Despite the great complexity and heterogeneity of cancers this study showed molecular changes that were shared across multiple cancers. The pathogenic and clinical importance of those changes was supported by enrichment of GWAS genes and association with mortality.
The study was based on scRNA-seq, which allows the characterization of molecular changes in all cell types in a tumor. This may be advantageous because increasing evidence points to the pathogenic importance of multiple cell types in the TME 28,29. This complexity leads to the problems of how best to organize systematically and prioritize mechanisms across cancers.
Previous scRNA-seq studies of complex diseases, which also are multicellular, have shown that these problems can be addressed by constructing multicellular network models based on connecting URs in any cell type with their DSs in other cell types, and prioritizing the URs with the largest effects on DSs 6,7. We applied these principles to scRNA-seq data from five cancers. In summary, we found that despite great cellular and molecular differences among the analyzed cancers, their MCTMs showed overarching similarities. These included pathogenic URs and DSs being dispersed across cell types, rather than only originating from cancer cells. A similar organization was found in the shMCTM, which showed a higher-order representation of the complex changes. In support of a shared multicellular pathogenesis across cancers, the shMCTM was enriched for GWAS genes and pathways associated with malignant transformation. Since shURs regulated the shDSs, the shURs would have a superior role relative to shDSs. The shURs that regulated more shDSs and cells were prioritized and considered as signature genes that could have important pathogenic roles.
Notably, these prioritized shURs exhibited elevated expression levels in fibroblasts compared to other cell types in the shMCTM. This agreed with the previous finding of a hierarchy of cell-cell interactions dominated by fibroblasts to macrophages in breast cancer 30. Moreover, we found CAF has a higher hierarchy over multiple cell types in five different tumors, supporting the crucial role of CAF in TME and tumor progression 4,31,32. This led us to subtype CAF cells into clusters, of which four were more common in cancer than in normal tissues. We found that most shURs and shDSs were mainly expressed in the largest cluster (CAF_0). This cluster is in agreement with previously reported mCAF, which shows high expression of ECM remodeling genes and a pro-angiogenic effects in TME 4,27. Interestingly, shURs located in mCAF regulated shDSs in all other cell types. KEGG pathway analysis of those shDSs revealed a wide variety of pathways related to cancer, vascular function, coagulation, immunity, and metabolism. In support of a direct tumorigenic role of the fibroblast shURs, their shDSs in epithelial cells encoded cancer-related pathways, namely proteoglycan- and AGE-RAGE signaling, as well as pathways associated with many specific cancers. This finding suggested a key regulatory role of mCAF which was mainly associated with ECM according to KEGG pathway enrichment analysis. Therefore, we hypothesized that mCAF could be used to add genes to the shared gene signature. This resulted in a gene signature with eight genes from mCAF and eight shURs.
Recently, CCI and shared mechanisms were discussed for their potential use relates to cancer’s clinical outcomes 29,33. In this study, we hypothesized that this signature was associated with the mortality of cancer patients and tested the hypothesis in two independent cohorts (TCGA and UKBB). The expression of signature mRNAs and proteins showed significant differences between tumor and normal samples in both cohorts, underscoring their pathogenic relevance. Additionally, our analysis revealed that each individual signature mRNA/protein was correlated with all-cause mortality in cancer patients from both cohorts. When evaluating the overall associations of the mRNA and protein signature scores within specific cancer types, we observed moderate to high associations with mortality in both datasets. The signature genes that belong to collagen family (e.g. COL18A1 and COL4A1) showed the highest association with increased risk of death. This is in line with previous findings implicating members of the collagen family as prognostic markers for cancers 34–36. Moreover, CTHRC1 also exhibited a high association with death risk in both mRNA and protein levels. This agrees with previous findings showing its association with tumor progression, metastasis and prognosis in several cancer types 34,37–39.
While both mRNA and protein scores were linked to all-cause mortality, the association differed between TCGA and UKBB. The association of signature score with mortality was demonstrated to be similar between females and males in TCGA, but it was notably associated with a greater risk of death in males compared to females in the UKBB dataset. Furthermore, the cancers with the highest associations in TCGA were located in the brain, mesothelioma and uterus, while the highest associations in UKBB were ovarian cancer, prostate and lymphoma, indicating differences between tissue mRNA and blood proteins. Nevertheless, the consistent significant association of both mRNA and protein scores with mortality underscores the pathogenic relevance of the signature.
Despite this, this study has potential limitations. Our analysis is limited to mRNAs and proteins, while multiple other types of molecules have been shown to play important pathogenic roles. Another limitation is that the scRNA-seq data were derived from a small number of patients from solid tumors. However, the relevance of the signature genes was supported in both cohorts by analyses of their associations with mortality in multiple other cancers including non-solid tumors like leukemia in independent cohorts. We propose that further studies are warranted to examine the signature genes in other cancers, as well as their associations with disease-relevant traits.
In conclusion, our findings support the pathogenic and clinical relevance of molecular interactions that are shared across cancers. We have made the methods and data underlying this study freely available for basic and translational studies (https://github.com/SDTC-CPMed/shMCTM_cancer_mortality).