Search

Abstract Background Malignant cells exhibit reduced period circadian regulator 3 (PER3) expression. However, the underlying mechanisms of variations in PER3 expression in cancers and the specific function of PER3 in tumor progression remain poorly understood. Results We explored multiple public databases, conducted bioinformatics analyses, and performed in vitro and in vivo experiments for validation. We found PER3 expression was decreased in most types of cancers, and PER3 downregulation was associated with a poor prognosis in 8 types of cancer. PER3 promoter methylation levels were increased in 11 types of cancer. Promoter hypermethylation (CpG islands [CGIs] cg12258811 and cg14204433) correlated with decreased PER3 expression in six cancers (breast invasive carcinoma, colon adenocarcinoma, head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma [KIRP], lung adenocarcinoma [LUAD], and uterine corpus endometrial carcinoma). CGI cg12258811 hypermethylation was associated with reduced survival time and advanced cancer stages. Moreover, the bisulfite pyrosequencing assay confirmed CGI cg12258811 hypermethylation and its negative correlation with PER3 expression. In vitro and in vivo experiments demonstrated that PER3 inhibited KIRP and LUAD progression. Decitabine enhanced PER3 expression and inhibited KIRP cell functions by reducing promoter (cg12258811) methylation level. Conclusions Our findings advanced the mechanistic understanding of variations in PER3 expression in cancers and confirmed the tumor-associated function of PER3 hypermethylation and downregulation.

Background: Mitochondrial fusion is vital for cellular function and has been increasingly linked to cancer development. Kidney renal papillary cell carcinoma (KIRP), the second most common renal cell carcinoma, presents diverse prognostic outcomes. Identifying novel biomarkers is critical for improving prognosis and treatment response in KIRP. Objective: This study aims to explore the gene expression associated with mitochondrial fusion and establish a novel gene signature model to predict KIRP prognosis and cisplatin sensitivity. Methods: We analyzed RNA sequencing data and clinical records of 285 KIRP patients from The Cancer Genome Atlas (TCGA). LASSO regression identified four key mitochondrial fusion-related genes (BNIP3, GDAP1, MIEF2, PRKN). Multivariate Cox regression evaluated their association with overall survival. Risk stratification was developed based on gene expression. We assessed immunotherapy responses using checkpoint inhibitor scores, tumor mutation burden, TIDE scores, and tumor microenvironment characteristics. Cisplatin sensitivity was evaluated via correlation analysis of gene expression levels and half-maximal inhibitory concentration (IC50). In vitro loss- and gain-of-function experiments in KIRP cell lines (Caki-2, ACHN) assessed MIEF2's role in cisplatin sensitivity. Results: The gene signature successfully stratified patients into high- and low-risk groups, with significant survival differences. The area under the ROC curve (AUC) for the risk model was 0.782. MIEF2 was notably associated with cisplatin sensitivity, confirmed through functional experiments. Patients in the high-risk group exhibited lower MIEF2 expression and increased cisplatin sensitivity. [ABSTRACT FROM AUTHOR]

Background: Malignant cells exhibit reduced period circadian regulator 3 (PER3) expression. However, the underlying mechanisms of variations in PER3 expression in cancers and the specific function of PER3 in tumor progression remain poorly understood. Results: We explored multiple public databases, conducted bioinformatics analyses, and performed in vitro and in vivo experiments for validation. We found PER3 expression was decreased in most types of cancers, and PER3 downregulation was associated with a poor prognosis in 8 types of cancer. PER3 promoter methylation levels were increased in 11 types of cancer. Promoter hypermethylation (CpG islands [CGIs] cg12258811 and cg14204433) correlated with decreased PER3 expression in six cancers (breast invasive carcinoma, colon adenocarcinoma, head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma [KIRP], lung adenocarcinoma [LUAD], and uterine corpus endometrial carcinoma). CGI cg12258811 hypermethylation was associated with reduced survival time and advanced cancer stages. Moreover, the bisulfite pyrosequencing assay confirmed CGI cg12258811 hypermethylation and its negative correlation with PER3 expression. In vitro and in vivo experiments demonstrated that PER3 inhibited KIRP and LUAD progression. Decitabine enhanced PER3 expression and inhibited KIRP cell functions by reducing promoter (cg12258811) methylation level. Conclusions: Our findings advanced the mechanistic understanding of variations in PER3 expression in cancers and confirmed the tumor-associated function of PER3 hypermethylation and downregulation. [ABSTRACT FROM AUTHOR]

The aim of our study was to identify differentially expressed genes associated with abnormal DNA methylation as biomarkers for predicting the outcome of Kidney renal papillary cell carcinoma (KIRP). We identified 8 differentially expressed genes (DEGs) (CSRP1, HLA-DMB, LIFR, LTB4R2, MAP3K14, NR2F1, SECTM1, and VIM) that were independently associated with the OS of KIRP. Time dependent AUC for each ROC of the risk assessment model at 1, 3, 5, and 10-years reached 0.8415, 0.8131, 0.7873, and 0.7667. The risk assessment model was correlated with several immune cells and factors. The AUC value of the diagnosis model using those 8 DEGs reached 0.99. The risk assessment model constructed by those 8 DEGs was well able to predict the prognosis and diagnose of KIRP. However, whether the prognosis and diagnosis model could be applied in clinical practice requires further study. Background: Kidney renal papillary cell carcinoma (KIRP) is a common type of renal cell carcinoma. DNA methylation plays an important role in the development of several cancers. The aim of our study was to identify differentially expressed genes associated with abnormal DNA methylation as biomarkers for predicting the outcome of KIRP. Method: We downloaded KIRP methylation data, RNA sequencing (RNAseq) data, and their corresponding clinical information from the Cancer Genome Atlas (TCGA) database. ChAMP and DEGseq2 packages in R software were used to screen differentially methylated probes (DMPs) and differentially expressed genes (DEGs). Univariate and multivariate Cox regression analyses were used to identify suitable immune related genes correlated with aberrant methylations as prognosis biomarkers. Results: We identified 8 DEGs (Cysteine And Glycine Rich Protein 1 [CSRP1], major histocompatibility complex, Class II, DM Beta [HLA-DMB], LIF Receptor Subunit Alpha [LIFR], Leukotriene B4 receptor 2 [LTB4R2], Mitogen-Activated Protein Kinase Kinase Kinase 14 [MAP3K14], Nuclear Receptor Subfamily 2 Group F Member 1 [NR2F1], Secreted And Transmembrane 1 [SECTM1], and Vimentin [VIM]) that were independently associated with the overall survival (months) (OS) of KIRP. The time dependent area under the curve (AUC) for each receiver operating characteristic (ROC) of the risk assessment model at 1, 3, 5, and 10-years reached 0.8415, 0.8131, 0.7873, and 0.7667. The risk assessment model was correlated with several immune cells and factors. The AUC value of the diagnosis model using those 8 DEGs reached 0.99. Conclusions: The risk assessment model constructed by those 8 DEGs was well able to predict the prognosis and diagnose of KIRP. However, whether the prognosis and diagnosis model could be applied in clinical practice requires further study. [ABSTRACT FROM AUTHOR]

