scholarly journals Identification and Validation of a Hypoxia-Immune-Based Prognostic mRNA Signature for Oral Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Shaohua Lv ◽  
Jianhao Li ◽  
Songlin Piao ◽  
jichen Li
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Status ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk

Abstract Background: Oral squamous cell carcinoma (OSCC) is a frequently encountered head and neck malignancy. Increasing evidence points towards an aberrant immune response and chronic cell hypoxia in the development of OSCC. However, there is a lack of a reliable hypoxia-immune-based gene signature that may serve to accurately prognosticate OSCC. Methods: The mRNA expression data of OSCC patients was extracted from the TCGA database. Hypoxia status was identified using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Both ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) was used for further evaluation of immune status. The DEGs in different hypoxia and immune status were determined. A Machine learning method-Least Absolute Shrinkage and Selection operator (LASSO) Cox regression analysis allowed us to select prognostically significant hypoxia- and immune-related mRNAs in order to construct prognostic gene signature to predict the overall survival (OS) of OSCC patients. Results: A total of 773 DEGs were classified into either Hypoxia_High and Hypoxia_Low groups. Immune-associated DEG expressions were used to divide individuals into Immune_High, Immune_ Medium and Immune_Low groups. A total of 193 mRNAs which were significant in both immune function and hypoxia status were identified. With the Lasso Cox regression model, 8 signature mRNAs (FAM122C, RNF157, RANBP17, SOWAHA, KIAA1211, RIPPLY2, INSL3, and DNAH1) associated with OS were selected for further calculation of their respective risk scores. The risk score showed a significant association with age, perineural and lymphovascular invasion. In the GEO validation cohort, a better OS was observed in patients from the low-risk group in comparison to those in the high-risk group. High-risk patients also demonstrated different immune infiltration characteristics from the low-risk group. All individuals from the TCGA OSCC cohort showed similar trends in all 6 immune checkpoints, with those of the low-risk group yielding higher immune indicator scores in contrast to their high-risk counterparts. Conclusion: The hypoxia-immune-based gene signature has prognostic potential in OSCC.

scholarly journals Ferroptosis-related gene signature predicts the prognosis in Oral squamous cell carcinoma patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hongyu Li ◽  
Xiliu Zhang ◽  
Chen Yi ◽  
Yi He ◽  
Xun Chen ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Status ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
High Risk Group

Abstract Background The 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. Methods The 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. Results A 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. Conclusion This 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.

scholarly journals Novel Autophagy-Related Gene Signature Investigation for Patients With Oral Squamous Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Lihong Huang ◽  
Xinghao Yu ◽  
Zhou Jiang ◽  
Ping Zeng
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Related Gene

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.

scholarly journals A Novel Clinical Six-Flavoprotein-Gene Signature Predicts Prognosis in Esophageal Squamous Cell Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Liu Peng ◽  
Jin-Cheng Guo ◽  
Lin Long ◽  
Feng Pan ◽  
Jian-Mei Zhao ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Clinical Significance ◽  
Squamous Cell ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk

Flavoproteins and their interacting proteins play important roles in mitochondrial electron transport, fatty acid degradation, and redox regulation. However, their clinical significance and function in esophageal squamous cell carcinoma (ESCC) are little known. Here, using survival analysis and machine learning, we mined 179 patient expression profiles with ESCC in GSE53625 from the Gene Expression Omnibus (GEO) database and constructed a signature consisting of two flavoprotein genes (GPD2 and PYROXD2) and four flavoprotein interacting protein genes (CTTN, GGH, SRC, and SYNJ2BP). Kaplan–Meier analysis revealed the signature was significantly associated with the survival of ESCC patients (mean survival time: 26.77 months in the high-risk group vs. 54.97 months in the low-risk group, P<0.001, n = 179), and time-dependent ROC analysis demonstrated that the six-gene signature had good predictive ability for six-year survival for ESCC (AUC = 0.86, 95% CI: 0.81–0.90). We then validated its prediction performance in an independent set by RT-PCR (mean survival: 15.73 months in the high-risk group vs. 21.1 months in the low-risk group, P=0.032, n = 121). Furthermore, RNAi-mediated knockdown of genes in the flavoprotein signature led to decreased proliferation and migration of ESCC cells. Taken together, CTTN, GGH, GPD2, PYROXD2, SRC, and SYNJ2BP have an important clinical significance for prognosis of ESCC patients, suggesting they are efficient prognostic markers and potential targets for ESCC therapy.

