mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning

Abstract

Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA stemness index-related metabolic risk score model, which was examined in The Cancer Genome Atlas and Gene Expression Omnibus databases (1323 patients) and validated using the Zhongshan Hospital cohort (200 patients). The high-risk group showed more immune infiltrations; higher levels of immunosuppressive checkpoints, such as CD274, tumor mutation burden, and resistance to chemotherapeutics; potentially better response to immune therapy; worse prognosis; and advanced stage of tumor node metastasis than the low-risk group. The combination of risk score and clinical characteristics was effective in predicting overall survival. Zhongshan cohort validated that high-risk score group correlated with malignant progression, worse prognosis, inferior adjuvant chemotherapy responsiveness of CRC, and shaped an immunoevasive contexture. This tool may provide a more accurate risk stratification in CRC and screening of patients with CRC responsive to immunotherapy.

Prediction of Prognosis and Chemotherapeutic Sensitivity Based on Cuproptosis-Associated lncRNAs in Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

“…Weng et al found that SCG2 was a risk factor with higher expression predicting poorer prognosis, and the SCG2 high expression subgroup was the immune hot type and considered more suitable for immunotherapy [53]. Weng et al found that a generated metabolic risk score model using a 21-gene mRNA dryness index could provide a more accurate stratification of colorectal risk and screening of colorectal patients who respond to immunotherapy [54]. In our present study, we found that this risk model was also closely related to several ICGs, CRSGs, and immune cells and factors, and many chemotherapy agents differ significantly between high-and low-risk CESC patients.…”

An individualized stemness‐related signature to predict prognosis and immunotherapy responses for gastric cancer using single‐cell and bulk tissue transcriptomes

“…6,7 In 2018, Malta et al developed a mRNA expression based-index (mRNAsi) model using machine learning algorithm to evaluate the stemness of tumors. 8 Based on mRNAsi, many stemness signatures have been constructed for prognostic analysis, [9][10][11] including GC. [12][13][14] However, the absolute stemness index calculated by mR-NAsi is easily influenced by sample composition, 15 and in some cancers, including GC, contrary to previous research, higher stemness corresponds to a better prognosis.…”