Integrated Multi-Omics Analysis Identified PTPRG and CHL1 as Key Regulators of Immunophenotypes in Clear Cell Renal Cell Carcinoma(ccRCC)

Metabolic classifications of renal cell carcinoma reveal intrinsic connections with clinical and immune characteristics

BACKGROUND

Kidney cancer undergoes a dramatic metabolic shift and has demonstrated responsiveness to immunotherapeutic intervention. However, metabolic classification and the associations between metabolic alterations and immune infiltration in Renal cell carcinoma still remain elucidative.

METHODS

Unsupervised consensus clustering was conducted on the TCGA cohorts for metabolic classification. GESA, mRNAsi, prognosis, clinical features, mutation load, immune infiltration and differentially expressed gene differences among different clusters were compared. The prognosis model and nomograms were constructed based on metabolic gene signatures and verified using external ICGC datasets. Immunohistochemical results from Human Protein Atlas database and Tongji hospital were used to validate gene expression levels in normal tissues and tumor samples. CCK8, apoptosis analysis, qPCR, subcutaneously implanted murine models and flowcytometry analysis were applied to investigate the roles of ACAA2 in tumor progression and anti-tumor immunity.

RESULTS

Renal cell carcinoma was classified into 3 metabolic subclusters and the subcluster with low metabolic profiles displayed the poorest prognosis, highest invasiveness and AJCC grade, enhanced immune infiltration but suppressive immunophenotypes. ACAA2, ACAT1, ASRGL1, AKR1B10, ABCC2, ANGPTL4 were identified to construct the 6 gene-signature prognosis model and verified both internally and externally with ICGC cohorts. ACAA2 was demonstrated as a tumor suppressor and was associated with higher immune infiltration and elevated PD-1 expression of CD8⁺ T cells.

CONCLUSIONS

Our research proposed a new metabolic classification method for RCC and revealed intrinsic associations between metabolic phenotypes and immune profiles. The identified gene signatures might serve as key factors bridging tumor metabolism and tumor immunity and warrant further in-depth investigations.

Prognosis and pain dissection of novel signatures in kidney renal clear cell carcinoma based on fatty acid metabolism-related genes

Renal cell carcinoma (RCC) is a malignant tumor that is characterized by the accumulation of intracellular lipid droplets. The prognostic value of fatty acid metabolism-related genes (FMGs) in RCC remains unclear. Alongside this insight, we collected data from three RCC cohorts, namely, The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts, and identified a total of 309 FMGs that could be associated with RCC prognosis. First, we determined the copy number variation and expression levels of these FMGs, and identified 52 overall survival (OS)-related FMGs of the TCGA-KIRC and the E-MTAB-1980 cohort data. Next, 10 of these genes-FASN, ACOT9, MID1IP1, CYP2C9, ABCD1, CPT2, CRAT, TP53INP2, FAAH2, and PTPRG-were identified as pivotal OS-related FMGs based on least absolute shrinkage and selection operator and Cox regression analyses. The expression of some of these genes was confirmed in patients with RCC by immunohistochemical analyses. Kaplan-Meier analysis showed that the identified FMGs were effective in predicting the prognosis of RCC. Moreover, an optimal nomogram was constructed based on FMG-based risk scores and clinical factors, and its robustness was verified by time-dependent receiver operating characteristic analysis, calibration curve analysis, and decision curve analysis. We have also described the biological processes and the tumor immune microenvironment based on FMG-based risk score classification. Given the close association between fatty acid metabolism and cancer-related pain, our 10-FMG signature may also serve as a potential therapeutic target with dual effects on ccRCC prognosis and cancer pain and, therefore, warrants further investigation.

