Overexpression of TP53INP2 Promotes Apoptosis in Clear Cell Renal Cell Cancer via Caspase-8/TRAF6 Signaling Pathway

Clear cell renal cell cancer (ccRCC) is a tumor of high malignancy, which can escape apoptosis. The tumor protein p53-inducible nuclear protein 2 (TP53INP2), known as an autophagy protein, is the essential part for autophagosome formation and sensitizes cells to apoptosis. Our study is aimed at exploring the role of TP53INP2 in ccRCC. We have identified the autophagy-related genes (ARGs) of differential expression in ccRCC patients with the help of the TCGA database by bioinformatics analysis. Our assays of quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were for the determination on the both levels of mRNA and protein. Overexpression of TP53INP2 on cellular proliferation, migration, and apoptosis of ccRCC was verified in the ways of performing CCK-8, wound scrape, transwell and flow cytometry assays in vitro, and a mice tumor model in vivo. Transmission electron microscopy was used to measure autophagy formation. The underlying mechanisms of TP53INP2 on ccRCC were determined via coimmunoprecipitation. TP53INP2 was found highly associated with an outcome of worse overall survival (OS) in Kaplan-Meier curves, and this parameter in ccRCC tissues was also lower than the normal tissues. Overexpression of TP53INP2 inhibited ccRCC cellular proliferation, migration, and invasion, as well as the tumor growth of mice. Those cells treated with autophagy inhibitor chloroquine (CQ) or TP53INP2 increased the apoptosis rate. TP53INP2 promoted autophagy formation and elevated the ratio of LC3 II/LC3 I. However, TP53INP2 did not significantly decrease the p-mTOR level. In addition, TP53INP2 activates the expressions of caspase-3, caspase-8, and PARP. Caspase-8 and TNF receptor associated factor 6 (TRAF6) were found to bind to each other in the presence of TP53INP2. TP53INP2 induces apoptosis in ccRCC cells through caspase-8/TRAF6 pathway, rather than the autophagy-dependent pathway.


