A Tumor-Infiltrating Lymphocyte-Based Risk Model Predicts Prognosis of Colon Cancer

Abstract Tumor-infiltrating lymphocytes are relevant to the tumor prognosis and response to immunotherapy in colon cancer. The gene expression data of colon cancer was obtained from the cancer genome atlas (TCGA) database and the components of immune cell types were analyzed by CIBERSORT. Selection operator (LASSO) and multivariate Cox regression filtered immune cells and selected the most significant cell types to construct an immune risk model including memory B cells, plasma cells, T follicular helper (Tfh) cells, M0 macrophages and resting dendritic cells. Receiver operating characteristic (ROC) curves were used to verify the sensitivity and specificity of the model, which was validated in Gene Expression Omnibus (GEO) dataset. Combined with the clinical traits, a nomogram was established to predict the prognosis of colon cancer. According to function analyses through weighted correlation network analysis (WGCNA) and gene set enrichment analysis (GSEA), tumor-infiltrating immune cells showed significant importance in tumor immune-associated regulation, especially the adhesion, migration and invasion in colon cancer.

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SERPINE1 associated with remodeling of the tumor microenvironment in colon cancer progression: a novel therapeutic target

BMC Cancer ◽

10.1186/s12885-021-08536-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shaokun Wang ◽

Li Pang ◽

Zuolong Liu ◽

Xiangwei Meng

Keyword(s):

Colorectal Cancer ◽

Colon Cancer ◽

Immune Cells ◽

Immune Cell ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

The Relationship

Abstract Background The change of immune cell infiltration essentially influences the process of colorectal cancer development. The infiltration of immune cells can be regulated by a variety of genes. Thus, modeling the immune microenvironment of colorectal cancer by analyzing the genes involved can be more conducive to the in-depth understanding of carcinogenesis and the progression thereof. Methods In this study, the number of stromal and immune cells in malignant tumor tissues were first estimated by using expression data (ESTIMATE) and cell-type identification with relative subsets of known RNA transcripts (CIBERSORT) to calculate the proportion of infiltrating immune cell and stromal components of colon cancer samples from the Cancer Genome Atlas database. Then the relationship between the TMN Classification and prognosis of malignant tumors was evaluated. Results By investigating differentially expressed genes using COX regression and protein-protein interaction network (PPI), the candidate hub gene serine protease inhibitor family E member 1 (SERPINE1) was found to be associated with immune cell infiltration. Gene Set Enrichment Analysis (GSEA) further projected the potential pathways with elevated SERPINE1 expression to carcinogenesis and immunity. CIBERSORT was subsequently utilized to investigate the relationship between the expression differences of SERPINE1 and immune cell infiltration and to identify eight immune cells associated with SERPINE1 expression. Conclusion We found that SERPINE1 plays a role in the remodeling of the colon cancer microenvironment and the infiltration of immune cells.

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RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.650499 ◽

2022 ◽

Vol 12 ◽

Author(s):

Lan-Xin Mu ◽

You-Cheng Shao ◽

Lei Wei ◽

Fang-Fang Chen ◽

Jing-Wei Zhang

Keyword(s):

Breast Cancer ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Impact ◽

Immune Microenvironment ◽

Model Based ◽

Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

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Comprehensive analysis of ceRNA networks reveals prognostic lncRNAs related to immune infiltration in colorectal cancer

BMC Cancer ◽

10.1186/s12885-021-07995-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jingyi Chen ◽

Yuxuan Song ◽

Mei Li ◽

Yu Zhang ◽

Tingru Lin ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Target Genes ◽

Immune Cell ◽

Pearson Correlation ◽

Enrichment Analysis ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cerna Network

Abstract Background Competing endogenous RNA (ceRNA) represents a class of RNAs (e.g., long noncoding RNAs [lncRNAs]) with microRNA (miRNA) binding sites, which can competitively bind miRNA and inhibit its regulation of target genes. Increasing evidence has underscored the involvement of dysregulated ceRNA networks in the occurrence and progression of colorectal cancer (CRC). The purpose of this study was to construct a ceRNA network related to the prognosis of CRC and further explore the potential mechanisms that affect this prognosis. Methods RNA-Seq and miRNA-Seq data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed lncRNAs (DElncRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs), and a prognosis-related ceRNA network was constructed based on DElncRNA survival analysis. Subsequently, pathway enrichment, Pearson correlation, and Gene Set Enrichment Analysis (GSEA) were performed to determine the function of the genes in the ceRNA network. Gene Expression Profiling Interactive Analysis (GEPIA) and immunohistochemistry (IHC) were also used to validate differential gene expression. Finally, the correlation between lncRNA and immune cell infiltration in the tumor microenvironment was evaluated based on the CIBERSORT algorithm. Results A prognostic ceRNA network was constructed with eleven key survival-related DElncRNAs (MIR4435-2HG, NKILA, AFAP1-AS1, ELFN1-AS1, AC005520.2, AC245884.8, AL354836.1, AL355987.4, AL591845.1, LINC02038, and AC104823.1), 54 DEmiRNAs, and 308 DEmRNAs. The MIR4435-2HG- and ELFN1-AS1-associated ceRNA subnetworks affected and regulated the expression of the COL5A2, LOX, OSBPL3, PLAU, VCAN, SRM, and E2F1 target genes and were found to be related to prognosis and tumor-infiltrating immune cell types. Conclusions MIR4435-2HG and ELFN1-AS1 are associated with prognosis and tumor-infiltrating immune cell types and could represent potential prognostic biomarkers or therapeutic targets in colorectal carcinoma.

