Qian Song

BACKGROUND: Lung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major type of lung cancer. This study aimed to establish a signature based on immune related genes that can predict patients' OS for LUAD. METHODS: The expression data of 976 LUAD patients from The Cancer Genome Atlas database (training set) and the Gene Expression Omnibus database (four testing sets) and 1534 immune related genes from the ImmPort database were used for generation and validation of the signature. The glmnet Cox proportional hazards model was used to find the best gene model and construct the signature. To assess the independently prognostic ability of the signature, the Kaplan-Meier survival analysis and Cox's proportional hazards model were performed. RESULTS: A gene model consisting of 30 immune related genes with the highest frequency after 1000 iterations was used as our signature. The signature demonstrated robust prognostic ability in both training set and testing set and could serve as an independent predictor for LUAD patients in all datasets except GSE31210. Besides, the signature could predict the overall survival (OS) of LUAD patients in different subgroups. And this signature was strongly associated with important clinicopathological factors like recurrence and TNM stage. More importantly, patients with high risk score presented high tumor mutation burden. CONCLUSIONS: This signature could predict prognosis and reflect the tumor immune microenvironment of LUAD patients, which can promote individualized treatment and provide potential novel targets for immunotherapy.

MicroRNA-221/222 (miRNA-221/222, miR-221/222) is a noncoding microRNA which is widely distributed in eukaryotic organisms and deeply involved in the posttranscriptional regulation of gene expressions. According to recent studies, abnormal expressions of miR-221/222 are closely related to the occurrence and development of various kinds of malignant tumors. The role of miR-221/222 in tumor development and their potential molecular mechanism in various cancers, including liver cancer, colorectal cancer, cervical cancer, ovarian cancer, and endometrial carcinoma, are summarized and reviewed in this paper. Moreover, the potential translational biomarker role of abnormal miR-221/222 level in tumor or blood circulation for tumor diagnosis is also discussed.

The kidney renal papillary cell carcinoma (KIRP) is a relatively rare type of kidney cancer. There has been no investigation to find a robust signature to predict the survival outcome of KIRP patients in the aspect of tumor immunology. In this study, 285 KIRP samples from The Cancer Genome Atlas (TCGA) were randomly divided into training and testing set. A total of 1534 immune-related genes from The Immunology Database and Analysis Portal (ImmPort) were used as candidates to construct the signature. Using univariate Cox analysis, we evaluated the relationship between overall survival and immune-related genes expression and found 272 immune-related genes with predicting prognostic ability. In order to construct an efficient predictive model, the Cox proportional hazards model with an elastic-net penalty was used and identified 23 groups after 1000 iterations. As a result, 15-genes model showing more stable than other gene groups was chosen to construct our immune-related risk signature. In line with our expectations, the signature can independently predict the survival outcome of KIRP patients. Patients with high-immune risk were found correlated with advanced stage. We also found that the high-immune risk patients with higher PBRM1 and SETD2 mutations, increasing chromosomal instability, together with the gene set enrichment analysis (GSEA) results showing intensive connection of our signature with immune pathways. In conclusion, our study constructs a robust 15-gene signature for predicting KIRP patients' survival outcome on the basis of tumor immune environment and may provide possible relationship between prognosis and immune-related biological function.