Comprehensive Analysis to Identify SPP1 as a Prognostic Biomarker in Cervical Cancer

Background:SPP1, secreted phosphoprotein 1, is a member of the small integrin-binding ligand N-linked glycoprotein (SIBLING) family. Previous studies have proven SPP1 overexpressed in a variety of cancers and can be identified as a prognostic factor, while no study has explored the function and carcinogenic mechanism of SPP1 in cervical cancer.Methods: We aimed to demonstrate the relationship between SPP1 expression and pan-cancer using The Cancer Genome Atlas (TCGA) database. Next, we validated SPP1 expression of cervical cancer in the Gene Expression Omnibus (GEO) database, including GSE7803, GSE63514, and GSE9750. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of SPP1 as a differentiating factor by the area under curve (AUC) score. Cox regression and logistic regression were performed to evaluate factors associated with prognosis. The SPP1-binding protein network was built by the STRING tool. Enrichment analysis by the R package clusterProfiler was used to explore potential function of SPP1. The single-sample GSEA (ssGSEA) method from the R package GSVA and TIMER database were used to investigate the association between the immune infiltration level and SPP1 expression in cervical cancer.Results: Pan-cancer data analysis showed that SPP1 expression was higher in most cancer types, including cervical cancer, and we got the same result in the GEO database. The ROC curve suggested that SPP1 could be a potential diagnostic biomarker (AUC = 0.877). High SPP1 expression was associated with poorer overall survival (OS) (P = 0.032). Further enrichment and immune infiltration analysis revealed that high SPP1 expression was correlated with regulating the infiltration level of neutrophil cells and some immune cell types, including macrophage and DC.Conclusion:SPP1 expression was higher in cervical cancer tissues than in normal cervical epithelial tissues. It was significantly associated with poor prognosis and immune cell infiltration. Thus, SPP1 may become a promising prognostic biomarker for cervical cancer patients.

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INHBA is a Prognostic Biomarker and Correlated With Immune Cell Infiltration in Cervical Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.705512 ◽

2022 ◽

Vol 12 ◽

Author(s):

Kaidi Zhao ◽

Yuexiong Yi ◽

Zhou Ma ◽

Wei Zhang

Keyword(s):

Cervical Cancer ◽

Immune Cell ◽

Enrichment Analysis ◽

R Package ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Cancer Data ◽

Pan Cancer ◽

Geo Database

Background: Inhibin A (INHBA), a member of the TGF-β superfamily, has been shown to be differentially expressed in various cancer types and is associated with prognosis. However, its role in cervical cancer remains unclear.Methods: We aimed to demonstrate the relationship between INHBA expression and pan-cancer using The Cancer Genome Atlas (TCGA) database. Next, we validated INHBA expression in cervical cancer using the Gene Expression Omnibus (GEO) database, including GSE7803, GSE63514, and GSE9750 datasets. Enrichment analysis of INHBA was performed using the R package “clusterProfiler.” We analyzed the association between immune infiltration level and INHBA expression in cervical cancer using the single-sample gene set enrichment analysis (ssGSEA) method by the R package GSVA. We explored the association between INHBA expression and prognosis using the R package “survival”.Results: Pan-cancer data analysis showed that INHBA expression was elevated in 19 tumor types, including cervical cancer. We further confirmed that INHBA expression was higher in cervical cancer samples from GEO database and cervical cancer cell lines than in normal cervical cells. Survival prognosis analysis indicated that higher INHBA expression was significantly associated with reduced Overall Survival (p = 0.001), disease Specific Survival (p = 0.006), and Progression Free Interval (p = 0.001) in cervical cancer and poorer prognosis in other tumors. GSEA and infiltration analysis showed that INHBA expression was significantly associated with tumor progression and some types of immune infiltrating cells.Conclusion:INHBA was highly expressed in cervical cancer and was significantly associated with poor prognosis. Meanwhile, it was correlated with immune cell infiltration and could be used as a promising prognostic target for cervical cancer.

