Immune Infiltration Subtypes Characterization and Identification of Prognosis-Related lncRNAs in Adenocarcinoma of the Esophagogastric Junction

The incidence of adenocarcinoma of the esophagogastric junction (AEG) has markedly increased worldwide. However, the precise etiology of AEG is still unclear, and the therapeutic options thus remain limited. Growing evidence has implicated long non-coding RNAs (lncRNAs) in cancer immunomodulation. This study aimed to examine the tumor immune infiltration status and assess the prognostic value of immune-related lncRNAs in AEG. Using the ESTIMATE method and single-sample GSEA, we first evaluated the infiltration level of 28 immune cell types in AEG samples obtained from the TCGA dataset (N=201). Patients were assigned into high- and low-immune infiltration subtypes based on the immune cell infiltration’s enrichment score. GSEA and mutation pattern analysis revealed that these two immune infiltration subtypes had distinct phenotypes. We identified 1470 differentially expressed lncRNAs in two immune infiltration subtypes. From these differentially expressed lncRNAs, six prognosis-related lncRNAs were selected using the Cox regression analysis. Subsequently, an immune risk signature was constructed based on combining the values of the six prognosis-associated lncRNAs expression levels and multiple regression coefficients. To determine the risk model’s prognostic capability, we performed a series of survival analyses with Kaplan–Meier methods, Cox proportional hazards regression models, and the area under receiver operating characteristic (ROC) curve. The results indicated that the immune-related risk signature could be an independent prognostic factor with a significant predictive value in patients with AEG. Furthermore, the immune-related risk signature can effectively predict the response to immunotherapy and chemotherapy in AEG patients. In conclusion, the proposed immune-related lncRNA prognostic signature is reliable and has high survival predictive value for patients with AEG and is a promising potential biomarker for immunotherapy.

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Identification and Validation of ATF3 Serving as a Potential Biomarker and Correlating With Pharmacotherapy Response and Immune Infiltration Characteristics in Rheumatoid Arthritis

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.761841 ◽

2021 ◽

Vol 8 ◽

Author(s):

Huan Hu ◽

Facai Zhang ◽

Li Li ◽

Jun Liu ◽

Qin Ao ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Differentially Expressed Genes ◽

Immune Cell ◽

Differentially Expressed ◽

Immune Cell Infiltration ◽

Immune Infiltration ◽

Potential Biomarker ◽

Gene Modules ◽

Atf3 Expression

Background: Although disease-modifying antirheumatic drugs (DMARDs) have significantly improved the prognosis of patients with rheumatoid arthritis (RA), approximately 40% of RA patients have limited response. Therefore, it was essential to explore new biomarkers to improve the therapeutic effects on RA. This study aimed to develop a new biomarker and validate it by an in vitro study.Methods: The RNA-seq and the clinicopathologic data of RA patients were downloaded from Gene Expression Omnibus (GEO) databases. Differentially expressed genes were screened in the GPL96 and GPL570 databases. Then, weighted gene co-expression network analysis (WGCNA) was used to explore the most correlated gene modules to normal and RA synovium in the GPL96 and GPL570 databases. After that, the differentially expressed genes were intersected with the correlated gene modules to find the potential biomarkers. The CIBERSORT tool was applied to investigate the relationship between activated transcription factor 3 (ATF3) expression and the immune cell infiltration, and Gene Set Enrichment Analysis (GSEA) was used to investigate the related signaling pathways of differentially expressed genes in the high and low ATF3 groups. Furthermore, the relationships between ATF3 expression and clinical parameters were also explored in the GEO database. Finally, the role of ATF3 was verified by in vitro cell experiments.Results: We intersected the differentially expressed genes and the most correlated gene modules in the GPL570 and GPL96 databases and identified that ATF3 is a significant potential biomarker and correlates with some clinical–pharmacological variables. Immune infiltration analysis showed that activated mast cells had a significant infiltration in the high ATF3 group in the two databases. GSEA showed that metabolism-associated pathways belonged to the high ATF3 groups and that inflammation and immunoregulation pathways were enriched in the low ATF3 group. Finally, we validated that ATF3 could promote the proliferation, migration, and invasion of RA fibroblast-like synoviocyte (FLS) and MH7A. Flow cytometry showed that ATF3 expression could decrease the proportion of apoptotic cells and increase the proportion of S and G2/M phase cells.Conclusion: We successfully identified and validated that ATF3 could serve as a novel biomarker in RA, which correlated with pharmacotherapy response and immune cell infiltration.