Introduction
Renal cell cancer (RCC) is a common urological malignancy, whose incidence was over an amount of 70,000 cases annually. It was in 2019 that there were about 14,000 patients died of RCC in America [1]. Clear cell renal cell cancer (ccRCC) is the major RCC subtype, whose patients made up for the majority of the diagnosed cases [2]. ccRCC is a high-risk vascular tumor with no clinical symptoms of manifestation at its early stage, and more than 25-30% of the patients were found along with cancer metastasis as soon as their initial diagnosis of ccRCC, resulted in patients' poor survival prognoses [3]. With the approval of tyrosine kinase inhibitors and rapamycin (mTOR) inhibitors, molecular targeted therapies have been shown to benefit the prognosis of ccRCC, improving overall survival (OS) by 30 months [4,5]. However, such targeted therapies have drawbacks such as drug resistance and more severe side effects [5]. Despite of the improved diagnostic and therapeutic methods, its recurrence and metastasis might still happen in some ccRCC patients with high mortality rates [6]. Hence, any possible effort to dig out novel biomarkers would benefit the early detection and the treatment of patients with ccRCC [6]. Autophagy, type II programmed cell death, is a means of cellular homeostasis to maintain itself stable under conditions of starvation and hypoxia [7]. Previous study has revealed the role of autophagy in numerous biological processes, and it was also closely related to multiple diseases, including cardiomyopathy, neurodegenerative diseases, and cancers [8]. LC3 is an autophagosome membrane protein, regarded as a biomarker to estimate autophagy activity in mammalian cells, initially found in eukaryotic cells of higher animals [9]. A study found that in many RCC cell lines, 30-60% of the growing cells showed significant LC3-II spots, while in normal primary renal cell culture, this proportion was only 1-5% [10]. Importantly, an inhibition to autophagy in RCC leads to an improvement in the efficiency of many treatment strategies [10,11]. A report of Zhang et al. also argued that the inhibition to autophagy would enhance the preferential toxicity of paclitaxel to renal cancer cells [11]. Chloroquine (CQ), an antimalarial drug that suppresses autophagy, makes these highly aggressive cells sensitive to the mTOR inhibitor tisirolimus [10]. Mammalian target of rapamycin (mTOR) is an essential molecule in the regulation to autophagy [12]. Activation of mTOR is a negative regulator in the process of autophagy, so the administration of mTOR inhibitors conducts an enhancement in both the inhibition to mTOR signaling pathway and the synergize cytotoxicity on RCC cells, thereby begetting the death of autophagic cells that modulate kinase-dependent proteins [13,14]. However, the pathogenesis of autophagy in RCC remains unclear.
Apoptosis is a description to the programmed death of cells, on which the stability of the organism's internal environment greatly relies [15]. Apoptosis is closely associated with caspase activation. Caspases is a class of apoptotic protein which is categorized into two main groups, which are the initiator caspases (caspase-2, -8, -9, and -10) and the executioner caspases (caspase-3, -6, and -7) [15]. The complexity of the interaction amid autophagy and apoptosis was extensively studied so far [16]. An inhibition to autophagy might lead to the amassing of autophagosome membranes, functioning as the platforms for the formation of intracellular disc. Procystease-8 binds to the inner disk of phagocytes via ATG12-ATG5-FADD axis on the outer membrane or LC3-p62 on the inner membrane of autophagy [17,18]. Ubiquitinated caspase-8's binding to p62 is accomplished with its ubiquitin binding domain, whereas its recruitment onto the autophagosome by binding to LC3-II is done with the LC3-interacting region (LIR) of p62 [17,18].In tumor protein p53 inducible nuclear protein 2 (TP53INP2), the nuclear protein participates in the interaction with the nuclear hormone receptor, which travels from the nucleus to the cytoplasm and prompts the synthesis of proteins by a way of facilitating the ribosomal biogenesis in the nucleoli [19]. However, when nutrients are depleted, TP53INP2 comes to an interaction with the nuclear and deacetylated LC3 pools; subsequently, they are both rapidly transported into the cytoplasm to activate autophagy [19]. TP53INP2 positive regulatory role in the process of autophagy is established with its direct interaction with the LIR sequences of the whole Atg8 family members [20]. It was recently discov-ered that TP53INP2 is an ubiquitin binding protein that favors single and K63-linked ubiquitin chains with an ability to promote cellular apoptosis with the death receptor ligands [21]; besides that, it is also a trigger for caspase-8 activation [22]. TP53INP2' combination to caspase-8 and the ubiquitin ligase TRAF6 would lead to a promotion in a TRAF6inducing ubiquitination and activation of caspase-8 [22].
In the present study, the gene expression microarray data was for the development in autophagy-related prognostic model, deemed as an independent OS index in ccRCC on the basis of the Cancer Genome Atlas (TCGA) database. We found that the TP53INP2's expression in ccRCC turned out a conspicuous lower level versus normal tissues, and it was proportional to the survival rate of patients with ccRCC, suggesting that TP53INP2 may be a tumor suppressor gene. However, the underlying mechanism of TP53INP2's role in ccRCC remains unclear. In summary, we proposed the following hypothesis: The autophagy-related gene TP53INP2 promotes the apoptosis of ccRCC cells by activating the caspase-8 apoptotic signaling pathway, functioning as an inhibitor in ccRCC. Both in vivo and in vitro experiments have verified that TP53INP2 inhibits ccRCC cells by regulating caspase-8 apoptotic signaling pathway, so as to provide theoretical basis for further understanding of TP53INP2 and drug therapy of ccRCC.

Materials and Methods
2.1. Autophagy-Related Genes and the Subjects. We retrieved a database from the Human Autophagy (HADb,http://www .autophagy.lu/index.html) for the identification of

