Systematic Analysis of the Expression and Prognostic Significance of P4HA1 in Pancreatic Cancer and Construction of a lncRNA-miRNA-P4HA1 Regulatory Axis

Objectives Prolyl 4-hydroxylase subunit alpha 1 (P4HA1) plays a crucial role in modulating extracellular matrix component and promoting tumor progression by changing tumor adhesion, migration, and other biological behaviors in some cancers. However, its expression pattern, biological function, and underlying mechanism in pancreatic cancer remain largely unclear. Materials and Methods In this study, a set of bioinformatics tools were used to analyze the expression of P4HA1 and its prognostic value in pancreatic cancer. In addition, the mechanism through which P4HA1 promotes the progression of pancreatic cancer was explored by constructing a competing endogenous RNA (ceRNA) regulatory axis. Results It was found that the mRNA and protein expression of P4HA1 was significantly higher in pancreatic cancer tissues than in normal tissues. Its high P4HA1 expression correlated with poor clinicopathological features (T stage: P = 0.0078; N stage: P = 0.0124; TNM stage: P = 0.0013; pathological grade: P = 0.0108) and poor prognosis [OS: HR = 1, 95% CI (1-1.01), P = 0.00028; DSS: HR = 1, 95% CI (1-1.01), P = 0.00049; PFI: HR = 1.01, 95% CI (1.01-1.02), P = 0.0057; and DFI: HR = 1, 95% CI (1-1.01), P = 0.0034]. The LINC01503/miR-335-5p/P4HA1 axis might mediate the effects of P4HA1 in promoting the progression on pancreatic cancer. Conclusions Collectively, our findings suggest that high expression of P4HA1 may be used as a promising prognostic biomarker and could be considered for the development of a novel therapeutic strategy for pancreatic cancer in the future.


Introduction
Pancreatic cancer is one of the most malignant tumors with the worst prognosis in the world, and it ranks seventh among the cancer-related deaths in the world and fourth amid all the cancer-related mortalities in the United States; annually, about 432,000 patients die of pancreatic cancer every year1. In the recent years, although some progress has been made in the field of combined and precision therapy for pancreatic cancer. However, overall prognosis remains very poor with a 5-year survival rate of less than 6%2. This makes the cancer one of the deadliest malignant tumors worldwide.
Prolyl 4-hydroxylase subunit α1 (P4HA1), located at 10q22.1, is the active catalytic subunit of prolyl 4hydroxylase (P4H). P4HA1 is responsible for newly synthesized collagen chains and plays a significant role in the formation and stability of the triple helix region of newly synthesized collagen chains. Additionally, it is the key catalytic subunit in regulating collagen biosynthesis3. Notably, recent studies found that P4HA1 not only could regulate extracellular collagen synthesis and secretion but also were closely related to the progression of some tumors, such as breast cancer4, prostate cancer5, and glioma6, 7 by changing tumor adhesion, migration, and other biological behaviors. However, the association between P4HA1 and pancreatic cancer as well as its role in the progression of the tumor was not clearly shown.
Human genome-wide and transcriptome research data shows that protein-coding genes account for only a tiny proportion of the genome (1-2%), while most of the genes in the genome are transcribed; they encode large amount of nonprotein coding RNA (ncRNA) much more than we expected8, 9. Interestingly, at least 80% of the ncRNA in the human genome is bioactive as opposed to the previous belief that it was simply "junk product" 10. Based on the length of the transcript, ncRNA is mainly divided into two categories: short-stranded noncoding RNA, such as micro-RNA (miRNA) (<200 bp), and long noncoding RNA (lncRNA) (>200 bp). In 2011, Salmena et al. 11 proposed a competing endogenous RNA (ceRNA) theory. They postulated that the 3 ′ UTR of lncRNA and mRNA are both rich in miRNA recognition elements (MRE). Moreover, lncRNA could adsorb miRNA through MRE and competitively bind miRNA to inhibit its activity. This happened in order to partially remove the inhibitory effect of miRNA on its target genes and upregulate their expression. Therefore, miRNA and lncRNA play a molecular regulatory role in many processes of gene expression, participate in the pathophysiological process of tumors, and are closely related to the occurrence and progression of cancer12. Moreover, the mechanism of the ceRNA of P4HA1 affecting the malignant progression of pancreatic cancer has not been reported.
Therefore, the expression and clinical value of P4HA1 in pancreatic cancer were comprehensively analyzed in this study. Additionally, a ceRNA regulatory axis was constructed to explore the potential mechanism of P4HA1 in the progression of pancreatic cancer.

