Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy

Abstract

IntroductionCellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-related genes in thyroid cancer (THCA) and their relationship with the TME.MethodsSenescence-related genes were identified from the Molecular Signatures Database and used to conduct consensus clustering across TCGA-THCA. Differentially expressed genes (DEGs) were identified between the clusters used to perform multivariate Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a senescence-related signature. TCGA dataset was randomly divided into training and test datasets to verify the prognostic ability of the signature. Subsequently, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. Finally, the expression of signature genes was detected across TCGA-THCA and GSE33630 datasets, and further validated by RT-qPCR.ResultsThree senescence clusters were identified based on the expression of 432 senescence-related genes. Then, 23 prognostic DEGs were identified in TCGA dataset. The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-risk THCA shows a better prognosis and higher immunotherapy response than high-risk THCA. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. The validation part demonstrated that ADAMTSL4, DOCK6, FAM111B, and SEMA6B were expressed at higher levels in the tumor tissue, whereas lower expression of MRPS10 and PSMB7 was observed.DiscussionIn conclusion, the senescence-related signature is a promising biomarker for predicting the outcome of THCA and has the potential to guide immunotherapy.

Development of a novel senescence‐related gene signature to predict clinical outcomes, immune landscape, and chemotherapeutic sensitivity in oral squamous cell carcinoma

“…Accumulating evidence has demonstrated a superior prognostic value of optimized combination of SRGs‐derived signature in multiple cancers 14,48 . We have effectively developed and verified a 7‐SRGs OSCC‐specific prognostic signature with favorable prognostic powers in multiple independent patient cohorts as supported by ROC and C‐index analyses.…”

“…40,47 These abovementioned findings suggest these SRGs identified here might have oncogenic or tumorsuppressive roles underlying OSCC progression by Accumulating evidence has demonstrated a superior prognostic value of optimized combination of SRGsderived signature in multiple cancers. 14,48 We have effectively developed and verified a 7-SRGs OSCC-specific prognostic signature with favorable prognostic powers in multiple independent patient cohorts as supported by ROC and C-index analyses. Furthermore, we generated a novel SRG nomogram integrating SRG signature and clinicopathological characteristics because of the nomogram benefits which elucidated the personalized requirement for therapeutic interventions or support.…”

Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells

“…The RS of each EC patient was calculated, and the median RS was used as the critical value to further divide the EC patients into high-risk group and low-risk group (high-risk group: RS≥median; low-risk group: RS < median). Considering the sample size and referring to previous literature [ 43 , 44 ], the total samples (Total Set, n =545) were randomly divided into a Train Set ( n =273) and a Test Set ( n =272) in a one-to-one ratio using the random sampling function in the R programming language to minimize information leakage and enhance model performance evaluation accuracy. Initially, the risk scores for patients in the Training Set were computed using the aforementioned formula.…”

“…The most significant GLRGs among the GLRGs in the GLRG-related signature were further screened out by integrating 12 machine learning (ML) algorithms and combining 113 algorithm combinations [ 55 ] to authenticate GLRGs with high accuracy and stability between EC and normal samples. As previously mentioned, considering the sample size and referring to previous literature [ 43 , 44 ], the total samples (All Set) were randomly and equally partitioned into a Train Set and a Test Set. The same Train Set was utilized to construct signatures among the 113 algorithms, and subsequently, the validation of these signatures was performed based on the calculation results obtained from both the same Test Set and All Set.…”

A prognostic signature associated with cell senescence predicts survival outcomes and strongly associates with immunotherapy and chemotherapy response in breast cancer

“…[7] Several prediction models derived from cell senescence-related genes (CSRGs) have been developed to predict prognosis and treatment efficacy for multiple types of cancer, highlighting the critical role of these genes in cancer biology. [8,9] In this study, we firstly generated molecular clusters derived from CSRGs, and then established a risk signature for the overall survival (OS) prediction of BC patients. Differences in clinical characteristics, immune infiltration, chemotherapy and radiotherapy response between different risk groups were further analyzed.…”