At present, there is no validated marker to identify the subpopulation of patients with advanced gastric cancer (AGC) who might benefit from neoadjuvant chemotherapy (NACT). In view of this clinical challenge, the identification of non-invasive biomarkers for efficacy prediction of NACT in patients with AGC is imperative. Herein, we aimed to develop a non-invasive, liquid-biopsy-based assay by using an exosome-derived RNAs model based on multi-omics characteristics of RNAs. We firstly used a multi-omics strategy to characterize the mRNAs, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) profiles of circulating exosome enriched fractions in responders to NACT paired with non-responders, using RNA sequencing. Finally, numerous miRNAs, mRNAs and lncRNAs were identified to be associated with the response to NACT in patients with AGC, and it was validated in an independent cohort with promising AUC values. Furthermore, we established a 6-exosome-RNA panel that could robustly identified responders from non-responders treated with fluorouracil-based neoadjuvant chemotherapy.

Background: The human copper Cu proteome, also termed Cu-binding proteins (CBP), is responsible for transporting “free” Cu to the cell that is related to cuproptosis. However, their role in gastric cancer (GC) has not been reported. Methods: RNA expression data of 946 GC patients were collected. A series of machine learning and bioinformatic approaches were combined to build a CBP signature to predict survival and immune microenvironment and guide the priority treatment. Immunohistochemistry and multicolor immunofluorescence (mIF) in 1076 resection slides were used to verify immune features. Results: A CBP signature was constructed using the machine learning method from TCGA that classifies cases as CBP_low and CBP_high groups. Multivariable Cox analysis confirmed that the CBP signature was an independent prognostic factor in the training and validation cohorts. Additionally, GC patients with low CBPscores showed an increase in anti-tumor immune microenvironment, which was further verified by mIF in pathological resections following immunotherapy. Importantly, patients with low CBPscores had higher levels of TMB/MSI and responded well to immunotherapy. Conclusions: We conducted the first and comprehensive CBP analysis of GC patients and established a clinically feasible CBP signature for predicting survival and response to treatment, which will be helpful for guiding personalized medicine.

Recent studies have found that m6A modification of mRNA may play important roles in the progression of various types of cancers. However, current knowledge about drugs that can interfere with m6A methylation and inhibit cancer cell proliferation is still far from comprehensive. To this end, we performed integrative analysis on transcriptome data with perturbation of m6A writers or erasers and identified consensus m6A-related differentially expressed genes (DEGs). Comparative analysis of these m6A-related DEGs with Connectivity Map signatures highlight potential m6A-targeted drugs. Among them, we experimentally verified the inhibitory effects of AZ628 on the proliferation of human breast cancer cell lines and R428 on the proliferation of human melanoma cell lines. Both drugs can significantly reduce the cellular level of m6A modification. These results suggest an m6A-related new target pathway by AZ628 and R428 and provide new candidate m6A-related drugs that inhibit cancer cell proliferation.