Molecular subtypes based on metabolic genes are potential biomarkers for predicting prognosis and immune responses of clear cell renal cell carcinoma.

Due to the existence of tumor molecular heterogeneity, even patients having similar clinicopathological features could have vastly different survival rates. Hence, we aimed to explore novel metabolism-associated genes (MAGs) related molecular subtypes for clear cell renal cell carcinoma (ccRCC) and their immune landscapes for predicting prognosis and immune responses. Gene matrices and clinical information were downloaded from TCGA and ICGC datasets. Consensus clustering was conducted by the R "ConsensusClusterPlus" package. ccRCC patients were successfully divided into three clusters (MC1, MC2, and MC3) based on MAGs in both TCGA and ICGC datasets. Our established three MAGs were significantly associated with chemokine/chemokine receptor, IFN, CYT, angiogenesis, immune checkpoint molecules, tumor-infiltrating immune cells, oncogenic pathways, pan-cancer immune subtypes, and tumor microenvironment (TME) scores or expressions. Moreover, these three metabolic ccRCC subtypes could predict immunotherapeutic responses. We further constructed a characteristic index (LDAscore) in three metabolic ccRCC subtypes and identified LDAscore-related modules by WGCNA. After deep data mining, 10 hub genes were obtained and seven genes (ATRX, BPTF, DHX9, EP300, POLR2B, SIN3A, UBE3A) were finally validated by qRT-PCR. Our results successfully established a novel ccRCC subtype based on MAGs, providing novel insights into metabolism-related ccRCC tumor heterogeneity and facilitating individualized therapy for future work.

Integrated analyses reveal the prognostic, immunological features and mechanisms of cuproptosis critical mediator gene FDX1 in KIRC.

The ferredoxin 1 (FDX1) gene had been recently reported as a critical mediator of cuproptosis, and without doubt, its roles in KIRC would be of importance. Hence, this paper was to explore the roles of FDX1 in kidney renal clear cell carcinoma (KIRC) and its potential molecular mechanisms via scRNA-sequencing and bulk RNA-sequencing analyses. FDX1 was lowly expressed in KIRC and validated both at the protein and mRNA levels (all p < 0.05). Moreover, its elevated expression was linked with a better overall survival (OS) prognosis in KIRC (p < 0.01). The independent impact of FDX1 on KIRC prognosis was demonstrated by univariate/multivariate regression analysis (p < 0.01). Gene set enrichment analysis (GSEA) identified seven pathways strongly associated with FDX1 in KIRC. Furthermore, FDX1 was also revealed to be significantly related with immunity (p < 0.05). In addition, patients with low expression of FDX1 might be more sensitive to immunotherapies. ScRNA-seq analysis found that FDX1 could be expressed in immune cells and was mainly differently expressed in Mono/Macro cells. Ultimately, we also identified several LncRNA/RBP/FDX1 mRNA networks to reveal its underlying mechanisms in KIRC. Taken together, FDX1 was closely related to prognosis and immunity in KIRC, and its RBP-involved mechanisms of LncRNA/RBP/FDX1 networks were also revealed by us.

Elevated CDC45 Expression Predicts Poorer Overall Survival Prognoses and Worse Immune Responses for Kidney Renal Clear Cell Carcinoma via Single-Cell and Bulk RNA-Sequencing.

The main objective of this paper is to analyze the prognostic and immunological value of CDC45 in kidney renal clear cell carcinoma (KIRC) using single-cell and bulk RNA-sequencing approaches. The expression of CDC45 in KIRC was evaluated by the HPA database, the TCGA-KIRC dataset and verified by PCR analysis and single-cell RNA-sequencing. The ability of CDC45 to independently predict prognosis in KIRC was confirmed by univariate/multivariate regression analysis. Gene set enrichment analysis (GSEA) was employed to explore CDC45-related pathways in KIRC. In addition, Relationships between CDC45 and immunity were also examined. Elevated CDC45 expression in KIRC was demonstrated at mRNA and protein levels. The results of the correlation analysis showed that as CDC45 expression increased, so did the histological grade, clinical stage, and TNM stage of the patients (p < 0.05). Univariate/multivariate regression analysis suggested CDC45 as an independent prognostic factor for KIRC. Seven pathways related to CDC45 were screened through GSEA. Meanwhile, we found that CDC45 was correlated with tumor mutational burden (TMB) and microsatellite instability (MSI) but not tumor neoantigen burden (TNB). Regarding immunity, CDC45 exhibited correlations with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Besides, low CDC45 expression was shown to be associated with a better response to immunotherapy. Single-cell RNA-sequencing revealed that CDC45 was differently expressed in T cells (p < 0.05). CDC45 showed potential as a prognostic biomarker and therapeutic target for KIRC. Meanwhile, the CDC45 low expression group was more sensitive to immunotherapy.