Abstract Purpose Kidney renal papillary cell carcinoma (KIRP) is a highly heterogeneous malignancy and current systemic therapeutic strategies are difficult to achieve a satisfactory outcome for advanced disease. Meanwhile, there is a lack of effective biomarkers to predict the prognosis of KIRP. Methods Using TCGA, GTEx, UALCAN, TIMER, TIMER 2.0 and STRING databases, we analyzed the relationship of SNHG6 with KIRP subtypes, tumor-infiltrating immune cells and potential target mRNAs. Based on TCGA data, ROC curves, Kaplan–Meier survival analysis and COX regression analysis were performed to evaluate the diagnostic and prognostic value of SNHG6 in KIRP. Nomogram was used to predict 3- and 5-year disease-specific survival in KIRP patients. In addition, with the help of Genetic ontology and Gene set enrichment analysis, the biological processes and signalling pathways that SNHG6 may be involved in KIRP were initially explored. Results In patients with KIRP, SNHG6 was significantly upregulated and associated with a more aggressive subtype (lymph node involvement, pathological stage IV, CIMP phenotype) and poor prognosis. The ROC curve showed good diagnostic efficacy (AUC value: 0.828) and the C-index of the Nomogram for predicting DSS at 3 and 5 years was 0.920 (0.898–0.941). In the immune microenvironment of KIRP, SNHG6 expression levels were negatively correlated with macrophage abundance and positively correlated with cancer-associated fibroblasts. Furthermore, SNHG6 may promote KIRP progression by regulating the expression of molecules such as AURKB, NDC80, UBE2C, NUF2, PTTG1, CENPH, SPC25, CDCA3, CENPM, BIRC5, TROAP, EZH2. Last, GSEA suggests that SNHG6 may be involved in the regulation of the PPAR signalling pathway and the SLIT/ROBO signalling pathway. Conclusions Our analysis suggests that a high SNHG6 expression status in KIRP is associated with a poorer prognosis for patients, and also elucidates some potential mechanisms contributing to this poorer outcome. This may provide new insights into the treatment and management of KIRP in the foreseeable future.

Background: Many studies have demonstrated the crucial roles of 5-methylcytosine (m5C) RNA methylation in cancer pathogenesis. Methods: Two datasets, including TCGA-KIRP and ICGC, and related clinical information were downloaded, where the expression of 13 m5C regulators was examined. We applied LASSO regression to construct a multi-m5C-regulator-based signature in the TCGA cohort, which was further validated using the ICGC cohort. Univariate and multivariate Cox regressions were applied to evaluate the independent prognostic value of our model. The differences in biological functions and immune characterizations between high and low-risk groups divided based on the risk scores were also investigated via multiple approaches, such as enrichment analyses, mutation mining, and immune scoring. Finally, the sensitivities of commonly used targeted drugs were tested, and the connectivity MAP (cMAP) was utilized to screen potentially effective molecules for patients in the high-risk group. Experimental validation was done following qPCR tests in Caki-2 and HK-2 cell lines. Results: 3 m5C regulators, including ALYREF, DNMT3B and YBX1, were involved in our model. Survival analysis revealed a worse prognosis for patients in the high-risk group. Cox regression results indicated our model's superior predictive performance compared to single-factor prognostic evaluation. Functional enrichment analyses indicated a higher mutation frequency and poorer tumor microenvironment of patients in the high-risk group. qPCR-based results revealed that ALYREF, DNMT3B, and YBX1 were significantly up-regulated in Caki-2 cell lines compared with HK-2 cell lines. Molecules like BRD-K72451865, Levosimendan, and BRD-K03515135 were advised by cMAP for patients in the high-risk group. Conclusion: Our study presented a novel predictive model for KIRP prognosis. Furthermore, the results of our analysis provide new insights for investigating m5C events in KIRP pathogenesis.

Background: Identifying Kidney Renal Papillary Cell Carcinoma (KIRP) patients with high-risk, guiding individualized diagnosis and treatment of patients, and identifying effective prognostic targets are urgent problems to be solved in current research on KIRP. Methods: In this study, data of multi omics for patients with KIRP were collected from TCGA database, including mRNAs, lncRNAs, miRNAs, data of methylation, and data of gene mutations. Data of multi-omics related to prognosis of patients with KIRP were selected for each omics level. Further, multi omics data related to prognosis were integrated into cluster analysis based on ten clustering algorithms using MOVICS package. The multi omics-based cancer subtype (MOCS) were compared on biological characteristics, immune microenvironmental cell abundance, immune checkpoint, genomic mutation, drug sensitivity using R packages, including GSVA, clusterProfiler, TIMER, CIBERSORT, CIBERSORT-ABS, quanTIseq, MCPcounter, xCell, EPIC, GISTIC, and pRRophetic algorithms. Results: The top ten OS-related factors for KIRP patients were annotated. Patients with KIRP were divided into MOCS1, MOCS2, and MOCS3. Patients in the MOCS3 subtype were observed with shorter overall survival time than patients in the MOCS1 and MOCS2 subtypes. MOCS1 was negatively correlated with immune-related pathways, and we found global dysfunction of cancerrelated pathways among the three MOCS subtypes. We evaluated the activity profiles of regulons among the three MOCSs. Most of the metabolism-related pathways were activated in MOCS2. Several immune microenvironmental cells were highly infiltrated in specific MOCS subtype. MOCS3 showed a significantly lower tumor mutation burden. The CNV occurrence frequency was higher in MOCS1. As for treatment, we found that these MOCSs were sensitive to different drugs and treatments. We also analyzed single-cell data for KIRP. Conclusion: Based on a variety of algorithms, this study determined the risk classifier based on multi-omics data, which could guide the risk stratification and medication selection of patients with KIRP. [ABSTRACT FROM AUTHOR]