Procedures and Statistical
Analysis. Our data analysis on the ARGs of differential expression within ccRCC and adjacent nontumor tissues was going along with our R package limma, with the thresholds of |log2 fold change ðFCÞ | >1 and adjusted P value <0.05. Heatmaps of ARGs were performed with package "pheatmap" in R. Then, we commenced a Gene Ontology (GO) enrichment analysis with DAVID to distinguish the differentially expressed ARGs in the course of major biological processes (BPs), and in sepa-rate the cellular components (CCs) and the molecular functions (MFs). Our KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis was for obtaining the signaling pathways where the ARGs of differential expression participate. We then had our univariate Cox and multivariate Cox proportional hazard regression analysis for the evaluation on the OS-related ARGs in ccRCC. Our prognostic model for OS was established on the basis of multivariate Cox proportional hazard regression analysis. In terms of the median value of the prognostic model, our subjects with ccRCC were grouped into the high-risk and the low-risk. Our Kaplan-Meier methods were built for the estimation on the OS between the groups of high-risk and low-risk. The R package of survival ROC was made for the creation of receiver operating characteristic (ROC) curves so as to Type  Type  10   5   0   -5   N   T   CDKN2A  NRG3  IL24  NLRC4  CCR2  BIRC5  SPHK1  DIRAS3  ATG9B  GRID1  NKX2-3  IFNG  TP73  TP63  GAPDH  SERPINA1  P4HB  CXCR4  VEGFA  APOL1  HSPB8  CX3CL1 BNIP3 HIF1A  Figure 1: The autophagy-related genes of differential expression. (a) Heatmap of the autophagy-related genes of differential expression (ARG). N was tantamount to nontumor tissues; T was tantamount to tumor tissues. (b) Volcano plot on the ARGs of differential expressions. Orange dots indicated the ones of high expression whereas the blues were for the low expressions. (c) The boxplot for the ARGs of differential expression. Red and green regions separately represented tumor tissues and normal tissues.
3 Journal of Immunology Research assess the sensitivity and specificity of prognostic model. This section comes to an end with all our following bioinformatics analysis basing on the R software (version 3.3.1).

Cell Culture.
We purchased the human kidney proximal tubular epithelial cell line (HK-2) cells from American Type Culture Collection (Manassas, VA, USA). Additionally, our embryonic kidney cells 293T as well as the human ccRCC cell lines 786-O, ACHN, and A498 were bought from Cell Bank, the Chinese Academy of Sciences (Shanghai, China). Those cells were grown in the standard DMEM containing 10% fetal bovine serum (FBS, Gibco, USA) under conventional culture conditions (5% CO 2 , 37°C).
2.5. RNA Extraction and RT-PCR. We extracted the total RNA from the cells following the TRIzol reagent  ) was utilized for the assessment on the longterm cell survival, and every procedure was performed following the manufacturer's instructions. Cell suspensions were grown at 5 × 10 3 cells per well in 96-well culture plates. We determined the cell viability with CCK-8 at a final concentration of 10% to each well with the absorbance at 450 nm by using a multiwell fluorescent plate reader (Thermo Scientific Varioskan Flash, Thermo Fisher Scientific, USA) every 24 h for 3 days. We plotted the proliferation curves in terms of the absorbance.
2.8. Wound Scrape Assay. Separately at 24 h and 48 h posttransfection, we grown those cells in 6-well plate and have them scratched with a 200 μL pipette tip in the middle of the wells; then, they were cultivated with serum-free medium. Twenty-four hours after that, we made an estimation to the width of wounds in three-independent wound sites per group; then, we normalized the data to the control.
2.9. Transwell Migration Assay. 786-O, ACHN, and A498 cells were inoculated into the transwell chamber of a 24well plate (1 × 10 [5]/well) with 600 μL of complete medium adding into the lower chamber. By observing the growth characteristics of the different cells, we took out the transwell chamber after 12-16 h cultivation. After 4% paraformaldehyde fixation, cotton swabs were to remove the remaining cells out of membrane surface. We finally performed a crystal violet staining assay and took the photographs with a microscope (MF53-N, Mingmei, China).

Cell Apoptosis Assay.
Collected cells were centrifugated in 1000 r/min for 3 min, then washed twice with ice-cold PBS, gently resuspended in 500 μL 1× annexin V binding buffer containing 5 μL annexin V-FITC and 3 μL of PI, and then incubated for 10 min at room temperature in dark. Apoptotic cell percentage was determined with flow cytometer (BD FACSCalibur, Becton-Dickinson, USA).
2.11. Cell Cycle Analysis. Procedures of cell cycle assay were shown as follow: cells from different groups were grown in 6-well plates and kept for 48 h. Then, we had them washed with ice-cold PBS and fixed them in 70% (v/v) ice-cold ethanol solution overnight at 4°C; these cells were analyzed in the following day with flow cytometry following the instruction of cell cycle analysis kit (Sigma, MO, USA). Information of cell cycle was determined with ModFit LT 4.0 software.