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VWCE as a potential biomarker associated with immune infiltrates in breast cancer

Cancer Cell International ◽

10.1186/s12935-021-01955-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qin Huo ◽

Zhenwei Li ◽

Siqi Chen ◽

Juan Wang ◽

Jiaying Li ◽

...

Keyword(s):

Breast Cancer ◽

Immune Cell ◽

Her2 Status ◽

Cancer Tissue ◽

M2 Macrophage ◽

Breast Cancer Patients ◽

Immune Infiltration ◽

Potential Biomarker ◽

Significant Difference ◽

Factor C

Abstract Purpose Von Willebrand Factor C and EGF Domains (VWCE) is an important gene that regulates cell adhesion, migration, and interaction. However, the correlation between VWCE expression and immune infiltrating in breast cancer remain unclear. In this study, we investigated the correlation between VWCE expression and immune infiltration levels in breast cancer. Methods The expression of VWCE was analyzed by the tumor immune estimation resource (TIMER) and DriverDB databases. Furthermore, genes co-expressed with VWCE and gene ontology (GO) enrichment analysis were investigated by the STRING and Enrichr web servers. Also, we performed the single nucleotide variation (SNV), copy number variation (CNV), and pathway activity analysis through GSCALite. Subsequently, the relationship between VWCE expression and tumor immunity was analyzed by TIMER and TISIDB databases, and further verified the results using Quantitative Real-Time PCR (RT-PCR), Western blotting, and immunohistochemistry. Results The results showed that the expression of VWCE mRNA in breast cancer tissue was significantly lower than that in normal tissues. We found that the expression level of VWCE was associated with subtypes, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status of breast cancer patients, but there was no significant difference in the expression of VWCE was found in age and nodal status. Further analyses indicated that VWCE was correlated with the activation or inhibition of multiple oncogenic pathways. Additionally, VWCE expression was negatively correlated with the expression of STAT1 (Th1 marker, r = − 0.12, p = 6e−05), but positively correlated with the expression of MS4A4A (r = 0.28, p = 0). These results suggested that the expression of VWCE was correlated with immune infiltration levels of Th1 and M2 macrophage in breast cancer. Conclusions In our study, VWCE expression was associated with a better prognosis and was immune infiltration in breast cancer. These findings demonstrate that VWCE is a potential prognostic biomarker and correlated with tumor immune cell infiltration, and maybe a promising therapeutic target in breast cancer.

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Profiles of immune infiltration and its relevance to survival outcome in meningiomas

Bioscience Reports ◽

10.1042/bsr20200538 ◽

2020 ◽

Vol 40 (5) ◽

Author(s):

Xiaodong Chen ◽

Fen Tian ◽

Peng Lun ◽

Yugong Feng

Keyword(s):

Immune Cells ◽

Clustering Analysis ◽

Immune Cell ◽

Cox Regression ◽

Cell Types ◽

Survival Outcome ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Online Databases ◽

The Relationship

Abstract Tumor-infiltrating immune cells play a decisive part in prognosis and survival. Until now, previous researches have not made clear about the diversity of cell types involved in the immune response. The objective of this work was to confirm the composition of tumor-infiltrating immune cells and their correlation with prognosis in meningiomas based on a metagene approach (known as CIBERSORT) and online databases. A total of 22 tumor-infiltrating immune cells were detected to determine the relationship between the immune infiltration pattern and survival. The proportion of M2 macrophages was more abundant in 68 samples, reaching more than 36%. Univariate Cox regression analysis displayed that the proportion of dendritic cells was obviously related to prognosis. Hierarchical clustering analysis identified two clusters by the method of within sum of squares errors, which exhibited different infiltrating immune cell composition and survival. To summarize, our results indicated that proportions of tumor-infiltrating immune cells as well as cluster patterns were associated with the prognosis, which offered clinical significance for research of meningiomas.

