Identification of Hypoxia-Related Molecular Classification and Associated Gene Signature in Oral Squamous Cell Carcinoma.
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ABSTRACT:
SUBMITTER: Li C
PROVIDER: S-EPMC8649955 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background Oral squamous cell carcinoma (OSCC), as the most common oral cancer globally, is very harmful to people’s health. Hypoxia is closely related to many cancers. In this study, we have conducted a comprehensive exploration of the impact of hypoxia on OSCC. Methods First, we calculated the enrichment score (ES) of hypoxia-related genes in the sample based on the enrichment analysis of the single sample Gene Set Enrichment Analysis (ssGSEA) and expressed it as a potential hypoxia index (HPI). We first identified the relationship between HPI and survival time in OSCC tumor samples. Then we assessed the correlation between hypoxia and the degree of infiltration of various immune cells in OSCC tissues, and screened out gene mutations that may be related to HPI in OSCC. Finally, we constructed a prognostic model of hypoxia-related genes. Results In the immune cell infiltration of OSCC, we found that hypoxia was significantly related to the infiltration of eosinophils, macrophages, neutrophils, T helper cells and Th1 cells. In addition, NSD1 mutations may become a signal to suggest that patients with OSCC have higher HPI. Finally, we constructed a prognostic model of 6 sets of hypoxia-related genes (PGK1, JMJD6, S100A4, SLC2A3, DDIT4 and HK1) in OSCC. Conclusions Hypoxia is closely related to immune cell infiltration, gene mutation, and prognosis in OSCC patients.
| S-EPMC8576654 | biostudies-literature
Project description:To identify an apoptosis-related gene (ARG) prediction model for oral squamous cell carcinoma (OSCC), we analyzed and validated the data from TCGA and GEO, respectively. Kaplan-Meier survival analysis and ROC curves showed a good prognostic ability of the model both in the internal training set and in the external testing set. Furthermore, we built a nomogram using these ARGs to forecast the survival probability of OSCC patients. Moreover, we evaluated the rate of immune cells infiltrating in the tumor samples and found obvious, different patterns between the high and low risk groups. GO and KEGG analyses demonstrated multiple molecular biological processes and signaling pathways connecting with this prognostic model in OSCC. The expression of these risk genes in clinical specimens was higher in the non-survival patients than in the well-survival patients by immunohistochemical staining analysis. In conclusion, we established a signature made up of six risk apoptosis-related genes to predict the survival rate of OSCC. These genes could also be targets for the treatment of OSCC.
| S-EPMC9235536 | biostudies-literature
Project description:The correlation between autophagy defects and oral squamous cell carcinoma (OSCC) has been previously studied, but only based on a limited number of autophagy-related genes in cell lines or animal models. The aim of the present study was to analyze differentially expressed autophagy-related genes through The Cancer Genome Atlas (TCGA) database to explore enriched pathways and potential biological function. Based on TCGA database, a signature composed of four autophagy-related genes (CDKN2A, NKX2-3, NRG3, and FADD) was established by using multivariate Cox regression models and two Gene Expression Omnibus datasets were applied for external validation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to study the function of autophagy-related genes and their pathways. The most significant GO and KEGG pathways were enriched in several key pathways that were related to the progression of autophagy and OSCC. Furthermore, a prognostic risk score was constructed based on the four genes; patients were then divided into two groups (i.e., high risk and low risk) in terms of the median of risk score. Prognosis of the two groups and results showed that patients at the low-risk group had a much better prognosis than those at the high-risk group, regardless of whether they were in the training datasets or validation datasets. Multivariate Cox regression results indicated that the risk score of the autophagy-related gene signatures could greatly predict the prognosis of patients after controlling for several clinical covariates. The findings of the present study revealed that autophagy-related gene signatures play an important role in OSCC and are potential prognostic biomarkers and therapeutic targets.