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Identification and Validation of a Novel Immune-Related Four-lncRNA Signature for Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.639254 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jixin Wang ◽

Xiangjun Yin ◽

Yin-Qiang Zhang ◽

Xuming Ji

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Immune Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Kaplan Meier

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, the prognosis of patients with which is associated with both lncRNAs and cancer immunity. In this study, we collected gene expression data of 585 LUAD patients from The Cancer Genome Atlas (TCGA) database and 605 subjects from the Gene Expression Omnibus (GEO) database. LUAD patients were divided into high and low immune-cell-infiltrated groups according to the single sample gene set enrichment analysis (ssGSEA) algorithm to identify differentially expressed genes (DEGs). Based on the 49 immune-related DE lncRNAs, a four-lncRNA prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) regression, univariate Cox regression, and stepwise multivariate Cox regression in sequence. Kaplan–Meier curve, ROC analysis, and the testing GEO datasets verified the effectiveness of the signature in predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression suggested that the signature was an independent prognostic factor. The correlation analysis revealed that the infiltration immune cell subtypes were related to these lncRNAs.

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Multiomic analysis of the function of SPOCK1 across cancers: an integrated bioinformatics approach

Journal of International Medical Research ◽

10.1177/0300060520962659 ◽

2021 ◽

Vol 49 (6) ◽

pp. 030006052096265

Author(s):

Jie Han ◽

Yihui Rong ◽

Xudong Gao

Keyword(s):

Gene Expression ◽

Cancer Progression ◽

Immune Cells ◽

Immune Cell ◽

Cancer Cell Line ◽

Enrichment Analysis ◽

Tissue Expression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Normal Tissues

Objective To investigate SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 ( SPOCK1) gene expression across The Cancer Genome Atlas (TCGA) cancers, both in cancer versus normal tissues and in different stages across the cancer types. Methods This integrated bioinformatics study used data from several bioinformatics databases (Cancer Cell Line Encyclopedia, Genotype-Tissue Expression, TCGA, Tumor Immune Estimation Resource [TIMER]) to define the expression pattern of the SPOCK1 gene. A survival analysis was undertaken across the cancers. The search tool for retrieval of interacting genes (STRING) database was used to identify proteins that interacted with SPOCK1. Gene Set Enrichment Analysis was conducted to determine pathway enrichment. The TIMER database was used to explore the correlation between SPOCK1 and immune cell infiltration. Results This multiomic analysis showed that the SPOCK1 gene was expressed differently between normal tissues and tumours in several cancers and that it was involved in cancer progression. The overexpression of the SPOCK1 gene was associated with poor clinical outcomes. Analysis of gene expression and tumour-infiltrating immune cells showed that SPOCK1 correlated with several immune cells across cancers. Conclusions This research showed that SPOCK1 might serve as a new target for several cancer therapies in the future.

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Construction of Prognostic Risk Model of 5-Methylcytosine-Related Long Non-Coding RNAs and Evaluation of the Characteristics of Tumor-Infiltrating Immune Cells in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.748279 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhidong Huang ◽

Junjing Li ◽

Jialin Chen ◽

Debo Chen

Keyword(s):

Breast Cancer ◽

Immune Cells ◽

Prognostic Value ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Non Coding Rnas

Purpose: The role of 5-methylcytosine-related long non-coding RNAs (m5C-lncRNAs) in breast cancer (BC) remains unclear. Here, we aimed to investigate the prognostic value, gene expression characteristics, and correlation between m5C-lncRNA risk model and tumor immune cell infiltration in BC.Methods: The expression matrix of m5C-lncRNAs in BC was obtained from The Cancer Genome Atlas database, and the lncRNAs were analyzed using differential expression analysis as well as univariate and multivariate Cox regression analysis to eventually obtain BC-specific m5C-lncRNAs. A risk model was developed based on three lncRNAs using multivariate Cox regression and the prognostic value, accuracy, as well as reliability were verified. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment of the risk model. CIBERSORT algorithm and correlation analysis were used to explore the characteristics of the BC tumor-infiltrating immune cells. Finally, reverse transcription-quantitative polymerase chain reaction was performed to detect the expression level of three lncRNA in clinical samples.Results: A total of 334 differential m5C-lncRNAs were identified, and three BC-specific m5C-lncRNAs were selected, namely AP005131.2, AL121832.2, and LINC01152. Based on these three lncRNAs, a highly reliable and specific risk model was constructed, which was proven to be closely related to the prognosis of patients with BC. Therefore, a nomogram based on the risk score was built to assist clinical decisions. GSEA revealed that the risk model was significantly enriched in metabolism-related pathways and was associated with tumor immune cell infiltration based on the analysis with the CIBERSORT algorithm.Conclusion: The efficient risk model based on m5C-lncRNAs associated with cancer metabolism and tumor immune cell infiltration could predict the survival prognosis of patients, and AP005131.2, AL121832.2, and LINC01152 could be novel biomarkers and therapeutic targets for BC.