scholarly journals Saliva protein biomarkers to detect oral squamous cell carcinoma in a high-risk population in Taiwan

2016 ◽  
Vol 113 (41) ◽  
pp. 11549-11554 ◽  
Author(s):  
Jau-Song Yu ◽  
Yi-Ting Chen ◽  
Wei-Fan Chiang ◽  
Yung-Chin Hsiao ◽  
Lichieh Julie Chu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Oral Cancer ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Low Risk ◽  
Healthy Controls ◽  
Protein Biomarkers

Most cases of oral squamous cell carcinoma (OSCC) develop from visible oral potentially malignant disorders (OPMDs). The latter exhibit heterogeneous subtypes with different transformation potentials, complicating the early detection of OSCC during routine visual oral cancer screenings. To develop clinically applicable biomarkers, we collected saliva samples from 96 healthy controls, 103 low-risk OPMDs, 130 high-risk OPMDs, and 131 OSCC subjects. These individuals were enrolled in Taiwan’s Oral Cancer Screening Program. We identified 302 protein biomarkers reported in the literature and/or through in-house studies and prioritized 49 proteins for quantification in the saliva samples using multiple reaction monitoring-MS. Twenty-eight proteins were successfully quantified with high confidence. The quantification data from non-OSCC subjects (healthy controls + low-risk OPMDs) and OSCC subjects in the training set were subjected to classification and regression tree analyses, through which we generated a four-protein panel consisting of MMP1, KNG1, ANXA2, and HSPA5. A risk-score scheme was established, and the panel showed high sensitivity (87.5%) and specificity (80.5%) in the test set to distinguish OSCC samples from non-OSCC samples. The risk score >0.4 detected 84% (42/50) of the stage I OSCCs and a significant portion (42%) of the high-risk OPMDs. Moreover, among 88 high-risk OPMD patients with available follow-up results, 18 developed OSCC within 5 y; of them, 77.8% (14/18) had risk scores >0.4. Our four-protein panel may therefore offer a clinically effective tool for detecting OSCC and monitoring high-risk OPMDs through a readily available biofluid.

A Four-gene Autophagy-related Prognostic Signature and Its Association With Immune Landscape in Lung Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Lumeng Luo ◽  
Minghe Lv ◽  
Xuan Li ◽  
Tiankui Qiao ◽  
Kuaile Zhao ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Immune Suppression ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
High Risk Group ◽  
Low Risk

Abstract Background: Recent advances in immune checkpoint inhibitors (ICIs) have dramatically changed the therapeutic strategy against lung squamous cell carcinoma (LUSC). In the era of immunotherapy, effective biomarkers to better predict outcomes and inform treatment decisions for patients diagnosed with LUSC are urgently needed. We hypothesized that immune contexture of LUSC is potentially dictated by tumor intrinsic events, such as autophagy. Thus, we attempted to construct an autophagy-related risk signature and examine its prediction value for immune phenotype in LUSC.Method: The expression profile of LUSC was obtained from the cancer genome atlas (TCGA) database and the profile of autophagy-related genes (ARGs) was extracted. The survival‑related ARGs (sARGs) was screened out through survival analyses. Random forest was performed to select the sARGs and construct a prognostic risk signature based on these sARGs. The signature was further validated by receiver operating characteristic (ROC) analysis and Cox regression. GEO dataset was used as an independent testing dataset. Patients were divided into high-risk and low-risk group based on the risk score. Then, gene set enrichment analysis (GSEA) was conducted between the two groups. The Single-Sample GSEA (ssGSEA) was introduced to quantify the relative infiltration of immune cells. The correlations between risk score and several main immune checkpoints were examined. And the ESTIMATE algorithm was used to calculate the estimate/immune/stromal scores of the LUSC. Results: Four ARGs (CFLAR, RGS19, PINK1 and CTSD) with the most significant prognostic values were enrolled to construct the risk signature. Patients in high-risk group had better prognosis than the low-risk group (P < 0.0001 in TCGA; P < 0.01 in GEO) and considered as an independent prognosis factor. We also found that high-risk group indicated an immune-suppression status and had higher levels of infiltrating regulatory T cells and macrophages, which are correlated with worse outcome. Besides, risk score showed a significantly positive correlation with the expression of PD-1 and CTLA4, as well as estimate score and immune score.Conclusion: This study established a novel autophagy-related four-gene prognostic risk signature, and the autophagy-related scores are associated with immune landscape of LUSC, with higher score indicating a stronger immune-suppression status.

