Integrative single-cell transcriptomic analyses reveal the cellular ontological and functional heterogeneities of primary and metastatic liver tumors

Functional heterogeneity of fibroblasts in primary tumors and metastases

Cancer-associated fibroblasts (CAFs) are abundant components of the tumor microenvironment (TME) of most solid malignancies and have emerged as key regulators of cancer progression and therapy response. Although recent technological advances have uncovered substantial CAF molecular heterogeneity at the single-cell level, defining functional roles for most described CAF populations remains challenging. With the aim of bridging CAF molecular and functional heterogeneity, this review focuses on recently identified functional interactions of CAF subtypes with malignant cells, immune cells, and other stromal cells in primary tumors and metastases. Dissecting the heterogeneous functional crosstalk of specific CAF populations with other components is starting to uncover candidate combinatorial strategies for therapeutically targeting the TME and cancer progression.

Bulk integrated single-cell-spatial transcriptomics reveals the impact of preoperative chemotherapy on cancer-associated fibroblasts and tumor cells in colorectal cancer, and construction of related predictive models using machine learning

BACKGROUND

Preoperative chemotherapy (PC) is an important component of Colorectal cancer (CRC) treatment, but its effects on the biological functions of fibroblasts and epithelial cells in CRC are unclear.

METHODS

This study utilized bulk, single-cell, and spatial transcriptomic sequencing data from 22 independent cohorts of CRC. Through bioinformatics analysis and in vitro experiments, the research investigated the impact of PC on fibroblast and epithelial cells in CRC. Subpopulations associated with PC and CRC prognosis were identified, and a predictive model was constructed using machine learning.

RESULTS

PC significantly attenuated the pathways related to tumor progression in fibroblasts and epithelial cells. NOTCH3 + Fibroblast (NOTCH3 + Fib), TNNT1 + Epithelial (TNNT1 + Epi), and HSPA1A + Epithelial (HSPA1A + Epi) subpopulations were identified in the adjacent spatial region and were associated with poor prognosis in CRC. PC effectively diminished the presence of these subpopulations, concurrently inhibiting pathway activity and intercellular crosstalk. A risk signature model, named the Preoperative Chemotherapy Risk Signature Model (PCRSM), was constructed using machine learning. PCRSM emerged as an independent prognostic indicator for CRC, impacting both overall survival (OS) and recurrence-free survival (RFS), surpassing the performance of 89 previously published CRC risk signatures. Additionally, patients with a high PCRSM risk score showed sensitivity to fluorouracil-based adjuvant chemotherapy (FOLFOX) but resistance to single chemotherapy drugs (such as Bevacizumab and Oxaliplatin). Furthermore, this study predicted that patients with high PCRSM were resistant to anti-PD1therapy.

CONCLUSION

In conclusion, this study identified three cell subpopulations (NOTCH3 + Fib, TNNT1 + Epi, and HSPA1A + Epi) associated with PC, which can be targeted to improve the prognosis of CRC patients. The PCRSM model shows promise in enhancing the survival and treatment of CRC patients.

Biological and clinical role of TREM2 in liver diseases

Liver diseases constitute a major health burden worldwide, accounting for more than 4% of all disease-related mortalities. While the incidence of viral hepatitis is expected to decrease, metabolic liver disorders are increasingly diagnosed. Liver pathology is diverse, with functional and molecular alterations in both parenchymal and mesenchymal cells, including immune cells. Triggering receptor expressed on myeloid cells 2 (TREM2) is a transmembrane receptor of the immunoglobulin superfamily and mainly expressed on myeloid cells. Several studies have demonstrated that TREM2 plays a critical role in tissue physiology and various pathological conditions. TREM2 is recognized as being associated with the development of liver diseases by regulating tissue homeostasis and the immune microenvironment. The biological and clinical impact of TREM2 is complex, given its diverse context-dependent functions. This review aims to summarize recent progress in understanding the association between TREM2 and different liver disorders and shed light on the clinical significance of targeting TREM2.

CMTM7 shapes the chronic inflammatory and immunosuppressive tumor microenvironment in hepatocellular carcinoma as an M2 macrophage biomarker

