Genes associated with N6-methyladenosine regulators provide insight into the prognosis and immune response to renal clear cell carcinoma.

As one of the most common messenger ribonucleic acid modifications in eukaryotic organisms, N6-methyladenosine (m6A) is involved in a wide variety of biological functions. The imbalance of m6A RNA modification may be linked to cancer and other disorders, according to a growing body of studies. Its effects on clear cell renal cell carcinoma (KIRC) have not been well discussed, though. Here, we acquired the expression patterns of 23 important regulators of m6A RNA modification and assess how they might fare in KIRC. We observed that 17 major m6A RNA modification regulatory factors had a substantial predictive influence on KIRC. Using the "ConsensusCluster" program, we defined two groupings (Cluster 1 and Cluster 2) depending on the expression of the aforementioned 17 key m6A RNA methylation regulators. The Cluster 2 has a less favorable outcome and is strongly related with a lesser immune microenvironment, according to the findings. We also developed a strong risk profile for three m6A RNA modifiers (METTL14, YTHDF1, and LRPPRC) using multivariate Cox regression analysis. According to further research, the aforementioned risk profile could serve as an independent predicting factor for KIRC, and the chemotherapy response sensitivity was analyzed between two risk groups. Moreover, to effectively forecast the future outlook of KIRC clients, we established a novel prognostic approach according to gender, age, histopathological level, clinical stage, and risk score. Finally, the function of hub gene METTL14 was validated by cell proliferation and subcutaneous graft tumor in mice. In conclusion, we discovered that m6A RNA modifiers play an important role in controlling KIRC and created a viable risk profile as a marker of prediction for KIRC clients.

Deciphering the Effects of the PYCR Family on Cell Function, Prognostic Value, Immune Infiltration in ccRCC and Pan-Cancer.

Pyrroline-5-carboxylate reductase (PYCR) is pivotal in converting pyrroline-5-carboxylate (P5C) to proline, the final step in proline synthesis. Three isoforms, PYCR1, PYCR2, and PYCR3, existed and played significant regulatory roles in tumor initiation and progression. In this study, we first assessed the molecular and immune characteristics of PYCRs by a pan-cancer analysis, especially focusing on their prognostic relevance. Then, a kidney renal clear cell carcinoma (KIRC)-specific prognostic model was established, incorporating pathomics features to enhance predictive capabilities. The biological functions and regulatory mechanisms of PYCR1 and PYCR2 were investigated by in vitro experiments in renal cancer cells. The PYCRs' expressions were elevated in diverse tumors, correlating with unfavorable clinical outcomes. PYCRs were enriched in cancer signaling pathways, significantly correlating with immune cell infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI). In KIRC, a prognostic model based on PYCR1 and PYCR2 was independently validated statistically. Leveraging features from H&E-stained images, a pathomics feature model reliably predicted patient prognosis. In vitro experiments demonstrated that PYCR1 and PYCR2 enhanced the proliferation and migration of renal carcinoma cells by activating the mTOR pathway, at least in part. This study underscores PYCRs' pivotal role in various tumors, positioning them as potential prognostic biomarkers and therapeutic targets, particularly in malignancies like KIRC. The findings emphasize the need for a broader exploration of PYCRs' implications in pan-cancer contexts.

LAMTOR3 is a prognostic biomarker in kidney renal clear cell carcinoma.

OBJECTIVE: The objective of the study was to investigate the expression of LAMTOR3 in kidney renal clear cell carcinoma (KIRC) and its clinical significance. METHODS: The expression of LAMTOR3 in KIRC and its relationship with clinical features were analyzed using the UALCAN online database. The results were verified using KIRC gene chip data and clinical specimens. The prognosis of KIRC patients was analyzed with the GEPIA2 database. GO, KEGG, and GSEA analyses were conducted to analyze the possible molecular mechanism of LAMTOR3 in KIRC. Immunohistochemical (IHC) and hematoxylin and eosin (H&E) staining were used for histopathological detection. RESULTS: UALCAN database analysis showed that LAMTOR3 expression in KIRC was significantly lower than in normal kidney tissues and correlated with the clinical stage, pathological grade, and tumor genotype (p < .05). GSE53757 dataset analysis consistently showed that the expression of LAMTOR3 in KIRC was significantly lower than in normal kidney tissues (p < .01). GEPIA2 database analysis indicated that patients with low LAMTOR3 expression had poor overall and disease-free survival rates. GSEA analysis suggested that LAMTOR3 positively regulated the citrate cycle and drug metabolism cytochrome P450 and negatively regulated folate biosynthesis and olfactory transduction. The expression of LAMTOR3 in KIRC was also significantly correlated with immune cell infiltration. Finally, IHC showed that LAMTOR3 expression in the KIRC tissues was lower than in the adjacent normal tissues. CONCLUSION: LAMTOR3 expression is significantly lower in KIRC. LAMTOR3 may be a potential marker for KIRC diagnosis and therapy.