Materials and Method
2.1. Pan-Cancer Analysis of P4HA1 in the Oncomine Database. Oncomine (http://www.oncomine.org/resource/ login.html) is an open-access cancer microarray database and an integrated data mining platform that facilitates the discovery of valuable clues from genome-wide expression analysis. So far, the database has 19 cancer types and contains 92 datasets13. In this study, Oncomine was used to compare the 19 cancer samples with normal samples and identify the expression of P4HA1 in various tumors. At the same time, the expression levels of P4HA1 mRNA (log2 transformed) in pancreatic cancer and normal tissues were examined in order to determine the transcription level of P4HA1 in the disease (P < 0:001, |fold change | >2, and the top 10% of the gene ranking as the threshold condition).

Pan-Cancer Analysis of P4HA1 in the Sangerbox
Database. The Sangerbox database (http://sangerbox.com/ Tool) is a comprehensive Chinese bioinformatics analysis platform. The pan-cancer analysis tool of this database was used to explore the expression of P4HA1 in pancreatic cancer and normal samples ( * P < 0:05, * * P < 0:01, and * * * P < 0:001). In addition, this analysis platform was also used to investigate the effect of P4HA1 on the survival and prognosis of pancreatic cancer. This included an analysis of overall survival (OS) and disease-specific survival (DSS). A log-rank P value < 0.05 was considered to be statistically significant.

Expression, Prognosis, Coexpression, and Correlation
Analysis of P4HA1 in the GEPIA Database. Gene Expression Profiling and Interactive Analysis (GEPIA) (http://gepia .cancer-pku.cn/index.html) is an interactive platform including 9,736 tumors and 8,587 normal samples from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects. It helps in the analysis of RNA sequencing expression data14. GEPIA provides customizable functions, such as tumor/normal differential expression analysis, patient survival analysis, similar gene detection, and correlation analysis. The database uses the log-rank (Mantel-Cox) test in the prognosis of 33 different types of cancer based on gene expression. Moreover, the box plot was used to display the ordinate of gene expression in tumor/normal differential expression analysis. The expression data was converted by log2 (TPM+1), |log2 FC | >1, and P value < 0.05 was set as the truncation criterion. Finally, gene expression correlation analysis of the given TCGA expression dataset was used to determine the correlation coefficient using the Pearson method.
2.4. The Expression of P4HA1 in the HPA Database. The Human Protein Atlas (HPA) (http://www.proteinatlas.org/) provides tissue and cellular distribution information for 26,000 types of human proteins15. In this database, the researchers used highly specific antibodies and immunodetection techniques (immunoblotting, immunofluorescence, and immunohistochemistry) to identify in detail the expression of each protein in 64 cell lines, 48 normal human tissues, and 20 tumor tissues. The immunohistochemical staining data from the Tissue and Pathology section of the HPA was used to detect the expression level of the P4HA1 protein in human pancreatic cancer and normal pancreatic tissues.
2.5. TCGA-PAAD Data Download and Data Analysis in UCSC Xena. The University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/) was used to obtain detailed information on mRNA expression of genes. In addition, the browser was employed to source clinical information on pancreatic adenocarcinoma (PAAD) patients in the TCGA cohort (Project ID: TCGA-PAAD)16, 17. Moreover, the relationship between the expression of P4HA1 and clinicopathological parameters (gender, T stage, N stage, M stage, TNM stage, and pathological grade) in pancreatic cancer was analyzed using the TCGA-PAAD data downloaded from Xena.
2.6. Screening for Genes Coexpressed with P4HA1. Screening for genes coexpressed with P4HA1 was done using the GEPIA and UALCAN databases. Afterwards, the same coexpression genes in the two databases were screened and a Venn diagram was drawn using the intersection method.
The UALCAN database is a user-friendly and interactive network resource, which provides a convenient way of publicly obtaining cancer transcriptome data from TCGA18. In this study, the database was used to find genes coexpressed To further explore the mechanism of P4HA1 underlying the prognosis of pancreatic cancer, the Gene-miRNA Interactome module of the NetworkAnalyst database was used to predict the upstream miRNAs that could regulate P4HA1. The predicted data originated from comprehensive experimental verifications collected in TarBase and miRTar-Base miRNA-Gene interaction information.
2.12. Predictive miRNA Expression, Correlation, and Prognostic Analysis. The UALCAN database was used to compare the expression of 5 miRNAs between normal and pancreatic cancer tissues. Moreover, the LinkedOmics database (http://www.linkedomics.org/login.php) 25 was used to analyze the correlation between 5 miRNAs and the expression level of P4HA1 in pancreatic cancer. It is well known that a negative regulatory relationship exists between miRNA and targeted mRNA. Therefore, miRNA that negatively correlated to P4HA1 expression was considered to have good research prospects. The Kaplan-Meier Plotter (http://kmplot.com/analysis/) is an online database established from gene expression data and survival information of patients with cancer26. Using TCGA data, this database was utilized to evaluate the prognostic value of the predicted miRNAs in PAAD. The miRNA name was keyed into the database, after which its Kaplan-Meier survival plots, hazard ratio (HR), 95% confidence interval (CI), and log-rank P values were displayed directly on the web page.
2.13. Analysis of Upstream lncRNA Targeted to miRNA. Here, the StarBase (http://starbase.sysu.edu.cn/index.php)27, 28 and the miRNet (https://www.mirnet.ca/) databases were introduced to predict lncRNAs that could bind miR-335-5p. Subsequently, the lncRNAs that predicted from the StarBase were intersected with the predicted from miRNet was considered to be the most potential regulatory lncRNA for miR-335-5p. According to the regulatory pattern of ceRNA, there is a negative correlation between the expression of lncRNA and miRNA and a positive correlation with the expression of target genes. Based on this feature, the study further screened for the most likely regulatory lncRNA targeted to miR-335-5p by correlation analysis. The StarBase was used to analyze the correlation between lncRNA-miRNA and lncRNA-P4HA1. R > |0:1 | and P < 0:05 were set as the critical criteria for identifying important miRNA-lncRNA and P4HA1-lncRNA pairs.