Subcutaneous Xenograft Model in BALB/c Nude Mice.
The animal study was carried out in terms of the Ethics Committee of Chongqing Medical University. The male BALB/c nude mice aged 4-6 weeks were purchased from Chongqing Medical University and maintained in accordance with the institutional policies. Stable cell lines (ACHN/vector cells and ACHN/shRNA cells) were used. Details of the experiments performed are as described in our previous report [23]. Briefly, ACHN/vector cells or ACHN/shRNA cells were injected, and the tumor volumes were observed every 3 days and calculated. Tumors were surgically removed 30 days after injection and photographed. Tumor volume was assessed by caliper measurement and calculated according to the formula (L × W × W/2), where L represents length and W represents width.

Immunoprecipitation. ACHN cells were transfected
with the plasmids ordered from the polyethyleneimine (PEI) of Polysciences Inc.; after the transfection, they were subjected to a 36-hour lysed with lysis buffer and underwent a pulling down assay with FLAG (Sigma) with all the procedures following the instructions. Immunocomplexes were done with SDS-PAGE, determined with western blot by using anti-FLAG, anti-TP53INP2, and anti-caspase-8 antibody.
2.16. Statistical Analysis. All data were analyzed by using GraphPad Prism 7 (San Diego, CA, USA) and shown in a way of the mean ± SD. Our statistical analysis was for the determination to the significance of the difference between the groups with one-way ANOVA and Turky test or Stu-dent's t-test. Outcomes with two-tailed and P values <0.05 were statistically significant.  c)). GO enrichment analysis was performed according to the differentially expressed ARGs. The top differentially expressed ARG-associated biological processes included regulation of endopeptidase activity, regulation of peptidase activity, autophagy, and process utilizing autophagic mechanism. Supplementary Figure 1(a) also showed the top cellular components including autophagosome and cytosolic part and top molecular functions including ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, and cytokine receptor binding. In the KEGG pathway enrichment analysis, differentially expressed ARGs were shown to be notably associated with human cytomegalovirus infection, shigellosis, and HIF-1 signaling pathway (Supplementary Figure 1(B)-1(D)).
In terms of our median value of the seventeen genes in prediction model, high levels of BID, ATG4B, CASP4, CAPN10, and ULK1 were conspicuously correlated with the worse overall survival in Kaplan-Meier curves (Supplementary Figure 2). On the contrary, low levels of ZFYVE1, PRKAR1A, NFKB1, TP53INP2, MAPK1, HSPA8, BAG1, EIF2S1, PTEN, and BNIP3 were in a significant association with the worse overall survival in our Kaplan-Meier curves (Supplementary Figure 3). To our surprise, the expression of NPC1 and CDKN2A showed no significant correlation with OS in Kaplan-Meier curves (Supplementary Figure 4).

Autophagy-Related
Signature is an Independent Factor for ccRCC Prognosis. Our univariate Cox analysis revealed that age, AJCC stage, Fuhrman grade, T status, M status, and the autophagy-related signature were all the associated factors to the OS of ccRCC patients (Table 4). Moreover, our multivariate Cox regression analysis was performed for the verification on the independent predictive value of clinicopathological parameters and autophagy-related signature  Journal of Immunology Research for OS. Age, AJCC stage, Fuhrman grade and the autophagyrelated signature were the independent predictive factors for the overall survival in ccRCC patients (Figure 3(a)). ROC curves shown in Figure 3(b) were the demonstration on clinicopathological parameters and autophagy-related signature for OS. The autophagy-related signature was the areas under the curve (AUC) in ROC curve with the value of 0.791. Of all the 539 ccRCC patients, there were large amounts of them scant of N status information, making our subsequent analysis on relationship amid N status and OS ambiguous.
The OS-related prediction model values of Fuhman grade were higher than those of the low grade (P < 0:001), and those of AJCC stage were higher than the low AJCC stage (P < 0:001); those values of T3/4 were also higher than the T1/2 (P < 0:001), and those of M1 are higher than the M0 (P < 0:001). There was no signal distinction amid the values of OS-related prediction model between patients aged >60 and patients aged ≤60 (P = 0:140), so was that of male and female (P = 0:878) (Figures 3(c)-3(h)).

