Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma

Abstract

Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear.Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated.Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response.Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.

Deciphering the role of NETosis-related signatures in the prognosis and immunotherapy of soft-tissue sarcoma using machine learning

“…The ESTIMATE scores were calculated using the R package “ESTIMATE” to estimate stromal and immune infiltration in the samples based on gene signatures ( Yoshihara et al, 2013 ). From previously published studies, we also retrieved signatures associated with pathways predicted to be involved in immunotherapy response and cancer-immunity cycles ( Chen and Mellman, 2013 ; Qi et al, 2023 ). The framework of cancer-immunity cycles guides cancer immunotherapy ( Chen and Mellman, 2013 ).…”

“…Information about the sources for the cell lines of sarcoma including SW-982, SW-872, hSS-005R and HSF used in this study was provided in previous publications ( Qi et al, 2022 ; Qi et al, 2023 ). In order to cultivate the cell lines of STS, Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS) were used at the temperature of 37°C with 5% CO 2 atmosphere.…”

“…The RevertAid First Strand cDNA Synthesis kit (K1622, Thermo Fisher Scientific, United States) was used to synthesize cDNA. The qPCR refers to the previous studies ( Qi et al, 2022 ; Qi et al, 2023 ). The primer sequences used for qPCR were summarized in Supplementary Table S2 .…”

Integration analysis of senescence-related genes to predict prognosis and immunotherapy response in soft-tissue sarcoma: evidence based on machine learning and experiments

“…We also utilized the R package “ESTIMATE” to compute ESTIMATE scores based on gene signatures ( Yoshihara et al, 2013 ), to assess the levels stromal and immune infiltrations within tumor. Potential signatures involved in immunotherapy response and cancer-immunity cycles were retrieved from previous research ( Chen and Mellman, 2013 ; Qi et al, 2023 ). The association between senescence scores and GSVA scores was evaluated by using the R package “ggcor”.…”

Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

“…16 The landscape changes at the genomic level in tumor tissues can significantly affect the prognosis and therapy response of patients with cancer. [17][18][19] Therefore, there is a need to develop an early warning and prediction tool with molecular signatures to predict the prognosis of patients with cancer and supplement the TNM staging system. In the era of precision treatment, the prognostic factors of patients with NCHCC must be identified to facilitate clinical counseling and individualized prognosis prediction for NCHCC.…”