Purpose: Kidney renal papillary cell carcinoma (KIRP) is a highly heterogeneous malignancy and current systemic therapeutic strategies are difficult to achieve a satisfactory outcome for advanced disease. Meanwhile, there is a lack of effective biomarkers to predict the prognosis of KIRP. Methods: Using TCGA, GTEx, UALCAN, TIMER, TIMER 2.0 and STRING databases, we analyzed the relationship of SNHG6 with KIRP subtypes, tumor-infiltrating immune cells and potential target mRNAs. Based on TCGA data, ROC curves, Kaplan–Meier survival analysis and COX regression analysis were performed to evaluate the diagnostic and prognostic value of SNHG6 in KIRP. Nomogram was used to predict 3- and 5-year disease-specific survival in KIRP patients. In addition, with the help of Genetic ontology and Gene set enrichment analysis, the biological processes and signalling pathways that SNHG6 may be involved in KIRP were initially explored. Results: In patients with KIRP, SNHG6 was significantly upregulated and associated with a more aggressive subtype (lymph node involvement, pathological stage IV, CIMP phenotype) and poor prognosis. The ROC curve showed good diagnostic efficacy (AUC value: 0.828) and the C-index of the Nomogram for predicting DSS at 3 and 5 years was 0.920 (0.898–0.941). In the immune microenvironment of KIRP, SNHG6 expression levels were negatively correlated with macrophage abundance and positively correlated with cancer-associated fibroblasts. Furthermore, SNHG6 may promote KIRP progression by regulating the expression of molecules such as AURKB, NDC80, UBE2C, NUF2, PTTG1, CENPH, SPC25, CDCA3, CENPM, BIRC5, TROAP, EZH2. Last, GSEA suggests that SNHG6 may be involved in the regulation of the PPAR signalling pathway and the SLIT/ROBO signalling pathway. Conclusions: Our analysis suggests that a high SNHG6 expression status in KIRP is associated with a poorer prognosis for patients, and also elucidates some potential mechanisms contributing to this poorer outcome. This may provide new insights into the treatment and management of KIRP in the foreseeable future. [ABSTRACT FROM AUTHOR]

To identify the autophagy-related long non-coding RNAs (ARlncRNAs) associated with the prognosis of kidney renal papillary cell carcinoma (KIRP), thereby establishing a clinical prognostic model. The gene expression matrix and clinical survival information of patients with KIRP were downloaded from The Cancer Genome Atlas database, and were divided into the training and testing groups. ARlncRNAs associated with the KIRP prognosis were analyzed by univariate, Least Absolute Shrinkage and Selection Operator (LASSO(, and multivariate Cox regression to construct a signature. We combined clinical factors associated with the prognosis with ARlncRNAs to establish a prognostic model of patients with KIRP. A nomogram was established to predict 1-year, 3-year, and 5-year survival of patients with KIRP. Besides, we built the lncRNA-messenger RNA co-expression network and used Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analysis to detect the biological functions of ARlncRNAs. LEF1-AS1, CU634019.6, C2orf48, AC027228.2, and AC107464.3 were identified. A prognosis-related ARlncRNAs signature was constructed in the training group and validated in the testing group. Patients with KIRP with a low risk score had significantly longer survival time than those with a high risk score. The risk score significantly affected the prognosis of patients, thereby being used for modeling. The area under the receiver operating characteristic curve values of 1-year, 3-year, and 5-year overall survival were 0.80, 0.78, and 0.84 in the training group, respectively. The signature had high concordance index and good accuracy in predicting the prognosis, which were confirmed by the nomogram. The prognosis-related ARlncRNAs signature we identified had a more accurate prediction for the prognosis of patients with KIRP. [ABSTRACT FROM AUTHOR]

Dechao Feng,* Facai Zhang,* Ling Liu,* Qiao Xiong, Hang Xu, Wuran Wei, Zhenghua Liu, Lu Yang Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhenghua Liu; Lu YangDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, 37 Guoxue Xiang, Chengdu, 610041, Sichuan, People’s Republic of ChinaTel +86 28-8542-2444Fax +86 28-85422451Email zhliu@scu.edu.cn; wycleflue@163.comBackground: There is a surprising paucity of studies investigating the potential mechanism of SKA3 in the progression and prognosis of kidney renal papillary cell carcinoma (KIRP).Methods: We used TCGA and other databases to analyze the expression, clinical value, and potential mechanisms of SKA3 in KIRP patients. We also explored therapeutic agents for KIRP through GSCALite.Results: SKA3 mRNA expression was significantly upregulated and the area under the curve was 0.792 (95% CI 0.727– 0.856). Increased SKA3 expression was related to shorter overall survival, disease-specific survival and progression-free survival. Hub genes in protein–protein interactions were CDK1, CDC20, CCNB1, CCNA2, BUB1, AURKB, BUB1B, PLK1, CCNB2, and MAD2L1, which were differentially expressed and also associated with KIRP prognosis. Gene-set enrichment analysis indicated that E2F targets, epithelial–mesenchymal transition, glycolysis, the WNT signaling pathway, and other pathways were highly enriched upon SKA3 upregulation. Gene-set variation analysis of SKA3 and its ten hub genes showed that the significant correlation of cancer-related pathways included the cell cycle, DNA damage, hormone androgen receptor, hormone estrogen receptor, PI3K/Akt, and Ras/MAPK. In addition, we found that MEK inhibitors, ie, trametinib, selumetinib, PD0325901, and RDEA119, may be feasible targeting agents for KIRP patients.Conclusion: SKA3 might contribute to poor prognosis of KIRP through cell cycle, DNA damage, hormone androgen receptor, hormone estrogen receptor, PI3K/Akt, and RAS/MAPK. SKA3 potentially serves as a prognostic biomarker and target for KIRP.Keywords: spindle and kinetochore–associated complex subunit 3, kidney renal papillary cell carcinoma, biomarker, enrichment analysis, targeted therapy

