Integrative genome-scale analysis of immune infiltration in esophageal carcinoma

Bian, Yunyi; Bi, Guoshu; Wei, Tengteng; Yao, Guangyu; Chen, Zhencong; Zhan, Cheng; Fan, Hong

doi:10.1016/j.intimp.2021.107371

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…As previously reported (12)(13)(14), the expression profile of 585 immune cell infiltration related genes was used to calculate the infiltrations of the 24 immune cell populations based on the Gene Set Variation Analysis (GSVA) method using R software (version 4.0.5). A matrix containing the infiltration enrichment scores ranging from -1 to 1 for each immune cell type in every tumor sample was obtained.…”

Section: Calculation and Clustering Of Immune Cell Infiltrationmentioning

confidence: 99%

Identification and Validation of Immune Infiltration Phenotypes in Laryngeal Squamous Cell Carcinoma by Integrative Multi-Omics Analysis

Song

Yang

et al. 2022

Front. Immunol.

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BackgroundLaryngeal squamous cell carcinoma (LSCC) is one of the world’s most common head and neck cancer. However, the immune infiltration phenotypes of LSCC have not been well investigated.MethodsThe multi-omics data of LSCC were obtained from the TCGA (n=111) and GEO (n=57) datasets. The infiltrations of the 24 immune cell populations were calculated using the GSVA method. Then LSCC samples with different immune cell infiltrating patterns were clustered, and the multi-omics differences were investigated.ResultsPatients were clustered into the high-infiltration and low-infiltration groups. The infiltration scores of most immune cells were higher in the high-infiltration group. Patients with high-infiltration phenotype have high N and TNM stages but better survival, as well as less mutated COL11A1 and MUC17. Common targets of immunotherapies such as PD1, PDL1, LAG3, and CTLA4 were significantly up-regulated in the high-infiltration group. The differentially expressed genes were mainly enriched in several immune-related GOs and KEGG pathways. Based on the genes, miRNAs, and lncRNAs differentially expressed in both the TCGA and GEO cohorts, we built a ceRNA network, in which BTN3A1, CCR1, miR-149-5p, and so on, located at the center. A predictive model was also constructed to calculate a patient’s immune infiltration phenotype using 16 genes’ expression values, showing excellent accuracy and specificity in the TCGA and GEO cohorts.ConclusionsIn this study, the immune infiltration phenotypes of LSCC and the corresponding multi-omics differences were explored. Our model might be valuable to predicting immunotherapy’s outcome.

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Section: Calculation and Clustering Of Immune Cell Infiltrationmentioning

confidence: 99%

Identification and Validation of Immune Infiltration Phenotypes in Laryngeal Squamous Cell Carcinoma by Integrative Multi-Omics Analysis

Song

Yang

et al. 2022

Front. Immunol.

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“…Immune Cell Infiltration in the Two Groups. We selected the gene markers reported by Bindea et al according to the previous studies [32][33][34]. A synopsis of genes associated with microenvironment cell sets was constructed precisely, which contained 585 genes depicting 24 tumor microenvironment-(TME-) infiltration cell populations related to innate immunity and adaptive immunity.…”

Section: Differentially Expressed Genes Micrornas (Mirnas)mentioning

confidence: 99%

Identification and Validation of a Proliferation-Associated Score Model Predicting Survival in Lung Adenocarcinomas