Integrated Analysis on ccRCC Unveils TP53INP2's Possible Biomarker Role in ccRCC.
Of the 17 ARGs we previously verified, genes BID, CASP4, ZFYVE1, PRKAR1A, NPC1, TP53INP2, HSPA8, EIF2S1, and BAG1 were detected in 293T, HK2, 786-O, ACHN, and A498 cell lines and they were all rarely studied in renal cell carcinoma. Our results showed that the mRNA expression of TP53INP2 was significantly lower in ccRCC cells when compared with the normal renal cells, and that difference turned the most remarkable versus other genes (Figures 4(a)-4(i)). We then combined the data of TP53INP2 expression and a clinical information from the TCGA dataset. To begin with our assay, we shut out the patients with incomplete clinical information. Then, the outcomes showed that the TP53INP2 expression of cancer tissues turned a significant low level than the normal tissues (P < 0:05), and that in high Fuhman, grade was also lower than the low grade (P < 0:001); the TP53INP2 expression of cancer tissues in high AJCC stage was lower than the low AJCC stage (P < 0:001), and it was

TP53INP2 Inhibited the Proliferation of ccRCC Cell
Lines. TP53INP2's biomarker role in ccRCC cells has been revealed in last section, and we would manage to dig out its biological role in 786-O, ACHN, and A498 cell lines by overexpressing this gene, respectively. qRT-PCR and western blot unveiled an outcome that TP53INP2 was highly expressed in all these cell lines on both mRNA and protein level (Figures 6(a)-6(c)). We had an overexpression of TP53INP2 in 786-O, ACHN, and A498 cell lines separately, and our CCK8 assays showed that the cell viability of these cell lines was all remarkably suppressed (Figures 6(d)-6(f)). Our subsequent flow cytometry assay revealed that all the cell lines manifested a notable decreasing proportion in phase S whereas turned an increasing proportion in phase G1 (Figures 6(g)-6(j)). And these results implied that all the ccRCC cell lines were arrested in phase G1, resulting in reduced cell proliferation.
3.6. TP53INP2 Inhibited Invasion and Promoted Apoptosis in ccRCC Cell Lines. We would have transwell and the scratch wound assays in this section to verify TP53INP2's effects on both the cellular invasion and migration of ccRCC. The scratch wound assay showed that overexpression of TP53INP2 disabled the migration of ccRCC cells after being scratched (Figures 7(a)-7(f)). Moreover, transwell assay showed that TP53INP2 significantly alleviated invasion in the 786-O, ACHN, and A498 cells (Figures 7(g)-7(j)).
Simultaneously, we detected the expressions of invasionrelated proteins in these cells. Our western blot assay showed that TP53INP2-overexpressed ccRCC cells come in an increasing levels of epithelial marker E-cadherin whereas resulted in a decrease in mesenchymal marker expression (Ncadherin and vimentin) (Figures 7(k)-7(n)). Then, flow cytometry showed that TP53INP2-overexpressed ccRCC cells turned a higher percentage of cell apoptosis in contrast to the control (Figures 8(a)-8(d)). The expressions of proapoptotic proteins cleaved caspase-3, cleaved caspase-8, and cleaved PARP were markedly upregulated in the TP53INP2-overexpressed ccRCC cell lines when compared with our control group (Figures 8(e)-8(h)).

