Machine learning identifies characteristics molecules of cancer associated fibroblasts significantly correlated with the prognosis, immunotherapy response and immune microenvironment in lung adenocarcinoma

Abstract

BackgroundLung adenocarcinoma (LUAD) is a highly lethal disease with a dramatic pro-fibrocytic response. Cancer-associated fibroblasts (CAFs) have been reported to play a key role in lung adenocarcinoma.MethodsMarker genes of CAFs were obtained from the Cell Marker website. Single sample gene set enrichment analysis (ssGSEA) was used for CAFs quantification. R and GraphPad Prism software were utilized for all analysis. Quantitative real-time PCR (qRT-PCR) was utilized to detect the RNA level of specific molecules.ResultsBased on the ssGSEA algorithm and obtained CAFs markers, the LUAD patients with low- and high-CAFs infiltration were successfully identified, which had different response patterns to immunotherapy. Through the machine learning algorithm – LASSO logistic regression, we identified 44 characteristic molecules of CAFs. Furthermore, a prognosis signature consisting of seven characteristic genes was established, which showed great prognosis prediction ability. Additionally, we found that patients in the low-risk group might have better outcomes when receiving immunotherapy of PD-1, but not CTLA4. Also, the biological enrichment analysis revealed that immune response-related pathways were significantly associated with CAFs infiltration. Meanwhile, we investigated the underlying biological and microenvironment difference in patients with high- and low-risk groups. Finally, we identified that AMPD1 might be a novel target for LUAD immunotherapy. Patients with a high level of AMPD1 were correlated with worse responses to immunotherapy. Moreover, immunohistochemistry showed that the protein level of AMPD1 was higher in lung cancer. Results of qRT-PCR demonstrated that AMPD1 was upregulated in A549 cells compared with BEAS-2B. Meanwhile, we found that the knockdown of AMPD4 can significantly reduce the expression of CTLA4 and PDCD1, but not CD274 and PDCD1LG2.ConclusionWe comprehensively explored the role of CAFs and its characteristics molecules in LUAD immunotherapy and developed an effective signature to indicate patients prognosis and immunotherapy response. Moreover, AMPD1 was identified as a novel target for lung cancer immunotherapy.

Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing

“…CAFs interact extensively with tumour cells throughout tumourigenesis and tumour development, affecting tumour growth, metastasis, invasion, treatment resistance and immunosuppression [32][33][34]. There is increasing evidence linking CAFs to the effectiveness of tumor immunotherapy, making them a potential target for improving immunotherapy outcomes [19,[35][36][37]. Previous studies have demonstrated the impact of CAFs on the biological behaviour of tumours and their involvement in targeted therapy and immunotherapy resistance [16,38,39].…”

Construction of a novel cancer-associated fibroblast-related signature to predict clinical outcome and immune response in colon adenocarcinoma

“…CAFs not only play a role in promoting tumour proliferation, metastasis and invasion but also in inducing anti-tumour drug resistance and immunosuppression [ 31 , 32 ]. Additionally, growing evidence suggests that CAFs are strongly associated with the efficacy of tumour immunotherapy [ 13 , 33 , 34 ]. Zheng et al reported that CAFs correlated with CD8+ T cells in the TME and also that the CD8+ T cell/CAFs ratio influenced the response to immunotherapy [ 35 ].…”