Pseudogene HSPA7 is a poor prognostic biomarker in Kidney Renal Clear Cell Carcinoma (KIRC) and correlated with immune infiltrates.

BACKGROUND: Pseudogenes played important roles in tumorigenesis, while there are nearly no reports about the expression and roles of HSPA7 in the cancer. METHODS: Firstly, we used Logistic regression, the KS test, the GEPIA database, UALCAN database and qRT-PCR to analyze the expression level of HSPA7 in KIRC, then we used the Cox regression and the Kaplan-Meier curve to analyze the overall survival (OS) of KIRC patients with different Clinico-pathological parameters. Thirdly, we used the multivariate Cox analysis of influencing factors to compare the correlation between the HSPA7 expression level and the clinical parameters. Finally, we used multi-GSEA analysis and the Tumor Immunoassay Resource (TIMER) database to explore the functional role of HSPA7 in KIRC RESULTS: The HSPA7 is highly expressed in KIRC tumor tissues, and its expression is related to clinico-pathological features and survival in KIRC patients. GSEA analysis displayed the high expression of HSPA7 in KIRC were related to several tumor-related and immune-related pathways. With the TIMER database analysis we showed that HSPA7 levels were correlated with the CD4+ T cells, neutrophils and Dendritic Cell. CONCLUSIONS: Our study showed that HSPA7 is very important in the tumor progression and may act as a poor prognostic biomarker for KIRC tumor by modulating immune infiltrating cells.

Multi-omics analysis of tumor angiogenesis characteristics and potential epigenetic regulation mechanisms in renal clear cell carcinoma.

BACKGROUND: Tumor angiogenesis, an essential process for cancer proliferation and metastasis, has a critical role in prognostic of kidney renal clear cell carcinoma (KIRC), as well as a target in guiding treatment with antiangiogenic agents. However, tumor angiogenesis subtypes and potential epigenetic regulation mechanisms in KIRC patient remains poorly characterized. System evaluation of angiogenesis subtypes in KIRC patient might help to reveal the mechanisms of KIRC and develop more target treatments for patients. METHOD: Ten independent tumor angiogenesis signatures were obtained from molecular signatures database (MSigDB) and gene set variation analysis was performed to calculate the angiogenesis score in silico using the Cancer Genome Atlas (TCGA) KIRC dataset. Tumor angiogenesis subtypes in 539 TCGA-KIRC patients were identified using consensus clustering analysis. The potential regulation mechanisms was studied using gene mutation, copy number variation, and differential methylation analysis (DMA). The master transcription factors (MTF) that cause the difference in tumor angiogenesis signals were completed by transcription factor enrichment analysis. RESULTS: The angiogenesis score of a prognosis related angiogenesis signature including 189 genes was significantly correlated with immune score, stroma score, hypoxia score, and vascular endothelial growth factor (VEGF) signal score in 539 TCGA KIRC patients. MMRN2, CLEC14A, ACVRL1, EFNB2, and TEK in candidate gene set showed highest correlation coefficient with angiogenesis score in TCGA-KIRC patients. In addition, all of them were associated with overall survival in both TCGA-KIRC and E-MTAB-1980 KIRC data. Clustering analysis based on 183 genes in angiogenesis signature identified two prognosis related angiogenesis subtypes in TCGA KIRC patients. Two clusters also showed different angiogenesis score, immune score, stroma score, hypoxia score, VEGF signal score, and microenvironment score. DMA identified 59,654 differential methylation sites between two clusters and part of these sites were correlated with tumor angiogenesis genes including CDH13, COL4A3, and RHOB. In addition, RFX2, SOX13, and THRA were identified as top three MTF in regulating angiogenesis signature in KIRC patients. CONCLUSION: Our study indicate that evaluation the angiogenesis subtypes of KIRC based on angiogenesis signature with 183 genes and potential epigenetic mechanisms may help to develop more target treatments for KIRC patients. Video Abstract.