Decreased expression of METTL14 predicts poor prognosis and construction of a prognostic signature for clear cell renal cell carcinoma.

BACKGROUND: METTL14, as one of N6-methyladenosine (m6A) related genes, has been found to be associated with promoting tumorigenesis in different types of cancers. This study was aimed to investigate the prognostic value of METTL14 in clear cell renal cell carcinoma (ccRCC). METHODS: We collected ccRCC patients' clinicopathological parameters information and 13 m6A related genes expression from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were conducted to investigate whether METTL14 could serve as an independent factor correlated with overall survival (OS). Gene Set Enrichment Analysis (GSEA) was carried out to identify METTL14-related signaling pathways. Moreover, a risk score (RS) was calculated to predict the prognosis of ccRCC. Quantitative real-time PCR (qRT-PCR) was also utilized to verify the expression of METTL14 in clinical specimens. RESULTS: Differently expressed m6A related genes were identified between ccRCC tissues and normal tissues. Therein, METTL14 was lowly expressed in ccRCC tissues and verified by qRT-PCR (all p < 0.01). Survival analysis indicated that high expression of METTL14 was associated with better OS (p = 1e-05). GSEA results revealed that high METTL14 expression was enriched in ERBB pathway, MAPK pathway, mTOR pathway, TGF-ß pathway and Wnt pathway. Moreover, METTL14 was proved to be an independent prognostic factor by means of univariate and multivariate Cox regression analyses. Nomogram integrating both the METTL14 expression and clinicopathologic variables was also established to provide clinicians with a quantitative approach for predicting survival probabilities of ccRCC. Furthermore, a METTL14-based riskscore (RS) was developed with significant OS (p = 6.661e-16) and increased AUC of 0.856. Besides, significant correlated genes with METTL14 were also provided. CONCLUSIONS: Our results indicated that METTL14 could serve as a favorable prognostic factor for ccRCC. Moreover, this study also provided a prognostic signature to predict prognosis of ccRCC and identified METTL14-related signaling pathways.

Prognostic value and immunological role of AXL gene in clear cell renal cell carcinoma associated with identifying LncRNA/RBP/AXL mRNA networks.

BACKGROUNDS: This article aimed to explore the prognostic and immunological roles of AXL gene in clear cell renal cell carcinoma (ccRCC) for overall survival (OS) and to identify the LncRNA/RBP/AXL mRNA networks. METHODS: AXL-related gene expression matrix and clinical data were obtained from The Cancer Genome Atlas (TCGA) dataset and AXL-related pathways were identified by gene set enrichment analysis (GSEA). We performed univariate/multivariate Cox regression analysis to evaluate independent prognostic factors and the relationships between AXL and immunity were also investigated. RESULTS: The outcomes of us indicated that the AXL mRNA expression was up-regulated in ccRCC samples and high expression of AXL was associated with worse OS in TCGA dataset (P < 0.01). Further external verification results from HPA, UALCAN, ICGC dataset, GSE6344, GSE14994, and qRT-PCR remained consistent (all P < 0.05). AXL was also identified as an independent prognostic factor for ccRCC by univariate/multivariate Cox regression analysis (both P < 0.05). A nomogram including AXL expression and clinicopathological factors was established by us and GSEA results found that elevated AXL expression was associated with the JAK-STAT, P53, WNT, VEGF and MAPK signaling pathways. In terms of immunity, AXL was dramatically linked to tumor microenvironment, immune cells, immune infiltration, immune checkpoint molecules and tumor mutational burden (TMB). As for its potential mechanisms, we also identified several LncRNA/RBP/AXL mRNA axes. CONCLUSIONS: AXL was revealed to play prognostic and immunological roles in ccRCC and LncRNA/RBP/AXL mRNA axes were also identified by us for its potential mechanisms.

High expression of CDCA7 predicts poor prognosis for clear cell renal cell carcinoma and explores its associations with immunity.

