Prognostic Value of Tumor Mutational Burden Related to Immune Infiltration in Cervical Squamous Cell Carcinoma

Cervical squamous cell carcinoma is one of the most common causes of female cancer deaths worldwide. At present, immunotherapy using immune checkpoint blockade (ICB) has improved the prognosis of many cancer patients, and neoantigens generated by mutations may serve as potential biomarkers for predicting the outcome of ICB therapy. In this study, we identified missense mutations as the most frequent in landscapes of gene mutation in cervical squamous cell carcinoma (CESC) samples. Patients with higher tumor mutation burden (TMB) presented higher overall survival (OS). In addition, there was a significant correlation between the high TMB group and fractions of most immune cells. Univariate and multivariate Cox regression analyses identified five hub genes (IFNG, SERPINA3, CCL4L2, TNFSF15, and IL1R1) that were used to build a prognostic model. In the prognostic model, the low-risk group achieved better OS. Mutations in the five hub genes mainly affected the infiltration level of CD8+ T cells and dendritic cells. In conclusion, our study is valuable for exploring the role of TMB and its relationship with immune infiltration in CESC. Moreover, the prognosis model may help predict the sensitivity of patients to immunotherapy and provide underlying biomarkers for personalized immunotherapy.

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Identification of an immune-based mRNA–lncRNA signature for overall survival in cervical squamous cell carcinoma

Future Oncology ◽

10.2217/fon-2020-1153 ◽

2021 ◽

Author(s):

Yifang Mao ◽

Run Chen ◽

Meng Xia ◽

Peng Guo ◽

Feitianzhi Zeng ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

Cervical Squamous Cell Carcinoma ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Rna Sequences ◽

Immune Infiltration

Aim: To better predict the survival of cervical squamous cell carcinoma (CESC) patients, we aimed to construct a signature according to different immune infiltration. Methods: We downloaded the RNA sequences of CESC patients from the Cancer Genome Atlas database. By using single-sample gene set enrichment analysis, we separated the samples into high- and low-immunity groups. Then we separated the samples into training and testing datasets and performed the following analyses: univariate, least absolute shrinkage and selection operator analysis, multivariate Cox regression analyses and weighted gene coexpression network analysis using R software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes studies were performed using the Database for Annotation, Visualization and Integrated Discovery website. Results & conclusion: We finally identified a signature with three mRNAs and two lncRNAs: ADGRG5, HSH2D, ZMAT4, RBAKDN and LINC00200. In short, our study constructed an mRNA–lncRNA signature related to immune infiltration to better predict the survival of CESC patients.

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Analysis of Key Pathways, Genes and Immune Cell Infiltration in Metastatic Esophageal Squamous Cell Carcinoma Based on Bioinformatics

10.21203/rs.3.rs-938022/v1 ◽

2021 ◽

Author(s):

Zhangjiao Wang ◽

Yong Shen ◽

Qingxia Xu

Keyword(s):

Squamous Cell Carcinoma ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Tumor Markers ◽

Squamous Cell ◽

Protein Interaction ◽

Signal Pathway ◽

Hub Genes ◽

Immune Infiltration ◽

Geo Database

Abstract Objective: To seek potential metastatic tumor markers in esophageal squamous cell carcinoma (ESCC) by bioinformatics. Methods: Four datasets (GSE70409, GSE26886, GSE161533, GSE17351) were downloaded from GEO database, and the differential expression of mRNA was screened by Limma software package. Gene ontology function and kyoto encyclopedia of genes and genomes pathway enrichment analysis were performed using the database for annotation; Protein–protein interaction network of these DEGs was constructed based on STRING database; then, the hub gene were identified by six algorithms of Cytoscape software. Using GEPIA2 database and OncoLnc database to analyze the survival of hub genes. Immune infiltration was analyzed by TIMER2 database and CIBERSORT deconvolution method. Results: A total of 244 differentially expressed genes(DEGs)were screened from 4 data sets of GEO database, including 164 up-regulated genes and 80 down-regulated genes, which were mainly enriched at extracellular region and extracellular space, and involved in biological processes such as cell adhesion and proteolysis related to tumor invasion and metastasis. KEGG results showed that DEGs were mainly enriched in PI3K-Akt signal pathway and extracellular matrix (ECM) receptor interaction signal pathway. Through the protein interaction analysis of DEGs, we found that three hub genes (CXCL8, MMP9 and MMP13) were highly expressed in ESCC. However, the GEPIA2 database shows that only CXCL8 is associated with the overall survival of ESCC patients. The results of immune infiltration showed that macrophages and resting dendritic cells increased significantly, while mast cells and monocytes decreased significantly. Conclusion: Three hub genes and immune infiltrating cells may play an important role in ESCC, and are expected to become potential tumor markers of ESCC and be used in the diagnosis and treatment of ESCC.