A ceRNA Regulation Axis and Brief Mechanism
Diagram of P4HA1. The study combined the coexpressed Hub genes directly interacting with P4HA1 obtained from above process, upstream miRNA for P4HA1 and upstream lncRNA for miRNA to construct a ceRNA regulatory lncRNA-miRNA-P4HA1 network diagram. Additionally, previous relevant reports from the literature were combined to draw a brief model diagram of the mechanism by which P4HA1 promotes the progression in pancreatic cancer.
2.15. Statistical Analysis. By default, all statistical analyses were performed as described in the web resources. Briefly, the t-test was performed to compare the expression of P4HA1 mRNA in the Oncomine database. The t-test was also used to perform comparative analysis of P4HA1 expression among different subgroups for each clinicopathological feature. The log-rank test was used to calculate the P value for survival data in the Sangerbox, GEPIA, and Kaplan-Meier Plotter databases. Furthermore, the Fisher's exact test was used to measure gene enrichment in annotated items of the Enrichr annotation system. Finally, linear correlation between mRNA-mRNA, miRNA-mRNA, and lncRNA-miRNA was measured using Pearson's correlation coefficient in the GEPIA, LinkedOmics, and StarBase databases. P < 0:05 was considered statistically significant ( * P < 0:05, * * P < 0:01, and * * * P < 0:001).

Results
3.1. The Expression and Prognosis of P4HA1 in Pan-Cancer. The overall expression profile of P4HA1 in various cancers was first analyzed using the Oncomine database by making comparisons between cancer and normal tissue samples. P4HA1 was markedly overexpressed in most tumors, especially in kidney and pancreatic cancers (Figure 1(a)). Afterwards, TCGA and GTEx data were used to analyze the expression of P4HA1 in pan-cancer, in order to further quantitatively verify its expression in various tumors. Figure 1(b) shows that there was a significant difference in the expression of P4HA1 between normal and tumor samples of most tissues except for the Kidney Chromophobe (KICH). Furthermore, P4HA1 was overexpressed in most tumors. Data from TCGA was also used to analyze the impact of high P4HA1 expression on the OS (Figure 1 In the above preliminary investigation, P4HA1 was significantly overexpressed in pancreatic cancer. Additionally, it was related to poor prognosis of survival in patients with PAAD warranting further study.