BACKGROUND: Cell division cycle-associated 7 (CDCA7), as a member of the cell division cycle associated family, was reported to be aberrantly expressed in both solid tumors and hematological tumors, suggesting its essential role in promoting tumorigenesis. Hence, we aimed to explore its comprehensive roles of overall survival (OS) in clear cell renal cell carcinoma (ccRCC) and emphasize its associations with immunity. METHODS: The RNA sequencing data and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was adopted to explore CDCA7 associated signaling pathways. Univariate and multivariate Cox regression analyses were carried out to assess independent prognostic factors. Furthermore, roles of CDCA7 in human immunity were also investigated. RESULTS: Our results suggested that CDCA7 was overexpressed in ccRCC and its elevated expression was related to shorter OS (P < 0.01). Univariate and multivariate Cox regression analyses identified CDCA7 as an independent prognostic factor (both P < 0.05). The prognostic nomogram integrating CDCA7 expression level and clinicopathologic variables was constructed to predict 1-, 3- and 5-year OS. GSEA indicated that high CDCA7 expression was related to the apoptosis pathway, cell cycle pathway, JAK-STAT pathway, NOD like receptor pathway, P53 pathway, T cell receptor pathway and toll like receptor pathway, etc. Moreover, CDCA7 was significantly related to microsatellite instability (MSI, P < 0.001) and tumor mutational burden (TMB, P < 0.001). As for immunity, CDCA7 was remarkably associated with immune infiltration, tumor microenvironment, immune checkpoint molecules and immune pathways. CONCLUSIONS: CDCA7 could serve as an independent prognostic factor for ccRCC and it was closely related to MSI, TMB, and immunity.

A novel 10 glycolysis-related genes signature could predict overall survival for clear cell renal cell carcinoma.

BACKGROUND: The role of glycolysis in tumorigenesis has received increasing attention and multiple glycolysis-related genes (GRGs) have been proven to be associated with tumor metastasis. Hence, we aimed to construct a prognostic signature based on GRGs for clear cell renal cell carcinoma (ccRCC) and to explore its relationships with immune infiltration. METHODS: Clinical information and RNA-sequencing data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) and ArrayExpress datasets. Key GRGs were finally selected through univariate COX, LASSO and multivariate COX regression analyses. External and internal verifications were further carried out to verify our established signature. RESULTS: Finally, 10 GRGs including ANKZF1, CD44, CHST6, HS6ST2, IDUA, KIF20A, NDST3, PLOD2, VCAN, FBP1 were selected out and utilized to establish a novel signature. Compared with the low-risk group, ccRCC patients in high-risk groups showed a lower overall survival (OS) rate (P = 5.548Ee-13) and its AUCs based on our established signature were all above 0.70. Univariate/multivariate Cox regression analyses further proved that this signature could serve as an independent prognostic factor (all P < 0.05). Moreover, prognostic nomograms were also created to find out the associations between the established signature, clinical factors and OS for ccRCC in both the TCGA and ArrayExpress cohorts. All results remained consistent after external and internal verification. Besides, nine out of 21 tumor-infiltrating immune cells (TIICs) were highly related to high- and low- risk ccRCC patients stratified by our established signature. CONCLUSIONS: A novel signature based on 10 prognostic GRGs was successfully established and verified externally and internally for predicting OS of ccRCC, helping clinicians better and more intuitively predict patients' survival.

Omega-3 polyunsaturated fatty acids ameliorates testicular ischemia-reperfusion injury through the induction of Nrf2 and inhibition of NF-κB in rats.

OBJECTIVE: This study was designed to investigate the protective effect of Omega-3 polyunsaturated fatty acids (n-3 PUFAs) on testicular ischemia-reperfusion (I/R) injury in rats. METHODS: A total of 24 rats were randomly divided into the following three groups: Group A (Control group, n=8), Group B (I/R group, n=8), Group C (I/R group treated with n-3 PUFAs, n=8). Histological examination was used to assess changes in testicular structure. Tissue oxidative stress biomarkers, malondialdehyde (MDA) as well as Superoxide, and antioxidant indexes, including total antioxidant capacity (T-AOC); catalase (CAT); glutathione (GSH); glutathione/glutathione disulfide ratio (GSH/GSSG); superoxide dismutase (SOD) were determined. In addition, nuclear transcription factor erythroid 2-related factor 2 (Nrf2), Nrf2-dependent antioxidant enzymes, such as heme oxygenase-1 (HO-1) and NADPH quinine oxidoreductase-1 (NQO-1), and nuclear factor kappa B (NF-κB) were detected. RESULTS: Compared to I/R group, n-3 PUFAs could obviously increase the mean seminiferous tubular diameter in the histological examination. After n-3 PUFAs treatment, the level of tissue MDA and Superoxide were significantly decreased, while tissue T-AOC, CAT, GSH, GSH/GSSG and SOD levels were significantly increased, compared to I/R group (P<0.05). Besides, the expression levels of Nrf2, HO-1, and NQO-1 were significantly higher and the NF-κB expression level was significantly lower in the n-3 PUFAs treated group than that in I/R group (P<0.05). CONCLUSIONS: These results provided evidences that n-3 PUFAs ameliorated testes damage caused by testicular I/R injury through its antioxidative capacity and anti-inflammatory effects, involving the activation of Nrf2 and the inhibition of NF-κB.