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Development of a Novel Immune Infiltration-Based Gene Signature to Predict Prognosis and Immunotherapy Response of Patients With Cervical Cancer

Frontiers in Immunology ◽

10.3389/fimmu.2021.709493 ◽

2021 ◽

Vol 12 ◽

Author(s):

Sihui Yu ◽

Xi Li ◽

Jiawen Zhang ◽

Sufang Wu

Keyword(s):

Cervical Cancer ◽

Immune Cell ◽

Cox Regression ◽

Patient Survival ◽

Checkpoint Inhibitors ◽

Cell Types ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cervical Lesions ◽

Immune Infiltration

Predictive models could indicate the clinical outcome of patients with carcinoma. Cervical cancer is one of the most frequently diagnosed female malignancies. Herein, we proposed an immune infiltration-related gene signature that predicts prognosis of patients with cervical cancer and depicts the immune landscape as well. We utilized the transcriptome data of The Cancer Genome Atlas (TCGA) and estimated the infiltration level of 28 immune cell types. We screened out four immune cell types conducive to patient survival and recognized their shared differentially expressed genes (DEGs). Four core genes (CHIT1, GTSF1L, PLA2G2D, and GNG8) that composed the ultimate signature were identified via univariate and multivariate Cox regression. The optimal model we built up could distinguish patients with cervical cancer into high-score and low-score subgroups. These two subgroups showed disparity in aspects of patient survival, immune infiltration landscape, and response to immune checkpoint inhibitors. Additionally, we found that GTSF1L was decreased gradually along with the severity of cervical lesions, and its potential role in immune contexture and clinical practice were also demonstrated. Our results suggested that the Immunoscore based on four immune-related genes could serve as a supplementary criterion to effectively foresee the survival outcome, tumor infiltration status, and immunotherapy efficacy of cervical cancer patients.

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Identification of three molecular subtypes based on immune infiltration in ovarian cancer and its prognostic value

Bioscience Reports ◽

10.1042/bsr20201431 ◽

2020 ◽

Vol 40 (10) ◽

Author(s):

Juan Liu ◽

Zongjian Tan ◽

Jun He ◽

Tingting Jin ◽

Yuanyuan Han ◽

...

Keyword(s):

Ovarian Cancer ◽

Drug Targets ◽

Immune Cell ◽

Cox Regression ◽

Molecular Subtypes ◽

Molecular Classification ◽

R Package ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Immune Infiltration

Abstract Background: Increasing studies suggest that tumor immune infiltration is a relative factor of prognosis in ovarian cancer (OvCa). The present study explored the composition of tumor-infiltrating immune cells (TIICs) in OvCa using CIBERSORT algorithm and further assessed their values for prognosis and therapeutic strategies by molecular subtypes. Methods: Publicly available databases including The Cancer Genome Atlas (TCGA) and GTEx were searched. Ovarian tumor samples were available from TCGA, and normal ovarian samples were obtained from the GTEx dataset. The relative proportions of immune cell profiling in OvCa and normal samples were evaluated by CIBERSORT algorithm. Association between each immune cell subtype and survival was inferred by the fractions of 22 immune cell types. “CancerSubtypes” R-package was employed to identify the three types of molecular classification and analyze the functional enrichment in each subclass. Response to immunotherapy and anticancer drug targets was predicted via TIDE algorithm and GDSC dataset. Results: Substantial variation reflecting individual difference was identified between cancer and normal tissues in the immune infiltration profiles. T cells CD4 memory activated, macrophages M1 were associated with improved overall survival (OS) as evaluated by univariate Cox regression and multivariate Cox. Three subtypes were identified by ´CancerSubtypes’ R-package and every sub-cluster possessed specific immune cell characterization. Meanwhile, Cluster II exhibited poor prognosis and sensitive response to immunotherapy. Conclusions: The cellular component of immune infiltration shows remarkable variation in OvCa. Profiling of immune infiltration is useful in prediction of prognosis of OvCa. The results from profiling might be considered in therapeutic modulation.

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Development and Validation of a Novel M6A-related LncRNA Prognostic Signature Associated with Immune Microenvironment for Ovarian Serous Cystadenocarcinoma

10.21203/rs.3.rs-1120501/v1 ◽

2021 ◽

Author(s):

Rui Geng ◽

Tian Chen ◽

Zihang Zhong ◽

Senmiao Ni ◽

Jianling Bai ◽

...

