Identification and Validation of a Malignant Cell Subset Marker-Based Polygenic Risk Score in Stomach Adenocarcinoma Through Integrated Analysis of Bulk and Single-Cell RNA Sequencing Data.

The aim of the present study was to construct a polygenic risk score (PRS) for poor survival among patients with stomach adenocarcinoma (STAD) based on expression of malignant cell markers. Integrated analyses of bulk and single-cell RNA sequencing (scRNA-seq) of STAD and normal stomach tissues were conducted to identify malignant and non-malignant markers. Analyses of the scRNA-seq profile from early STAD were used to explore intratumoral heterogeneity (ITH) of the malignant cell subpopulations.

Identification of the association between HMMR expression and progression of hepatocellular carcinoma via construction of a co-expression network.

The aim of the present study was to identify key genes involved in the progression of hepatocellular carcinoma (HCC). According to the theory of the multistep process of hepatocarcinogenesis and weighted gene co-expression network analysis, hub genes associated with the progression of HCC were identified using the gene expression profiles of patients with normal to chronic hepatitis/cirrhosis and dysplastic nodules to HCC. An independent dataset was used to verify the association between hub gene and clinical phenotype.

A Five-microRNA Signature as Risk Stratification System in Uterine Corpus Endometrial Carcinoma.

Background: MicroRNAs (miRs) have been shown to play important roles in various cancers and may be a reliable prognostic marker. However, its prognostic value in endometrial carcinoma (UCEC) needs to be further explored.

Objectives: The aim of this study was to create a miR-based signature to effectively predict the prognosis for patients with uterine corpus endometrial carcinoma (UCEC).

Construction and validation of a seven-microRNA signature as a prognostic tool for lung squamous cell carcinoma.

The aim of this study was to construct and validate a microRNA (miR)-based signature as a prognostic tool for lung squamous cell carcinoma (LUSC). With the use of mature miR expression profiles downloaded from The Cancer Genome Atlas database, we identified differentially expressed miRs between LUSC and matched healthy lung tissue. Thereafter, we carried out an evaluation of the association of differentially expressed miRs with overall survival (OS) with the use of univariate and multivariate Cox regression analysis.

Identification of a 13-gene-based classifier as a potential biomarker to predict the effects of fluorouracil-based chemotherapy in colorectal cancer.

The aim of the current study was to develop a predictor classifier for response to fluorouracil-based chemotherapy in patients with advanced colorectal cancer (CRC) using microarray gene expression profiles of primary CRC tissues. Using two expression profiles downloaded from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between responders and non-responders to fluorouracil-based chemotherapy were identified. A total of 791 DEGs, including 303 that were upregulated and 488 that were downregulated in responders, were identified.

Identification of key genes involved in the metastasis of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common and lethal renal malignant tumor in adults. The aim of the present study was to identify the key genes involved in ccRCC metastasis. Expression profiling data for ccRCC patients with metastasis and without metastasis were obtained from The Cancer Genome Atlas database.