Identification of Prognostic and Tumor Microenvironment by Shelterin Complex-Related Signatures in Oral Squamous Cell Carcinoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhang S
PROVIDER: S-EPMC9217620 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundOral squamous cell carcinoma (OSCC) is the most common type of oral cancer, which remains a major cause of morbidity and mortality in patients with head and neck cancers. However, the critical immune-related signatures and their prognostic values have rarely been investigated.Materials and methodsGene differential analysis was used to measure the differences of gene expression between the groups. Correlation analysis was used to assess the association between the gene expression levels and immune-related risk score/DNA methylation levels. The gene set enrichment analysis (GSEA) was used to identify the pathways or cell types enriched by those identified differentially expressed genes (DEGs).ResultsIn this study, we identified four immune-related gene signatures, including CTSG, TNFRSF4, LCORL, and PLAU, that were significantly associated with the overall survival in OSCC patients from the Cancer Genome Atlas (TCGA) OSCC cohort. Moreover, these four immune-related signatures were differentially expressed between the OSCC and nontumor tissues. The two groups (high and low risk) stratified by the immune-related risk scores had significantly different OS and mortality rates. The gene expression patterns and prognostic values of these immune-related signatures were also verified in two independent validation cohorts. Furthermore, the downregulated genes in the high-risk group (which were also upregulated in the low-risk group) were significantly enriched in the cell type-specific signatures of type 2 T helper cell (Th2), plasmacytoid dendritic cell (pDC), and memory B cell. In contrast, the upregulated genes in the high-score group were enriched in growth factor receptor-related signaling pathways, such as the VEGFA-VEGFR2 signaling pathway, PI3K-Akt signaling pathway, focal adhesion-PI3K-Akt-mTOR signaling pathway, and PDGF pathway, suggesting that those pathways were inversely correlated with immune cell infiltration.ConclusionIn summary, the immune-related signatures had the potential for predicting the risk of OSCC patients. Moreover, the present study also improved our understanding of the association between the growth factor receptor pathways and immune cell infiltration in OSCC.
| S-EPMC8420972 | biostudies-literature
Project description:BackgroundWe aimed to develop novel diagnostic and prognostic signatures based on preoperative inflammatory, immunological, and nutritional parameters in blood (PIINPBs) by machine learning algorithms for patients with oral squamous cell carcinoma (OSCC).MethodsA total of 486 OSCC patients and 200 age and gender-matched non-OSCC patients who were diagnosed and treated at our institution for noninfectious, nontumor diseases were retrospectively enrolled and divided into training and validation cohorts. Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASSO) analyses were employed to construct prognostic signatures. A novel prognostic nomogram integrating a PIINPB-derived prognostic signature and selected clinicopathological parameters was further developed. Performances of these signatures were assessed by receiver operating characteristic (ROC) curves, calibrating curves, and decision tree.ResultsDiagnostic models developed by machine learning algorithms from 13 PIINPBs, which included counts of white blood cells (WBC), neutrophils (N), monocytes (M), lymphocytes (L), platelets (P), albumin (ALB), and hemoglobin (Hb), along with albumin-globulin ratio (A/G), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), displayed satisfactory discriminating capabilities in patients with or without OSCC, and among OSCC patients with diverse pathological grades and clinical stages. A prognostic signature based on 6 survival-associated PIINPBs (L, P, PNI, LMR, SII, A/G) served as an independent factor to predict patient survival. Moreover, a novel nomogram integrating prognostic signature and tumor size, pathological grade, cervical node metastasis, and clinical stage significantly enhanced prognostic power [3-year area under the curve (AUC) =0.825; 5-year AUC =0.845].ConclusionsOur results generated novel and robust diagnostic and prognostic signatures derived from PIINPBs by machine learning for OSCC. Performance of these signatures suggest the potential for PIINPBs to supplement current regimens and provide better patient stratification and prognostic prediction.