Abstract Background GPRIN1 may be a novel tumor regulator, but its role and mechanism in tumors are still unclear. Methods First, a pan‐cancer correlation analysis was conducted on the expression and prognosis of GPRIN1 based on the data downloaded from The Cancer Genome Atlas (TCGA) database. Second, the Starbase database was used to predict the upstream miRNAs and lncRNAs of GPRIN1, and the expression analysis, survival analysis, and correlation analysis were performed to screen the microRNA (miRNAs)/long non‐coding RNAs (lncRNAs) that had a correlation with kidney renal papillary cell carcinoma (KIRP) or lung adenocarcinoma (LUAD). Third, the CIBERSORT algorithm was employed to calculate the proportion of various types of immune cells, and then the R packages were used for evaluating the relation between GPRIN1 expression and tumor immune cell infiltration as well as between GPRIN1 and the immune cell biomarker. Finally, the correlation analysis was made on GPRIN1 and immune checkpoints (CD274, CTLA4, and PDCD1). Results The pan‐cancer analysis suggested that GPRIN1 was up‐expressed in KIRP and LUAD, and it correlated with poor prognosis. LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG‐miR‐140‐3p was likely to be the most promising upstream regulation pathway of GPRIN1. Upexpression of LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG and downexpression of miR‐140‐3p were found relevant with poor outcomes of KIRP and LUAD. GPRIN1 expression was significantly correlated with tumor immune cell infiltration, immune cell biomarkers, and immune checkpoints. Conclusions The competitive endogenous (ceRNA) of miR‐140‐3p‐GPRIN1 axis and its upstream lncRNAs are closely related to KIRP and LUAD, and might affect the prognosis and therapeutic effect of KIRP and LUAD.

Abstract Background Little data is available on prognostic biomarkers and effective treatment options for Kidney Renal Papillary Cell Carcinoma (KIRP) patients, to find potential prognostic biomarkers and new targets was an urgent mission for KIRP therapy. Methods The differentially expressed autophagy-related genes (DEARGs) were screened out according to the RNA sequencing data in The Cancer Genome Atlas database, then identified survival-related DEARGs to establish a prognostic model for survival predicting of KIRP patients. Then we verified the robustness and validity of the prognostic risk model through clinicopathological data. At last, we evaluate the prognostic value of genes that formed the prognostic risk model individually. Results We analyzed the expression of 232 autophagy-related genes (ARGs) in 289 KIRP and 32 non-tumor tissue cases, and 40 mRNAs were screened out as DEARGs. The functional and pathway enrichment analysis was done and protein–protein interaction network was constructed for all DEARGs. To sift candidate DEARGs associated with KIRP patients’ survival and create an autophagy-related risk prognostic model, univariate and multivariate Cox regression analysis were did separately. Eventually 3 desirable independent prognostic DEARGs (P4HB, NRG1, and BIRC5) were picked out and used for construct the autophagy-related risk model. The accuracy of the prognostic risk model for survival prediction was assessed by Kaplan–Meier plotter, receiver-operator characteristic curve, and clinicopathological correlational analyses. The prognostic value of above 3 genes was verified individually by survival analysis and expression analysis on mRNA and protein level. Conclusions The autophagy-related prognostic model is accurate and applicable, it can predict OS independently for KIRP patients. Three independent prognostic DEARGs can benefit for facilitate personalized target treatment too.

Pyroptosis is defined as an inflammatory form of programmed cell death. Increasing studies have demonstrated that pyroptosis is closely related to tumor development and antitumor process. However, the role of pyroptosis in kidney renal papillary cell carcinoma (KIRP) remains obscure. In this study, we analyzed the expression of 52 pyroptosis-related genes (PRGs) in KIRP, of which 20 differentially expressed PRGs were identified between tumor and normal tissues. Consensus clustering analysis based on these PRGs was used to divided patients into two clusters, from which a significant difference in survival was found (p = 0.0041). The prognostic risk model based on six PRGs (CASP8, CASP9, CHMP2A, GPX4, IL6, and IRF1) was built using univariate Cox regression and LASSO–Cox regression analysis, with good performance in predicting one-, three-, and five-year overall survival. Kaplan–Meier survival analysis showed that the high-risk group had a poor survival outcome (p < 0.001) and risk score was an independent prognostic factor (HR: 2.655, 95% CI 1.192–5.911, p = 0.016). Immune profiling revealed differences in immune cell infiltration between the two groups, and the infiltration of M2 macrophages was significantly upregulated in the tumor immune microenvironment, implying that tumor immunity participated in the KIRP progression. Finally, we identified two hub genes in tumor tissues (IL6 and CASP9), which were validated in vitro. In conclusion, we conducted a comprehensive analysis of PRGs in KIRP and tried to provide a pyroptosis-related signature for predicting the prognosis.

Pyroptosis is defined as an inflammatory form of programmed cell death. Increasing studies have demonstrated that pyroptosis is closely related to tumor development and antitumor process. However, the role of pyroptosis in kidney renal papillary cell carcinoma (KIRP) remains obscure. In this study, we analyzed the expression of 52 pyroptosis-related genes (PRGs) in KIRP, of which 20 differentially expressed PRGs were identified between tumor and normal tissues. Consensus clustering analysis based on these PRGs was used to divided patients into two clusters, from which a significant difference in survival was found (p = 0.0041). The prognostic risk model based on six PRGs (CASP8 , CASP9 , CHMP2A , GPX4 , IL6 , and IRF1) was built using univariate Cox regression and LASSO–Cox regression analysis, with good performance in predicting one-, three-, and five-year overall survival. Kaplan–Meier survival analysis showed that the high-risk group had a poor survival outcome (p < 0.001) and risk score was an independent prognostic factor (HR: 2.655, 95% CI 1.192–5.911, p = 0.016). Immune profiling revealed differences in immune cell infiltration between the two groups, and the infiltration of M2 macrophages was significantly upregulated in the tumor immune microenvironment, implying that tumor immunity participated in the KIRP progression. Finally, we identified two hub genes in tumor tissues (IL6 and CASP9), which were validated in vitro. In conclusion, we conducted a comprehensive analysis of PRGs in KIRP and tried to provide a pyroptosis-related signature for predicting the prognosis. [ABSTRACT FROM AUTHOR]