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening.

BACKGROUND: In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC's prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA). RESULTS: With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data. CONCLUSIONS: Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients' survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.

Development and Validation of Anoiki-Related Lncrna Signature Prediction Model for KIRC Prognosis.

BACKGROUND: Various cancer types have been studied and understood using long noncoding RNA (lncRNA). Despite this, only a few studies have examined anoikis-related lncRNAs in kidney renal clear cell carcinoma (KIRC). As a result, this study evaluated a powerful prognostic model for KIRC patients based on anoikis-lncRNAs and identified potential biological targets. METHODS: Anoikis-related lncRNAs associated with patient prognosis were identified using Pearson correlation, variance, and univariate Cox regression analyses. A predictive model that incorporated 4 anoikis-related lncRNAs has been constructed using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The prognostic performance of the proposed model has also been assessed utilizing Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analyses. An ESTIMATE analysis was carried out on the low- as well as high-risk subtypes to evaluate immune cell infiltration status. Furthermore, CIBERSORT, TIMER, and QUANTISEQ along with other algorithms were applied for determining the infiltration status of numerous immune cells across both groups. In addition, immune checkpoint gene expression in both groups was also determined. Finally, drug sensitivity assays and in vitro experiments were performed to validate the results. RESULTS: A total of sixty-three lncRNAs associated with anoikis and KIRC prognosis were identified via univariate cox analysis, and four lncRNAs (Z99289.2, AC084876.1, LINC00460, and AC090337.2.) were selected as hub lncRNAs. A prognostic signature has been developed based on the expression levels and coefficiency of these four lncRNAs while establishing its efficacy in part and whole TCGA KIRC cohort. Furthermore, by using this risk signature, high- as well as low-risk KIRC patients could be distinguished more precisely it can predict patient outcomes as well. The survival predictions by the nomogram exhibited an absolute degree of concordance with actual situations. In vitro experiments verified that LINC00460 downregulation contributed to the growth inhibition of KIRC cell lines and promoted apoptosis of cancer cells. CONCLUSION: This study suggests that anoikis-related lncRNAs could serve as valuable prognostic markers for KIRC. Additionally, they may provide insight into future KIRC treatment options by reflecting on the situation of the kidney immune microenvironment.

Identification of a lactate metabolism-related lncRNAs signature for predicting the prognosis in patients with kidney renal clear cell carcinoma.

Background: Lactate metabolism-related (LMR) long noncoding RNAs (lncRNAs) play significant roles in various cancers, but their impact on kidney renal clear cell carcinoma (KIRC) remains unclear. This study aimed to explore the value of LMR lncRNA and develop a risk model for KIRC. Methods: Data on KIRC patients were downloaded from The Cancer Genome Atlas (TCGA) database. LMR lncRNAs were identified by co-expression, univariate and multivariate analyses, and least absolute shrinkage selection operator (LASSO) regression analysis. Subsequently, a prognostic signature was constructed and its accuracy was verified. To predict the prognosis of KIRC effectively, we established a nomogram based on this information. Enrichment analysis, tumor mutational burden (TMB) analysis, immune status and the therapeutic sensitivities of KIRC patients were also investigated. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the expression of lncRNAs. Results: We constructed and verified a predictive signature based on six LMR lncRNA (LINC00944, AC090772.3, Z83745.1, AP001267.3, AC092296.1, and AL162377.1) to assess the patient prognoses of KIRC. Survival analyses showed a more unfavorable outcome in high-risk patients (P<0.001). Enrichment analysis demonstrated that immune-related pathways were enriched in the high-risk group. Besides, patients classified by risk scores had distinguishable immune status, TMB, response to immunotherapy, and sensitivity to chemotherapy and targeted drugs. Conclusions: The LMR lncRNAs signature has significant implications for prognostic assessment and clinical treatment guidance in KIRC.

