IMC-MDA: Prediction of miRNA-disease association based on induction matrix completion.

To comprehend the etiology and pathogenesis of many illnesses, it is essential to identify disease-associated microRNAs (miRNAs). However, there are a number of challenges with current computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "isolated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases: colon cancer, kidney cancer, and lung cancer.

Seven bacterial response-related genes are biomarkers for colon cancer.

BACKGROUND: Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis. METHODS: The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues. RESULTS: A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480). CONCLUSIONS: We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

CIRCULATE-Japan: Circulating tumor DNA-guided adaptive platform trials to refine adjuvant therapy for colorectal cancer.

Adjuvant chemotherapy has reduced the risk of tumor recurrence and improved survival in patients with resected colorectal cancer. Potential utility of circulating tumor DNA (ctDNA) prior to and post surgery has been reported across various solid tumors. We initiated a new type of adaptive platform trials to evaluate the clinical benefits of ctDNA analysis and refine precision adjuvant therapy for resectable colorectal cancer, named CIRCULATE-Japan including three clinical trials. The GALAXY study is a prospectively conducted large-scale registry designed to monitor ctDNA for patients with clinical stage II to IV or recurrent colorectal cancer who can undergo complete surgical resection. The VEGA trial is a randomized phase III study designed to test whether postoperative surgery alone is noninferior to the standard therapy with capecitabine plus oxaliplatin for 3 months in patients with high-risk stage II or low-risk stage III colon cancer if ctDNA status is negative at week 4 after curative surgery in the GALAXY study. The ALTAIR trial is a double-blind, phase III study designed to establish the superiority of trifluridine/tipiracil as compared with placebo in patients with resected colorectal cancer who show circulating tumor-positive status in the GALAXY study. Therefore, CIRCULATE-Japan encompasses both "de-escalation" and "escalation" trials for ctDNA-negative and -positive patients, respectively, and helps to answer whether measuring ctDNA postoperatively has prognostic and/or predictive value. Our ctDNA-guided adaptive platform trials will accelerate clinical development toward further precision oncology in the field of adjuvant therapy. Analysis of ctDNA status could be utilized as a predictor of risk stratification for recurrence and to monitor the effectiveness of adjuvant chemotherapy. ctDNA is a promising, noninvasive tumor biomarker that can aid in tumor monitoring throughout disease management.