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Autophagy-Related Three-Gene Prognostic Signature for Predicting Survival in Esophageal Squamous Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.650891 ◽

2021 ◽

Vol 11 ◽

Author(s):

Heyang Cui ◽

Yongjia Weng ◽

Ning Ding ◽

Chen Cheng ◽

Longlong Wang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Discovery Cohort ◽

Cox Regression Analysis

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in China, and its prognosis remains poor. Autophagy is an evolutionarily conserved catabolic process involved in the occurrence and development of ESCC. In this study, we described the expression profile of autophagy-related genes (ARGs) in ESCC and developed a prognostic prediction model for ESCC patients based on the expression pattern of ARGs. We used four ESCC cohorts, GSE53624 (119 samples) set as the discovery cohort, The Cancer Genome Atlas (TCGA) ESCC set (95 samples) as the validation cohort, 155 ESCC cohort, and Oncomine cohort were used to screen and verify differentially expressed ARGs. We identified 34 differentially expressed genes out of 222 ARGs. In the discovery cohort, we divided ESCC patients into three groups that showed significant differences in prognosis. Then, we analyzed the prognosis of 34 differentially expressed ARGs. Three genes [poly (ADP-ribose) polymerase 1 (PARP1), integrin alpha-6 (ITGA6), and Fas-associated death domain (FADD)] were ultimately obtained through random forest feature selection and were constructed as an ARG-related prognostic model. This model was further validated in TCGA ESCC set. Cox regression analysis confirmed that the three-gene signature was an independent prognostic factor for ESCC patients. This signature effectively stratified patients in both discovery and validation cohorts by overall survival (P = 5.162E-8 and P = 0.052, respectively). We also constructed a clinical nomogram with a concordance index of 0.713 to predict the survival possibility of ESCC patients by integrating clinical characteristics and the ARG signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. In conclusion, we constructed a new ARG-related prognostic model, which shows the potential to improve the ability of individualized prognosis prediction in ESCC.

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A Novel Prognostic Model for Oral Squamous Cell Carcinoma: The Functions and Prognostic Values of RNA-Binding Proteins

Frontiers in Oncology ◽

10.3389/fonc.2021.592614 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yingjuan Lu ◽

Yongcong Yan ◽

Bowen Li ◽

Mo Liu ◽

Yancan Liang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Squamous Cell ◽

Prognostic Model ◽

Binding Proteins ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins

PurposeThe biological roles and clinical significance of RNA-binding proteins (RBPs) in oral squamous cell carcinoma (OSCC) are not fully understood. We investigated the prognostic value of RBPs in OSCC using several bioinformatic strategies.Materials and MethodsOSCC data were obtained from a public online database, the Limma R package was used to identify differentially expressed RBPs, and functional enrichment analysis was performed to elucidate the biological functions of the above RBPs in OSCC. We performed protein-protein interaction (PPI) network and Cox regression analyses to extract prognosis-related hub RBPs. Next, we established and validated a prognostic model based on the hub RBPs using Cox regression and risk score analyses.ResultsWe found that the differentially expressed RBPs were closely related to the defense response to viruses and multiple RNA processes. We identified 10 prognosis-related hub RBPs (ZC3H12D, OAS2, INTS10, ACO1, PCBP4, RNASE3, PTGES3L-AARSD1, RNASE13, DDX4, and PCF11) and effectively predicted the overall survival of OSCC patients. The area under the receiver operating characteristic (ROC) curve (AUC) of the risk score model was 0.781, suggesting that our model exhibited excellent prognostic performance. Finally, we built a nomogram integrating the 10 RBPs. The internal validation cohort results showed a reliable predictive capability of the nomogram for OSCC.ConclusionWe established a novel 10-RBP-based model for OSCC that could enable precise individual treatment and follow-up management strategies in the future.