PTK6: An emerging biomarker for prognosis and immunotherapeutic response in clear cell renal carcinoma (KIRC).

Kidney renal clear cell carcinoma (KIRC), one of the most prevalent form of kidney carcinoma, is highly aggressive cancer known for significant immune infiltration and high mortality rates. The absence of sensitivity to traditional therapy has spurred the search for new treatments. Protein Tyrosine Kinase 6 (PTK6) is implicated in promoting cancer growth, spread, and metastasis. Our review of The Cancer Genome Atlas database revealed PTK6 overexpression in KIRC, though its specific role in this cancer type was unclear. We investigated PTK6's cancer-promoting roles in KIRC using the database and confirmed our findings with patient-derived tissues. Our analysis showed that elevated PTK6 expression is linked to worse outcomes and higher levels of immune infiltration. It also correlates positively with neo-antigens (NEO) and DNA ploidy changes in KIRC. This research delves into PTK6's role in KIRC development, suggesting PTK6 as a possible biomarker for prognosis and treatment in KIRC.

Development of a prognostic model based on different disulfidptosis related genes typing for kidney renal clear cell carcinoma.

Background: Kidney renal clear cell carcinoma (KIRC) is a common and clinically significant subtype of kidney cancer. A potential therapeutic target in KIRC is disulfidptosis, a novel mode of cell death induced by disulfide stress. The aim of this study was to develop a prognostic model to explore the clinical significance of different disulfidptosis gene typings from KIRC. Methods: A comprehensive analysis of the chromosomal localization, expression patterns, mutational landscape, copy number variations, and prognostic significance of 10 disulfide death genes was conducted. Patients were categorized into distinct subtypes using the Non-negative Matrix Factorization (NMF) typing method based on disulfidptosis gene expression patterns. Weighted Gene Co-expression Network Analysis (WGCNA) was used on the KIRC dataset to identify differentially expressed genes between subtype clusters. A risk signature was created using LASSO-Cox regression and validated by survival analysis. An interaction between risk score and immune cell infiltration, tumor microenvironment characteristics and pathway enrichment analysis were investigated. Results: Initial findings highlight the differential expression of specific DRGs in KIRC, with genomic instability and somatic mutation analysis revealing key insights into their role in cancer progression. NMF clustering differentiates KIRC patients into subgroups with distinct survival outcomes and immune profiles, and hierarchical clustering identifies gene modules associated with key biological and clinical parameters, leading to the development of a risk stratification model (LRP8, RNASE2, CLIP4, HAS2, SLC22A11, and KCTD12) validated by survival analysis and predictive of immune infiltration and drug sensitivity. Pathway enrichment analysis further delineates the differential molecular pathways between high-risk and low-risk patients, offering potential targets for personalized treatment. Lastly, differential expression analysis of model genes between normal and KIRC cells provides insights into the molecular mechanisms underlying KIRC, highlighting potential biomarkers and therapeutic targets. Conclusion: This study contributes to the understanding of KIRC and provides a potential prognostic model using disulfidptosis gene for personalized management in KIRC patients. The risk signature shows clinical applicability and sheds light on the biological mechanisms associated with disulfide-induced cell death.

Landscape analysis of alternative splicing in kidney renal clear cell carcinoma and their clinical significance.

A growing number of studies reveal that alternative splicing (AS) is associated with tumorigenesis, progression, and metastasis. Systematic analysis of alternative splicing signatures in renal cancer is lacking. In our study, we investigated the AS landscape of kidney renal clear cell carcinoma (KIRC) and identified AS predictive model to improve the prognostic prediction of KIRC. We obtained clinical data and gene expression profiles of KIRC patients from the TCGA database to evaluate AS events. The calculation results for seven types of AS events indicated that 46276 AS events from 10577 genes were identified. Next, we applied Cox regression analysis to identify 5864 prognostic-associated AS events. We used the Metascape database to verify the potential pathways of prognostic-associated AS. Moreover, we constructed KIRC prediction systems with prognostic-associated AS events by the LASSO Cox regression model. AUCs demonstrated that these prediction systems had excellent prognostic accuracy simultaneously. We identified 34 prognostic associated splicing factors (SFs) and constructed homologous regulatory networks. Furthermore, in vitro experiments were performed to validate the favorable effect of SFs FMR1 in KIRC. In conclusion, we overviewed AS events in KIRC and identified AS-based prognostic models to assist the survival prediction of KIRC patients. Our study may provide a novel predictive signature to improve the prognostic prediction of KIRC, which might facilitate KIRC patient counseling and individualized management.