Bian

Sui

et al. 2021

Disease Markers

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Aim. This study is aimed at building a risk model based on the genes that significantly altered the proliferation of lung adenocarcinoma cells and exploring the underlying mechanisms. Methods. The data of 60 lung adenocarcinoma cell lines in the Cancer Dependency Map (Depmap) were used to identify the genes whose knockout led to dramatical acceleration or deacceleration of cell proliferation. Then, univariate Cox regression was performed using the survival data of 497 patients with lung adenocarcinoma in The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) model was used to construct a risk prediction score model. Patients with lung adenocarcinoma from TCGA were classified into high- or low-risk groups based on the scores. The differences in clinicopathologic, genomic, and immune characteristics between the two groups were analyzed. The prognosis of the genes in the model was verified with immunohistochemical staining in 100 samples from the Department of Thoracic Surgery, Zhongshan Hospital, and the alteration in the proliferation rate was checked after these genes were knocked down in lung adenocarcinoma cells (A549 and H358). Results. A total of 55 genes were found to be significantly related to survival by combined methods, which were crucial to tumor progression in functional enrichment analysis. A six-gene-based risk prediction score, including the proteasome subunit beta type-6 (PSMB6), the heat shock protein family A member 9 (HSPA9), the deoxyuridine triphosphatase (DUT), the cyclin-dependent kinase 7 (CDK7), the polo-like kinases 1 (PLK1), and the folate receptor beta 2 (FOLR2), was built using the LASSO method. The high-risk group classified with the score model was characterized by poor overall survival (OS), immune infiltration, and relatively higher mutation load. A total of 9864 differentially expressed genes and 138 differentially expressed miRNAs were found between the two groups. Also, a nomogram comparing score model, age, and the stage was built to predict OS for patients with lung adenocarcinoma. Using immunohistochemistry, the expression levels of PSMB6, HSPA9, DUT, CDK7, and PLK1 were found to be higher in lung adenocarcinoma tissues of patients, while the expression of FOLR2 was low, which was consistent with survival prediction. The knockdown of PSMB6 and HSPA9 by siRNA significantly downregulated the proliferation of A549 and H358 cells. Conclusion. The proposed score model may function as a promising risk prediction tool for patients with lung adenocarcinoma and provide insights into the molecular regulation mechanism of lung adenocarcinoma.

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“…Finally, the expression level of the common co-stimulators and immune checkpoint inhibitors ( Bian et al, 2021 ) were compared between the two groups ( Figures 3E,F ). Most of them were positively correlated ( Figure 3G ) and differentially expressed.…”

Section: Resultsmentioning

confidence: 99%

Multi-Omics Analysis of Cancer Cell Lines with High/Low Ferroptosis Scores and Development of a Ferroptosis-Related Model for Multiple Cancer Types

Shan

Zhang

et al. 2021

Front. Cell Dev. Biol.

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Background: Ferroptosis is a newly identified regulated cell death characterized by iron-dependent lipid peroxidation and subsequent membrane oxidative damage, which has been implicated in multiple types of cancers. The multi-omics differences between cancer cell lines with high/low ferroptosis scores remain to be elucidated.Methods and Materials: We used RNA-seq gene expression, gene mutation, miRNA expression, metabolites, copy number variation, and drug sensitivity data of cancer cell lines from DEPMAP to detect multi-omics differences associated with ferroptosis. Based on the gene expression data of cancer cell lines, we performed LASSO-Logistic regression analysis to build a ferroptosis-related model. Lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), esophageal cancer (ESCA), bladder cancer (BLCA), cervical cancer (CESC), and head and neck cancer (HNSC) patients from the TCGA database were used as validation cohorts to test the efficacy of this model.Results: After stratifying the cancer cell lines into high score (HS) and low score (LS) groups according to the median of ferroptosis scores generated by gene set variation analysis, we found that IC50 of 66 agents such as oxaliplatin (p < 0.001) were significantly different, among which 65 were higher in the HS group. 851 genes such as KEAP1 and NRAS were differentially muted between the two groups. Differentially expressed genes, miRNAs and metabolites were also detected—multiple items such as IL17F (logFC = 6.58, p < 0.001) differed between the two groups. Unlike the TCGA data generated by bulk RNA-seq, the gene expression data in DEPMAP are from pure cancer cells, so it could better reflect the traits of tumors in cancer patients. Thus, we built a 15-signature model (AUC = 0.878) based on the gene expression data of cancer cell lines. The validation cohorts demonstrated a higher mutational rate of NFE2L2 and higher expression levels of 12 ferroptosis-related genes in HS groups.Conclusion: This article systemically analyzed multi-omics differences between cancer cell lines with high/low ferroptosis scores and a ferroptosis-related model was developed for multiple cancer types. Our findings could improve our understanding of the role of ferroptosis in cancer and provide new insight into treatment for malignant tumors.

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Integrative genome-scale analysis of immune infiltration in esophageal carcinoma

Cited by 5 publications

References 34 publications

Identification and Validation of Immune Infiltration Phenotypes in Laryngeal Squamous Cell Carcinoma by Integrative Multi-Omics Analysis

Identification and Validation of Immune Infiltration Phenotypes in Laryngeal Squamous Cell Carcinoma by Integrative Multi-Omics Analysis

Identification and Validation of a Proliferation-Associated Score Model Predicting Survival in Lung Adenocarcinomas

Multi-Omics Analysis of Cancer Cell Lines with High/Low Ferroptosis Scores and Development of a Ferroptosis-Related Model for Multiple Cancer Types

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