Integrative analysis deciphers the heterogeneity of cancer-associated fibroblast and implications on clinical outcomes in ovarian cancers

Zhao, Yan; Song, Mei; Huang, Yixuan; Chen, Junru; Zhang, Xinlei; Zhang, Peng

doi:10.1016/j.csbj.2022.11.025

Cited by 12 publications

(12 citation statements)

References 50 publications

(55 reference statements)

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“…The TME comprises a complex milieu of cells, vasculature and signalling molecules that provide structural and biochemical support for tumour progression. The immune cells present in TME play a critical role in regulating inflammation and facilitating the tumour's ability to escape the immune system, in turn promoting tumour growth and invasion 62 . Emerging evidence suggests that TME is involved in oncogenesis and cancer progression, and plays a crucial role in resistance to cancer therapy 63 .…”

Section: Discussionmentioning

confidence: 99%

“…The immune cells present in TME play a critical role in regulating inflammation and facilitating the tumour's ability to escape the immune system, in turn promoting tumour growth and invasion. 62 Emerging evidence suggests that TME is involved in oncogenesis and cancer progression, and plays a crucial role in resistance to cancer therapy. 63 Here, we report that PCD cluster B and high levels of eosinophils and neutrophils in this cluster are related to a better prognosis in HCC.…”

Section: Discussionmentioning

confidence: 99%

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The cell death‐related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma

Du,

Qu,

Qin

et al. 2024

J Cellular Molecular Medi

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Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD‐related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low‐risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI‐207 show a greater efficacy in patients in the low‐risk category. Conversely, for the high‐risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision‐making for HCC patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The cell death‐related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma

Du,

Qu,

Qin

et al. 2024

J Cellular Molecular Medi

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“…The tumor microenvironment consists of abundant stromal components, which are composed of various cell types, including the extracellular matrix, fibroblasts, chondrocytes, and mesothelial cells, non-stromal components comprising different immune cells and adipocytes, and microbiota, such as mycoplasma [ 17 , 18 ]. Indubitably, the diverse cellular compositions play a crucial role in the heterogeneity of serous ovarian cancers and induce the initiation, progression, and resistance to the anti-tumor therapy of serous ovarian cancer [ 19 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

Identifying ITGB2 as a Potential Prognostic Biomarker in Ovarian Cancer

Deng

Cao

et al. 2023

Diagnostics

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Epithelial ovarian cancer is by far the most lethal gynecological malignancy. The exploration of promising immunomarkers to predict prognosis in ovarian cancer patients remains challenging. In our research, we carried out an integrated bioinformatic analysis of genome expressions and their immune characteristics in the ovarian cancer microenvironment with validation in different experiments. We filtrated 332 differentially expressed genes with 10 upregulated hub genes from the Gene Expression Omnibus database. These genes were closely related to ovarian tumorigenesis. Subsequently, the survival and immune infiltration analysis demonstrated that the upregulation of five candidate genes, ITGB2, VEGFA, CLDN4, OCLN, and SPP1, were correlated with an unfavorable clinical outcome and increased immune cell infiltration in ovarian cancer. Of these genes, ITGB2 tended to be the gene most correlated with various immune cell infiltrations and had a strong correlation with significant M2 macrophages infiltration (r = 0.707, p = 4.71 × 10−39), while it had a moderate correlation with CD4+/CD8+ T cells and B cells. This characteristic explains why the high expression of ITGB2 was accompanied by immune activation but did not reverse carcinogenesis. Additionally, we confirmed that ITGB2 was over-expressed in ovarian cancer tissues and was mainly located in cytoplasm, detected by Western blotting and the immunohistochemical method. In summary, ITGB2 may serve as a prognostic immunomarker for ovarian cancer patients.

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“…Moreover, the high content of CAFs and a subtype of CAFs expressing the EMT features is enriched in metastatic tumors. In addition, several signaling pathways, ligands, and receptors are involved in the communication between CAFs and cancer cells with prognostic and therapeutic relevance [ 7 – 12 ]. An abundance of CAFs is associated with advanced stage and metastasis to the omentum and lymph nodes, contributing to the formation of the premetastatic niche and the failure of drug treatments by exchanging signals with tumor cells [ 7 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

The β-arrestin1/endothelin axis bolsters ovarian fibroblast-dependent invadosome activity and cancer cell metastatic potential

Del Rio,

Masi,

Caprara

et al. 2024

Cell Death Dis

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Recruitment of fibroblasts to tumors and their activation into cancer-associated fibroblasts (CAFs) is a strategy used by tumor cells to direct extracellular matrix (ECM) remodeling, invasion, and metastasis, highlighting the need to investigate the molecular mechanisms driving CAF function. Endothelin-1 (ET-1) regulates the communication between cancer and stroma and facilitates the progression of serous ovarian cancer (SOC). By binding to Endothelin A (ETA) and B (ETB) receptors, ET-1 enables the recruitment of β-arrestin1 (β-arr1) and the formation of signaling complexes that coordinate tumor progression. However, how ET-1 receptors might “educate” human ovarian fibroblasts (HOFs) to produce altered ECM and promote metastasis remains to be elucidated. This study identifies ET-1 as a pivotal factor in the activation of CAFs capable of proteolytic ECM remodeling and the generation of heterotypic spheroids containing cancer cells with a propensity to metastasize. An autocrine/paracrine ET-1/ETA/BR/β-arr1 loop enhances HOF proliferation, upregulates CAF marker expression, secretes pro-inflammatory cytokines, and increases collagen contractility, and cell motility. Furthermore, ET-1 facilitates ECM remodeling by promoting the lytic activity of invadosome and activation of integrin β1. In addition, ET-1 signaling supports the formation of heterotypic HOF/SOC spheroids with enhanced ability to migrate through the mesothelial monolayer, and invade, representing metastatic units. The blockade of ETA/BR or β-arr1 silencing prevents CAF activation, invadosome function, mesothelial clearance, and the invasive ability of heterotypic spheroids. In vivo, therapeutic inhibition of ETA/BR using bosentan (BOS) significantly reduces the metastatic potential of combined HOFs/SOC cells, associated with enhanced apoptotic effects on tumor cells and stromal components. These findings support a model in which ET-1/β-arr1 reinforces tumor/stroma interaction through CAF activation and fosters the survival and metastatic properties of SOC cells, which could be counteracted by ETA/BR antagonists.

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Integrative analysis deciphers the heterogeneity of cancer-associated fibroblast and implications on clinical outcomes in ovarian cancers

Cited by 12 publications

References 50 publications

The cell death‐related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma

The cell death‐related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma

Identifying ITGB2 as a Potential Prognostic Biomarker in Ovarian Cancer

The β-arrestin1/endothelin axis bolsters ovarian fibroblast-dependent invadosome activity and cancer cell metastatic potential

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