| S-EPMC8421978 | biostudies-literature
Project description:Background: Esophageal squamous cell carcinoma (ESCC) is the most prevalent histological type of esophageal cancer, but there is a lack of definite prognostic markers for this cancer. Methods: We used the ESTIMATE algorithm to access the tumor microenvironment (TME) of ESCC cases deposited in the TCGA database, and identified TME-related prognostic genes using Cox regression analysis. A least absolute shrinkage and selector operation or LASSO algorithm was used to identify key prognostic genes. Risk scores were calculated, and a clinical predictive model was constructed to evaluate the prognostic value of TME-related genes. Results: We found that high immune and stromal scores were significantly associated with poor overall survival (p < 0.05). We identified a total of 1,151 TME-related differently expression genes, among which 67 were prognosis-related genes. Through the LASSO method, 13 key prognostic genes were selected, namely, ADAMTS16, LOC51089, CH25H, CORO2B, DLGAP1, GYS2, HAL, MXRA8, NPTX1, OTX1, RET, SLC24A2, and SPI1, and a 13-gene risk score was constructed. A higher score was indicative of a poorer prognosis than a lower risk score (hazard ratio = 8.21, 95% confidence interval: 2.56-26.31; P < 0.001). The risk score was significantly correlated with immune/stromal scores and various types of infiltrating immune cells, including CD8 cells, regulatory T cells, and resting macrophages. Conclusion: We characterized the tumor microenvironment in ESCC, and identified the key prognosis genes. The risk score based on the expression profiles of these genes is proposed as an indicator of TME status and is instrumental in predicting patient prognosis.
| S-EPMC7767869 | biostudies-literature
Project description:BackgroundAlternative splicing (AS) plays an essential role in tumorigenesis and progression. This study aimed to develop a novel prognostic model based on the AS events to obtain more accurate survival prediction and search for potential therapeutic targets in oral squamous cell carcinoma (OSCC).MethodsSeven types of AS events in 326 OSCC patients with RNA-seq were obtained from the TCGA SpliceSeq tool and the TCGA database. Cox analysis, the least absolute shrinkage and selection operator Cox regression and random forest were employed to establish prognostic models. Genomics of Drug Sensitivity in Cancer (GDSC) was adopted to estimate the possible drug sensiticity. Prognostic splicing factor (SF)-AS network was constructed by Cytoscape.ResultsThe final model included 12 AS events, showing satisfactory performance. The area under the curve for 3- and 5-year survival in the training cohort was 0.83 and 0.82, respectively while that in internal validation was 0.83 and 0.82 accordingly. The calibration curve also indicated a satisfactory agreement between the observation and the predictive values. Low-risk patients stratified by the final model presented higher sensitivity to three chemo drugs. Besides, the prognostic SF-AS regulatory network contained five key SFs and 62 AS events.ConclusionsWe developed a powerful prognostic AS signature for OSCC and deepened the understanding of SF-AS network regulatory mechanisms. Low-risk patients tended to be more sensitive to the three chemo drugs while five key SFs including CELF2, TIA1, HNRNPC, HNRNPK, and SRSF9 were identified as potential prognostic biomarkers, which may offer new prospects for effective therapies of OSCC.
| S-EPMC7485395 | biostudies-literature
Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is one of the most fatal cancers in the world. The 5-year survival rate of ESCC is <30%. However, few biomarkers can accurately predict the prognosis of patients with ESCC. We aimed to identify potential survival-associated biomarkers for ESCC to improve its poor prognosis.MethodsImmuneAI analysis was first used to access the immune cell abundance of ESCC. Then, ESTIMATE analysis was performed to explore the tumor microenvironment (TME), and differential analysis was used for the selection of immune-related differentially expressed genes (DEGs). Weighted gene coexpression network analysis (WGCNA) was used for selecting the candidate DEGs. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to build the immune-cell-associated prognostic model (ICPM). Kaplan-Meier curve of survival analysis was performed to evaluate the efficacy of the ICPM.ResultsBased on the ESTIMATE and ImmuneAI analysis, we obtained 24 immune cells' abundance. Next, we identified six coexpression module that was associated with the abundance. Then, LASSO regression models were constructed by selecting the genes in the module that is most relevant to immune cells. Two test dataset was used to testify the model, and we finally, obtained a seven-genes survival model that performed an excellent prognostic efficacy.ConclusionIn the current study, we filtered seven key genes that may be potential prognostic biomarkers of ESCC, and they may be used as new factors to improve the prognosis of cancer.