Keyword(s):

Immune Modulation ◽

Cox Regression ◽

Great Influence ◽

Clinical Samples ◽

Consensus Clustering ◽

Prognostic Signature ◽

Cancer Stemness ◽

Gynecological Malignancy ◽

Pan Cancer ◽

Geo Database

Abstract Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have great influence on OV development and patients' immunotherapy response. Here, we decided to establish a reliable signature in the light of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis in the light of TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model in the light of the mRLs. K-M analysis, PCA, GSEA, and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune features comparison, consensus clustering analysis, and Pan-cancer analysis.Results: A prognostic signature comprising four mRLs: WAC-AS1, LINC00997, DNM3OS, and FOXN3-AS1, was constructed and verified for OV according to TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, people can identify patients more effectively. All the sample were assigned into two clusters, and the clusters had different overall survival, clinical features, and tumor microenvironment. Finally, Pan-cancer analysis further demonstrated the four mRLs significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusion: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.

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Prognostic and predictive value of FCER1G in glioma outcomes and response to immunotherapy

10.21203/rs.3.rs-98505/v2 ◽

2021 ◽

Author(s):

Houshi Xu ◽

Qingwei Zhu ◽

Lan Tang ◽

Junkun Jiang ◽

Huiwen Yuan ◽

...

Keyword(s):

T Cell Activation ◽

Predictive Value ◽

Cell Activation ◽

Immune Cell ◽

Cox Regression ◽

Glioma Patient ◽

Clinical Prognosis ◽

Multiple Factors ◽

Dismal Prognosis ◽

Geo Database

Abstract Purpose: Glioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma. Methods: The expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups.Results: FCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95% CI is 0.54 to 0.79 (P <0.001), whereas age (HR=1.26, 95% CI=1.04-1.52), grade (HR=2.75, 95% CI=2.06-3.68), tumor recurrence (HR=2.17, 95% CI=1.81-2.62), IDH mutant (HR=2.46, 95% CI=1.97-3.01) and chemotherapeutic status (HR=1.4, 95% CI=1.20-1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients.Conclusions: This study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy.

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Expression profiling analysis of autophagy-related genes in cervical cancer

10.21203/rs.3.rs-595676/v1 ◽

2021 ◽

Author(s):

Zhiyuan Huang ◽

He Wang ◽

Min Liu ◽

Xinrui Li ◽

Lei Zhu ◽

...

Keyword(s):

Risk Factors ◽

Cervical Cancer ◽

Overall Survival ◽

Risk Score ◽

Roc Curve ◽

Normal Tissue ◽

Cox Regression ◽

Risk Group ◽

Differentially Expressed ◽

Tissue Samples

Abstract Background: It has been demonstrated by studies globally that autophagy took part in the development of cervical cancer (CC). Few studies concentrated on the correlation between overall survival and CC patients. We retrieved significant autophagy-related genes (ARGs) correlated to the process of cervical cancer. They may be used as prognosis marker or treatment target for clinical application.Methods: Expressions level of genes in cervical cancer and normal tissue samples were obtained from GTEx and TCGA database. Autophagy-related genes (ARGs) were retrieved accroding to the gene list from HaDB. Differentially expressed autophagy related genes (DE-ARGs) related to cervical cancer were identified by Wilcoxon signed-rank test. ClusterProfiler package worked in R software was used to perform GO and KEGG enrichment analyses. Univariate propotional hazard cox regression and multivariate propotional hazard cox regressions were applied to identify DE-ARGs equipped with prognostic value and other clinical independent risk factors. ROC curve was drawn for comparing the survival predict feasibility of risk score with other risk factors in CC patients. Nomogram was drawn to exhibit the prediction model constructed accroding to multivariate cox regression. Correlations between Differentially expressed autophagy related genes (DE-ARGs) and other clinical features were investigated by t test or Cruskal wallis analysis. Correlation between Immune and autophagy in cervical cancer was investigated by ssGSEA and TIMER database. Results: Fifty-six differentially expressed ARGs (DE-ARGs) were retrieved from cervical cancer tissue and normal tissue samples. GO enrichment analysis showed that these ARGs involved in autophagy, ubiquitination of protein and apoptosis. Cox regression medel showed that there were six ARGs significantly associated with overall survival of cervical caner patients. VAMP7 (HR = 0.599, P= 0.033) and TP73 (HR = 0.671, P= 0.014) played protective roles in survival among these six genes. Stage (Stage IV vs Stage I HR = 3.985, P<0.001) and risk score (HR = 1.353, P< 0.001) were sorted as independent prognostic risk factors based on multivariate cox regression. ROC curve validated that risk score was preferable to predict survival of CC patients than other risk factors. Additionally, we found some of these six predictor ARGs were correlated significantly in statistic with tumor grade or stage, clinical T stage, clinical N stage, pathology or risk score (all P< 0.05). The immune cells and immune functions showed a lower activity in high risk group than low risk group which is distincted by median risk score. Conclusion: Our discovery showed that autophagy genes involved in the progress of cervical cancer. Many autophagy-related genes could probably serve as prognostic biomarkers and accelerate the discovery of treatment targets for CC patients.