Research on molecular characteristics of ADME-related genes in kidney renal clear cell carcinoma.

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.

Disulfidptosis-associated LncRNA signature predicts prognosis and immune response in kidney renal clear cell carcinoma.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) represents a significant proportion of renal cell carcinomas and is characterized by high aggressiveness and poor prognosis despite advancements in immunotherapy. Disulfidptosis, a novel cell death pathway, has emerged as a critical mechanism in various cellular processes, including cancer. This study leverages machine learning to identify disulfidptosis-related long noncoding RNAs (DRlncRNAs) as potential prognostic biomarkers in KIRC, offering new insights into tumor pathogenesis and treatment avenues. RESULTS: Our analysis of data from The Cancer Genome Atlas (TCGA) led to the identification of 431 DRlncRNAs correlated with disulfidptosis-related genes. Five key DRlncRNAs (SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3, and AC108673.3) were used to develop a prognostic model that effectively distinguished between low- and high-risk patients with significant differences in overall survival and progression-free survival. The low-risk group had a favorable prognosis associated with a protective immune microenvironment and a better response to targeted drugs. Conversely, the high-risk group displayed aggressive tumor features and poor immunotherapy outcomes. Validation through qRT‒PCR confirmed the differential expression of these DRlncRNAs in KIRC cells compared to normal kidney cells, underscoring their potential functional significance in tumor biology. CONCLUSIONS: This study established a robust link between disulfidptosis-related lncRNAs and patient prognosis in KIRC, underscoring their potential as prognostic biomarkers and therapeutic targets. The differential expression of these lncRNAs in tumor versus normal tissue further highlights their relevance in KIRC pathogenesis. The predictive model not only enhances our understanding of KIRC biology but also provides a novel stratification tool for precision medicine approaches, improving treatment personalization and outcomes in KIRC patients.

SOX11 as a prognostic biomarker linked to m6A modification and immune infiltration in renal clear cell carcinoma.

Background: The prognosis for patients with kidney renal clear cell carcinoma (KIRC) remains unfavorable, and the understanding of SRY-box transcription factor 11 (SOX11) in KIRC is still limited. The purpose of this paper is to explore the role of SOX11 in the prognosis of KIRC. Methods: We analyzed SOX11 expression in KIRC and adjacent normal tissues using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Our study aims to establish a correlation between SOX11 expression and clinical pathological features. Differentially expressed genes (DEGs) were assessed using R software. Furthermore, we conducted Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and gene set enrichment analysis (GSEA). Integration of data from the Tumor Immune Estimation Resource (TIMER) and TCGA databases allowed us to assess the association between SOX11 expression and immune infiltration in KIRC. Additionally, we analyzed the association between SOX11 gene expression and N6-methyladenosine (m6A) modification in KIRC using TCGA and GEO data. Results: Our findings revealed high SOX11 expression in KIRC, which showed a significant correlation with tumor staging and prognosis. GO/KEGG and GSEA analyses indicated that SOX11 was closely associated with sodium ion transport, synaptic vesicle circulation, and oxidative phosphorylation. Analysis of the TIMER and TCGA databases demonstrated correlations of SOX11 expression levels with the presence of CD8+ T lymphocytes, neutrophils, CD4+ T cells, as well as B cells. Moreover, both the TCGA and GEO datasets showed a substantial association between SOX11 and m6A modification-related genes, namely ZC3H13, FTO, METTL14, YTHDC1, IGF2BP1, and IGF2BP2. Conclusions: SOX11 exhibits a correlation with m6A modification and immune infiltration, suggesting its potential as a prognostic biomarker for KIRC.

Low OGDHL expression affects the prognosis and immune infiltration of kidney renal clear cell carcinoma.