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DNM1: A Prognostic Biomarker Associated with Immune Infiltration in Colon Cancer—A Study Based on TCGA Database

BioMed Research International ◽

10.1155/2021/4896106 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Mingchao Hu ◽

Jianchun Gu ◽

Wenzhao Su ◽

Zhenjie Zhang ◽

Baosong Zhu ◽

...

Keyword(s):

Colon Cancer ◽

Immune Cell ◽

Cox Regression ◽

Univariate Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Receptor Interaction ◽

Regression Analyses ◽

Normal Tissues

Aim. The aim of our work was to determine the utility of DNM1 as a biomarker for the diagnosis and prognosis of colon cancer (CC). Methods. DNM1 expression variations in CC vs. normal tissues were investigated using The Cancer Genome Atlas (TCGA) database. The association of DNM1 expression levels with the clinicopathological variables in CC prognosis was investigated using logistic regression analyses. Independent prognostic factors for CC were evaluated using univariate and multivariate Cox regression analyses. The correlation between DNM1 expression and immune cell infiltration was estimated using single-sample Gene Set Enrichment Analysis (ssGSEA). Results. DNM1 expression in CC tissues was significantly higher than that in normal tissues. High DNM1 expression was significantly correlated with M stage, N stage, perineural invasion and lymphatic invasion and predicted poor prognosis. The univariate analysis highlighted that DNM1 was an independent CC risk factor. Results of ssGSEA showed that DNM1 was linked to several cancer-related pathways, including the neuroactive ligand-receptor interaction, hypertrophic cardiomyopathy, ECM-receptor interaction, dilated cardiomyopathy, and calcium signaling pathway. Moreover, DNM1 expression was positively correlated with the level of infiltration by Neutrophils, Tregs, NK cells, and Macrophages. Conclusion. DNM1 has a significant function and has diagnostic and prognostic potential for CC.

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Profiles and clinical significance of immune cell infiltration in pancreatic adenocarcinoma

10.1101/2020.03.30.017327 ◽

2020 ◽

Author(s):

Jie Mei ◽

Rui Xu ◽

Dandan Xia ◽

Xuejing Yang ◽

Huiyu Wang ◽

...

Keyword(s):

Clinical Significance ◽

Immune Cells ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Risk Groups ◽

Cell Types ◽

Biological Processes ◽

Protein Protein Interaction ◽

Kaplan Meier

AbstractBackgroundIt has been well defined that tumor-infiltrating immune cells (TIICs) play critical roles in pancreatic cancer (PAAD) progression. The aim of this research was to comprehensively explore the composition of TIICs in PAAD and their potential clinical significance.Methods178 samples from TCGA and 63 samples from GSE57495 dataset were enrolled into our study. ImmuCellAI was applied to calculate the infiltrating abundance of 24 immune cell types in PAAD and further survival analysis revealed the prognostic values of TIICs in PAAD. Moreover, Gene ontology (GO) enticement analysis of differentially expressed genes (DEGs) between low- and high-risk groups was performed as well.ResultsDifferent kinds of TIICs had distinct infiltrating features. Besides, Specific TIICs subsets had notable prognostic values in PAAD. We further established a 6-TIICs signature to assess the prognosis of PAAD patients. Kaplan-Meier and Cox regression analyses both suggested the significant prognostic value of the signature in PAAD. We next extracted 1,334 DEGs based on the risk model, and the hub modules in the protein-protein interaction (PPI) network of DEGs were involved in regulating immune-related biological processes.ConclusionsOverall, the current study illuminated the immune cells infiltrating landscape in PAAD and developed a TIICs-dependent prognostic signature, which could be used as an effective prognostic classifier for PAAD patients.

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A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02329-9 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Wanting Song ◽

Yi Bai ◽

Jialin Zhu ◽

Fanxin Zeng ◽

Chunmeng Yang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Risk Model ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Critical Function ◽

Mesenchymal Transition ◽

Patient Prognosis

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

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Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.611058 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhuomao Mo ◽

Daiyuan Liu ◽

Dade Rong ◽

Shijun Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Checkpoints ◽

Immune Cell Infiltration ◽

Immunosuppressive Microenvironment

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

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