Expression of P4HA1 in Pancreatic
Cancer. The analysis of tumor vs. normal samples in different datasets from Oncomine showed that P4HA1 was significantly overexpressed in multiple pancreatic cancer datasets (Figures 2(a)-2(e)). The GEPIA database was also used to further confirm that P4HA1 was significantly highly expressed in the TCGA-PAAD dataset (Figure 2(f)). In addition, the HPA database showed that immunohistochemical staining of cancer tissues was deeper than that of normal pancreatic tissues. This suggests that there was remarkable expression of the P4HA1 protein in pancreatic cancer tissues.
The study further explored the association between P4HA1 expression and different clinical-pathological features in patients with PAAD ( Figure 3). The results showed that there was no significant difference in the expression of P4HA1 between male and female patients as shown in Figure 3(a) (P = 0:5868). Additionally, there was no significant difference between the positive and the negative metastasis groups as highlighted in Figure 3(d) (P = 0:9642). In terms of T stage (T1-2 vs. T3-4), N stage (N-vs. N+), TNM stage (I vs. II-III), and pathological grade (G1 vs. G2-4), expression of P4HA1 was significantly high in the subgroups with high stage/grade (T stage: P = 0:0078; N stage: P = 0:0124; TNM stage: P = 0:0013; pathological grade: P = 0:0108). Therefore, these results indicate that P4HA1 expression was associated with unfavorable clinicopathological characteristics in patients with pancreatic cancer and might play a detrimental role in pancreatic cancer.
All in all, the findings show that P4HA1 is a reliable and promising biomarker of pancreatic cancer. and 4(f)). All these findings showed that P4HA1 was associated with poor prognosis in patients with pancreatic cancer. It could therefore be used as a predictor of poor prognosis in the disease.

GO and KEGG Pathway Analysis of Genes Coexpressed with P4HA1.
Identifying genes coexpressed with P4HA1 would help to better understand the potential function of the protein in pancreatic cancer. Therefore, the UALCAN and GEPIA databases were used to identify these coexpressed genes, as shown in Figure 5(a). A total of 139 coexpressed genes were predicted by both databases. Afterwards, the GO enrichment (Figures 5(b)-5(d)) and KEGG pathway enrichment analyses were performed on the genes ( Figure 5(e)). Additionally, the Enrichr database was used to perform the KEGG pathway enrichment and functional annotation analyses including three types of GO analysis: biological processes (BP), cellular components (CC), and molecular functions (MF).

Identification and Analysis
Core Genes Coexpressed with P4HA1. A PPI network of the coexpressed genes was constructed using both the STRING database and Cytoscape software. Initially, the CytoHubba plug-in in the Cytoscape software was used to identify the top 15 node genes coexpressed with P4HA1 and were highlighted as potential key genes (Hub genes), based on the score of the degree among nodes. The genes included GAPDH, FN1, VEGFA, HSPA5, ENO1, PGK1, HSP90B1, TPI1, LDHA, HK1, HK2, ALDOA, PFKP, SLC2A1, and HSP90AA1 ( Figure 6(a)). Afterwards, the MCODE plug-in was used to obtain the most important interaction module (Figure 6(b)). The results showed that Hub genes identified by the two methods were generally the same.
The expression heat map of P4HA1 and the Hub genes was displayed using the UCSC Cancer Genomics Browser. The findings showed that the expression pattern of 10 Hub genes was consistent with that of P4HA1 in pancreatic cancer ( Figure 6C). Additionally, correlation heat maps showed that the expression of P4HA1 was positively associated with the expression patterns of these Hub genes ( Figure 6(d)). Furthermore, the KEGG circus diagram of the top 15 Hub genes showed that they were mainly involved in the HIF-1 signaling pathway, the PI3K-Akt signaling pathway, glycolysis/ gluconeogenesis, central carbon metabolism in cancer, proteoglycans in cancer, protein processing in the endoplasmic reticulum, fructose and mannose metabolism, and the pentose phosphate pathway (Figure 6(e)).
Moreover, P4HA1 and the top 15 Hub genes were subjected to PPI analysis (Figure 7(a)). Hub genes that directly interacted with P4HA1 were selected, including PGK1, ALDOA, and LDHA (Figure 7(b)). Expression level and correlation analyses showed that these three genes were significantly overexpressed in pancreatic cancer (Figure 7(c)) and had a positive correlation with P4HA1 (Figure 7(d)). Therefore, the results suggested that PGK1, ALDOA, and LDHA may play important synergistic roles for P4HA1 in pancreatic cancer.
3.6. Prediction miRNA, lncRNA, and Building a ceRNA Regulatory Network. In order to further explore the      BioMed Research International mechanism of P4HA1 in the prognosis of pancreatic cancer, the potential mechanism of noncoding RNA regulating P4HA1 was explored. First, NetworkAnalyst was used to predict the upstream miRNAs that could target and regulate P4HA1, including hsa-mir-145-5p, hsa-mir-320a, hsa-mir-335-5p, hsa-mir-877-3p, hsa-mir-122-5p, and hsa-mir-941 (Figure 8(e)). Afterwards, the UALCAN database, TCGAbased data, was used to analyze the expression of these miR-NAs in pancreatic cancer (hsa-mir-941 was not reported in TCGA). The results showed that hsa-mir-320a and hsamir-335-5p had significantly low expression in pancreatic cancer (Figures 8(b) and 8(c)). Additionally, the LinkedOmics database was used to analyze the correlation between the five predicted miRNAs and P4HA1. The results showed that only miR-335-5p had a significant negative correlation with P4HA1 (Figure 8(h)). Furthermore, the Kaplan-Meier Plotter was used to analyze the effects of these miRNAs on the OS of patients with pancreatic cancer. The findings revealed that miR-122-5p was a poor prognostic marker [HR = 1:64, 95% CI (1.00-2.68), P = 0:046] while miR-335-5p was a protective factor in patients with pancreatic cancer [HR = 0:53, 95% CI (0.34-0.83), P = 0:0049]. However, the other three miRNAs had no effect on the overall survival of patients (Figures 8(k) and 9(l)). These results suggest that miR-335-5p was the most promising upstream miRNA regulating P4HA1.