Background: GPRIN1 may be a novel tumor regulator, but its role and mechanism in tumors are still unclear. Methods: First, a pan‐cancer correlation analysis was conducted on the expression and prognosis of GPRIN1 based on the data downloaded from The Cancer Genome Atlas (TCGA) database. Second, the Starbase database was used to predict the upstream miRNAs and lncRNAs of GPRIN1, and the expression analysis, survival analysis, and correlation analysis were performed to screen the microRNA (miRNAs)/long non‐coding RNAs (lncRNAs) that had a correlation with kidney renal papillary cell carcinoma (KIRP) or lung adenocarcinoma (LUAD). Third, the CIBERSORT algorithm was employed to calculate the proportion of various types of immune cells, and then the R packages were used for evaluating the relation between GPRIN1 expression and tumor immune cell infiltration as well as between GPRIN1 and the immune cell biomarker. Finally, the correlation analysis was made on GPRIN1 and immune checkpoints (CD274, CTLA4, and PDCD1). Results: The pan‐cancer analysis suggested that GPRIN1 was up‐expressed in KIRP and LUAD, and it correlated with poor prognosis. LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG‐miR‐140‐3p was likely to be the most promising upstream regulation pathway of GPRIN1. Upexpression of LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG and downexpression of miR‐140‐3p were found relevant with poor outcomes of KIRP and LUAD. GPRIN1 expression was significantly correlated with tumor immune cell infiltration, immune cell biomarkers, and immune checkpoints. Conclusions: The competitive endogenous (ceRNA) of miR‐140‐3p‐GPRIN1 axis and its upstream lncRNAs are closely related to KIRP and LUAD, and might affect the prognosis and therapeutic effect of KIRP and LUAD. [ABSTRACT FROM AUTHOR]

Background: Pyrroline-5-carboxylate reductase (PYCR) includes three human genes encoding three isozymes, PYCR1, PYCR2, and PYCR3 (or PYCRL), which facilitate the final step in the conversion of glutamine to proline. These genes play important roles in regulating the cell cycle and redox homeostasis as well as promoting growth signaling pathways. Proline is abnormally upregulated in a variety of cancers, and as the last key enzyme in proline production, PYCR plays an integral role in promoting tumorigenesis and cancer progression. However, its role in patients with kidney renal papillary cell carcinoma (KIRP) has not been fully elucidated. In this study, we aimed to systematically analyze the expression, gene regulatory network, prognostic value, and target prediction of PYCR in patients with KIRP, elucidate the association between PYCR expression and KIRP, and identify potential new targets for the clinical treatment of KIRP. Methods: We systematically analyzed the expression, prognosis, gene regulatory network, and regulatory targets of PYCR1, PYCR2, and PYCRL in KIRP using multiple online databases including cBioPortal, STRING, MethSurv, GeneMANIA, Gene Expression Profiling Interactive Analysis (GEPIA), Metascape, UALCAN, LinkedOmics, and TIMER. Results: The expression levels of PYCR1, PYCR2, and PYCRL were considerably upregulated in patients with KIRP based on sample type, sex, age, and individual cancer stage. PYCR1 and PYCR2 transcript levels were markedly upregulated in females than in males, and patients aged 21–40 years had higher PYCR1 and PYCR2 transcript levels than those in other age groups. Interestingly, PYCR2 transcript levels gradually decreased with age. In addition, the expressions of PYCR1 and PYCR2 were notably correlated with the pathological stage of KIRP. Patients with KIRP with low PYCR1 and PYCR2 expression had longer survival than those with high PYCR1 and PYCR2 expression. PYCR1, PYCR2, and PYCRL were altered by 4%, 7%, and 6%, respectively, in 280 patients with KIRP. The methylation levels of cytosine-phosphate-guanine (CpG) sites in PYCR were markedly correlated with the prognosis of patients with KIRP. PYCR1, PYCR2, PYCRL, and their neighboring genes form a complex network of interactions. The molecular functions of the genes, as demonstrated by their corresponding Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, included calcium channel activity, phospholipid binding, RNA polymerase II-specificity, and kinase and GTPase-regulatory activities. PYCR1, PYCR2, and PYCRL targeted miR-21, miR-221, and miR-222, resulting in a better prognosis of KIRP. We analyzed mRNA sequencing data from 290 patients with KIRP and found that ADA, NPM3, and TKT were positively associated with PYCR1 expression; PFDN2, JTB, and HAX1 were positively correlated with PYCR2 expression; SHARPIN, YDJC, and NUBP2 were positively correlated with PYCRL expression; PYCR1 was positively correlated with B cell and CD8+ T-cell infiltration levels; macrophage infiltration was negatively correlated with PYCR2 expression; and PYCRL expression was negatively correlated with B-cell, CD8+ T cell, and dendritic cell infiltration levels. Conclusions: PYCR1, PYCR2, and PYCRL may be potential therapeutic and prognostic biomarkers for patients with KIRP. The regulation of microRNAs (miRNAs), including miR-21, miR-221, and miR-222, may prove an important strategy for KIRP treatment.