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Acetyl Coenzyme A Synthase 2 Acts as a Prognostic Biomarker Associated with Immune Infiltration in Cervical Squamous Cell Carcinoma

Cancers ◽

10.3390/cancers13133125 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3125

Author(s):

Chia-Jung Li ◽

Yi-Han Chiu ◽

Chung Chang ◽

Yuan-chin Ivan Chang ◽

Jim Jinn-Chyuan Sheu ◽

...

Keyword(s):

Cervical Cancer ◽

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Coenzyme A ◽

Prognostic Biomarker ◽

Cervical Squamous Cell Carcinoma ◽

Acetyl Coenzyme A ◽

Immune Infiltration ◽

Acetyl Coenzyme A Synthase

Cervical squamous cell carcinoma (CESC) is one of the most common malignant tumors in women worldwide with a low survival rate. Acetyl coenzyme A synthase 2 (ACSS2) is a conserved nucleosidase that converts acetate to acetyl-CoA for energy production. Our research intended to identify the correlations of ACSS2 with clinical prognosis and tumor immune infiltration in CESC. ACSS2 is highly expressed in many tumors and is involved in the progression and metastasis of these tumors. However, it is not clear how ACSS2 affects CESC progression and immune infiltration. Analysis of the cBioPortal, GEPIA2, UALCAN, and TCGA databases showed that ACSS2 transcript levels were significantly upregulated in multiple cancer types including CESC. Quantitative RT-PCR analysis confirmed that ACSS2 expression was significantly upregulated in human cervical cancer cells. Here, we performed tissue microarray analysis of paraffin-embedded tissues from 240 cervical cancer patients recorded at FIGO/TNM cancer staging. The results showed that ACSS2 and PDL1 were highly expressed in human CESC tissues, and its expression was associated with the clinical characteristics of CESC patients. TIMER database analysis showed that ACSS2 expression in CESC was associated with tumor infiltration of B cells, CD4+ and CD8+ T cells, and cancer-associated fibroblasts (CAF). Kaplan–Meier survival curve analysis showed that CESC with high ACSS2 expression was associated with shorter overall survival. Collectively, our findings establish ACSS2 as a potential diagnostic and prognostic biomarker for CESC.

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Prognostic values of lymphocyte and eosinophil counts in resectable cervical squamous cell carcinoma

Future Oncology ◽

10.2217/fon-2018-0879 ◽

2019 ◽

Vol 15 (30) ◽

pp. 3467-3481 ◽

Cited By ~ 1

Author(s):

Jie Zhu ◽

Han Wang ◽

Min-Jie Gao ◽

Yi-Fan Li ◽

Yue-Qing Huang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Cox Regression ◽

Cell Cancer ◽

Progression Free Survival ◽

Cervical Squamous Cell Carcinoma ◽

Analysis Model ◽

Cox Regression Analysis ◽

Cell Parameters

Aim: Cervical cancer is one of the leading causes of cancer mortality in women. Peripheral white blood cell parameters such as neutrophil (NE), eosinophil (EO), basophil (BA), as well as lymphocyte (LY) and monocyte (MO), are correlated with tumor outcomes. Methods: In total, 110 cervical squamous cell carcinoma patients were recruited in this study. The potential prognostic factors were evaluated by univariate and multivariate survival analysis. Results: Cox regression analysis model indicated that higher pretreatment EO level and increased post-/preradiotherapy EO ratio were independently associated with worse progression-free survival. Lower pretreatment LY or higher EO levels and increased post-/preradiotherapy EO ratio were independently associated with worse overall survival. Conclusion: LY and EO are correlated with outcomes of cervical squamous cell cancer.