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Bioinformatic Analysis: The Role of LINC00634 in Colorectal Carcinoma

10.21203/rs.3.rs-801188/v1 ◽

2021 ◽

Author(s):

Fang Liu ◽

Fengyihuan Fu ◽

Yuqiang Nie

Keyword(s):

Esophageal Cancer ◽

Regression Analysis ◽

Prognostic Factors ◽

Colorectal Carcinoma ◽

Roc Curve ◽

Cox Regression ◽

Survival Outcomes ◽

Immune Infiltration ◽

The Relationship

Abstract Background: LINC00634 is highly expressed in esophageal cancer, and its depletion can suppress the viability and induce the apoptosis of esophageal cancer cells. However, there is a lack of studies that examine the relationship between LINC00634 expression and the clinicopathological features, survival outcomes, prognostic factors and tumor immune cell infiltration of colorectal carcinoma (CRC) patients.Objective: We aim at investigating the role of LINC00634 in colorectal carcinoma.Methods: We obtained data from the TCGA (The Cancer Genome Atlas) public database, GTEx (Genotype-Tissue Expression) database and clinical samples. Wilcoxon rank-sum test, Kruskal-Wallis test and logistic regression analysis were employed to assess the relationship between LINC00634 expression and the clinicopathological characteristics of CRC patients. Receiver operating characteristic (ROC) curve was constructed to evaluate the ability of LINC00634 for distinguishing between CRC patients and normal subjects based on the area under the curve (AUC) score. Univariate and multivariate analyses were conducted to evaluate the association between prognostic factors and survival outcomes. Kaplan-Meier curves and Cox regression analysis were employed to determine the contribution of LINC00634 expression to the prognosis of colorectal carcinoma patients. Immune infiltration analysis and Gene Set Enrichment Analysis (GSEA) were conducted to identify the significantly involved functions of LINC00634. Finally, a nomogram was constructed for internal verification based on the Cox regression data.Results: The expression of LINC00634 was upregulated in CRC patients, and markedly associated with N stage, residual tumor, pathological stage, and overall survival (OS) event. ROC curve showed that LINC00634 had strong diagnostic and prognostic abilities (AUC=0.74). The high expression of LINC00634 could predict poor disease specific survival (DSS; P=0.008) and poor overroll survival (OS;P<0.01). The expression of LINC00634 was independently associated with OS in CRC patients (P=0.019). GSEA and immune infiltration analysis demonstrated that LINC00634 expression was involved in gene transcription, epigenetic regulation and the functions of certain types of immune infiltrating cells. The c-index of the nomogram was 0.772 (95％CI: 0.744-0.799).Conclusions: Our study reveals that LINC00634 can serve as a potential prognostic biomarker for CRC patients.