scholarly journals Comprehensive analysis of the significance of ferroptosis-related genes in the prognosis and immunotherapy of oral squamous cell carcinoma

2020 ◽  
Author(s):  
Junhao Yin ◽  
Xiaoli Zeng ◽  
Zexin Ai ◽  
Miao Yu ◽  
Yang'ou Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Oral Squamous Cell Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Regression Analyses

Abstract Background: Oral squamous cell carcinoma (OSCC) is a life-threatening disease that emerged as a major international health concern, associated with poor prognosis and the absence of specific biomarkers. Studies have shown that the ferroptosis-related genes (FRGs) can be used as tumor prognostic markers. However, FRGs’ prognostic value in OSCC needs further exploration. Our aim was to construct a novel FRG signature for overall survival (OS) prediction in OSCC patients and explore its role in immunotherapy.Methods: In our study, gene expression profile and clinical data of OSCC patients were collected from a public domain. FRGs were available from the FerrDb database. We performed univariate and multivariate Cox regression analyses to construct a multigene signature. The Kaplan-Meier (K-M) and receiver operating characteristic (ROC) methods were utilized to test the effectiveness of the FRG signature. A differential gene expression analysis was performed by the limma R package, followed by functional enrichment analyses. CIBERSORT was applied to analyze the tumor microenvironment (TME). Finally, the expression of human leukocyte antigen (HLA) and immune checkpoint molecules were analyzed to confirm the sensitivity of immunotherapy.Results: A total of 103 FRGs, expressed in OSCC (FRGs-OSCC), were identified from the two datasets by the Venn analysis. The Cox regression analysis identified 5 FRGs-OSCC that were associated with overall survival (all P < 0.01). The FRGs-OSCC risk model was established to classify patients into high risk and low risk groups. Compared with the low risk group, the survival time of the high-risk group was significantly reduced (P < 0.001). According to the multivariate Cox regression analyses, the risk score acted as an independent predictor for OS (HR > 1, P < 0.001). The accuracy of the FRGs-OSCC risk predictive model was confirmed by ROC curve analysis. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed significant enrichment of immune-related pathways, and a difference in tumor microenvironment between the two groups. The low risk group had the characteristics of higher expression of HLA and immune checkpoints (IDO1, LAG3, PDCD1 and TIGHT), a lower tumor purity and a higher infiltration of immune cells, indicating a more sensitive response to immunotherapy.Conclusions: The novel FRGs-OSCC risk score system can be used to predict OSCC prognosis. Ferroptosis targeting may be a therapeutic option for OSCC.

scholarly journals The Immune Heterogeneity Between Pulmonary Adenocarcinoma and Squamous Cell Carcinoma: A Comprehensive Analysis Based on lncRNA Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Yan ◽  
Guoyuan Ma ◽  
Kai Wang ◽  
Weidong Liu ◽  
Weiqing Zhong ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Squamous Cell Carcinoma ◽  
T Cell ◽  
High Risk ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores

Adenocarcinoma (AD) and squamous cell carcinoma (SCC) are both classified as major forms of non-small cell lung cancer, but differences in clinical prognoses and molecular mechanisms are remarkable. Recent studies have supported the importance of understanding immune status in that it influences clinical outcomes of cancer, and immunotherapies based on the theory of “immune editing” have had notable clinical success. Our study aimed to identify specific long non-coding (lnc) RNAs that control key immune-related genes and to use them to construct risk models for AD and SCC. Risk scores were used to separate patients into high- and low-risk groups, and we validated the prognostic significance of both risk scores with our own cohorts. A Gene Set Enrichment Analysis suggested that the immune responses of patients in the AD high-risk group and the SCC low-risk group tended to be weakened. Evaluation of immune infiltration revealed that the degree of infiltration of dendritic cells is of particular importance in AD. In addition, prediction of responses to immune checkpoint inhibitor (ICI) treatments, based on the T Cell Immune Dysfunction and Exclusion and immunophenoscore models, indicated that deterioration of the immune microenvironment is due mainly to T cell exclusion in AD patients and T cell dysfunction in SCC patients and that high-risk patients with SCC might benefit from ICI treatment. The prediction of downstream targets via The Cancer Proteome Atlas and RNA-seq analyses of a transfected lung cancer cell line indicated that the lncRNA LINC00996 is a potential therapeutic target in AD.

