An Analysis Regarding the Association Between Connexins and Colorectal Cancer (CRC) Tumor Microenvironment

GJA4 expressed on cancer associated fibroblasts (CAFs)-A 'promoter' of the mesenchymal phenotype

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer worldwide. Connexin is a transmembrane protein involved in gap junctions (GJs) formation. Our previous study found that connexin 37 (Cx37), encoded by gap junction protein alpha 4 (GJA4), expressed on fibroblasts acts as a promoter of CRC and is closely related to epithelial-mesenchymal transition (EMT) and tumor immune microenvironment. However, to date, the mechanism concerning the malignancy of GJA4 in tumor stroma has not been studied.

METHODS

Hematoxylin-eosin (HE) and immunohistochemical (IHC) staining were used to validate the expression and localization of GJA4. Using single-cell analysis, enrichment analysis, spatial transcriptomics, immunofluorescence staining (IF), Sirius red staining, wound healing and transwell assays, western blotting (WB), Cell Counting Kit-8 (CCK8) assay and in vivo experiments, we investigated the possible mechanisms of GJA4 in promoting CRC.

RESULTS

We discovered that in CRC, GJA4 on fibroblasts is involved in promoting fibroblast activation and promoting EMT through a fibroblast-dependent pathway. Furthermore, GJA4 may act synergistically with M2 macrophages to limit T cell infiltration by stimulating the formation of an immune-excluded desmoplasic barrier. Finally, we found a significantly correlation between GJA4 and pathological staging (P < 0.0001) or D2 dimer (R = 0.03, P < 0.05).

CONCLUSION

We have identified GJA4 expressed on fibroblasts is actually a promoter of the tumor mesenchymal phenotype. Our findings suggest that the interaction between GJA4+ fibroblasts and M2 macrophages may be an effective target for enhancing tumor immunotherapy.

EBF1-COX4I2 signaling axis promotes a myofibroblast-like phenotype in cancer-associated fibroblasts (CAFs) and is associated with an immunosuppressive microenvironment

Immunotherapy has limited response rates in colorectal cancer (CRC) due to an immunosuppressive tumor microenvironment (TME). Combining transcriptome sequencing, clinical specimens, and functional experiments, we identified a unique group of CAF subpopulations (COX4I2 + ) with inhibited mitochondrial respiration and enhanced glycolysis. Through bioinformatics predictions and luciferase reporter assays, we determined that EBF1 can upstreamly regulate COX4I2 transcription. COX4I2 + CAFs functionally and phenotypically resemble myofibroblasts, are important for the formation of the fibrotic TME, and are capable of activating the M2 phenotype of macrophages. In vitro experiments demonstrated that COX4I2 + CAFs promote immunosuppressive TME by blocking CD8 + T cell infiltration and inducing CD8 + T cell dysfunction. Using multiple independent cohorts, we also found a strong correlation between the immunotherapy response rate of CRC patients and COX4I2 expression in their tumors. Our results identify a CAF subpopulation characterized by activation of the EBF1-COX4I2 axis, and this group of CAFs can be targeted to improve cancer immunotherapy outcomes.

Spatiotemporal heterogeneity of LMOD1 expression summarizes two modes of cell communication in colorectal cancer

Cellular communication (CC) influences tumor development by mediating intercellular junctions between cells. However, the role and underlying mechanisms of CC in malignant transformation remain unknown. Here, we investigated the spatiotemporal heterogeneity of CC molecular expression during malignant transformation. It was found that although both tight junctions (TJs) and gap junctions (GJs) were involved in maintaining the tumor microenvironment (TME), they exhibited opposite characteristics. Mechanistically, for epithelial cells (parenchymal component), the expression of TJ molecules consistently decreased during normal-cancer transformation and is a potential oncogenic factor. For fibroblasts (mesenchymal component), the expression of GJs consistently increased during normal-cancer transformation and is a potential oncogenic factor. In addition, the molecular profiles of TJs and GJs were used to stratify colorectal cancer (CRC) patients, where subtypes characterized by high GJ levels and low TJ levels exhibited enhanced mesenchymal signals. Importantly, we propose that leiomodin 1 (LMOD1) is biphasic, with features of both TJs and GJs. LMOD1 not only promotes the activation of cancer-associated fibroblasts (CAFs) but also inhibits the Epithelial-mesenchymal transition (EMT) program in cancer cells. In conclusion, these findings demonstrate the molecular heterogeneity of CC and provide new insights into further understanding of TME heterogeneity.