Elevated PTTG1 predicts poor prognosis in kidney renal clear cell carcinoma and correlates with immunity.

Background: PTTG1 has been reported to be linked with the prognosis and progression of various cancers, including kidney renal clear cell carcinoma (KIRC). In this article, we mainly investigated the associations between prognosis, immunity, and PTTG1 in KIRC patients. Method: We downloaded transcriptome data from the TCGA-KIRC database. PCR and immunohistochemistry were used, respectively, to validate the expression of PTTG1 in KIRC at the cell line and the protein levels. Survival analyses as well as univariate or multivariate Cox hazard regression analyses were used to prove whether PTTG1 alone could affect the prognosis of KIRC. The most important point was to study the relationship between PTTG1 and immunity. Results: The results of the paper revealed that the expression levels of PTTG1 were elevated in KIRC compared with para-cancerous normal tissues, validated by PCR and immunohistochemistry at the cell line and the protein levels (P < 0.05). High PTTG1 expression was related to shorter overall survival (OS) in patients with KIRC (P < 0.05). Through univariate or multivariate regression analysis, PTTG1 was confirmed to be an independent prognostic factor for OS of KIRC (P < 0.05), and its related seven pathways were obtained through gene set enrichment analysis (GSEA; P < 0.05). Moreover, tumor mutational burden (TMB) and immunity were found to be significantly connected with PTTG1 in KIRC (P < 0.05). Correlations between PTTG1 and immunotherapy responses implied that the low-PTTG1 group was more sensitive to immunotherapy (P < 0.05). Conclusions: PTTG1 was closely associated with TMB or immunity, and it had a superior ability to forecast the prognosis of KIRC patients.

Identification of hub biomarkers and exploring the roles of immunity, M6A, ferroptosis, or cuproptosis in rats with diabetic erectile dysfunction.

BACKGROUND: Currently, patients with diabetic erectile dysfunction (DMED) were not satisfied with the effects of first-line phosphodiesterase type 5 inhibitors (PDE5Is). Hence, this paper was designed to mine hub biomarkers in DMED and explore its potential mechanisms. METHODS: Gene expression matrix of DMED was downloaded from the gene expression omnibus (GEO; GSE2457) dataset. The top 20 genes were selected based on the connectivity degrees in protein-protein interaction (PPI) network. Functional enrichment analysis was utilized to reveal DMED-related signaling pathways. We also explored the roles of immunity, m6A, ferroptosis, or cuproptosis in DMED and constructed Sprague Dawley (SD) rats DMED model to verify gene expressions by quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: Based on the threshold, a total of 122 differently expressed genes (DEGs) were identified in DMED, including 39 up-regulated and 83 down-regulated genes. Functional enrichment analysis implied that these DEGs were significantly enriched in peroxisome proliferator-activated receptors, ferroptosis, hypoxia-inducible factor 1 signaling pathways, and so on. SD rats DMED model was also successfully established by us and validated by intracavernous pressure/mean arterial pressure, Masson's trichrome staining, and immunohistochemical analysis. We further verified the expression of these top 20 genes from the PPI network by qRT-PCR in the SD rats DMED model and finally identified Sparc, Lox, Srebf1, and Mmp3 as hub biomarkers (all p < 0.05). As for immunity and cuproptosis, our analysis indicated that DMED had nothing to do with them (all p > 0.05). Actually, DMED was markedly associated with m6A regulators and ferroptosis. CONCLUSIONS: We identified Sparc, Lox, Srebf1, and Mmp3 as potential hub biomarkers in the SD rats DMED model for future drug development and found its significant associations with m6A regulators and ferroptosis, but not with immunity or cuproptosis.

Six RNA binding proteins (RBPs) related prognostic model predicts overall survival for clear cell renal cell carcinoma and is associated with immune infiltration.