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Identification of the Pyroptosis-related Signature for Predicting Prognosis in Cervical Cancer

10.21203/rs.3.rs-870182/v1 ◽

2021 ◽

Author(s):

Cankun Zhou ◽

Chaomei Li ◽

Yuhua Zheng ◽

Xiaochun Liu

Keyword(s):

Cervical Cancer ◽

Tumor Microenvironment ◽

Cox Regression ◽

Experimental Studies ◽

Risk Groups ◽

Core Gene ◽

Low Risk ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Infiltration

Abstract Background: Cervical cancer (CC) is one of the most common malignancies in gynecology. There is still a lack of specific biomarkers for the diagnosis and prognosis of CC. Pyroptosis is one of the methods of programmed cell death, and its various components are related to the occurrence, invasion, and metastasis of tumors. However, the role of pyroptosis in CC has not yet been elucidated.Methods: This study focuses on the development of a prognostic signature associated with pyroptosis for CC patients using integrated bioinformatics to elucidate the relationship between the signature and the tumor microenvironment and immune response.Results: We identified a prognostic signature based on eight pyroptosis-related genes (PRGs), with better prognostic survival in the low-risk group (P<0.05) and AUC values greater than 0.7. The results of the multi-factor Cox regression analysis indicated that the signature could be used as an independent prognostic factor, and both the DCA and the Nomogram suggested that the prognostic signature had good predictive power. Interestingly, this prognostic signature can also be applied to multiple tumors. In addition, the tumor microenvironment and immune infiltration status were significantly different between high and low-risk groups (P<0. 05). The core gene GZMB was screened and the CC-associated GZMB/ miR-378a/TRIM52-AS1 regulatory axis was constructed.Conclusion: The study successfully established the prognostic signature based on eight PRGs and reflected their tumor microenvironment and immune infiltration. The GZMB/ miR-378a/TRIM52-AS1 regulatory axis may play an important regulatory role in the development of CC, and further experimental studies are needed to validate these results subsequently.

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SLC2A5 Correlated with Immune Infiltration: A Candidate Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma

Journal of Immunology Research ◽

10.1155/2021/9938397 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Lianxiang Luo ◽

Jiating Su ◽

Yushi Zheng ◽

Fangfang Huang ◽

Riming Huang ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Immune Cell ◽

Prognostic Biomarker ◽

Clinical Feature ◽

Lung Cancer Patients ◽

Immune Infiltration ◽

Kaplan Meier ◽

Tumor Tissues ◽

Kaplan Meier Plotter

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer with a relatively poor prognosis, requiring novel therapeutic approaches. Great advances in new immunotherapy strategies have shown encouraging results in lung cancer patients. This study is aimed at elucidating the function of SLC2A5 in the prognosis and pathogenesis of LUAD by analyzing public databases. The differential expression of SLC2A5 in various tissues from Oncomine, GEPIA, and other databases was obtained, and SLC2A5 expression at the protein level in normal and tumor tissues was detected with the use of the HPA database. Then, we used the UALCAN database to analyze the expression of SLC2A5 in different clinical feature subgroups. Notably, in both PrognoScan and Kaplan-Meier plotter databases, we found a certain association between SLC2A5 and poor OS outcomes in LUAD patients. Studies based on the TIMER database show a strong correlation between SLC2A5 expression and various immune cell infiltrates and markers. The data analysis in the UALCAN database showed that the decreased promoter methylation level of SLC2A5 in LUAD may lead to the high expression of SLC2A5. Finally, we used the LinkedOmics database to evaluate the SLC2A5-related coexpression and functional networks in LUAD and to investigate their role in tumor immunity. These findings suggest that SLC2A5 correlated with immune infiltration can be used as a candidate diagnostic and prognostic biomarker in LUAD patients.

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Development and validation of an immune-related prognostic signature in cervical cancer

10.21203/rs.3.rs-227250/v1 ◽

2021 ◽

Author(s):

Rongjia Su ◽

Chengwen Jin ◽

Hualei Bu ◽

Xiaoyun Wang ◽

Menghua Kuang ◽

...

Keyword(s):

Cervical Cancer ◽

Cox Regression ◽

Clinical Information ◽

Risk Groups ◽

Gene Signature ◽

Cancer Prognosis ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Gynecological Malignancy ◽

Immune Infiltration

Abstract Background Cervical cancer is the fourth most frequently gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods Raw data and clinical information of cervical cancer samples were download from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results There were 8 immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing PD-L1 expression and PD-1 checkpoint pathway differences between high risk and low risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusion The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

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