Single-cell RNA-Seq and bulk RNA-Seq reveal reliable diagnostic and prognostic biomarkers for CRC

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Identification and validation of cuproptosis and disulfidptosis related genes in colorectal cancer

Colorectal cancer, the third most prevalent malignant cancer, is associated with poor prognosis. Recent studies have investigated the mechanisms underlying cuproptosis and disulfidptosis in colorectal cancer. However, whether genes linked to these processes impact the prognosis of colorectal cancer patients through analogous mechanisms remains unclear. In this study, we developed a model of cuproptosis and disulfidptosis in colorectal cancer and concurrently explored the role of the pivotal model gene HSPA8 in colorectal cancer cell lines. Our results revealed a positive correlation between cuproptosis and disulfidptosis, both of which are emerging as protective factors for the prognosis of CRC patients. Consequently, a prognostic model encompassing HSPA8, PDCL3, CBX3, ATP6V1G1, TAF1D, RPL4, and RPL14 was constructed. Notably, the key gene in our model, HSPA8, exhibited heightened expression and was validated as a protective prognostic factor in colorectal cancer, exerting inhibitory effects on colorectal cancer cell proliferation. This study offers novel insights into the interplay between cuproptosis and disulfidptosis. The application of the prognostic model holds promise for more effectively predicting the overall survival of colorectal cancer patients.

Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence

Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.