Single-cell transcriptome analysis reveals heterogeneity and convergence of the tumor microenvironment in colorectal cancer

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.

The Predictive Value of Neutrophil Extracellular Trap-Related Risk Score in Prognosis and Immune Microenvironment of Colorectal Cancer Patients

Colorectal cancer (CRC) has brought great healthy burden for patients. Neutrophil extracellular traps (NETs) have been explored in several tumors, while it remains largely unclear in CRC. CRC-related data were downloaded from Cancer Genome Atlas and Gene Expression Omnibus databases. Then, a NET risk score was built after univariate Cox and LASSO Cox regression analysis. Prognostic value was evaluated via survival analysis, stratification analysis, and ROC analysis. The functional enrichment analysis was conducted basing on bulk and scRNA-seq data. The immune landscape difference was analyzed using CIBERSORT, XCell, and MCPcounter portals. NET risk score was built for CRC patients, basing on G0S2, HIST1H2BC, CRISPLD2, and IL17A. In TCGA-CRC and validation datasets, regardless of age or gender, high-risk CRC patients had significantly worse prognosis, besides higher NET risk score was mainly found in samples with MSI-H and advanced T, N, and M stages. Employing multiple databases, we noticed that M0 and M2 Macrophages infiltrated the most in high-risk CRC patients, besides M2 Macrophages and neutrophils showed positive correlation with NET risk score. A novel reliable prognostic NET risk score was developed for CRC patients, and high-risk patients had unfavorable prognosis with advanced disease status.

Unlocking the potential of T-cell metabolism reprogramming: Advancing single-cell approaches for precision immunotherapy in tumour immunity

As single-cell RNA sequencing enables the detailed clustering of T-cell subpopulations and facilitates the analysis of T-cell metabolic states and metabolite dynamics, it has gained prominence as the preferred tool for understanding heterogeneous cellular metabolism. Furthermore, the synergistic or inhibitory effects of various metabolic pathways within T cells in the tumour microenvironment are coordinated, and increased activity of specific metabolic pathways generally corresponds to increased functional activity, leading to diverse T-cell behaviours related to the effects of tumour immune cells, which shows the potential of tumour-specific T cells to induce persistent immune responses. A holistic understanding of how metabolic heterogeneity governs the immune function of specific T-cell subsets is key to obtaining field-level insights into immunometabolism. Therefore, exploring the mechanisms underlying the interplay between T-cell metabolism and immune functions will pave the way for precise immunotherapy approaches in the future, which will empower us to explore new methods for combating tumours with enhanced efficacy.

An Analysis Regarding the Association Between DAZ Interacting Zinc Finger Protein 1 (DZIP1) and Colorectal Cancer (CRC)

Colorectal cancer (CRC) is the third most common malignant disease worldwide, and its incidence is increasing, but the molecular mechanisms of this disease are highly heterogeneous and still far from being fully understood. Increasing evidence suggests that fibrosis mediated by abnormal activation of fibroblasts based in the microenvironment is associated with a poor prognosis. However, the function and pathogenic mechanisms of fibroblasts in CRC remain unclear. Here, combining scrna-seq and clinical specimen data, DAZ Interacting Protein 1 (DZIP1) was found to be expressed on fibroblasts and cancer cells and positively correlated with stromal deposition. Importantly, pseudotime-series analysis showed that DZIP1 levels were up-regulated in malignant transformation of fibroblasts and experimentally confirmed that DZIP1 modulates activation of fibroblasts and promotes epithelial-mesenchymal transition (EMT) in tumor cells. Further studies showed that DZIP1 expressed by tumor cells also has a driving effect on EMT and contributes to the recruitment of more fibroblasts. A similar phenomenon was observed in xenografted nude mice. And it was confirmed in xenograft mice that downregulation of DZIP1 expression significantly delayed tumor formation and reduced tumor size in CRC cells. Taken together, our findings suggested that DZIP1 was a regulator of the CRC mesenchymal phenotype. The revelation of targeting DZIP1 provides a new avenue for CRC therapy.

Single-cell N6-methyladenosine-related genes function within the tumor microenvironment to affect the prognosis and treatment sensitivity in patients with gastric cancer

BACKGROUND

Gastric cancer (GC) ranks fifth for morbidity and third for mortality worldwide. The N6-methyladenosine (m6A) mRNA methylation is crucial in cancer biology and progression. However, the relationship between m6A methylation and gastric tumor microenvironment (TME) remains to be elucidated.

METHODS

We combined single-cell and bulk transcriptome analyses to explore the roles of m6A-related genes (MRG) in gastric TME.

RESULTS

Nine TME cell subtypes were identified from 23 samples. Fibroblasts were further grouped into four subclusters according to different cell markers. M6A-mediated fibroblasts may guide extensive intracellular communications in the gastric TME. The m6A-related genes score (MRGs) was output based on six differentially expressed single-cell m6A-related genes (SCMRDEGs), including GHRL, COL4A1, CAV1, GJA1, TIMP1, and IGFBP3. The protein expression level was assessed by immunohistochemistry. We identified the prognostic value of MRGs and constructed a nomogram model to predict GC patients' overall survival. MRGs may affect treatment sensitivity in GC patients.

CONCLUSION

Our study visualized the cellular heterogeneity of TME at the single-cell level, revealed the association between m6A mRNA modification and intracellular communication, clarified MRGs as an independent risk factor of prognosis, and provided a reference for follow-up treatment.

The Application of Single-Cell RNA Sequencing in the Inflammatory Tumor Microenvironment

The initiation and progression of tumors are complex. The cancer evolution-development hypothesis holds that the dysregulation of immune balance is caused by the synergistic effect of immune genetic factors and environmental factors that stimulate and maintain non-resolving inflammation. Throughout the cancer development process, this inflammation creates a microenvironment for the evolution and development of cancer. Research on the inflammatory tumor microenvironment (TME) explains the initiation and progression of cancer and guides anti-cancer immunotherapy. Single-cell RNA sequencing (scRNA-seq) can detect the transcription levels of cells at the single-cell resolution level, reveal the heterogeneity and evolutionary trajectory of infiltrated immune cells and cancer cells, and provide insight into the composition and function of each cell group in the inflammatory TME. This paper summarizes the application of scRNA-seq in inflammatory TME.