The aim of this article was to construct an accurate prognostic model by using RNA-binding proteins (RBPs) to predict overall survival (OS) for patients with clear cell renal cell carcinoma (ccRCC) as well as to reveal its associations with immune infiltration. Expression profiles based on RNA-binding proteins (RBPs) and  clinical follow-up parameters were obtained from the Cancer Genome Atlas (TCGA) and the ArrayExpress databases. Through univariate COX and LASSO regression analyses, the RBPs based signature was developed. A total of six RBPs (CLK2, IGF2BP2, RNASE2, EZH2, PABPC1L, RPL22L1) were eventually used to establish a prognostic signature. Based on this signature, ccRCC patients were classified into high-risk and low-risk subgroups and significant OS was obtained in both the internal and external datasets (p<0.05). AUCs of its ROC curve were all above 0.70 and this signature was an independent prognostic factor of OS for ccRCC (p<0.05). Nomograms were also constructed to visualize the relationships among individual predictors and 1-, 3- and 5-year OS for ccRCC. Furthermore, the established RBPs based signature was strongly related to critical clinicopathologic characteristics such as grade (p=8.921e-12), stage (p=1.421e-11), M (p=1.662e-05), and T stage (p=7.907e-10). Moreover, 12 kinds of tumor-infiltrating immune cells were significantly linked to high-risk and low-risk groups classified by our constructed model (all p<0.05). Our study successfully identified six RBPs as a robust prognostic signature in ccRCC by both external and internal verification. Besides, our established model displayed significant associations with immune infiltration. In addition to original clinical parameters, our findings may further help clinicians in predicting patients' survival status and creating individualized treatment plans.

Survival Prognosis, Tumor Immune Landscape, and Immune Responses of PPP1R18 in Kidney Renal Clear Cell Carcinoma and Its Potentially Double Mechanisms.

Background: The aim of this study was to investigate the immunological and prognostic roles of protein phosphatase 1 regulatory subunit 18 (PPP1R18) for overall survival (OS) in kidney renal clear cell carcinoma (KIRC) patients, as well as the prediction of its potential mechanisms. Methods: Based on PPP1R18 single gene expression matrices and clinical information from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and GSE6344 datasets, the relationships between PPP1R18 and prognosis/immunity were fully explored. Univariate and multivariate Cox regression analyses were applied to assess its independent prognostic ability and gene set enrichment analysis (GSEA) was implemented to find its related pathways. Besides, we also explored possible mechanisms of PPP1R18 involved in KIRC. Results: PPP1R18 was highly expressed in KIRC samples than in non-tumor tissues in TCGA, ICGC and GSE6344 datasets, with validations from quantitative real-time polymerase chain reaction (qRT-PCR) in both cell lines and KIRC tissues (all P < 0.05). Univariate and multivariate Cox regression analyses indicated that PPP1R18 was also considered to be with independent prognostic ability in KIRC (both P < 0.01). GSEA results showed that PPP1R18 gene expression was significantly related to Chemokine, JAK STAT, MAPK, and NOTCH pathways. Furthermore, PPP1R18 was also firmly associated with microsatellite instability (MSI), tumor mutational burden (TMB), immune microenvironment, immune cells, immune checkpoints and immune infiltration (all P < 0.05). Analysis of tumor immune dysfunction and exclusion (TIDE) and Imvigor210 datasets suggested that patients with low PPP1R18 expression are more likely to benefit from immunotherapy. Finally, we identified two potential mechanisms of a classical competing endogenous RNA (ceRNA) mechanism and an RNA-binding protein (RBP) involved mechanism of PPP1R18 in KIRC. Conclusions: PPP1R18 played oncogenic and immunological roles of OS in KIRC, offering potential antigens for developing KIRC mRNA vaccines.

Survival Prognosis, Tumor Immune Landscape, and Immune Responses of ADAMTS14 in Clear Cell Renal Cell Carcinoma and Its Potential Mechanisms.