| S-EPMC8573319 | biostudies-literature
Project description:BackgroundThe interplay between cancer cells and the immune system is crucial in cancer progression and treatment. In this regard, the tumor immune microenvironment and macroenvironment, marked by systemic inflammation markers and TILs, could be considered key prognostic factors in tumors, including oral and lung squamous cell carcinoma.MethodsWe conducted a retrospective clinical study on patients with Oral Squamous Cell Carcinoma (OSCC) and Lung Squamous Cell Carcinoma (LUSCC), examining stages, comorbidities, treatments, and outcomes. We evaluated the prognostic significance of pre-surgical systemic inflammation markers and tumor microenvironment composition.ResultsAssociations were found between systemic inflammation markers-NLR, MLR, and PLR-and tumor microenvironment factors, such as TILs and CD8+ cell prevalence-elevated inflammation markers correlated with advanced stages. Specifically, NLR was prognostic in OSCC, whereas PLR was prognostic in LUSCC. Using a cutoff value, we divided our tumor samples into two prognostic groups. Moreover, TILs levels >15% of tumor stroma correlated with prolonged overall survival in both OSCC and LUSCC, while increased CD8+ expression was linked to extended disease-free survival in LUSCC.DiscussionSystemic inflammation markers and TILs can be valuable prognostic factors of survival, highlighting the immune response's role in OSCC and LUSCC. Despite limited clinical integration of the presented cohorts due to a lack of standardization, we concluded that analyzing tumor immune profiles may offer novel prognostic insights.ConclusionsFuture integration into cancer classification could improve risk stratification and treatment guidance.
| S-EPMC11312115 | biostudies-literature
Project description:Cervical squamous cell carcinoma (CSCC) is one of the most commonly occurring gynecological malignancies. Because CSCC is a biologically heterogeneous disease, its prognosis varies. Therefore, identifying prognostic biomarkers that reflect its biological heterogeneity could lead to better interventions for patients with a poor prognosis. This study used the ESTIMATE algorithm to identify immune related prognostic genes within the tumor microenvironment of CSCC. The results revealed that high immune scores were associated with better overall survival (P = 0.029). Differential expression analysis revealed 384 intersection genes influencing both the immune and stromal scores. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed the 384 intersection genes to be mainly enriched for T cell activation, the region of the membrane, carbohydrate binding, and cytokine-cytokine receptor interaction. Among them, 149 immune genes were predictive of overall survival in CSCC. These findings provide a more comprehensive understanding of immune genes within the tumor microenvironment as well as a list of immune genes prognostic in CSCC.
| S-EPMC6914434 | biostudies-literature
Project description:Background: Oral squamous cell carcinoma (OSCC) is the most common cancer of oral and maxillofacial region. A recent clinical research has shown that tumor immune microenvironment (TIME)cells are closely related to immunotherapy sensitivity and OSCC prognosis. Nonetheless, a comprehensive analysis of TIME in OSCC has not been reported. Methods: Bioinformatics and computational algorithms were employed to determine the significance of TIME cells in 257 OSCC patients. TIME scores were measured by three TIME models, and then used to evaluate the prognosis of OSCC patients. Results: High TIME score was characterized by better prognosis in OSCC patients less than 60 years old, overexpression of immunotherapy targets (e.g., PD-1 and CLTA-4), and higher T-cell activity to inhibit tumor growth. Besides, poor prognosis was associated with low time score. Conclusion: TIME score exhibited potential as a prognostic biomarker and an indicator in predict immunotherapeutic outcomes. Through the understanding of TIME model, this study can provide a better scheme for immunotherapy as the effective treatment of OSCC patients in the future.