TP53INP2 Accelerated Autophagy and Apoptosis in ACHN Cells.
Microscopically speaking, administration of TP53INP2 or mTOR inhibitor Torin1 might lead to an increasement in autophagic vacuoles' number in ACHN cells. Moreover, amount of autophagosomes in the cells of TP53INP2+Torin1 was more than that of merely TP53INP2 or Torin1 (Figure 9(a)). Western blot assay revealed that the overexpression of TP53INP2 in ACHN cells come into no significant effect on mTOR phosphorylation, while the phosphorylation level of mTOR was decreased in Torin1-treated ACHN cells. In contrast to the control, both western blot and immunofluorescence assays turned out a significant increase in LC3 levels in the TP53INP2-overexpressed or Torin1-treated ACHN cells, as well as an enhancement in cells treated with TP53INP2+Torin1 (Figures 9(b)-9(e)). The expression trend of p62 was opposite to LC3 (Figures 9(b)-9(c)). It suggests that TP53INP2 can activate autophagy. Since autophagy and apoptosis are closely related, we added the autophagy inhibitor chloroquine (CQ) to investigate whether TP53INP2 induces cell apoptosis through the activation of autophagy. Western blot assay showed that merely CQ or TP53INP2 might elevate the expressions of proapoptotic proteins (cleaved caspase-3, cleaved caspase-8, and cleaved PARP) versus the control group. Furthermore, the expressions of proapoptotic proteins were signally elevated in TP53INP2+CQ group when compared with the TP53INP2 group (Figures 9(f) and  9(g)). The flow cytometry exhibited that the cell apoptosis 13 Journal of Immunology Research was markedly enhanced in CQ-, TP53INP2-, and CQ+TP53INP2-treated ACHN cells, and the apoptosis rate in cells treated with CQ+TP53INP2 was the highest (Figures 9(h) and 9(i)). These results demonstrated that the TP53INP2 activated autophagy is a protective effect on ACHN, and the proapoptotic effect of TP53INP2 on ACHN is independent of the autophagy pathway.

Overexpression TP53INP2 Inhibits Tumor Growth In
Vivo. Our further experiments would be conducted in animals for the detection on TP53INP2's effects on the tumor evolution. TP53INP2-or empty vector-transfected ACHN cells were injected into nude mice. After overexpressing TP53INP2 in vivo, the size of the tumor was significantly reduced (Figures 10(a) and 10(b)). Western blot showed that the LC3 II/I ratio and cleaved caspase-3 level of mice transfected with TP53INP2 were greatly higher than those of the control, while the p62 level was lower versus the control (Figures 10(c) and 10(d)). In addition, the experimental results showed that the TP53INP2 was successfully overexpressed in mice (Figures 10(c)-10(e)). The H&E staining images showed that the cell morphology was normal in the control group, whereas the number of tumor cells decreased and cell borders were indistinct in the TP53INP2. TUNEL assay revealed that the overexpression of TP53INP2 promoted apoptosis of tumor cells (Figure 10(e)).

TP53INP2 Promoted Cell Apoptosis by Regulating
Caspase-8/TRAF6 Signaling Pathway. Our exploration on the regulatory mechanism of TP53INP2 to ccRCC cell apoptosis was enforced, the proteins binding to TP53INP2 were obtained by co-IP assay, and the proteins directly binding to TP53INP2 were detected by western blot assay. TRAF6 was reported to add K63 chains to caspase-8 when an apoptosis was induced by death receptor. The caspase-8, TRAF6, and TP53INP2 are the components on a same complex; besides that, TP53INP2 works as a scaffold for TRAF6-induced caspase-8 ubiquitination [22]. Therefore, the expressions of TRAF6 and cleaved caspase-8 in TP53INP2-treated ACHN cells were determined by qRT-PCR, western blot, and immunofluorescence. And the results turned out that the TP53INP2 upregulated both the expressions of cleaved caspase-8 and TRAF6 when they were compared with the control (Figures 11(a)-11(e)). The interaction between cleaved caspase-8 and TRAF6 was observed in the presence of TP53INP2 by co-IP assay, thus suggesting a fact that cleaved caspase-8, TRAF6, and TP53INP2 are on the same complex (Figure 11(f)). Subsequently, we silenced TRAF6 in ACHN cells (Figures 11(g) and 11(h)). We found that the absence of TRAF6 resulted in an impaired increasement of proapoptotic proteins (cleaved caspase-3, cleaved caspase-8, and cleaved PARP) and apoptosis rates induced by the overexpression of TP53INP2 (Figures 11(i)-11(n)).