BioMed Research International
These results suggested that LINC01503 was the most promising lncRNA targeting to regulate miR-335-5p.
Finally, based on the coexpressed genes, the miRNA and lncRNA selected above, a LINC01503/miR-335-5p/P4HA1 ceRNA regulatory axis was constructed (Figure 9(g)). Additionally, a brief model diagram involving the potential mechanism of P4HA1 underlying the progression of pancreatic cancer was drawn. (Figure 9(h)).

Discussion
P4HA1 is a key intracellular enzyme that catalyzes the formation of 4-hydroxyproline, and it is necessary for the correct three-dimensional folding of newly synthesized procollagen chains, thereby maintaining the steady state of the extracellular matrix3, 29. P4HA1 is involved in regulating extracellular collagen synthesis and secretion and regulating extracellular matrix components. However, increasing evidence shows that P4HA1 is related to the occurrence, invasion, and metastasis of some human cancers including breast cancer and prostate cancer, glioma, and hepatocellular carcinoma. Gilkes et al.4 found that knocking down P4HA1 could reduce collagen deposition and inhibit the growth and metastasis of breast cancer cells. Additionally, the overexpression of P4HA1 was shown to promote the migration and metastasis of prostate cancer through the P4HA1-MMP1 signaling pathway5. Zhou et al.7 found that the expression of P4HA1 may be regulated by HIF1α, and its overexpression could promote invasive growth of gliomas by upregulating EMT induced by SNAI1 and SNAI2 and by causing angiogenesis of HBMECs. It was also shown that P4HA1 could be targeted by miR-30e, thereby inhibiting cell proliferation and colony formation in hepatocellular carcinoma30. Therefore, previous studies showed that P4HA1 played an important role in the process of malignant tumor progression. However, it was unclear whether P4HA1 plays an important role in the occurrence and malignant progression of pancreatic cancer, which was worthy of further exploration.
In this study, a brief pan-cancer analysis of P4HA1 in various tumors was first conducted. This facilitated an