Identification of a novel CpG methylation signature to predict prognosis in lung squamous cell carcinoma

2020 ◽  
pp. 1-11
Author(s):  
Nan Lee ◽  
Xuelian Xia ◽  
Hui Meng ◽  
Weiliang Zhu ◽  
Xiankai Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Patients

BACKGROUND: DNA methylation plays a vital role in modulating genomic function and warrants evaluation as a biomarker for the diagnosis and treatment of lung squamous cell carcinoma (LUSC). OBJECTIVE: In this study, we aimed to identify effective potential biomarkers for predicting prognosis and drug sensitivity in LUSC. METHODS: A univariate Cox proportional hazards regression analysis, a random survival forests-variable hunting (RSFVH) algorithm, and a multivariate Cox regression analysis were adopted to analyze the methylation profile of patients with LUSC included in public databases: The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO). RESULTS: A methylated region consisting of 3 sites (cg06675147, cg07064331, cg20429172) was selected. Patients were divided into a high-risk group and a low-risk group in the training dataset. High-risk patients had shorter overall survival (OS) (hazard ratio [HR]: 2.72, 95% confidence interval [CI]: 1.82–4.07, P< 0.001) compared with low-risk patients. The accuracy of the prognostic signature was validated in the test and validation cohorts (TCGA, n= 94; GSE56044, n= 23). Gene set variation analysis (GSVA) showed that activity in the cell cycle/mitotic, ERBB, and ERK/MAPK pathways was higher in the high-risk compared with the low-risk group, which may lead to differences in OS.Interestingly, we observed that patients in the high-risk group were more sensitive to gemcitabine and docetaxel than the low-risk group, which is consistent with results of the GSVA. CONCLUSION: We report novel methylation sites that could be used as powerful tools for predicting risk factors for poorer survival in patients with LUSC.

scholarly journals Prognostic biomarkers and therapeutic targets in oral squamous cell carcinoma: a study based on cross-database analysis

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Wanli Yang ◽  
Wei Zhou ◽  
Xinhui Zhao ◽  
Xiaoqian Wang ◽  
Lili Duan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Gene Signature ◽  
Genetic Alterations ◽  
Prognostic Biomarkers ◽  
Hub Genes

Abstract Background Oral squamous cell carcinoma (OSCC) is a malignant cancer, the survival rate of patients is disappointing. Therefore, it is necessary to identify the driven-genes and prognostic biomarkers in OSCC. Methods Four Gene Expression Omnibus (GEO) datasets were integratedly analyzed using bioinformatics approaches, including identification of differentially expressed genes (DEGs), GO and KEGG analysis, construction of protein-protein interaction (PPI) network, selection of hub genes, analysis of prognostic information and genetic alterations of hub genes. ONCOMINE, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases were used to evaluate the expression and prognostic value of hub genes. Tumor immunity was assessed to investigate the functions of hub genes. Finally, Cox regression model was performed to construct a multiple-gene prognostic signature. Results Totally 261 genes were found to be dysregulated. 10 genes were considered to be the hub genes. The Kaplan-Meier analysis showed that upregulated SPP1, FN1, CXCL8, BIRC5, PLAUR, and AURKA were related to poor outcomes in OSCC patients. FOXM1 and TPX2 were considered as the potential immunotherapeutic targets with future clinical significance. Moreover, we constructed a nine-gene signature (TEX101, DSG2, SCG5, ADA, BOC, SCARA5, FST, SOCS1, and STC2), which can be utilized to predict prognosis of OSCC patients effectively. Conclusion These findings may provide new clues for exploring the molecular mechanisms and targeted therapy in OSCC. The hub genes and risk gene signature are helpful to the personalized treatment and prognostic judgement.

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