Discussion
Autophagy is an essential procedure in the biological function accomplishment of ccRCC [24]. The autophagyrelated genes, signaling pathways, and markers are associated with ccRCC, and to further analyze the inner link between autophagy molecules to clarify conversion between autophagy and apoptosis, they can provide new ways for an early diagnosis on ccRCC and provide novel strategy to clinical treatment [10,24,25]. In this study, large-scale gene expression profile was paged for the investigation on the association within autophagy-related genes and clinicopathological characteristics, as well as OS in ccRCC. Autophagyrelated genes (ARGs) of differential expression were identified in ccRCC patients with the help of the TCGA database.
A univariate Cox regression model was built for the analysis on the expression level of ARGs and overall survival for the purpose of selecting autophagy-related prognostic genes. Then, we developed a novel prognostic model on the basis of 17 ARGs (BID, ATG4B, CASP4, ZFYVE1, PRKAR1A, CAPN10, NFKB1, NPC1, TP53INP2, ULK1, MAPK1, HSPA8, EIF2S1, CDKN2A, PTEN, BAG1, and BNIP3), all of which together could be the independent prognostic indicators for ccRCC patients. Of those genes, ATG4B, CAPN10, NFKB1, ULK1, MAPK1, CDKN2A, PTEN, and BNIP3 were reported to be the closely associated factors in renal cancer development. ATG4B was involved in the progression-free survival of ccRCC patients of pazopanib treatment [26]. CAPN10 has an inseparable relationship with renal function [27]. The promoter polymorphism of NFKB1 is known as an increased risk of RCC [28]. High expression level of ULK1 has been observed and closely related to poor survival in renal cancer [29]. Wei et al. found that MAPK1 is a vital gene in the VHL-HIF1α pathway in RCC patients [30]. Previous research has shown that the inactivation of CDKN2A is associated with the occurrence and development of ccRCC [31]. Lee et al. reported that low expression of PTEN tended to correlate with poor progression-free survival of RCC patients treated with sunitinib [32]. Restoration of BNIP3 expression would lead to inhibition to growth and a promotion in apoptotic for RCC cell lines [33]. However, the functions of BID, CASP4, ZFYVE1, PRKAR1A, NPC1, TP53INP2, HSPA8, EIF2S1, and BAG1 gene have not been reported in RCC, suggesting that the validation at cellular level, as well as cellular level studies, may reveal the function of these genes in both RCC development and progression.
Our analysis results showed that TP53INP2 was signally associated with worse overall survival (OS) in Kaplan-Meier curves. Moreover, TP53INP2 was hardly expressed in ccRCC tissues when compared with normal tissues. The above results suggest that TP53INP2 is indispensable for ccRCC development. TP53INP2 has not only the function on tumor inhibition but also on the ability to inhibit tumor metastasis, which was consistent with our results [34,35]. Screening to cancer cell lines also implied that those cells with higher protein expressions of TP53INP2 are more prone to being the ones of receptor-induced apoptosis. Expression of TP53INP2 might be deemed as the biomarker in predicting human liposarcoma malignancies [36]. In bladder cancer, TP53INP2 is a modulator in cell migration, invasion, and EMT by going with the GSK-3beta/betacatenin/Snail1 pathway [34]. Low expression of TP53INP2 in neck squamous cell carcinoma is known as a poor prognosis for an intervention in TP53INP2 expression that would lead to an advancement in cell proliferation [35]. Our results also showed that TP53INP2 has tumor inhibition function. Overexpressed TP53INP2 is found to suppress the activity, migration, and invasion of ccRCC cells, thereby inhibiting tumor growth in mice and promoting cell apoptosis.
Autophagy is an evolutionarily conserved biological progression, where the cytoplasmic materials and the organelles are sequestered into autophagosomes, and those molecules are further disintegrated in the autolysosomes so that the material cycle and the cellular homeostasis would be maintained [37]. On the basis of current studies, the importance of autophagy to tumorous biological development is well     [38][39][40]. TP53INP2 involves in basic autophagy by the way of modulating the autophagosome formation [21]. After the autophagy is triggered, TP53INP2 would be relocated out of nucleus to autophagic vacuoles so as to facilitate recruit LC3 the autophagy mediator into these structures [41]. Overexpression