Oxidative Phosphorylation-Related Signature Participates in Cancer Development, and PTPRG Overexpression Suppresses the Cancer Progression in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) was a common cancer type diagnosed with frequent metastases, harboring an unfavorable therapeutic response, and results in a poor prognosis. More promising therapeutic targets are urgently required for treating ccRCC. This study was conducted to explore the role of oxidative phosphorylation in ccRCC development and reveal its clinical potential. We first identified oxidative phosphorylation-related clusters based on consensus clustering and validated their diversity in the genome instability, environmental infiltration, and immunosuppression by Gistic, ESTIMATE, GSVA, and TIDE web tools. We also compared their prognostic and clinical feature differences and predicted the IC50 level between the clusters using pRRophetic. Subsequently, we performed weighted gene coexpression network analysis to select cluster-related genes and performed functional analysis for them. The cluster-related genes were adopted to construct a risk score and nomogram for predicting patient prognosis with predictive accuracy evaluated. Finally, we performed lentivirus to induce ccRCC cell PTPRG overexpression and conducted western blot experiments to detect the critical protein expression of oxidative phosphorylation, apoptosis, cell cycle, and epithelial-mesenchymal transition processes. Also, the cell cycle and apoptosis level were evaluated by flow cytometry. As a result, we discovered that both the C1 cluster and high-risk group predicted patient survival with high accuracy and characterized lower survival rates, lower oxidative phosphorylation levels, higher immune infiltration, and malignant clinical features. Besides, we observed that overexpression of PTPRG activated oxidative phosphorylation and inhibited apoptosis. Its overexpression also depressed the epithelial-mesenchymal transition and promoted G1/S cell cycle arrest. Comprehensively, we confirmed the anticancer role of oxidative phosphorylation in ccRCC cells and discovered its association with immune and immunosuppression. PTPRG was also identified as a potential therapeutic target due to its multiple anticancer effects. We believe this study discovered a novel mechanism of ccRCC pathological progression and will provide promising targets for therapeutic strategy development.

A Potential Fatty Acid Metabolism-Related Gene Signature for Prognosis in Clear Cell Renal Cell Carcinoma

Simple Summary

Clear Cell Renal Cell Carcinoma (ccRCC) is the most common and aggressive subtype of renal cancer. Abnormal fatty acid metabolism (FAM) is reported to be strongly associated with multiple malignancies, yet there is limited research in ccRCC. In this manuscript, we reported the significant role of abnormal FAM in predicting the prognosis of ccRCC. Three independent clinical cohorts (TCGA, EMTAB and our clinical cohorts with prognostic profiles and gene expression data, including RNA-seq, microarray and RT-qPCR) were applied as training and two external validation cohorts. As a result, we successfully constructed and validated a novel FAM-related gene signature (FAMGS) and nomogram for the overall survival of patients with ccRCC. Additionally, our study further elucidated the potential immune relevance and molecular mechanisms of abnormal FAM and the signature. In conclusion, the novel FAMGS constructed in this study offered a promising prognostic tool in clinic and potential therapeutic targets for ccRCC patients.

Abstract

This study aims to explore the role of abnormal fatty acid metabolism (FAM) in ccRCC and construct a novel fatty acid metabolism-related gene signature (FAMGS) for prognosis. Three independent ccRCC cohorts, including The Cancer Genome Atlas, E-MTAB-1980 and our clinical cohort (including RNA-seq, microarray and RT-qPCR data), were applied as training and two independent validation cohorts. Firstly, FAM levels were found to be significantly decreased in ccRCC and correlated with degrees of malignancy, confirming the pivotal role of FAM in ccRCC. Applying the least absolute shrinkage and selection operator cox regression, we established a novel FAMGS for overall survival (OS). The FAMGS divided patients into low or high-risk groups in the training cohort and were successfully validated in both the EMTAB and our clinical validation cohorts. Additionally, the FAMGS serves as an independent risk factor for OS of ccRCC. Results of the immune cell abundance identifier (ImmuCellAI) algorithm and gene set variation analysis (GSVA) revealed that patients in the high-risk group have comprehensively impaired metabolism, including lipids, amino acids and tricarboxylic acid cycle-related pathways and a more immunosuppressive tumor microenvironment. In conclusion, our study constructed and validated a novel FAMGS, which may improve the risk stratification optimization and personalized management of ccRCC.