Background: Oxoglutarate dehydrogenase-like (OGDHL) modulates glutamine metabolism to influence tumor progression. Therefore, we aimed to explore the potential role of OGDHL in the prognosis of kidney renal clear cell carcinoma (KIRC) and its effect on immune infiltration. Methods: The Cancer Genome Atlas, Tumor Immune Estimation Resource, Gene Expression Profiling Interactive Analysis, Human Protein Atlas, and The University of Alabama at Birmingham Cancer databases and the GSE53757 dataset were utilized to analyze expression difference and prognosis of OGDHL in tumor and normal tissue; diagnostic value was assessed using receiver operating characteristic curves. Correlations with clinical features and survival prognosis were analyzed. Independent prognostic factors were identified using univariate and multifactorial Cox regression analysis. We used the CIBERSORT analysis tool to discover the proportion of tumor-infiltrating immune cells (TIICs) in KIRC patients. Next, the differences in the proportion of TIICs under different OGDHL expression were analyzed. Finally, we explored the potential mechanisms by which OGDHL expression affects patient survival using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Results: OGDHL expression was markedly downregulated in KIRC tissues compared to in normal tissues, and the downregulation of OGDHL expression was significantly associated with tumor progression (including tumor stage and grade) and poor prognosis. Cox regression analyses revealed OGDHL to be an independent prognostic factor for KIRC. CIBERSORT analysis showed that OGDHL expression is associated with differences in the proportion of several TIICs, particularly resting mast cells. Finally, GO and KEGG analysis showed that OGDHL was associated with extracellular matrix and epithelial cell differentiation involved in kidney development. GSEA indicated that low OGDHL was closely related to the activation of carcinogenic signaling pathways, including epithelial mesenchymal transition, tumor necrosis factor alpha and nuclear factor kappa B signaling pathway, negative regulation of apoptotic signaling, collagen formation, etc. Conclusions: OGDHL level can be monitored for diagnosing KIRC. Reduced expression is associated with poor prognosis and immune infiltration of KIRC. OGDHL is expected to become a new target for the treatment of KIRC.

SEMA3G, downregulated by ncRNAs, correlates with favorable prognosis and tumor immune infiltration in kidney renal clear cell carcinoma.

Kidney renal clear cell carcinoma (KIRC), relatively aggressive subtype of renal cell carcinoma, lacks of effective targets and promising biomarkers. Recently, although the function and immune correlation of semaphorin 3G (SEMA3G) in cancer draw more and more attention, its specific role and mechanism in KIRC are still not fully understood. In this work, we firstly conducted pan-cancer expression and survival bioinformatic analysis for SEMA3G and showed that SMEA3G might be a potential tumor suppressor and favorable prognostic biomarker in KIRC. Next, upstream noncoding RNA (ncRNA) regulatory mechanism of SEMA3G in KIRC was explored. By performing a series of in silico analyses, we identified that TBX2-AS1-miR-146a/b-5p axis was partially responsible for SEMA3G downregulation in KIRC. Furthermore, we also confirmed significant correlation of SEMA3G expression with tumor immune infiltration levels, expression of biomarkers of immune cells or immune checkpoints in KIRC. Taken together, the current data elucidated that ncRNA-caused downregulation of SEMA3G markedly linked to favorable prognosis and tumor immune infiltration in KIRC.

Comprehensive analysis of LAMC1 expression and prognostic value in kidney renal papillary cell carcinoma and clear cell carcinoma.