of TP53INP2 in human liposarcoma cells would lead to an increasement in LC3 II/ LC3 I ratio as well as a decreasement in p62 expression [36]. We observed in the present study that merely TP53INP2 or mTOR inhibitor Torin1 could initiate the autophagy in ACHN cell lines. Parameter of the LC3 II/ LC3 I ratio is proved to be an indicator of autophagy, which was significantly increased in this context. The positive expression rate of LC3 in renal cancer tissues was shown evidently lower than the adjacent tissues, and the expression of LC3 was correlated with renal cancer development. Moreover, treatment with autophagy inhibitor CQ or TP53INP2 alone will increase the apoptosis rate of ccRCC cells. Aberrant AKT/mTOR pathway activation is broadly seen in various malignant tumors with a consequence of accelerating cellular proliferation, increasing cancerous resistance to apoptosis and advancing the invasion and metastasis of tumors [42]. MTOR is capable of tremendously affecting autophagy process, so the AKT/mTOR signaling pathway is able to negatively manipulate autophagy, but an intervention in the activation of mTOR would beef up autophagy [43,44]. However, our results showed that the TP53INP2 overexpression did not efficiently downregulate mTOR phosphorylation. This finding suggests that TP53INP2-induced autophagy is not dependent on mTOR signaling pathway. TP53INP2 induces apoptosis in ccRCC cells through a nonautophagy dependent pathway.
Autophagy presents a complex and contradictory process in each stage of tumor. On the one hand, basic autophagy could slow down normal-cell transformation; on the other hand, under the condition of long-term metabolic damage, oxidative stress and lack of nutrients and persistent and excessive upregulation of autophagy would also cause cellular death [45]. Growing clinical evidence have been done and found that both autophagy enhancers and inhibitors may induce tumor cell apoptosis [46][47][48]. Caspase is the main executor of apoptosis, caspase-8 initiates apoptosis through autoactivation, and caspase-3 breaks down cellular proteins and promotes apoptosis [15,49]. TP53INP2 is a member of those dual-function proteins to initiate autophagy or apoptosis in different conditions. In this study, we found that the TP53INP2 and autophagy inhibitor treatment can activate caspase-3, caspase-8, and PARP and further promote ccRCC cells into apoptosis. TP53INP2 interplays and initiates caspase-8 in a way of facilitating its TRAF6inducing ubiquitination, thus shifting the response on cell death of apoptosis [22]. Silencing TRAF6 alone did not significantly promote the expressions of cleaved caspase-3, cleaved caspase-8, and cleaved PARP and apoptosis in this study. The apoptotic rate of cells treated with both overexpression of TP53INP2 and silencing of TRAF6 decreased compared with those treated with the overexpression of TP53INP2. Our experiment further confirms this caspase-8 , which was verified with immunofluorescence. (f) ACHN cells expressing FLAG-TRAF6 and TP53INP2 were immunoprecipitated with FLAG resin, and immunocomplexes were subjected to western blot analysis with anti-FLAG, anti-TP53INP2, and anti-caspase-8 antibody. (g) and (h) TRAF6 silence was transiently transfected into the ACHN cell lines. The relative mRNA level and protein level of TRAF6 was estimated with qRT-PCR and western blot. (i) and (k)-(m) Protein levels of cleaved caspase-3, cleaved caspase-8, and cleaved PARP were measured in cells administrated with TP53INP2, siTRAF6, and TP53INP2+siTRAF6. (j) and (n) Flow cytometry was accomplished for the detection on the apoptosis rate in different groups. Data were presented as mean ± SD of three or four independent experiments. * P < 0:05; * * P < 0:01; * * P < 0:001. and TRAF6 bind to each other in the presence of TP53INP2. These results imply that TP53INP2 inhibited renal cell carcinoma by regulating caspase-8 apoptotic pathway.
All the results demonstrated that the inhibitory effects of autophagy-related gene TP53INP2 work out in tumor growth in both vitro and vivo. Moreover, TP53INP2 induces apoptosis in ccRCC cells through a nonautophagy dependent pathway, suggesting that the caspase-8/TRAF6 pathway may be the potential mechanism of TP53INP2-induced apoptosis. We hence draw a conclusion that the gene TP53INP2 could be deemed as a biomarker in prognosis and a target in ccRCC treatment.

Data Availability
The data used to support the findings of this study are included within the article.