Background: ADAMTS14 played a crucial role in the formation and development of various cancers. Currently, no associations had been revealed between ADAMTS14 and clear cell renal cell carcinoma (ccRCC). Hence, this study was designed to assess the prognostic values and immunological roles of ADAMTS14 in ccRCC and to reveal its potential mechanisms. Methods: ADAMTS14-related expression profiles and related clinical data were downloaded from The Cancer Genome Atlas (TCGA) dataset, validated by the ICGC dataset, qRT-PCR, and immunohistochemistry. We utilized gene set enrichment analysis (GSEA) to find potentially ADAMTS14-related pathways and applied univariate/multivariate Cox regression analyses to identify independent factors significantly related to overall survival (OS) for ccRCC. A nomogram consisted of independent prognostic factors was also conducted. We further explored the associations between ADAMTS14 with immunity and revealed its potential mechanisms. Results: ADAMTS14 displayed a higher expression in ccRCC tumor than in adjacent normal tissues, and further validated results of the ICGC dataset; qRT-PCR and immunohistochemistry remained consistent (all p < 0.05). Moreover, elevated ADAMTS14 expression was significantly associated with poor OS (p < 0.001). Through univariate/multivariate Cox regression analyses, ADAMTS14 was found to be an independent prognostic factor for ccRCC (both p < 0.05) and GSEA identified several signaling pathways including INSULIN, MTOR, and PPAR pathways. The nomogram based on independent prognostic factors was successfully established and well evaluated. Moreover, the expression of ADAMTS14 was remarkably associated with immune checkpoint molecules, tumor mutational burden (TMB), immune cells, and tumor immune microenvironment (all p < 0.05). Results from TIDE and TCIA showed that highly expressed ADAMTS14 could predict worse efficacy of immunotherapy (all p < 0.05). As for its potential mechanisms, we also revealed several LncRNA/RNA binding protein (RBP)/ADAMTS14 mRNA networks. Conclusions: ADAMTS14 was found to play oncogenic roles in ccRCC and to be significantly associated with immunity. Several LncRNA/RBP/ADAMTS14 mRNA networks were also identified for its potential mechanisms.

Chromatin regulators-related lncRNA signature predicting the prognosis of kidney renal clear cell carcinoma and its relationship with immune microenvironment: A study based on bioinformatics and experimental validation.

Background: Kidney Renal Clear cell carcinoma (KIRC) is a major concern in the urinary system. A lot of researches were focused on Chromatin Regulators (CRs) in tumors. In this study, CRs-related lncRNAs (CRlncRNAs) were investigated for their potential impact on the prognosis of KIRC and the immune microenvironment. Methods: The TCGA database was used to obtain transcriptome and related clinical information. CRs were obtained from previous studies, whereas CRlncRNAs were obtained by differential and correlation analysis. We screened the lncRNAs for the signature construction using regression analysis and LASSO regression analysis. The effectiveness of the signature was evaluated using the Kaplan-Meier (K-M) curve and Receiver Operating Characteristic curve (ROC). Additionally, we examined the associations between the signature and Tumor Microenvironment (TME), and the efficacy of drug therapy. Finally, we further verified whether these lncRNAs could affect the biological function of KIRC cells by functional experiments such as CCK8 and transwell assay. Results: A signature consisting of 8 CRlncRNAs was constructed to predict the prognosis of KIRC. Quantitative Real-Time PCR verified the expression of 8 lncRNAs at the cell line and tissue level. The signature was found to be an independent prognostic indicator for KIRC in regression analysis. This signature was found to predict Overall Survival (OS) better for patients in the subgroups of age, gender, grade, stage, M, N0, and T. Furthermore, a significant correlation was found between riskScore and immune cell infiltration and immune checkpoint. Finally, we discovered several drugs with different IC50 values in different risk groups using drug sensitivity analysis. And functional experiments showed that Z97200.1 could affect the proliferation, migration and invasion of KIRC cells. Conclusion: Overall, the signature comprised of these 8 lncRNAs were reliable prognostic biomarkers for KIRC. Moreover, the signature had significant potential for assessing the immunological landscape of tumors and providing individualized treatment.

A novel 17 apoptosis-related genes signature could predict overall survival for bladder cancer and its associations with immune infiltration.

Background: Apoptosis-related genes (ARGs) were used to develop a novel signature for forecasting overall survival (OS) and examining their relationships with immune infiltrates in bladder cancer (BC). Methods: Gene expression matrices as well as related clinical data were acquired for BC samples from online datasets. According to differentially expressed ARGs acquired from normal bladder tissues and cancer samples, functional enrichment analyses were conducted. With the assistance of LASSO and Cox regression analysis, a novel model was successfully established and evaluated by external and internal validations. Results: Eventually, 17 ARGs (SLC5A6, GULP1, TAP1, MMP9, P4HB, FOXL2, CIDEC, EN2, NES, EPHA7, SUSD2, TMPRSS3, HOXB7, SATB1, MEST, PCDHGC3, ASPM) were utilized to construct the signature. Our constructed signature significantly distinguished high-risk from low-risk BC patients of OS by internal and external validations and was also proven to be able to serve as an independent prognostic biomarker (all P < 0.05). Furthermore, a prognostic nomogram was also constructed based on TCGA dataset to predict OS prognosis in BC suffers. Besides, this ARG based model was markedly associated with clinical characteristics like tumor stage (P = 3.98e-06), race (P = 8.255e-06), N stage (P = 0.002), T stage (P = 3.679e-05) and M stage (P = 0.002). As for immune infiltration, our established model was significantly associated with seven tumor-infiltrating immune cells. Conclusions: A prognostic signature was successfully developed by us according to 17 ARGs in BC using external and internal verifications, enabling clinicians to predict BC suffers' OS and promote specific individualization of patient care.