| S-EPMC8248343 | biostudies-literature
Project description:BackgroundThe prognosis of oral squamous cell carcinoma (OSCC) patients is difficult to predict or describe due to its high-level heterogeneity and complex aetiologic factors. Ferroptosis is a novel form of iron-dependent cell death that is closely related to tumour growth and progression. This study aims to clarify the predictive value of ferroptosis-related genes (FRGs) on the overall survival(OS) of OSCC patients.MethodsThe mRNA expression profile of FRGs and clinical information of patients with OSCC were collected from the TCGA database. Candidate differentially expressed ferroptosis-related genes (DE-FRGs) were identified by analysing differences between OSCC and adjacent normal tissues. A gene signature of prognosis-related DE-FRGs was established by univariate Cox analysis and LASSO analysis in the training set. Patients were then divided into high- and low-risk groups according to the cut-off value of risk scores, A nomogram was constructed to quantify the contributions of gene signature and clinical parameters to OS. Then several bioinformatics analyses were used to verify the reliability and accuracy of the model in the validation set. Finally, single-sample gene set enrichment analysis (ssGSEA) was also performed to reveal the underlying differences in immune status between different risk groups.ResultsA prognostic model was constructed based on 10 ferroptosis-related genes. Patients in high-risk group had a significantly worse OS (p < 0.001). The gene signature was verified as an independent predictor for the OS of OSCC patients (HR > 1, p < 0.001). The receiver operating characteristic curve displayed the favour predictive performance of the risk model. The prediction nomogram successfully quantified each indicator's contribution to survival and the concordance index and calibration plots showed its superior predictive capacity. Finally, ssGSEA preliminarily indicated that the poor prognosis in the high-risk group might result from the dysregulation of immune status.ConclusionThis study established a 10-ferroptosis-releated gene signature and nomogram that can be used to predict the prognosis of OSCC patients, which provides new insight for future anticancer therapies based on potential FRG targets.
| S-EPMC8290602 | biostudies-literature
Project description:The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan-Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients.
| S-EPMC8724436 | biostudies-literature
Project description:PurposeHypoxia is a leading hallmark of tumors, which is associated with carcinogenicity and dismal patient outcome. In this project, we tended to detect the prognostic value of hypoxic lncRNA and further generate a hypoxic lncRNA-based model in head and neck squamous cell carcinoma (HNSCC).MethodsWe integrated the transcriptome and clinical information of HNSCC based on TCGA dataset. Univariate-multivariate Cox analysis was implemented to develop the signature according to hypoxia-related lncRNAs (HRlncRNAs) with greatly prognostic power in HNSCC. Next, the biomarker signature was tested using survival analysis and ROC plots. Moreover, we used GSEA to uncover the potential pathways of HRlncRNAs, and CIBERSORT and ssGSEA tools were applied to mirror the immune status of HNSCC patients.ResultsNine HRlncRNAs (LINC00460, AC144831.1, AC116914.2, MIAT, MSC-AS1, LINC01980, MYOSLID, AL357033.4, and LINC02195) were determined to develop a HRlncRNA-related signature (HRLS). High-HRLS group was associated with dismal patient outcome using survival analysis. Moreover, the HRLS was superior to classical clinical traits in forecasting survival rate of samples with HNSCC. GSEA unearthed the top six hallmarks in the HRLS-high group individuals. In addition, the HRLS was also bound up with the infiltration of macrophages, CD8 T cells, and activated mast cells.ConclusionOur nominated nine-HRlncRNA risk model is robust and valuable tool for forecasting patient outcome in HNSCC.
| S-EPMC8791745 | biostudies-literature
Project description:BackgroundOral squamous cell carcinoma (OSCC) is one of the most common maligancies of the head and neck. The prognosis was is significantly different among OSCC patients. This study aims to identify new biomarkers to establish a prognostic model to predict the survival of OSCC patients.MethodsThe mRNA expression and corresponding clinical information of OSCC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Additionally, a total of 26 hypoxia-related genes were also obtained from a previous study. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal hypoxia-related genes which were associated with the prognosis of OSCC. to establish the predictive model (Risk Score) was established for estimating the patient's overall survival (OS). Multivariate Cox regression analysis was used to determine whether the Risk Score was an independent prognostic factor. Based on all the independent prognostic factors, nomogram was established to predict the OS probability of OSCC patients. The relative proportion of 22 immune cell types in each patient was evaluated by CIBERSORT software.ResultsWe determined that a total of four hypoxia-related genes including ALDOA, P4HA1, PGK1 and VEGFA were significantly associated with the prognosis of OSCC patients. The nomogram established based on all the independent factors could reliably predict the long-term OS of OSCC patients. In addition, our resluts indicated that the inferior prognosis of OSCC patients with high Risk Score might be related to the immunosuppressive microenvironments.ConclusionThis study shows that high expression of hypoxia-related genes including ALDOA, P4HA1, PGK1 and VEGFA is associated with poor prognosis in OSCC patients, and they can be used as potential markers for predicting prognosis in OSCC patients.
