Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future.

Prospective identification of robust biomarkers related to prognosis and adjuvant chemotherapy has become a necessary and critical step to predict the benefits of adjuvant therapy for patients with stage II–III colorectal cancer (CRC) before clinical treatment. We proposed a single-cell-based prognostic biomarker recognition approach to identify and construct CRC up- and down-regulated prognostic signatures (CUPsig and CDPsig) by integrating scRNA-seq and bulk datasets. We found that most genes in CUPsig and CDPsig were known disease genes, and they had good prognostic abilities in CRC validation datasets. Multivariate analysis confirmed that they were two independent prognostic factors of disease-free survival (DFS). Significantly, CUPsig and CDPsig could effectively predict adjuvant chemotherapy benefits in drug-treated validation datasets. Additionally, they also performed well in patients with CMS4 subtype. Subsequent analysis of drug sensitivity showed that expressions of these two signatures were significantly associated with the sensitivities of CRC cell lines to multiple drugs. In summary, we proposed a novel prognostic biomarker identification approach, which could be used to identify novel prognostic markers for stage II–III CRC patients who will undergo adjuvant chemotherapy and facilitate their further personalized treatments.

Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality in women. We integrated predicted compound targets of Fufang Banmao Capsules with BRCA-associated genes and prioritized candidates using LASSO regression, correlation analysis, and ROC analysis. Single-cell RNA sequencing analysis then characterized their expression patterns, endothelial heterogeneity, and inferred endothelial–macrophage communication. The four key genes CAV1, CES1, PPARG, and RUNX1T1 showed the highest AUCell scores in endothelial cells. Cell–cell communication analysis inferred MIF signaling involving CD74-containing receptor complexes between endothelial cells and macrophages. Virtual knockout analysis identified CD74 among the genes predicted to be perturbed following CAV1, CES1, or RUNX1T1 knockout in endothelial cells. Further analysis revealed heterogeneous CAV1 expression across endothelial subpopulations, with relatively higher expression in ACKR1-positive and metabolic endothelial cells. Pseudotime analysis characterized variation in CAV1 expression along inferred trajectories, while regulon analysis identified distinct regulatory programs across endothelial subpopulations. Together, these findings prioritize candidate genes linked to the predicted targets of Fufang Banmao Capsules and generate hypotheses concerning their cellular context in BRCA.

Consensus molecular subtype 4 (CMS4) colorectal cancer (CRC) is associated with an aggressive clinical course and poor survival, yet the biological basis of heterogeneity within this subtype remains incompletely understood. DNA methylation is an epigenetic mechanism involved in transcriptional regulation, cellular differentiation, and colorectal tumorigenesis. Here, we integrated single-cell RNA sequencing (scRNA-seq), bulk data, and promoter DNA methylation data to characterize CMS4-associated cancer cell states and methylation-related features. Using the scAB algorithm, we integrated scRNA-seq with bulk CMS4 data and identified CMS4-related cells distributed across multiple patients. Single-cell analyses of cell–cell communication and transcriptional regulation revealed a CMS4-related cancer cell population characterized by macrophage migration inhibitory factor (MIF)-centered intercellular communication, enhanced caudal type homeobox 1 (CDX1) and Kruppel-like factor 5 (KLF5) regulon activity, and gene modules enriched in differentiation-related pathways. CytoTRACE analysis further stratified CMS4 cancer cells into poorly and well-differentiated states, yielding 802 differentially expressed genes (DEGs). Linking these differentiation-associated DEGs with bulk expression and promoter methylation data identified 218 methylation-associated DEGs showing significant inverse methylation expression correlations, suggesting a link between differentiation-related heterogeneity and promoter methylation. Univariable Cox regression followed by LASSO regression further prioritized eight genes for construction of the methylation and differentiation-related prognostic model (MeDiff-PM). MeDiff-PM consistently stratified overall survival in the TCGA CMS4 cohort and two independent validation cohorts, with cutoff-independent continuous Cox analyses further supporting its prognostic association across cohorts. And MeDiff-PM remained prognostically significant after adjustment for available clinical variables. High MeDiff-PM risk scores were associated with activation of P53, WNT, and ubiquitin-mediated proteolysis pathways and with consistent predicted drug response differences for compounds across three CMS4 cohorts. While individual in silico knockout analysis suggested links between MeDiff-PM genes and metallothionein-related and immune-associated transcriptional responses. Collectively, these findings indicate that methylation-associated differentiation features represent a molecular dimension of intra-CMS4 heterogeneity and provide a biologically informed framework for prognostic stratification within CMS4 CRC.

Colorectal cancer (CRC) is a highly aggressive cancer, with its treatment and prognosis particularly challenging due to metastasis. The immune response is involved in the whole process of CRC development, and immunotherapy has increasingly become a part of CRC patients’ treatment. However, comprehensive research on the immune microenvironment driving CRC metastasis remains limited. Given this limitation, we proposed a bioinformatics method to construct a metastasis–based immune prognostic model (MIPM) by integrating CRC single–cell RNA sequencing (scRNA–seq) and bulk data. Our study identified several MIPM genes significantly associated with CRC metastasis and progression. MIPM reliably predicted overall survival (OS) and tumor recurrence in CRC across eleven bulk validation datasets. Notably, MIPM could independently predict outcomes beyond traditional clinical factors such as age, sex, and stage. It showed high predictive accuracy in CRC patients treated with chemotherapy. Drug sensitivity and multifaceted immune analyses further underscored the importance of MIPM in therapeutic and immunotherapy response modulation. In conclusion, our findings have profound implications for the illustration of MIPM, which could serve as a new plausible prognostic marker for CRC patients and provide new insights for treatment strategies. The further evaluation and investigation of MIPM will enhance the prognosis and precision therapy of CRC patients.