RESUMO
Background: Intratumoral hypoxia is widely associated with the development of malignancy, treatment resistance, and worse prognoses. The global influence of hypoxia-related genes (HRGs) on prognostic significance, tumor microenvironment characteristics, and therapeutic response is unclear in patients with non-small cell lung cancer (NSCLC). Method: RNA-seq and clinical data for NSCLC patients were derived from The Cancer Genome Atlas (TCGA) database, and a group of HRGs was obtained from the MSigDB. The differentially expressed HRGs were determined using the limma package; prognostic HRGs were identified via univariate Cox regression. Using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, an optimized prognostic model consisting of nine HRGs was constructed. The prognostic model's capacity was evaluated by KaplanâMeier survival curve analysis and receiver operating characteristic (ROC) curve analysis in the TCGA (training set) and GEO (validation set) cohorts. Moreover, a potential biological pathway and immune infiltration differences were explained. Results: A prognostic model containing nine HRGs (STC2, ALDOA, MIF, LDHA, EXT1, PGM2, ENO3, INHA, and RORA) was developed. NSCLC patients were separated into two risk categories according to the risk score generated by the hypoxia model. The model-based risk score had better predictive power than the clinicopathological method. Patients in the high-risk category had poor recurrence-free survival in the TCGA (HR: 1.426; 95% CI: 0.997-2.042; p = 0.046) and GEO (HR: 2.4; 95% CI: 1.7-3.2; p < 0.0001) cohorts. The overall survival of the high-risk category was also inferior to that of the low-risk category in the TCGA (HR: 1.8; 95% CI: 1.5-2.2; p < 0.0001) and GEO (HR: 1.8; 95% CI: 1.4-2.3; p < 0.0001) cohorts. Additionally, we discovered a notable distinction in the enrichment of immune-related pathways, immune cell abundance, and immune checkpoint gene expression between the two subcategories. Conclusion: The proposed 9-HRG signature is a promising indicator for predicting NSCLC patient prognosis and may be potentially applicable in checkpoint therapy efficiency prediction.
RESUMO
Endometrial carcinoma (EC) is a malignant neoplasm of the endometrial epithelium, which may be diagnosed by pathological investigations. The aim of the current study was to identify new markers for the diagnosis of EC using machine learning. The association between human T cell lymphotropic virus type 1 (HTLV-1) infection and endometrial cancer risk have not been widely reported. It remains ambiguous whether HTLV-1 infection is associated with several types of cancer. The present study investigated the association between HTLV-1 infection-associated genes and EC risk. RNA sequencing uterine cancer expression data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified between normal and matched tumor samples. A total of 41 genes were selected by an overlap between HTLV-1 infection pathway-associated genes and the DEGs. Two-way hierarchical clustering analysis (HCA) and a support vector machine (SVM) classifier were constructed using the 41 genes. The accuracy of the candidate genes in risk-stratifying the samples was 100%. The accuracy of the proposed SVM model was 100%. In addition, the classification power of the SVM model was validated using a merged dataset (TCGA and the Genotype-Tissue Expression project). This predictive feature achieved reliable outcomes with risk-stratifying samples of almost 99% in two-way HCA, and an accuracy yield of 98% of the SVM classifier. In conclusion, the 41 genes identified in the current study may be implicated in the development of EC and may be of prognostic value for the disease. The results obtained the current study suggest that HTLV-1 may be potentially associated with EC and highlight potential disease mechanisms.