Background: Little data is available on prognostic biomarkers and effective treatment options for Kidney Renal Papillary Cell Carcinoma (KIRP) patients, to find potential prognostic biomarkers and new targets was an urgent mission for KIRP therapy.Methods: The differentially expressed autophagy-related genes (DEARGs) were screened out according to the RNA sequencing data in The Cancer Genome Atlas database, then identified survival-related DEARGs to establish a prognostic model for survival predicting of KIRP patients. Then we verified the robustness and validity of the prognostic risk model through clinicopathological data. At last, we evaluate the prognostic value of genes that formed the prognostic risk model individually.Results: We analyzed the expression of 232 autophagy-related genes (ARGs) in 289 KIRP and 32 non-tumor tissue cases, and 40 mRNAs were screened out as DEARGs. The functional and pathway enrichment analysis was done and protein-protein interaction network was constructed for all DEARGs. To sift candidate DEARGs associated with KIRP patients' survival and create an autophagy-related risk prognostic model, univariate and multivariate Cox regression analysis were did separately. Eventually 3 desirable independent prognostic DEARGs (P4HB, NRG1, and BIRC5) were picked out and used for construct the autophagy-related risk model. The accuracy of the prognostic risk model for survival prediction was assessed by Kaplan-Meier plotter, receiver-operator characteristic curve, and clinicopathological correlational analyses. The prognostic value of above 3 genes was verified individually by survival analysis and expression analysis on mRNA and protein level.Conclusions: The autophagy-related prognostic model is accurate and applicable, it can predict OS independently for KIRP patients. Three independent prognostic DEARGs can benefit for facilitate personalized target treatment too. [ABSTRACT FROM AUTHOR]

Kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) are the most common RCC types. RCC has high immune infiltration levels, and immunotherapy is currently one of the most promising treatments for RCC. Collagen triple helix repeat containing 1 (CTHRC1) is an extracellular matrix protein that regulates tumor invasion and modulates the tumor microenvironment. However, the association of CTHRC1 with the prognosis and tumor-infiltrating lymphocytes of KIRP and KIRC has not been reported. We examined the CTHRC1 expression differences in multiple tumor tissues and normal tissues via exploring TIMER, Oncomine, and UALCAN databases. Then, we searched the Kaplan-Meier plotter database to evaluate the correlation of CTHRC1 mRNA level with clinical outcomes. Subsequently, the TIMER platform and TISIDB website were chosen to assess the correlation of CTHRC1 with tumor immune cell infiltration level. We further explored the causes of aberrant CTHRC1 expression in tumorigenesis. We found that CTHRC1 level was significantly elevated in KIRP and KIRC tissues relative to normal tissues. CTHRC1 expression associates with tumor stage, histology, lymph node metastasis, and poor clinical prognosis in KIRP. The CTHRC1 level correlates to tumor grade, stage, nodal metastasis, and worse survival prognosis. Additionally, CTHRC1 is positively related to different tumor-infiltrating immune cells in KIRP and KIRC. Moreover, CTHRC1 was closely correlated with the gene markers of diverse immune cells. Also, high CTHRC1 expression predicted a worse prognosis in KIRP and KIRC based on immune cells. Copy number variations (CNV) and DNA methylation might contribute to the abnormal upregulation of CTHRC1 in KIRP and KIRC. In conclusion, CTHRC1 can serve as a biomarker to predict the prognosis and immune infiltration in KIRP and KIRC.

Kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) are the most common RCC types. RCC has high immune infiltration levels, and immunotherapy is currently one of the most promising treatments for RCC. Collagen triple helix repeat containing 1 (CTHRC1) is an extracellular matrix protein that regulates tumor invasion and modulates the tumor microenvironment. However, the association of CTHRC1 with the prognosis and tumor-infiltrating lymphocytes of KIRP and KIRC has not been reported. We examined the CTHRC1 expression differences in multiple tumor tissues and normal tissues via exploring TIMER, Oncomine, and UALCAN databases. Then, we searched the Kaplan-Meier plotter database to evaluate the correlation of CTHRC1 mRNA level with clinical outcomes. Subsequently, the TIMER platform and TISIDB website were chosen to assess the correlation of CTHRC1 with tumor immune cell infiltration level. We further explored the causes of aberrant CTHRC1 expression in tumorigenesis. We found that CTHRC1 level was significantly elevated in KIRP and KIRC tissues relative to normal tissues. CTHRC1 expression associates with tumor stage, histology, lymph node metastasis, and poor clinical prognosis in KIRP. The CTHRC1 level correlates to tumor grade, stage, nodal metastasis, and worse survival prognosis. Additionally, CTHRC1 is positively related to different tumor-infiltrating immune cells in KIRP and KIRC. Moreover, CTHRC1 was closely correlated with the gene markers of diverse immune cells. Also, high CTHRC1 expression predicted a worse prognosis in KIRP and KIRC based on immune cells. Copy number variations (CNV) and DNA methylation might contribute to the abnormal upregulation of CTHRC1 in KIRP and KIRC. In conclusion, CTHRC1 can serve as a biomarker to predict the prognosis and immune infiltration in KIRP and KIRC. [ABSTRACT FROM AUTHOR]

• 368 immune-related genes and 60 TFs were differentially expressed in KIRP. • 9 genes were statistically significant prognostic-related genes. • Created a nomogram with good predictive power and clinical practicability. • B cells and CD4 + T cells were positively correlated with risk score model. Kidney renal papillary cell carcinoma (KIRP) is frequently associated with an unfavorable prognosis for affected individuals. Unfortunately, there has been insufficient exploration in search for a reliable prognosis signature and predictive indicators to forecast outcomes for KIRP patients. The aim of this study is to employ a comprehensive analysis of data for the identification of prognosis genes, leading to the development of a nomogram with strong predictive capabilities. The objective is to provide a valuable statistical tool that, when implemented in a clinical setting, can offer patients an early opportunity for treatment and enhance their chances of ultimate recovery from this life-threatening disease. Different packages in R were used to analyze RNA-seq data from the TCGA data portal. Multivariate Cox regression analysis and Kaplan-Meier analysis were also used to investigate the prognostic values of immune-related genes and construct the predictive model and nomogram. A p-value < 0.05 was considered to be significant. A total of 368 immune-related genes and 60 TFs were identified as differentially expressed in KIRP tissues compared with normal tissues. Of the 368, 23 were found to be related to overall survival. GO and KEGG analysis suggested that these prognostic immune-related genes mainly participated in the ERK1 and ERK2 cascades, Rap1 signaling pathway, and the PI3K-Akt signaling pathway. 9 genes were identified from Cox regression to be statistically significant prognostic-related genes. Survival analysis showed that a model based on these 9 prognostic-related genes has high predictive performance. Immunohistochemistry results show that APOH, BIRC5, CCL19, and GRN were significantly increased in kidney cancer. B cells and CD4 + T cells were positively correlated with risk score model. A prognostic model was successfully created based on 9 immune-related genes correlated with overall survival in KIRP. This work aims to provide some insight into therapeutic approaches and prognostic predictors of KIRP. [ABSTRACT FROM AUTHOR]