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Development and validation of a robust immune-related risk signature for hepatocellular carcinoma

10.21203/rs.3.rs-16346/v1 ◽

2020 ◽

Author(s):

Zaoqu Liu ◽

Dechao Jiao ◽

Xueliang Zhou ◽

Yuan Yao ◽

Zhaonan Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Functional Enrichment ◽

Differentially Expressed ◽

Risk Genes ◽

Cox Regression Analysis ◽

Related Risk

Abstract Background: A growing amount of evidence has suggested immune-related genes (IRGs) play a key role in the development of hepatocellular carcinoma (HCC). However, there have been no investigations proposing a reliable prognostic signature in terms of tumor immunology. This study aimed to develop a robust signature based on IRGs in HCC.Methods: A total of 597 HCC patients were enrolled. The TCGA database was utilized for discovery, and the ICGC database was utilized for validation. Multiple algorithms (including univariate Cox, LASSO, and multivariate Cox regression) were performed to identify key prognostic IRGs and establish an immune-related risk signature. Bioinformatics analysis and R soft tools were utilized to annotate underlying biological functions. Results: A total of 1416 differentially expressed mRNAs (DEMs) were screened in the TCGA cohort, of which 90 were differentially expressed IRGs (DEIRGs). Using univariate Cox regression analysis, we identified 33 prognostically relevant DEIRGs. Using LASSO regression and multivariate Cox regression analysis, we extracted 8 optimal DEIRGs (APLN, CDK4, CXCL2, ESR1, IL1RN, PSMD2, SEMA3F, and SPP1) to construct a risk signature with the ability to distinguish cases as having a high or low risk of unfavorable prognosis in the TCGA cohort, and the signature was verified in the ICGC cohort. The signature was prognostically significant in all stratified cohorts and was deemed an independent prognostic factor for HCC. We also built a nomogram with good performance by combining the signature with clinicopathological factors to increase the accuracy of predicting HCC prognosis. By investigating the relationship of the risk score and 8 risk genes from our signature with clinical traits, we found that the aberrant expression of the immune-related risk genes is correlated with the development of HCC. Moreover, the high-risk group was higher than the low-risk group in terms of tumor mutation burden (TMB), immune cell infiltration, and the expression of immune checkpoints (PD-1, PD-L1, and CTLA-4), and functional enrichment analysis indicated the signature enriched an intensive immune phenotype.Conclusions: This study developed a robust immune-related risk signature and built a predictive nomogram that reliably predict overall survival in HCC, which may be helpful for clinical management and personalized immunotherapy decisions.

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Identification of recurrence marker associated with immune infiltration in prostate cancer with radical resection and build prognostic nomogram

BMC Cancer ◽

10.1186/s12885-019-6391-9 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 2

Author(s):

Xin Rui ◽

Siliang Shao ◽

Li Wang ◽

Jiangyong Leng

Keyword(s):

Prostate Cancer ◽

Immune Cells ◽

Prediction Models ◽

Immune Cell ◽

Cox Regression ◽

Cancer Recurrence ◽

Th2 Cells ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Prostate Cancer Recurrence

Abstract Background Some historic breakthroughs have been made in immunotherapy of advanced cancer. However, there is still little research on immunotherapy in prostate cancer. We explored the relationship between immune cell infiltration and prostate cancer recurrence and tried to provide new ideas for the treatment of prostate cancer. Methods Prostate cancer RNA-seq data and clinical information were downloaded from the TCGA database and GEO database. The infiltration of 24 immune cells in tissues was quantified by ssGSEA. Univariate Cox regression analysis was used to screen for immune cell types associated with tumor recurrence, weighted gene co-expression network analysis (WGCNA) and LASSO were used to identify hub genes which regulate prognosis in patients through immune infiltration. Then, the nomogram was constructed based on the hub gene to predict the recurrence of prostate cancer, and the decision curve analysis (DCA) was used to compare the accuracy with the PSA and Gleason prediction models. Result Analysis showed that Th2 cells and Tcm related to prostate cancer recurrence after radical prostatectomy, and they are independent protective factors for recurrence. Through WGCNA and Lasso, we identified that NDUFA13, UQCR11, and USP34 involved in the infiltration of Th2 cells and Tcm in tumor tissues, and the expression of genes is related to the recurrence of patients. Based on the above findings, we constructed a clinical prediction model and mapped a nomogram, which has better sensitivity and specificity for prostate cancer recurrence prediction, and performed better in comparison with PSA and Gleason’s predictions. Conclusion The immune cells Th2 cells and Tcm are associated with recurrence of PCa. Moreover, the genes NDUFA13, UQCR11, and USP34 may affect the recurrence of PCa by affecting the infiltration of Th2 cells and Tcm. Moreover, nomogram can make prediction effectively.

