Understanding the intrinsic biology of diffuse large B-cell lymphoma: recent advances and future prospects.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoid tumor, and accounts for approximately 30-40% of non-Hodgkin lymphomas. Although the prognosis has significantly improved with the advent of rituximab combination chemotherapy in the early 2000s, recurrence still occurs in about 40% of cases. Even though chemotherapy with increased dose-intensity is used in recurrent cases, the prognosis of such patients remains poor. Thus, the development of personalized medicine, including molecular-targeted drugs, is required to improve the prognosis of DLBCL patients, and further understanding of the molecular pathogenesis of DLBCL is essential for this purpose. With recent advances in genetic analysis technology, unknown genetic abnormalities and gene expression patterns have been discovered, and based on these discoveries, progress is being made in elucidating and subdividing molecular pathologies. This article summarizes recent findings regarding molecular pathogenesis in DLBCL using transcriptome and genomics technologies, and outlines the path to personalized medicine.

Multi-omics cluster defines the subtypes of CRC with distinct prognosis and tumor microenvironment.

BACKGROUND: Colorectal cancer (CRC) is a complex malignancy characterized by diverse molecular profiles, clinical outcomes, and limited precision in prognostic markers. Addressing these challenges, this study utilized multi-omics data to define consensus molecular subtypes in CRC and elucidate their association with clinical outcomes and underlying biological processes. METHODS: Consensus molecular subtypes were obtained by applying ten integrated multi-omics clustering algorithms to analyze TCGA-CRC multi-omics data, including mRNA, lncRNA, miRNA, DNA methylation CpG sites, and somatic mutation data. The association of subtypes with prognoses, enrichment functions, immune status, and genomic alterations were further analyzed. Next, we conducted univariate Cox and Lasso regression analyses to investigate the potential prognostic application of biomarkers associated with multi-omics subtypes derived from weighted gene co-expression network analysis (WGCNA). The function of one of the biomarkers MID2 was validated in CRC cell lines. RESULTS: Two CRC subtypes linked to distinct clinical outcomes were identified in TCGA-CRC cohort and validated with three external datasets. The CS1 subtype exhibited a poor prognosis and was characterized by higher tumor-related Hallmark pathway activity and lower metabolism pathway activity. In addition, the CS1 was predicted to have less immunotherapy responder and exhibited more genomic alteration compared to CS2. Then a prognostic model comprising five genes was established, with patients in the high-risk group showing substantial concordance with the CS1 subtype, and those in the low-risk group with the CS2 subtype. The gene MID2, included in the prognostic model, was found to be correlated with epithelial-mesenchymal transition (EMT) pathway and distinct DNA methylation patterns. Knockdown of MID2 in CRC cells resulted in reduced colony formation, migration, and invasion capacities. CONCLUSION: The integrative multi-omics subtypes proposed potential biomarkers for CRC and provided valuable knowledge for precision oncology.