A Novel Nine Apoptosis-Related Genes Signature Predicting Overall Survival for Kidney Renal Clear Cell Carcinoma and its Associations with Immune Infiltration.

Background: This study was designed to establish a sensitive prognostic model based on apoptosis-related genes to predict overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC). Methods: Obtaining the expression of apoptosis-related genes and associated clinical parameters from online datasets (The Cancer Genome Atlas, TCGA), their biological function analyses were performed through differently expressed genes. By means of LASSO, unadjusted and adjusted Cox regression analyses, this predictive signature was constructed and validated by internal and external databases (both TCGA and ArrayExpress). Results: A total of nine apoptosis-related genes (SLC27A2, TNFAIP2, IFI44, CSF2, IL4, MDK, DOCK8, WNT5A, APP) were ultimately screened as associated hub genes and utilized to construct a prognosis model. Then our constructed riskScore model significantly passed the validation in both the internal and external datasets of OS (all p < 0.05) and verified their expressions by qRT-PCR. Moreover, we conducted the Receiver Operating Characteristic (ROC), finding the area under the ROC curves (AUCs) were all above 0.70 which indicated that riskScore was a stable independent prognostic factor (p < 0.05). Furthermore, prognostic nomograms were established to figure out the relationship between 1-, 3- and 5-year OS and individual parameters for ccRCC patients. Additionally, survival analyses indicated that our riskScore worked well in predicting OS in subgroups of age, gender, grade, stage, T, M, N0, White (all p < 0.05), except for African, Asian and N1 (p > 0.05). We also explored its association with immune infiltration and applied cMap database to seek out highly correlated small molecule drugs. Conclusion: Our study successfully constructed a prognostic model containing nine hub apoptosis-related genes for ccRCC, helping clinicians predict patients' OS and making the prognostic assessment more standardized. Future prospective studies are required to validate our findings.

A novel 13 RNA binding proteins (RBPs) signature could predict prostate cancer biochemical recurrence.

BACKGROUND: Cancer precision medicine requires biomarkers or signatures to predict prognosis and therapeutic benefits. Driven by this, we established a biochemical recurrence (BCR) predictive model for prostate cancer (PCA) patients based on RNA-binding proteins (RBPs). METHODS: RNA-sequencing and corresponding clinicopathological data were downloaded from the Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Univariate COX, LASSO and multivariate COX regression analyses were carried out to develop the BCR predictive riskScore model. Survival analysis, ROC curve, independent prognostic analysis, nomogram were also performed to evaluate this signature internally and externally. RESULTS: A total of 13 RBPs including TRMT1L, WBP4, MBNL3, SMAD9, NSUN7, ENG9, PIWIL4, PEG10, CSDC2, HELZ2, CELF2, YBX2 and ESRP2 were eventually identified as BCR-related hub biomarkers and utilized to establish a riskScore. Further analysis including external and internal verification indicated that the patients with high riskScores had shorter time to BCR compared to those with low riskScores in both TCGA and GSE116918. The area under the curve (AUC) of the time-dependent receiver operator characteristic curve (ROC) of the predictive model exhibited a good predictive performance. The signature was also proven to be a valuable independent prognostic factor (all P < 0.05). We also established a nomogram based on the 13 RBPs to visualize the relationships between individual predictors and 1-, 3- and 5-year BCR for PCA. CONCLUSIONS: Our results successfully screened out 13 RBPs as a robust BCR-predictive signature in PCA by external and internal verification, helping clinician predict patients' cancer progression status and promoting the specific individualized treatment than original clinical parameters.

Metastasectomy could not improve the survival of metastatic urothelial carcinoma: evidence from a meta-analysis.