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IL-11RA is a Prognostic Biomarker and Correlated with Immune Infiltration in Lung Adenocarcinoma

10.21203/rs.3.rs-673757/v1 ◽

2021 ◽

Author(s):

Aitao Nai ◽

SHOAIB BASHIR ◽

Ling Jin ◽

Zirui He ◽

Shuwen Zeng ◽

...

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Progression Free Survival ◽

Clinicopathological Parameters ◽

Free Survival ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Potential Biomarker

Abstract Background: Interleukin-11 receptor subunit alpha (IL-11RA) contributes to multiple biological processes in various tumors. However, the role of IL-11RA in Lung adenocarcinoma (LUAD) is still undetermined. The study aims to explore the role of IL-11RA in LUAD via an integrated bioinformatics analysis. Methods: TIMER, GEPIA, TCGA and HPA databases analysis were used to detect IL-11RA expression. UALCAN database was used to analysis the correlation between IL-11RA expression and clinicopathological parameters of LUAD. Kaplan-Meier Plotter, TCGA and GEO databases were used to analysis overall survival (OS) and progression-free survival (PFS) of the LUAD patients. Univariate Cox regression analysis was used to assess the prognostic value of IL-11RA in different clinical characteristics. GSEA, and TIMER were used to investigate the relationship between IL-11RA and immune infiltration.Results: The expression of IL-11RA was down-regulated in LUAD tissues. Furthermore, IL-11RA expression was closely associated with clinical stage, lymph node stage and smoking habits. The patients with lower IL-11RA expression had poorer overall survival (OS) and progression-free survival (PFS). Lower IL-11RA expression was significantly associated with its hypermethylation, and the hypermethylation of CpG site at cg14609668 and cg21504624 was obviously correlated with poorer OS. Then, we found that IL-11RA may play an important role in LUAD progression and immune regulations. Notably, High expression of IL-11RA may suppress the progression of LUAD through inhibiting cell proliferation and immune cell infiltration, especially in B cells, CD4+ T cells, and Dendritic Cell. Conclusions: Decreased IL-11RA expression correlates with poor prognosis and immune infiltration in LUAD. Our work highlights IL-11RA might be a potential biomarker for prognosis and provide a new therapeutic target for LUAD patients.

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Development of an Immune-Related LncRNA Prognostic Signature for Glioma

Frontiers in Genetics ◽

10.3389/fgene.2021.678436 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yudong Cao ◽

Hecheng Zhu ◽

Jun Tan ◽

Wen Yin ◽

Quanwei Zhou ◽

...

Keyword(s):

Regression Analysis ◽

Clinical Outcome ◽

Immune Cell ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Infiltration

IntroductionGlioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management.Materials and methodsSingle-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature.ResultsA total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p < 0.05) in ssGSEA except neutrophils (p = 0.43).ConclusionThe study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.

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Proteomics of the ascitic fluid for the differentiation of advanced ovarian cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.e15510 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. e15510-e15510

Author(s):

Gururao Hariprasad ◽

Roopa Hariprasad ◽

Lalit Kumar ◽

Amit Kaushik ◽

Alagiri Srinivasan

Keyword(s):