| S-EPMC9024147 | biostudies-literature
Project description:PurposeThe aim of this study was to investigate the prognostic significance of preoperative inflammatory markers in peripheral blood of patients with oral squamous cell carcinoma (OSCC), and to establish correlations with the infiltrate of macrophages and lymphocytes in the local immune tumor microenvironment (TME).Materials and methodsNeutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and systemic immune-inflammation index (SII) were retrospectively evaluated in a cohort of 348 OSCC patients, and correlated with overall (OS) and disease-specific survival (DSS). Immunohistochemical analysis of tumoral and stromal infiltration of CD8+, CD4+, FOXP3+ and CD20+ lymphocytes and CD68+ and CD163+ macrophages was performed in a subset of 119 OSCC patient samples, and correlations further assessed.ResultsNLR, SII, and LMR were significantly associated with a poorer OS in univariate analysis; however, only NLR remained a significant independent predictor in the multivariate analysis (HR = 1.626, p = 0.04). NLR and SII were inversely and significantly correlated with stromal infiltration of CD8+, CD4+, and CD20+ lymphocytes. Moreover, a significant correlation between LMR was also found to significantly associate with stromal infiltration of CD8+, CD4+, and CD20+ lymphocytes, stromal CD68+ and CD163+ macrophages, and also tumoral infiltration of CD4+ and CD20+ lymphocytes.ConclusionsPreoperative NLR, SII, and LMR may serve as valuable systemic markers to predict OSCC patient survival, with NLR emerging as an independent predictor of poor OS. Moreover, strong significant correlations were exclusively observed between systemic inflammatory markers and the local stromal infiltration of lymphocytes in the TME.
| S-EPMC9360320 | biostudies-literature
Project description:Background Esophageal cancer (EC) is a highly aggressive malignancy that is classified as esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Infiltrating stromal/immune cells, a major component of the tumor immune microenvironment (TIME), have prognostic significance in various cancers. Methods In this study we investigated genes and immune factors in the tumor microenvironment (TME) of ESCC and EAC that can serve as prognostic biomarkers. Stromal and immune scores were calculated using the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) algorithm based on gene expression profiles of patient-derived tumor tissues in The Cancer Genome Atlas database. The correlation between ESTIMATE scores and survival rates in EC were analyzed. A comparison of high and low stromal/immune score groups revealed multiple differentially expressed genes (DEGs) as candidate prognostic genes; their role in immune-related biological processes was evaluated by functional and protein-protein interaction (PPI) network analyses, and the genes were validated using Gene Expression Omnibus datasets. Additionally, 22 tumor-infiltrating immune cell (TIIC) subsets were analyzed using the CIBERSORT algorithm. Results Median stromal score was higher whereas immune score was lower in ESCC than in EAC (both P<0.01). Stromal score was lower in female as compared to male ESCC patients (P<0.05), and was significantly correlated with T stage (P<0.05). In EAC, median immune score was higher in female as compared to male patients (P<0.05) and was correlated with tumor-node-metastasis stage (P<0.05). The identified DEGs were mainly involved in lymphocyte (especially T-lymphocyte) activation and carbohydrate binding. Moreover, the levels of infiltrating resting-stage dendritic cells, CD8+ T cells, naïve B cells, activated mast cells, and resting memory CD4+ T cells were significantly correlated with EC prognosis (P<0.05). Conclusions The immune microenvironment of ESCC and EAC are quite different. We have found genes with prognostic value in multiple tumor databases.
| S-EPMC8797718 | biostudies-literature