| S-EPMC8094530 | biostudies-literature
Project description:Oral squamous cell carcinoma (OSCC) is one of the leading cancers with poor disease survival rate. Herein, we explored molecular basis, in silico identification and in vitro verification of genes associated with OSCC. Five gene expression series including, GSE30784, GSE13601, GSE9844, GSE23558 and GSE37991 were screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by cluster Profiler. Further, protein-protein interaction network was analysed and hub genes were verified. A total of 6476 (up-regulated: 2848; down-regulated: 3628) DEGs were identified among OSCC patients and healthy controls. Gene Ontology analysis indicated DEGs enrichment in cellular motility, invasion and adhesion processes. KEGG analysis revealed enrichment of PI3K-Akt signalling, focal adhesion and regulation of actin cytoskeleton pathways. Subsequently, nine DEGs including APP, EHMT1, ACACB, PCNA, PLAU, FST, HMGA2, LAMC2 and SPP1 were correlated with TCGA expression data along with significant association towards patient's survival, recognized as hub genes. This dysregulated mRNA signature of genes was validated in two OSCC cell lines with an anti-cancer agent, fisetin. Fisetin inhibited the expression of APP, EHMT1, PCNA, PLAU, FST, HMGA2, LAMC2, SPP1 and upregulated the expression of ACACB gene which were associated with growth inhibition of both the OSCC cell lines. The regulatory effect of fisetin supported crucial role of nine hub genes identified in OSCC. This study signified that hub genes and pathways might influence the aggressiveness of OSCC. Thus, the proposed hub genes could be potential diagnostic biomarker and drug targets for OSCC.Supplementary informationThe online version contains supplementary material available at 10.1007/s13205-021-02737-4.
| S-EPMC8046867 | biostudies-literature
Project description:Elevated polyamine levels are required for tumor transformation and development; however, expression patterns of polyamines and their diagnostic potential have not been investigated in oral squamous cell carcinoma (OSCC), and its impact on prognosis has yet to be determined. A total of 440 OSCC samples and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Consensus clustering was conducted to classify OSCC patients into two subgroups based on the expression of the 17 polyamine regulators. Polyamine-related differentially expressed genes (PARDEGs) among distinct polyamine clusters were determined. To create a prognostic model, PARDEGs were examined in the training cohorts using univariate-Lasso-multivariate Cox regression analyses. Six prognostic genes, namely, "CKS2," "RIMS3," "TRAC," "FMOD," CALML5," and "SPINK7," were identified and applied to develop a predictive model for OSCC. According to the median risk score, the patients were split into high-risk and low-risk groups. The predictive performance of the six gene models was proven by the ROC curve analysis of the training and validation cohorts. Kaplan-Meier curves revealed that the high-risk group had poorer prognosis. Furthermore, the low-risk group was more susceptible to four chemotherapy drugs according to the IC50 of the samples computed by the "pRRophetic" package. The correlation between the risk scores and the proportion of immune cells was calculated. Meanwhile, the tumor mutational burden (TMB) value of the high-risk group was higher. Real-time quantitative polymerase chain reaction was applied to verify the genes constructing the model. The possible connections of the six genes with various immune cell infiltration and therapeutic markers were anticipated. In conclusion, we identified a polyamine-related prognostic signature, and six novel biomarkers in OSCC, which may provide insights to identify new treatment targets for OSCC.
| S-EPMC9887031 | biostudies-literature
Project description:ObjectivesOral squamous cell carcinoma (OSCC) is the most common oral cancer and has a poor prognosis. We aimed to identify new biomarkers or potential therapeutic targets for OSCC.Materials and methodsFour pairs of tumor and adjacent normal tissues were collected from OSCC patients, and differentially expressed genes (DEGs) were screened via high-throughput RNA sequencing (RNA-seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze the DEGs. A protein-protein interaction (PPI) network was established with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape, and two significant clusters were found. Candidate genes were screened by analyzing head and neck squamous cell carcinoma (HNSCC) data from The Cancer Genome Atlas (TCGA). A DEG-based risk model was established to predict the overall survival (OS) of OSCC patients via Kaplan-Meier analysis and the log-rank test. Furthermore, univariate Cox regression analysis was applied to assess associations between potential biomarkers and the overall survival rate.ResultsOf 720 total DEGs, fifty-two DEGs in the two subclusters of the PPI network analysis were selected. A risk model was established, and five candidate genes (SPRR2E, ICOS, CTLA4, HTR1D, and CCR4) were identified as biomarkers of OS in OSCC patients.ConclusionsWe successfully constructed a prognostic signature to predict prognosis and identified five candidate genes associated with the OS of OSCC patients that are potential tumor biomarkers and targets in OSCC.
| S-EPMC8032525 | biostudies-literature