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFK-2/FBPase-2, PFKFB3) is a glycolysis regulatory enzyme and plays a key role in oncogenesis of several cancers. However, the systematic study of crosstalk between PFKFB3 and Tumor microenvironment (TME) in pan-cancer has less been examined. In this study, we conducted a comprehensive analysis of the relationship between PFKFB3 expression, patient prognostic, Tumor mutational burden (TMB), Microsatellite instability (MSI), DNA mismatch repair (MMR), and especially TME, including immune infiltration, immune regulator, and immune checkpoint, across 33 types of tumors using datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We found that PFKFB3 expression was significantly correlated with patient prognostic and TME factors in various tumors. Moreover, we confirmed that PFKFB3 was an independent prognostic factor for kidney renal papillary cell carcinoma (KIRP), and established a risk prognostic model based on the expression of PFKFB3 as a clinical risk factor, which has a good predictive ability. Our study indicated that PFKFB3 is a potent regulatory factor for TME and has the potential to be a valuable prognostic biomarker in human tumor therapy.

Background: A signal transduction pathway (STP) is a network of intercellular information flow initiated when extracellular signaling molecules bind to cell-surface receptors. Many aberrant STPs have been associated with various cancers. To develop optimal treatments for cancer patients, it is important to discover which STPs are implicated in a cancer or cancer-subtype. The Cancer Genome Atlas (TCGA) makes available gene expression level data on cases and controls in ten different types of cancer including breast cancer, colon adenocarcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian carcinoma, rectum adenocarcinoma, and uterine corpus endometriod carcinoma. Signaling Pathway Impact Analysis (SPIA) is a software package that analyzes gene expression data to identify whether a pathway is relevant in a given condition. Methods: We present the results of a study that uses SPIA to investigate all 157 signaling pathways in the KEGG PATHWAY database. We analyzed each of the ten cancer types mentioned above separately, and we perform a pan-cancer analysis by grouping the data for all the cancer types. Results: In each analysis several pathways were found to be markedly more significant than all the other pathways. We call them notable. Research has already established a connection between many of these pathways and the corresponding cancer type. However, some of our discovered pathways appear to be new findings. Altogether there were 37 notable findings in the separate analyses, 26 of them occurred in 7 pathways. These 7 pathways included the 4 notable pathways discovered in the pan-cancer analysis. So, our results suggest that these 7 pathways account for much of the mechanisms of cancer. Furthermore, by looking at the overlap among pathways, we identified possible regions on the pathways where the aberrant activity is occurring. Conclusions: We obtained 37 notable findings concerning 18 pathways. Some of them appear to be new discoveries. Furthermore, we identified regions on pathways where the aberrant activity might be occurring. We conclude that our results will prove to be valuable to cancer researchers because they provide many opportunities for laboratory and clinical follow-up studies. [ABSTRACT FROM AUTHOR]

To identify the autophagy-related long non-coding RNAs (ARlncRNAs) associated with the prognosis of kidney renal papillary cell carcinoma (KIRP), thereby establishing a clinical prognostic model. The gene expression matrix and clinical survival information of patients with KIRP were downloaded from The Cancer Genome Atlas database, and were divided into the training and testing groups. ARlncRNAs associated with the KIRP prognosis were analyzed by univariate, Least Absolute Shrinkage and Selection Operator (LASSO(, and multivariate Cox regression to construct a signature. We combined clinical factors associated with the prognosis with ARlncRNAs to establish a prognostic model of patients with KIRP. A nomogram was established to predict 1-year, 3-year, and 5-year survival of patients with KIRP. Besides, we built the lncRNA-messenger RNA co-expression network and used Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analysis to detect the biological functions of ARlncRNAs. LEF1-AS1, CU634019.6, C2orf48, AC027228.2, and AC107464.3 were identified. A prognosis-related ARlncRNAs signature was constructed in the training group and validated in the testing group. Patients with KIRP with a low risk score had significantly longer survival time than those with a high risk score. The risk score significantly affected the prognosis of patients, thereby being used for modeling. The area under the receiver operating characteristic curve values of 1-year, 3-year, and 5-year overall survival were 0.80, 0.78, and 0.84 in the training group, respectively. The signature had high concordance index and good accuracy in predicting the prognosis, which were confirmed by the nomogram. The prognosis-related ARlncRNAs signature we identified had a more accurate prediction for the prognosis of patients with KIRP., Competing Interests: Competing interests: None declared., (© American Federation for Medical Research 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: VEGFA is one of the most important regulators of angiogenesis and plays a crucial role in cancer angiogenesis and progression. Recent studies have highlighted a relationship between VEGFA expression and renal cell carcinoma occurrence. However, the expression level, gene regulation network, prognostic value, and target prediction of VEGFA in renal cell carcinoma remain unclear. Therefore, system analysis of the expression, gene regulation network, prognostic value, and target prediction of VEGFA in patients with renal cell carcinoma is of great theoretical significance as there is a clinical demand for the discovery of new renal cell carcinoma treatment targets and strategies to further improve renal cell carcinoma treatment efficacy., Methods: This study used multiple free online databases, including cBioPortal, TRRUST, GeneMANIA, GEPIA, Metascape, UALCAN, LinkedOmics, Metascape, and TIMER for the abovementioned analysis., Results: VEGFA was upregulated in patients with kidney renal clear cell carcinoma (KIRC) and kidney chromophobe (KICH), and downregulated in patients with kidney renal papillary cell carcinoma (KIRP). Moreover, genetic alterations of VEGFA were found in patients with renal cell carcinoma as follows: 4% (KIRC), 8% (KICH), and 4% (KIRP). The promoter methylation of VEGFA was lower and higher in patients with clinical stages of KIRC and stage 1 KIRP, respectively. VEGFA expression significantly correlated with KIRC and KIRP pathological stages. Furthermore, patients with KICH and KIRP having low VEGFA expression levels had a longer survival than those having high VEGFA expression levels. VEGFA and its neighboring genes functioned in the regulation of protein methylation and glycosylation, as well as muscle fiber growth and differentiation in patients with renal cell carcinoma. Gene Ontology enrichment analysis revealed that the functions of VEGFA and its neighboring genes in patients with renal cell carcinoma are mainly related to cell adhesion molecule binding, catalytic activity, acting on RNA, ATPase activity, actin filament binding, protease binding, transcription coactivator activity, cysteine-type peptidase activity, and calmodulin binding. Transcription factor targets of VEGFA and its neighboring genes in patients with renal cell carcinoma were found: HIF1A, TFAP2A, and ESR1 in KIRC; STAT3, NFKB1, and HIPK2 in KICH; and FOXO3, TFAP2A, and ETS1 in KIRP. We further explored the VEGFA -associated kinase (ATM in KICH as well as CDK1 and AURKB in KIRP) and VEGFA -associated microRNA (miRNA) targets (MIR-21 in KICH as well as MIR-213, MIR-383, and MIR-492 in KIRP). Furthermore, the following genes had the strongest correlation with VEGFA expression in patients with renal cell carcinoma: NOTCH4 , GPR4 , and TRIB2 in KIRC; CKMT2 , RRAGD , and PPARGC1A in KICH; and FLT1 , C6orf223 , and ESM1 in KIRP. VEGFA expression in patients with renal cell carcinoma was positively associated with immune cell infiltration, including CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells., Conclusions: This study revealed VEGFA expression and potential gene regulatory network in patients with renal cell carcinoma, thereby laying a foundation for further research on the role of VEGFA in renal cell carcinoma occurrence. Moreover, the study provides new renal cell carcinoma therapeutic targets and prognostic biomarkers as a reference for fundamental and clinical research.