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BioMed Research International understanding of the expression pattern and prognosis of P4HA1 in various tumors. In pancreatic cancer, P4HA1 not only was highly expressed but also was related to prognosis in patients. This provided a basis for further analysis of the relationship between P4HA1 and pancreatic cancer. The study then sought to further verify the correlation between expression of P4HA1 and prognosis of pancreatic cancer. To achieve this, GEO data from the Oncomine database was used to observe the expression level of P4HA1 in pancreatic cancer tissues compared to normal ones. It was shown that expression in pancreatic cancer was significantly higher than that in normal tissues, consistent with the results obtained from analysis using the GEPIA database. In addition, the HPA database confirmed that the level of the P4HA1 protein in pancreatic cancer was higher than that in normal pancreatic tissues. Moreover, this study found that high expression of P4HA1 was associated with poor clinicopathological features in pancreatic cancer. Our results showed that P4HA1 expression was not related to the M stage but considered the low number of M-positive patients included in the TCGA database; it may also have a certain deviation to our results. Therefore, this still needs to be confirmed by largescale studies in the future. Subsequently, the prognostic analysis revealed that high expression of P4HA1 was related to poor prognosis in patients with pancreatic cancer. These results indicated that P4HA1 played an important and unfavorable role in the malignant transformation of pancreatic cancer and could be a promising biomarker of pancreatic cancer in the future. In order to better understand the potential function of P4HA1 in pancreatic cancer, genes coexpressed with the P4HA1 were predicted. GO enrichment analysis      Figure 6(e)) of these genes showed that they were mainly involved in the biological processes of glycolysis, glucose metabolism, and production of ATP from ADP. The genes were mainly associated with glycolysis/gluconeogenesis, the HIF-1 signaling pathway, central carbon metabolism of cancer, fructose and mannose metabolism, the PI3K-Akt signaling pathway, and the pentose phosphate pathway. These results were consistent with previous reports on P4HA1 and collagen synthesis as well as tissue cell hypoxia29, 31, 32. Therefore, we speculated that P4HA1 plays an important role in tumors probably through these biological processes. Moreover, three genes (PGK1, ALDOA, and LDHA) directly interacting with P4HA1 were selected from the top 15 Hub genes. Analysis using the GEPIA database further confirmed that the genes were overexpressed in pancreatic cancer. Additionally, there was a significant positive correlation between the three genes and P4HA1. Therefore, the results suggested that these coexpressed genes might play an important synergistic role with P4HA1 in the progression of pancreatic cancer through glycolysis, HIF-1α, and metabolism.
Previous studies showed that the interaction of lncRNAs and miRNAs played an important role in the process of cancer and miRNAs could mediate tumor progression by regulating their downstream mRNA12. Therefore, in order to explore the potential regulatory mechanism of P4HA1 in the progression of pancreatic cancer, a lncRNA-miRNA-mRNA interaction network was constructed. First, six potential miRNAs were identified as candidates that targeted P4HA1 (miR-145-5p, miR-320a, miR-335-5p, miR-877-3p, miR-122-5p, and miR-941), and predicted targeting relationship was verified in TarBase and miRTarBase. Further, expression levels of the six potential miRNAs in pancreatic cancer were analyzed. The results showed that two of them (miR-320a and miR-335-5p) were significantly downregulated in pancreatic cancer. According to the ceRNA hypothesis, miRNA has a negative regulatory relationship with the target gene, so miR-335-5p was further screened with negative correlation between miRNA and P4HA1 by correlation analysis.
The effect of these miRNAs on the OS of patients with pancreatic cancer was then analyzed using the Kaplan-Meier Plotter database. The results further proved that miR-335-5p was a protective factor in pancreatic cancer [HR = 0:53, 95% CI (0.34-0.83), P = 0:0049]. Moreover, previous studies reported that miR-335-5p had tumor suppressive effects in lung cancer33, gastric cancer34, colorectal cancer35, and breast cancer36. Therefore, miR-335-5p was considered to be the most promising miRNA that could target and regulate P4HA1.
Similarly, StarBase and miRNet were used to predict upstream lncRNA targeting miR-335-5p. 18 lncRNAs predicted by the two both databases were obtained. According to the ceRNA theory, lncRNAs are negatively correlated with miRNAs but positively correlated with mRNA; therefore, the only one lncRNA (LINC01503) was eligible. The expression level of LINC01503 in pancreatic cancer was then examined and found that it was significantly upregulated in PAAD. In addition, previous studies identified LINC01503 played an oncogene role in tumors such as colorectal cancer37, gastric

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BioMed Research International cancer38, glioma39, cholangiocarcinoma40, and cervical cancer41 and promotes tumor proliferation and invasion. Therefore, LINC01503 was considered to be the most promising lncRNA that could target and regulate miR-335-5p. Finally, the LINC01503/miR-335-5p/P4HA1 ceRNA regulatory axis was constructed by combining the miRNA and lncRNA selected in the above analytical process. In summary, the study utilized bioinformatics tools to provide a reliable, comprehensive analysis of the expression pattern, prognostic value, and potential regulatory mechanism of P4HA1 in pancreatic cancer. It was confirmed that the overexpression of P4HA1 not only was associated with poor prognosis but was also related to poor clinicopathology in the disease. In addition, the potential regulatory mechanism of P4HA1 in promoting the progression of pancreatic cancer was explored by constructing a ceRNA regulatory axis.

Conclusion
P4HA1 can be used as a novel and promising prognostic biomarker for patients with pancreatic cancer, and it may provide a new therapeutic direction for the treatment of pancreatic cancer. However, considering that the findings were based on bioinformatics analyses, more laboratory experiments and clinical trials are needed to further validate in the future.