C1QTNF6 is a Prognostic Biomarker and Related to Immune Infiltration and Drug Sensitivity: A Pan-Cancer Analysis

Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.

Pan-Cancer Analysis of Prognostic and Immune Infiltrates for the TMEM65, Especially for the Breast Cancer

Introduction

Transmembrane protein 65 (TMEM65) is an inner mitochondrial membrane protein, which played important role in mediating autophagy, smooth muscle contraction, protein glycosylation, and immune response. In recent years, the interest had risen for exploring the function of the TMEM genes in the cancer fields. As a consequence, in our pan-cancer research of the TMEM65, we explored the function of the gene in kinds of database and tried to apply the finding in the clinical practice.

Methods

In this research, we provide a comprehensive investigation of TMEM65 expression in a pan-cancer manner containing 33 cancer types. We evaluated the association of TMEM65 with the prognosis, immune infiltration, drug sensitivity analysis, GSVA enrichment analysis, TMB, MSI, NEO, and hotspot mechanisms.

Results

TMEM65 was abnormally expressed in 24 types of cancers and showed correlation with the OS for 6 cancers and PFI for 9 cancers and kpI for 3 types. Moreover, the TME score, CD8 T effector, and immune checkpoint scoring systems showed a close correlation with the TMEM65. Moreover, TMEM65 was strongly correlated with some of the most common tumor-related genes and certain pathways (TGF beta signaling, TNFA signaling, hypoxia, pyroptosis, DNA repairing, autophagy, ferroptosis, and other related genes). Additionally, the TMEM65 showed correlations with the tumor mutational burden (TMB), microsatellite instability (MSI), NEO, and drug sensitivity. Finally, we confirmed several pathways by the GSEA and GSVA for the TMEM65 at the breast cancer aspects. Nomogram prediction model was also established for the breast tumors based on the TMEM65 level and other variables.

Conclusion

Above all, the TMEM65 played important roles in predicting the prognosis of the cancers and correlated with the tumor immunity in the pan-cancer analysis.

A signature constructed with mitophagy-related genes to predict the prognosis and therapy response for breast cancer

Over the past decades, the incidence and mortality rates of breast cancer (BC) have increased rapidly; however, molecular biomarkers that can reliably detect BC are yet to be discovered. Our study aimed to identify a novel signature that can predict the prognosis of patients with BC. Data from the TCGA-BRCA cohort were analyzed using univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis was performed to build a stable prognostic model. Subsequently, Kaplan–Meier (K–M) and receiver operating characteristic (ROC) analyses were performed to demonstrate the predictive power of our gene signature. Each patient was assigned to either a low- or high-risk group. Patients with high-risk BC had poorer survival than those with low-risk BC. Cox regression analysis suggested that our signature was an independent prognostic factor. Additionally, decision curve analysis and calibration accurately predicted the capacity of our nomogram. Thus, based on the differentially expressed genes (DEGs) of mitophagy-related tumor classification, we established a 13-gene signature and robust nomogram for predicting BC prognosis, which can be beneficial for the diagnosis and treatment of BC.