Background and Objective: SPC24 was reported to be correlated with the development of many cancers. However, its role in renal cancer was unclear. Our aim was to explore the role of SPC24 in kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) in types of renal cancer., Methods: SPC24 expressions in KIRC and KIRP were firstly analyzed. Subsequently, the correlation between SPC24 expression and TNM staging of KIRC and KIRP and the accuracy of SPC24 in diagnosing KIRC and KIRP were explored. Moreover, the correlation between SPC24 expression and prognosis of KIRC and KIRP were analyzed. Univariate and multivariate analyses were performed to identify prognostic factors in KIRC and KIRP, and nomograms were constructed. The correlation between SPC24 expression and immune cell infiltration, immune molecules, microsatellite instability (MSI), and tumor mutational burden (TMB) were further explored. Finally, the correlations between SPC24 expression and prognosis of KIRC based on different immune cell enrichment were analyzed., Results: SPC24 was significantly up-regulated in multiple cancers, especially KIRC and KIRP. SPC24 expression was significantly correlated with the TNM stage of KIRC and KIRP, and upregulated SPC24 suggested a worse prognosis. Besides, SPC24 possesses good accuracy in diagnosing KIRC and KIRP. The SPC24-based nomograms displayed satisfactory efficacy in KIRC and KIRP. Moreover, we found that SPC24 expression was closely correlated with immune cell infiltration, immune molecules, and TMB in KIRC, and up-regulated SPC24 revealed poor prognosis based on different immune cell enrichment., Conclusion: SPC24 has the potential to be a biomarker predicting the prognosis and/or immune infiltration of KIRC and KIRP., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Kidney renal papillary cell carcinoma (KIRP) is a type of low-grade malignant renal cell carcinoma. Huge challenges remain in the treatment of KIRP. Cell division cycle associated 3 (CDCA3) participates in human physiological and pathological processes. However, its role in KIRP has not been established. Here, we evaluated the prognostic value of CDCA3 in KIRP using a comprehensive bioinformatics approach. Data for CDCA3 expression in KIRP were obtained from online database. Different expression genes between high and low CDCA3 expression groups were identified and evaluated by performing Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. A gene set enrichment analysis was performed to elucidate the function and pathway differences between the different. Differences in immune cell infiltration between low and high CDCA3 expression groups were analyzed by a single-sample GSEA method for immune cells. A protein-protein interaction network was generated and hub genes were identified. UALCAN was used to analyze associations between the mRNA expression levels of CDCA3 in KIRP tissues with clinicopathologic parameters. The diagnostic efficacy of CDCA3 for KIRP was analyzed by ROC analysis. Logistic regression was used to analyze relationships between the clinicopathological characteristics and CDCA3 expression. Our results indicated that high CDCA3 mRNA expression is significantly associated with some clinicopathologic parameters in KIRP patients High CDCA3 mRNA expression associated with a shorter overall survival, progression-free interval, and disease-special survival. Taken together, CDCA3 is a potential target for the development of anti-KIRP therapeutics and is an efficient prognostic marker.

In this study, we investigated the prognostic significance of the expression of N6-methyladenosine (m6A) RNA methylation regulatory genes in kidney renal papillary cell carcinoma (KIRP). RNA-sequencing data analysis showed that 14 of 20 major m6A RNA methylation regulatory genes were differentially expressed in the KIRP tissues from The Cancer Genome Atlas (TCGA) database. We constructed a prognostic risk signature with three m6A RNA methylation regulatory genes, IGF2BP3, KIAA1429 and HNRNPC , based on the results from univariate and LASSO Cox regression analyses. Multivariate Cox regression analysis confirmed that the risk score based on the three-gene prognostic risk signature was an independent predictive factor in KIRP. The overall survival of high-risk KIRP patients was significantly shorter than the low-risk KIRP patients. Expression of the three prognostic risk-related genes correlated with the AJCC and TNM stages of KIRP patients from TCGA and GEPIA datasets. ROC curve analysis showed that the three-gene prognostic risk signature precisely predicted the 1-year, 3-year and 5-year survival of KIRP patients. These findings demonstrate that expression of three prognostic risk-related m6A RNA methylation regulatory genes accurately predicts survival outcomes in KIRP patients.