Identification and characterization of aging/senescence-induced genes in osteosarcoma and predicting clinical prognosis

Abstract

BackgroundAging is an influential risk factor for progression of both degenerative and oncological diseases of the bone. Osteosarcoma, considered the most common primary mesenchymal tumor of the bone, is a worldwide disease with poor 5-year survival. This study investigated the role of aging-/senescence-induced genes (ASIGs) in contributing to osteosarcoma diagnosis, prognosis, and therapeutic agent prediction.MethodsTherapeutically Applicable Research to Generate Effective Treatments (TARGET), Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) were used to collect relevant gene expression and clinical data of osteosarcoma and paracancerous tissues. Patients were clustered by consensus using prognosis-related ASIGs. ssGSEA, ESTIMATE, and TIMER were used to determine the tumor immune microenvironment (TIME) of subgroups. Functional analysis of differentially expressed genes between subgroups, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analyses (GSVAs), was performed to clarify functional status. Prognostic risk models were constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. SCISSOR was used to identify relevant cells in osteosarcoma single-cell data for different risk groups. The effect of immunotherapy was predicted based on TIDE scores and chemotherapy drug sensitivity using CTRP and PRISM.ResultsThree molecular subgroups were identified based on prognostic differentially expressed ASIGs. Immunological infiltration levels of the three groups differed significantly. Based on GO and KEGG analyses, differentially expressed genes between the three subgroups mainly relate to immune and aging regulation pathways; GSVA showed substantial variations in multiple Hallmark pathways among the subgroups. The ASIG risk score built based on differentially expressed genes can predict patient survival and immune status. We also developed a nomogram graph to accurately predict prognosis in combination with clinical characteristics. The correlation between the immune activation profile of patients and the risk score is discussed. Through single-cell analysis of the tumor microenvironment, we identified distinct risk-group-associated cells with significant differences in immune signaling pathways. Immunotherapeutic efficacy and chemotherapeutic agent screening were evaluated based on risk score.ConclusionAging-related prognostic genes can distinguish osteosarcoma molecular subgroups. Our novel aging-associated gene signature risk score can be used to predict the osteosarcoma immune landscape and prognosis. Moreover, the risk score correlates with the TIME and provides a reference for immunotherapy and chemotherapy in terms of osteosarcoma.

CEP55, serving as a diagnostic marker gene for osteosarcoma, triggers the JAK2-STAT3-MMPs axis

“…So far, the causes of OS, the most common primary bone cancer in clinical practice, are still largely unknown and uncertain. Although some studies have suggested a higher incidence of OS in genetic cases with mutations in tumor suppressor genes 24 , 25 , these findings have not provided novel and effective means for the treatment of OS. Unfortunately, the traditional and conservative treatment approach of systemic chemotherapy combined with surgical resection has not changed consistently for more than three decades 26 .…”

HELLS modulates the stemness of intrahepatic cholangiocarcinoma through promoting senescence-associated secretory phenotype

“…Its driving action to cancers can be summarized into the following three parts: 1) SASP of senescent cancer cells yield a massive bioactive factors, and thereby affect the adjacent viable cancer cells, as well as other cells of TME via a paracrine manner, contributing to cell proliferation, apoptosis resistance, angiogenesis, invasion and metastasis, and tumor immunosuppression; 2) Senescent cancer cells can exit from their SASP and nonproliferative status, and restore associated capabilities necessary for viable oncogenic cells; 3) Senescence occurred in other cells of TME is also a driving force for tumor evolution [4] . Previous studies established different SRGs-related risk score models in osteosarcoma, bladder cancer, and colorectal cancer [42] , [43] , [44] . They provided comprehensive insights into the correlation between cell senescence and tumor immunity, clinical prognosis or some other cancer features.…”

Cellular senescence in cancer: clinical detection and prognostic implications

“…However, in certain tumor types, the (abundant) presence of OIS or expression of senescence-associated markers is also linked to worse prognosis [132,197,221,237,238,248]. Perhaps even more surprisingly, both absence and extensive presence of senescence in CRC was associated with negative prognosis whereas moderate presence was associated with the best prognosis [174], demonstrating that an extensive senescence burden can paradoxically impair clinical outcome in contrast to a moderate senescence burden.…”