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.

Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores

Large-scale short hairpin RNA (shRNA) screens on well-characterized human cancer cell lines have been widely used to identify novel cancer dependencies. However, the off-target effects of shRNA reagents pose a significant challenge in the analysis of these screens. To mitigate these off-target effects, various approaches have been proposed that aggregate different shRNA viability scores targeting a gene into a single gene-level viability score. Most computational methods for discovering cancer dependencies rely on these gene-level scores. In this paper, we propose a computational method, named NBDep, to find cancer self-dependencies by directly analyzing shRNA-level dependency scores instead of gene-level scores. The NBDep algorithm begins by removing known batch effects of the shRNAs and selecting a subset of concordant shRNAs for each gene. It then uses negative binomial random effects models to statistically assess the dependency between genetic alterations and the viabilities of cell lines by incorporating all shRNA dependency scores of each gene into the model. We applied NBDep to the shRNA dependency scores available at Project DRIVE, which covers 26 different types of cancer. The proposed method identified more well-known and putative cancer genes compared to alternative gene-level approaches in pan-cancer and cancer-specific analyses. Additionally, we demonstrated that NBDep controls type-I error and outperforms statistical tests based on gene-level scores in simulation studies.

Cancer-associated fibroblasts and its derived exosomes: a new perspective for reshaping the tumor microenvironment

Cancer-associated fibroblasts (CAFs) are the most abundant stromal cells within the tumor microenvironment (TME). They extensively communicate with the other cells. Exosome-packed bioactive molecules derived from CAFs can reshape the TME by interacting with other cells and the extracellular matrix, which adds a new perspective for their clinical application in tumor targeted therapy. An in-depth understanding of the biological characteristics of CAF-derived exosomes (CDEs) is critical for depicting the detailed landscape of the TME and developing tailored therapeutic strategies for cancer treatment. In this review, we have summarized the functional roles of CAFs in the TME, particularly focusing on the extensive communication mediated by CDEs that contain biological molecules such as miRNAs, proteins, metabolites, and other components. In addition, we have also highlighted the prospects for diagnostic and therapeutic applications based on CDEs, which could guide the future development of exosome-targeted anti-tumor drugs.

A Connexin-Based Biomarker Model Applicable for Prognosis and Immune Landscape Assessment in Lung Adenocarcinoma

Purpose

Gap junction protein (Connexin) family is the basic unit of cellular connection, whose multiple members were recently demonstrated to be associated with tumor progression. However, the expression pattern and prognostic value of connexin in lung adenocarcinoma (LUAD) have not yet been elucidated.

Methods

Consensus cluster algorithm was first applied to determine a novel molecular subtype in LUAD based on connexin genes. The differentially expressed genes (DEGs) between two clusters were obtained to include in Cox regression analyses for the model construction. To examine the predictive capacity of the signature, survival curves and ROC plots were conducted. We implemented GSEA method to uncover the function effects enriched in the risk model. Moreover, the tumor immune microenvironment in LUAD was depicted by CIBERSORT and ssGSEA methods.

Results

The integrated LUAD cohort (TCGA-LUAD and GSE68465) were clustered into two subtypes (C1 = 217 and C2 = 296) based on 21 connexins and the clinical outcomes of LUAD cases in the two clusters showed remarkable discrepancy. Next, we collected 222 DEGs among two subclusters to build a prognostic model using stepwise Cox analyses. Our proposed model consisted of six genes that accurately forecast patient outcomes and differentiate patient risk. GSEA indicated that high-risk group was involved in tumor relevant pathways were activated in high-risk group, such as PI3K/AKT signaling, TGF-β pathway, and p53 pathway. Furthermore, LUAD cases with high-risk presented higher infiltration level of M2 macrophage and neutrophil, suggesting high-risk group were more likely to generate an immunosuppressive status.

Conclusion

Our data identified a novel connexin-based subcluster in LUAD and further created a risk signature which plays a central part in prognosis assessment and clinical potency.