al., “Infection susceptibility in gastric intrinsic factor (vitamin B12)-defective mice is subject to maternal influences,” mBio, vol. 7, no. 3, 2016.

J. Dai, C. Qian, M. Su, M. Chen, and J. Chen, “Gastrokine-2 suppresses epithelial mesenchymal transition through PI3K/AKT/GSK3β signaling in gastric cancer,” Tumor Biology, vol. 37, no. 9, pp. 12403–12410, 2016.

T. K. Roepke, A. Anantharam, P. Kirchhoff et al., “The KCNE2 potassium channel ancillary subunit is essential for gastric acid secretion,” Journal of Biological Chemistry, vol. 281, no. 33, pp. 23740–23747, 2006.

A. Magalhães, Y. Rossez, C. Robbe-Masselot et al., “Muc5ac gastric mucin glycosylation is shaped by FUT2 activity and functionally impacts Helicobacter pylori binding,” Scientific Reports, vol. 6, no. 1, p. 25575, 2016.

R. T. Taggart and I. M. Samloff, “Immunochemical, electrophoretic, and genetic heterogeneity of pepsinogen I,” Gastroenterology, vol. 92, no. 1, pp. 143–150, 1987.

M. Soutto, J. Romero-Gallo, U. Krishna et al., “Loss of TFF1 promotes Helicobacter pylori-induced β-catenin activation and gastric tumorigenesis,” Oncotarget, vol. 6, no. 20, pp. 17911–17922, 2015.

M. Nishi, F. Aoyama, F. Kisa et al., “TRIM50 protein regulates vesicular trafficking for acid secretion in gastric parietal cells,” Journal of Biological Chemistry, vol. 287, no. 40, pp. 33523–33532, 2012.

O. Oidovsambuu, G. Nyamsuren, S. Liu, W. Göring, W. Engel, and I. M. Adham, “Adhesion protein VSIG1 is required for the proper differentiation of glandular gastric epithelia,” PLoS One, vol. 6, no. 10, Article ID e25908, 2011.

A. A. Dabaja, A. Mielnik, B. D. Robinson, M. S. Wosnitzer, P. N. Schlegel, and D. A. Paduch, “Possible germ cell-Sertoli cell interactions are critical for establishing appropriate expression levels for the Sertoli cell-specific MicroRNA, miR-202-5p, in human testis,” Basic and Clinical Andrology, vol. 25, no. 1, p. 2, 2015.

R. Kallel, M. Niasme-Grare, S. Belguith-Maalej et al., “Screening of SLC26A4 gene in autoimmune thyroid diseases,” International Journal of Immunogenetics, vol. 40, no. 4, pp. 284–291, 2013.

L. Vitale, A. Piovesan, F. Antonaros, P. Strippoli, M. C. Pelleri, and M. Caracausi, “A molecular view of the normal human thyroid structure and function reconstructed from its reference transcriptome map,” BMC Genomics, vol. 18, no. 1, p. 739, 2017.