Transmembrane domain-containing 7 (CMTM7) is a protein located at the plasma membrane. It plays a role in regulating the development and immune microenvironment of tumor cells. However, the impact of CMTM7 on hepatocellular carcinoma (HCC) is not well understood. To better understand the role of CMTM7 in HCC, the correlations of CMTM7 with clinical characteristics, patient prognosis, chronic inflammation, and immune cell infiltration were analyzed using tissue microarray slides, sequencing datasets and various analysis tools (Web). The bulk sequencing analysis indicated that elevated expression of CMTM7 appears to promote chronic inflammation, immunosuppression, M2 macrophage infiltration, a diminished response to cancer immunotherapy, and an unfavorable clinical prognosis in patients with hepatocellular carcinoma (HCC). Further investigation through single-cell RNA sequencing and multiple fluorescence staining demonstrated that CMTM7 serves as a molecular marker for M2 macrophages and is associated with T cell exhaustion as well as highly plastic stem-like characteristics. We propose that CMTM7 may represent a novel immune checkpoint for HCC patients experiencing suboptimal therapeutic outcomes. Utilizing the Connectivity Map and AutoDock Vina, we predicted two potential compounds targeting CMTM7-fasudil and arachidonyltrifluoromethane-as promising therapeutic candidates. Collectively, these findings suggest that CMTM7-positive macrophages play significant roles in establishing an immunosuppressive tumor microenvironment while promoting highly plastic and stem-like traits in HCC cells, ultimately contributing to poor prognostic outcomes.

Immune dynamics shaping pre-metastatic and metastatic niches in liver metastases: from molecular mechanisms to therapeutic strategies

Liver metastases are commonly detected in the advanced stages of various malignant tumors, representing a significant clinical challenge. Throughout the process of liver metastases formation, immune cells play a pivotal role, particularly in the pre-metastatic and metastatic niches within the liver. Immune cells establish extensive and intricate interactions with tumor cells and other components in the liver, collectively promoting and sustaining the growth of liver metastases. Despite the limited efficacy of existing therapeutic modalities against some advanced liver metastases, novel immune-based treatment approaches are continuously being explored and validated. Building on the systematic elucidation of the immunosuppressive characteristics of liver metastases, we explored the potential of novel immunotherapies applicable to patients with liver metastases from multiple dimensions.

Consensus clustering and development of a risk signature based on trajectory differential genes of cancer-associated fibroblast subpopulations in colorectal cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, the impact of CAF subpopulation trajectory differentiation on CRC remains unclear.

METHODS

In this study, we first explored the trajectory differences of CAFs subpopulations using bulk and integrated single-cell sequencing data, and then performed consensus clustering of CRC samples based on the trajectory differential genes of CAFs subpopulations. Subsequently, we analyzed the heterogeneity of CRC subtypes using bioinformatics. Finally, we constructed relevant prognostic signature using machine learning and validated them using spatial transcriptomic data.

RESULTS

Based on the differential genes of CAFs subpopulation trajectory differentiation, we identified two CRC subtypes (C1 and C2) in this study. Compared to C1, C2 exhibited worse prognosis, higher immune evasion microenvironment and high CAF characteristics. C1 was primarily associated with metabolism, while C2 was primarily associated with cell metastasis and immune regulation. By combining 101 combinations of 10 machine learning algorithms, we developed a High-CAF risk signatures (HCAFRS) based on the C2 characteristic gene. HCAFRS was an independent prognostic factor for CRC and, when combined with clinical parameters, significantly predicted the overall survival of CRC patients. HCAFRS was closely associated with epithelial-mesenchymal transition, angiogenesis, and hypoxia. Furthermore, the risk score of HCAFRS was mainly derived from CAFs and was validated in the spatial transcriptomic data.

CONCLUSION

In conclusion, HCAFRS has the potential to serve as a promising prognostic indicator for CRC, improving the quality of life for CRC patients.

Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning

BACKGROUND

Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC.

METHODS

The data for this study were obtained from bulk, single-cell, and spatial transcriptomic sequencing data. Bioinformatics analysis, in vitro experiments, and machine learning methods were employed to investigate the functional characteristics of CAF subtypes and construct prognostic models.

RESULTS

Our study demonstrates that Biglycan (BGN) positive cancer-associated fibroblasts (BGN + Fib) serve as a driver in colorectal cancer (CRC). The proportion of BGN + Fib increases gradually with the progression of CRC, and high infiltration of BGN + Fib is associated with poor prognosis in terms of overall survival (OS) and recurrence-free survival (RFS) in CRC. Downregulation of BGN expression in cancer-associated fibroblasts (CAFs) significantly reduces migration and proliferation of CRC cells. Among 101 combinations of 10 machine learning algorithms, the StepCox[both] + plsRcox combination was utilized to develop a BGN + Fib derived risk signature (BGNFRS). BGNFRS was identified as an independent adverse prognostic factor for CRC OS and RFS, outperforming 92 previously published risk signatures. A Nomogram model constructed based on BGNFRS and clinical-pathological features proved to be a valuable tool for predicting CRC prognosis.

CONCLUSION

In summary, our study identified BGN + Fib as drivers of CRC, and the derived BGNFRS was effective in predicting the OS and RFS of CRC patients.