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.
BackgroundIt is well known that the prognosis of Gastric cancer (GC) patient is affected by many factors. However, the latent impact of anoikis on the prognosis of GC patients is insufficient understood.MethodsAccording to the Cancer Genome Atlas (TCGA) database, we elected discrepantly expressed anoikis-related genes (ARGs). Univariate cox and the least absolute shrinkage and selection operator (lasso) analysis were applied to build the ARGs signature. The prognostic effect of the ARGs signature was also evaluated. A series of algorithms were performed to evaluate the discrepancies in the immune microenvironment. Moreover, the correlation between drug sensitivity and ARGs signature was analyzed. We also performed Real-Time Polymerase Chain Reaction (RT-PCR) to probe the signature.ResultsThe ARGs signature of 9 genes was constructed, which was apparently interrelated with the prognosis. The nomogram was established by combining the ARGs signature with clinicopathological characteristics. We found that the predictive power was noteworthily superior to other individual predictors. The immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, StromalScores, tumor immune dysfunction and exclusion (TIDE) score were lower in the low-risk group, while immunophenoscore (IPS) was on the contrary. The infiltrated immune cells and immune checkpoint (ICP) expression levels were significantly different between the two groups. Furthermore, nine drugs were positively associated with the ARGs signature score. The results of RT-PCR analysis were consistent with our previous differential expression analysis.ConclusionThe developed ARGs signature could act as the biomarker and provide a momentous reference for Individual therapy of GC patients.
BackgroundPatients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients’ prognoses.MethodsThe URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.ResultsThe URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes.ConclusionThe URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.
Reactive oxygen species play a crucial role in the prognosis and tumor microenvironment (TME) of malignant tumors. An ROS-related signature was constructed in gastric cancer (GC) samples from TCGA database. ROS-related genes were obtained from the Molecular Signatures Database. Consensus clustering was used to establish distinct ROS-related subtypes related to different survival and immune cell infiltration patterns. Sequentially, prognostic genes were identified in the ROS-related subtypes, which were used to identify a stable ROS-related signature that predicted the prognosis of GC. Correlation analysis revealed the significance of immune cell iniltration, immunotherapy, and drug sensitivity in gastric cancers with different risks. The putative molecular mechanisms of the different gastric cancer risks were revealed by functional enrichment analysis. A robust nomogram was established to predict the outcome of each gastric cancer. Finally, we verified the expression of the genes involved in the model using RT-qPCR. In conclusion, the ROS-related signature in this study is a novel and stable biomarker associated with TME and immunotherapy responses.
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