BACKGROUND: With the advancement of surgical techniques and instruments, surgeries had been increasingly applied to patients with metastatic urothelial carcinoma. However, their survival benefits had not been carefully evaluated. METHODS: Eligible articles were conducted by comprehensively searching three online databases (PubMed, EMBASE and Web of Science), published before May 1st, 2019. Overall survival (OS) and cancer-specific survival/progression-free survival (CSS/PFS) were analyzed to clarify their associations. RESULTS: Finally, eight out of 3,581 articles were enrolled in this meta-analysis. In terms of OS, our results indicated that OS was positively associated with the patients underwent radical cystectomy (RC) (pooled HR =0.72, 95% CI, 0.64-0.81, P=0.158, I 2=39.4%), but it was not significantly associated with the patients underwent metastasectomy (MC) (pooled HR =0.78, 95% CI, 0.56-1.08, P=0.093, I 2=49.7%). As for CSS/PFS, our results displayed that patients could benefit from surgery (RC or MC) (pooled HR =0.56, 95% CI, 0.42-0.75, P=0.213, I 2=35.3%). CONCLUSIONS: Despite the positive role of the RC in treating metastatic urothelial carcinoma, MC did not suggest a survival benefit in terms of OS. More strictly designed randomized controlled trials (RCTs) were needed to validate our findings.

Identification of small molecule drugs and development of a novel autophagy-related prognostic signature for kidney renal clear cell carcinoma.

Abnormal autophagic levels have been implicated in the pathogenesis of multiple cancers, however, its role in tumors is complex and has not yet been explored clearly. Hence, we aimed to explore the prognostic values of autophagy-related genes (ARGs) for kidney renal clear cell carcinoma (KIRC). Differentially expressed ARGs and transcription factors (TFs) were identified in KIRC patients obtaining from the The Cancer Genome Atlas (TCGA) database. Then, networks between TFs and ARGs, gene ontology functional annotations and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted. Next, we performed consensus clustering, COX regression analysis and Lasso regression analysis to identify the prognostic ARGs. Finally, an individual prognostic index (PI, riskScore) was established. Based on TCGA cohort and ArrayExpress cohort, Survival analysis, ROC curve, independent prognostic analysis, and clinical correlation analysis were also performed to evaluate this PI. Based on differentially expressed ARGs, KIRC patients were successfully divided into two clusters (P = 5.916e-04). AS for PI, it was constructed based on 11 ARGs and significantly classified KIRC patients into high-risk group and low-risk group in terms of OS (P = 4.885e-15 for TCGA cohort, P = 6.366e-03 for ArrayExpress cohort). AUC of its ROC curve reached 0.747 for TCGA cohort and 0.779 for ArrayExpress cohort. What's more, this PI was proven to be a valuable independent prognostic factor in both univariate and multivariate COX regression analysis (P < .001). Prognostic nomograms were also performed to visualize the relationship between individual predictors and survival rates in patients with KIRC. By means of connectivity map database, emetine, cephaeline and co-dergocrine mesilate related to ARGs were found to be negatively correlated with KIRC. This study provided an effective PI for KIRC and also displayed networks between TFs and ARGs. KIRC patients were successfully divided into two clusters based on differentially expressed ARGs. Besides, small molecule drugs related to ARGs were also identified for KIRC.

Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA).

BACKGROUND: We aimed to establish an immune-related gene (IRG) based signature that could provide guidance for clinical bladder cancer (BC) prognostic surveillance. METHODS: Differentially expressed IRGs and transcription factors (TFs) between BCs and normal tissues were extracted from transcriptome data downloaded from the TCGA database. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to identify related pathways based on differently expressed IRGs. Then, univariate Cox regression analysis was performed to investigate IRGs with prognostic values and LASSO penalized Cox regression analysis was utilized to develop the prognostic index (PI) model. RESULTS: A total of 411 BC tissue samples and 19 normal bladder tissues in the TCGA database were enrolled in this study and 259 differentially expressed IRGs were identified. Networks between TFs and IRGs were also provided to seek the upstream regulators of differentially expressed IRGs. By means of univariate Cox regression analysis, 57 IRGs were analyzed with prognostic values and 10 IRGs were finally identified by LASSO penalized Cox regression analysis to construct the PI model. This model could significantly classified BC patients into high-risk group and low-risk group in terms of OS (P=9.923e-07) and its AUC reached 0.711. By means of univariate and multivariate COX regression analysis, this PI was proven to be a valuable independent prognostic factor (HR =1.119, 95% CI =1.066-1.175, P<0.001). CMap database analysis was also utilized to screen out 10 small molecules drugs with the potential for the treatment of BC. CONCLUSIONS: Our study successfully provided a novel PI based on IRGs with the potential to predict the prognosis of BC and screened out 10 small molecules drugs with the potential to treat BC. Besides, networks between TFs and IRGs were also displayed to seek its upstream regulators for future researches.