Identification of fatty acid metabolism-based molecular subtypes and prognostic signature to predict immune landscape and guide clinical drug treatment in renal clear cell carcinoma

Identification of Prognostic and Immune Characteristics of Two Lung Adenocarcinoma Subtypes Based on TRPV Channel Family Genes

Lung adenocarcinoma (LUAD) is one of the deadliest malignant tumors worldwide. Transient receptor potential vanilloid (TRPV) channels take pivotal parts in many cancers, but their impact on LUAD remains unexplored. In this study, LUAD samples were classified into two subtypes according to the expression characteristics of TRPV1-6 genes, with LUAD subtype cluster2 exhibiting significantly higher survival rates than cluster1. Subsequently, analysis of differentially expressed genes (DEGs) was performed between cluster1 and cluster2, revealing enrichment of DEGs in channel activity and Ca2+ signaling pathways. We established a protein-protein interaction network based on DEGs and constructed a LUAD prognostic model by using Cox regression analysis based on genes corresponding to 170 protein nodes. The prognostic model demonstrated good predictive ability for patient prognosis, with higher survival rates observed in the low-risk (LR) group. The risk score was validated as an independent prognostic indicator, according to Cox regression analysis. A clinically applicable nomogram was plotted. Immunological analysis indicated that the LR and high-risk (HR) groups had varied proportions of immune cell infiltration. The immunotherapy prediction indicated that LUAD patients in LR group had a greater likelihood to benefit from immune checkpoint blockade therapy. Furthermore, we hypothesized that the expression patterns of feature genes in the LUAD model were related to the sensitivity to lung cancer therapeutic drugs TAS-6417 and Erlotinib. To sum up, our LUAD prognostic model possessed clinical applicability for prognosis and immunotherapy response prediction.

TMX family genes and their association with prognosis, immune infiltration, and chemotherapy in human pan-cancer

BACKGROUND

The thioredoxin (TMX) system, an important redox system, plays crucial roles in several immune-related diseases. However, there is limited research on the correlation of TMX family gene expression with human pan-cancer prognosis, tumor microenvironment (TME), and immunotherapy.

METHODS

Based on the integration of several bioinformatics analysis methods, we explored the expression levels and prognostic value of TMX family members in pan-cancer and analyzed their association between TME, immune infiltration, stemness scores, and drug sensitivity. Using KEGG enrichment analysis, we explored the potential signaling pathways of their regulation. Additionally, we conducted a transwell assay to verify the relationship between TMX family gene expression and epithelial-mesenchymal transition (EMT) in liver cancer.

RESULTS

Expression of the TMX family genes was shown to have an obvious intratumoral heterogeneity. In some cancers, TMX family members expression was also been found to correlate with poor prognosis of patients. Furthermore, TMX family genes may serve important roles in TME. The expression of TMX family genes was found to have a strong correlation with the stromal scores, immune scores, DNAss and RNAss in pan-cancer. Specifically, the expression levels of TMX family genes have been found to be associated with immune subtypes of renal clear cell carcinoma and liver hepatocellular carcinoma. High TMX2 expression promote EMT in liver cancer.

CONCLUSIONS

The findings of this study may elucidate the biological roles of TMX family genes as potential targets for pan-cancer and also offer valuable insights for further investigating how these genes function in the development and spreading of cancer.

A prognostic risk model for programmed cell death and revealing TRIB3 as a promising apoptosis suppressor in renal cell carcinoma

Programmed cell death (PCD), a common modality of cell death, affects tumor development and acts as a target for tumor therapeutics. Many modalities of PCD regulate genesis, progression and metastasis of cancers, thus affecting the patients' prognosis, but the comprehensive molecular mechanisms of PCD in tumors are lacking, especially in renal cancer. Here, seventeen PRPCDGs were identified from 1257 genes associated with thirteen PCD modalities, which were highly differentially expressed and significantly affected patients' prognosis. Then, LASSO regression analysis of these PRPCDGs screened the 9-gene PRPCDGs risk signature in TCGA-KIRC database. The PRPCDGs risk signature was closely associated with the patients' prognosis and presented stable prediction efficacy for 5- and 7-year overall survival (OS) in three different cohorts of renal cancer. Immune cell infiltration, immune checkpoint expression and pathway enrichment (including GO, KEGG pathway, tumor-associated pathways and metabolism-associated pathways) were significantly different in the high- or low-PRPCDGs-risk group. Finally, we illustrated that TRIB3 might be a protumor factor responsible for the elevated proliferation and invasion capacities of renal cell carcinoma (RCC) cells. In summary, the PRPCDGs risk signature was developed and showed stable prediction efficacy for the prognosis of patients and that (such as TRIB3) could be a potential target for RCC management.

Identification of DNA repair gene signature and potential molecular subtypes in hepatocellular carcinoma

DNA repair is a critical factor in tumor progression as it impacts tumor mutational burden, genome stability, PD-L1 expression, immunotherapy response, and tumor-infiltrating lymphocytes (TILs). In this study, we present a prognostic model for hepatocellular carcinoma (HCC) that utilizes genes related to the DNA damage response (DDR). Patients were stratified based on their risk score, and groups with lower risk scores demonstrated better survival rates compared to those with higher risk scores. The prognostic model's accuracy in predicting 1-, 3-, and 5-year survival rates for HCC patients was analyzed using receiver operator curve analysis (ROC). Results showed good accuracy in predicting survival rates. Additionally, we evaluated the prognostic model's potential as an independent factor for HCC prognosis, along with tumor stage. Furthermore, nomogram was employed to determine the overall survival year of patients with HCC based on this independent factor. Gene set enrichment analysis (GSEA) revealed that in the high-risk group, apoptosis, cell cycle, MAPK, mTOR, and WNT cascades were highly enriched. We used training and validation datasets to identify potential molecular subtypes of HCC based on the expression of DDR genes. The two subtypes differed in terms of checkpoint receptors for immunity and immune cell filtration capacity.Collectively, our study identified potential biomarkers of HCC prognosis, providing novel insights into the molecular mechanisms underlying HCC.