Prognostic Role of M6A-Associated Immune Genes and Cluster-Related Tumor Microenvironment Analysis: A Multi-Omics Practice in Stomach Adenocarcinoma

N6-methylandrostenedione (m6A) methylation plays a very important role in the development of malignant tumors. The immune system is the key point in the progression of tumors, particularly in terms of tumor treatment and drug resistance. Tumor immunotherapy has now become a hot spot and a new approach for tumor treatment. However, as far as the stomach adenocarcinoma (STAD) is concerned, the in-depth research is still a gap in the m6A-associated immune markers. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases is extremely important for our research, where we obtained gene mutation, gene expression data and relevant clinical information of STAD patients. Firstly, the samples from GEO were used as external validation groups, while the TCGA samples were divided into a training group and an internal validation group randomly. Using the way of Single factor COX-LASSO- and multi-factor Cox to construct the prognostic model. Then, all samples were subjected to cluster analysis to generate high and low expression groups of immune gene. Meanwhile, we also collected the correlation between these types and tumor microenvironment. On this basis, a web version of the dynamic nomogram APP was developed. In addition, we performed microenvironmental correlation, copy number variation and mutation analyses for model genes. The prognostic model for STAD developed here demonstrated a very strong predictive ability. The results of cluster analysis manifested that the immune gene low expression group had lower survival rate and higher degree of immune infiltration. Therefore, the immune gene low expression group was associated with lower survival rates and a higher degree of immune infiltration. Gene set enrichment analysis suggested that the potential mechanism might be related to the activation of immunosuppressive functions and multiple signaling pathways. Correspondingly, the web version of the dynamic nomogram APP produced by the DynNom package has successfully achieved rapid and accurate calculation of patient survival rates. Finally, the multi-omics analysis of model genes further enriched the research content. Interference of RAB19 was confirmed to facilitate migration of STAD cells in vitro, while its overexpression inhibited these features. The prognostic model for STAD constructed in this study is accurate and efficient based on multi-omics analysis and experimental validation. Additionally, the results of the correlation analysis between the tumor microenvironment and m6Ascore are the basics of further exploration of the pathophysiological mechanism in STAD.

A four-microRNA panel in serum may serve as potential biomarker for renal cell carcinoma diagnosis

BACKGROUND

Renal cell carcinoma (RCC) is one out of the most universal malignant tumors globally, and its incidence is increasing annually. MicroRNA (miRNA) in serum could be considered as a non-invasive detecting biomarker for RCC diagnosis.

METHOD

A total of 224 participants (112 RCC patients (RCCs) and 112 normal controls (NCs)) were enrolled in the three-phrase study. Reverse transcription quantitative PCR (RT-qPCR) was applied to reveal the miRNA expression levels in RCCs and NCs. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to predict the diagnostic ability of serum miRNAs for RCC. Bioinformatic analysis and survival analysis were also included in our study.

RESULTS

Compared to NCs, the expression degree of miR-155-5p, miR-224-5p in serum was significantly upregulated in RCC patients, and miR-1-3p, miR-124-3p, miR-129-5p, and miR-200b-3p were downregulated. A four-miRNA panel was construed, and the AUC of the panel was 0.903 (95% CI: 0.847-0.944; p < 0.001; sensitivity = 75.61%, specificity = 93.67%). Results from GEPIA database indicated that CHL1, MPP5, and SORT1 could be seen as promising target genes of the four-miRNA panel. Survival analysis of candidate miRNAs manifested that miR-155-5p was associated with the survival rate of RCC significantly.

CONCLUSIONS

The four-miRNA panel in serum has a great potential to be non-invasive biomarkers for RCC sift to check.