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A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma

BMC Medical Genomics ◽

10.1186/s12920-021-00885-3 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Rui Qin ◽

Lu Cao ◽

Cong Ye ◽

Junrong Wang ◽

Ziqian Sun

Keyword(s):

Gene Expression ◽

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Cox Regression ◽

Risk Groups ◽

Cervical Squamous Cell Carcinoma ◽

Regression Analyses ◽

Prognostic Prediction ◽

Immune Related Genes

Abstract Background In this study, we aimed to mine immune-related RNAs expressed in early cervical squamous cell carcinoma to construct prognostic prediction models. Methods The RNA sequencing data of 309 cervical squamous cell carcinoma (CSCC) cases, including data of individuals with available clinical information, were obtained from The Cancer Genome Atlas (TCGA) database. We included 181 early-stage CSCC tumor samples with clinical survival and prognosis information (training dataset). Then, we downloaded the GSE44001 gene expression profile data from the National Center for Biotechnology Information Gene Expression Omnibus (validation dataset). Gene ontology annotation and the Kyoto Encyclopedia of Genes and Genomes pathway analyses were used to analyze the biological functions of differentially expressed immune-related genes (DEIRGs). We established protein–protein interactions and competing endogenous RNA networks using Cytoscape. Using the Kaplan–Meier method, we evaluated the association between the high- and low-risk groups and the actual survival and prognosis information. Our univariate and multivariate Cox regression analyses screened for independent prognostic factors. Results We identified seven prognosis-related signature genes (RBAKDN, CXCL2, ZAP70, CLEC2D, CD27, KLRB1, VCAM1), the expression of which was markedly associated with overall survival (OS) in CSCC patients. Also, the risk score of the seven-gene signature discripted superior ability to categorize CSCC patients into high-risk and low-risk groups, with a observablydifferent OS in the training and validation datasets. We screened two independent prognostic factors (Pathologic N and prognostic score model status) that correlated significantly by univariate and multivariate Cox regression analyses in the TCGA dataset. To further explore the potential mechanism of immune-related genes, we observed associated essential high-risk genes with a cytokine–cytokine receptor interaction. Conclusions This study established an immune-related RNA signature, which provided a reliable prognostic tool and may be of great significance for determining immune-related biomarkers in CSCC.

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

Hereditas ◽

10.1186/s41065-021-00181-1 ◽

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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Expression and Prognosis Analysis of SUMOylation Regulators in Oral Squamous Cell Carcinoma Based on High-Throughput Sequencing

Frontiers in Genetics ◽

10.3389/fgene.2021.671392 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yutong Meng ◽

Xiaozhi Li

Keyword(s):

Squamous Cell Carcinoma ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Survival Data ◽

Prognostic Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Wilcoxon Test ◽

Model Based

IntroductionOral squamous cell carcinoma (OSCC) originates from oral mucosal epithelial cells, accounting for more than 90% of oral cancers. The relationship between the expression and prognostic role of SUMOylation regulators in OSCC is rarely studied.Materials and methodsThe expression and survival data of OSCC were derived from TCGA and GEO databases. Wilcoxon test was used to determine the differential expression of the SUMOylation regulators. A prognostic model based on SUMOylation regulator-related genes was constructed by Cox regression. Gene set enrichment analysis was applied to predict the potential biological functions that the genes might be involved in.ResultsRANBP2 and SENP6 had the highest SNV frequency. Eleven genes including PIAS3, RANBP2, USPL1, SENP6, SENP2, SENP5, SAE1, UBA2, PIAS4, UBE2I, and SENP3 were highly expressed in OSCC. The prognostic model based on nine SUMOylation-regulated genes (TRIM37, UFM1, FUBP1, CCNT1, FXR1, HMG20A, RANBP3, SPATA5, and DDX23) had a strong ability to predict the prognosis of OSCC.ConclusionThis study might provide targets for prognostic evaluation and targeted therapy of patients with OSCC.

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