Background: Laminin subunit gamma 1 (LAMC1) protein is associated with tumor cell invasion and metastasis. However, its role in kidney cancer remains unclear. In this work, we sought to probe the expression as well as its carcinogenic mechanisms of LAMC1 in kidney renal papillary cell carcinoma (KIRP) and kidney renal clear cell carcinoma (KIRC). Methods: Public databases including TIMER, Oncomine, UALCAN, TISIDB, TCGA, Kaplan-Meier plotter, UCSC Xena, cBioPortal, SurvivalMeth, KEGG, GeneMANIA, Metascape, GSCALite and GDSC were adopted, and the expression, clinical pathological correlation, prognostic signatures, dominant factors influencing LAMC1 expression, DNA methylation levels, gene mutations, copy number variations, functional networks, and drug sensitivity were analyzed. Expression of LAMC1 protein in clinical KIRP and KIRC was validated using tissue array. Results: LAMC1 expression in KIRP and KIRC were significantly higher than those in normal tissues. High LAMC1 expression indicated poor overall survival in KIRP patients and better overall survival in KIRC patients. Through the univariate and multivariate Cox analysis, we found that high LAMC1 expression was a potential independent marker for poor prognosis in KIRP, however it implied a better prognosis in KIRC by univariate Cox analysis. In addition, the LAMC1 expression in KIRP and KIRC was negatively correlated with methylation levels of LAMC1 DNA. Interestingly, LAMC1 expression was positively correlated with the infiltration of CD8+ T cells, dendritic cells and neutrophils in KIRP; however, it was positively correlated with the infiltration of CD4+ T cells, macrophages and neutrophils but negatively correlated with B cells in KIRC. Moreover, high level of CD8+ T cells is beneficial for KIRC prognosis but opposite for KIRP. LAMC1 may participate in signaling pathways involved in formation of adherens junction and basement membrane in KIRP and KIRC, and the high expression of LAMC1 is resistant to most drugs or small molecules of the Genomics of Drug Sensitivity in Cancer database. Conclusion: Enhanced LAMC1 expression suggests a poor prognosis in KIRP while a better prognosis in KIRC, and these opposite prognostic signatures of LAMC1 may be related to different immune microenvironments.

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.

Molecular Subtyping Based on Cuproptosis-Related Genes and Characterization of Tumor Microenvironment Infiltration in Kidney Renal Clear Cell Carcinoma.

The incidence of kidney renal clear cell carcinoma (KIRC) is rising worldwide, and the prognosis is poor. Cuproptosis is a new form of cell death that is dependent on and regulated by copper ions. The relationship between cuproptosis and KIRC remains unclear. In the current study, changes in cuproptosis-related genes (CRGs) in TCGA-KIRC transcriptional datasets were characterized, and the expression patterns of these genes were analyzed. We identified three main molecular subtypes and discovered that multilayer CRG changes were associated with patient clinicopathological traits, prognosis, elesclomol sensitivity, and tumor microenvironment (TME) cell infiltration characteristics. Then, a CRG score was created to predict overall survival (OS). The CRG score was found to be strongly linked to the TME. These findings may help elucidate the roles of CRGs in KIRC, potentially enhancing understanding of cuproptosis and supporting the development of more effective immunotherapy strategies.

Diagnostic and prognostic role of basic leucine zipper transcription factor in kidney renal clear cell carcinoma.

Background: Basic leucine zipper transcription factor (BATF) plays a crucial role in development and progression of different types of carcinomas. However, its prognostic value in kidney renal clear cell carcinoma (KIRC) is yet to be elucidated. Methods: We obtained clinicopathological data and expression profiles of BATF from The Cancer Genome Atlas (TCGA) on the pan-cancer and KIRC perspectives. We calculated the area under the curve (AUC) of the receiver-operating characteristic curves to understand the discriminatory capacity of BATF. Next, we generated Kaplan-Meier curves to assess the effect of BATF on the overall survival (OS) of patients, then performed univariate and multivariate Cox regression analyses. Subsequently, we used multivariate regression to construct a nomogram for predicting prognosis. Furthermore, we construct a protein-protein interaction (PPI) network, then performed gene set enrichment and pathway enrichment analyses to determine the biological function of the co-expression genes. Finally, we performed tumor microenvironment analyses to establish the relationship between BATF expression and infiltrating immune cells. Results: BATF was significantly upregulated in KIRC relative to normal kidney tissues. Upregulation of BATF mRNA was associated with higher TNM pathological stage, histological grade, and poor OS/PFI (progression-free interval). Receiver-operating characteristic (ROC) curves showed that BATF had excellent diagnostic value in KIRC, as evidenced by the AUC and cutoff values of 0.942 and 2.033, respectively. Kaplan-Meier survival curves demonstrated that KIRC patients with high-BATF were associated with worse prognosis (hazard ratio =1.42, P=0.024). Results from Cox univariate analyses indicated that BATF was an independent prognostic factor in KIRC patients, and survival probabilities were predicted by the established nomogram. Results from the Tumor Immune Estimation Resource (TIMER) and BATF mRNA expression showed an association with immune cell infiltration. Conclusions: BATF is a prognostic biomarker and a potential target for immune therapies in KIRC.