Ovarian Cancer ◽

Ascitic Fluid ◽

Predictive Value ◽

Advanced Ovarian Cancer ◽

The Body ◽

Apolipoprotein A1 ◽

Differentially Expressed ◽

Ovarian Cancers ◽

Potential Biomarker ◽

Clinical Scenarios

e15510 Background: Ovarian cancers are classified as primary, if it arises in the ovary and secondary or metastatic, if the origin is from other parts of the body. The clinical manifestations of these cancers in the advanced stage are very similar making it difficult to distinguish clinically, hiostopathologically and radiologicaly. The therapeutics and management of the primary and secondary malignancies are completely different. While, the advanced primary malignancies are treated by cytoreduction followed by chemotherapy, the metastatic tumors are treated mainly with palliative chemotherapy. The prognosis is better for primary than secondary ovarian cancer making their diagnosis very crucial for patient care. Methods: 1D and 2D-gel based proteomic approaches were used to study the differentially expressed proteins in the ascitic fluid of patients (10 primary and 4 secondary) with advanced ovarian cancer. The relative ratios of the protein expression were estimated by densitometric analysis. The bands/spots with more than three fold difference were subjected to in-gel trypsin digestion and identified by mass spectrometric analysis. The differential expression of one of the proteins was further validated by western blot experiments and ELISA. Results: Programmed Cell Death 1-Ligand 2 and apolipoprotein A1 were seen to be up regulated in the advanced primary ovarian cancer while apolipoprotein A4, and chain L, humanized version of the anti-human fas antibody Hfe7a were seen to be up regulated in the metastatic variant. Validation for the expression of apolipoprotein A1 shows that a 61.8ng/ml cut off is ideal to differentiate the primary and secondary advanced ovarian cancers. The assay has 100% sensitivity, 75% specificity, positive predictive value of 90.9%, negative predictive value of 100%, accuracy of 92.85% and a pre-test odds positive of 2.5. Conclusions: Proteomics of ascitic fluid is useful for the differentiation of advanced ovarian cancers. There are proteins which are differentially expressed in the ascitic fluid of patients with primary and secondary ovarian cancer. Apolipoprotein A1 is a potential biomarker that can be used to differentiate the closely mimicking clinical scenarios of advanced ovarian cancer.

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Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer

BioMed Research International ◽

10.1155/2020/3909416 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Huiling Wang ◽

Shuo You ◽

Meng Fang ◽

Qian Fang

Keyword(s):

Breast Cancer ◽

T Cells ◽

Regression Analysis ◽

Clustering Algorithm ◽

Immune Cell ◽

Cox Regression ◽

Therapeutic Potential ◽

Enrichment Analysis ◽

Cox Regression Analysis ◽

Immune Infiltration

Background. Breast cancer (BC) is the most common malignant tumor in women. The immunophenotype of tumor microenvironment (TME) has shown great therapeutic potential in tumor. Method. The transcriptome was obtained from TCGA and GEO data. Immune infiltration was analyzed by single-sample gene set enrichment (ssGSEA). The immune feature was constructed by Cox regression analysis. In addition, the coexpression of differential expression genes (DEGs) was identified. Through enrichment analysis, the function and pathway of module genes were identified. The somatic mutations related to immune characteristics were analyzed by Maftools. By using the consistency clustering algorithm, the molecular subtypes were constructed, and the overall survival time (OS) was predicted. Results. Immune landscape can be divided into low immune infiltration and high immune infiltration. Cox regression analysis identified seven immune cells as protective factors of BC. In the coexpression modules for DEGs of high and low immune infiltration, module 1 was related to T cells and high immune infiltration. In particular, the area under the curve (AUC) value of hub gene SASH3 was the highest, and the correlation with T cells was stronger in the high immune infiltration. Enrichment analysis found that oxidative stress, T cell aggregation, and apoptosis were observed in high immune infiltration. In addition, TP53 was identified as the most important somatic gene mutation related to immune characteristics. Importantly, we also constructed seven immune cell-based breast cancer subtypes to predict OS. Conclusion. We evaluated the immune landscape of BC and constructed the gene characteristics related to the immune landscape. The potential of T cells and SASH3 in immunotherapy of BC was revealed, which may guide the development of new clinical treatment strategies.

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Development of a Novel Immune Infiltration-Related ceRNA Network and Prognostic Model for Sarcoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.652300 ◽

2021 ◽

Vol 9 ◽

Author(s):

Deyao Shi ◽

Shidai Mu ◽

Feifei Pu ◽

Binlong Zhong ◽

Binwu Hu ◽

...

Keyword(s):

Regulatory Network ◽

Immune Cell ◽

Tumor Development ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Immune Infiltration ◽

Cerna Network ◽

Related Risk ◽

Study Gene Expression ◽

Score Model

Due to the rarity and heterogeneity, it is challenging to explore and develop new therapeutic targets for patients with sarcoma. Recently, immune cell infiltration in the tumor microenvironment (TME) was widely studied, which provided a novel potential approach for cancer treatment. The competing endogenous RNA (ceRNA) regulatory network has been reported as a critical molecular mechanism of tumor development. However, the role of the ceRNA regulatory network in the TME of sarcoma remains unclear. In this study, gene expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) sarcoma datasets, and an immune infiltration-related ceRNA network was constructed, which comprised 14 lncRNAs, 13 miRNAs, and 23 mRNAs. Afterward, we constructed an immune infiltration-related risk score model based on the expression of IRF1, MFNG, hsa-miR-940, and hsa-miR-378a-5p, presenting a promising performance in predicting the prognosis of patients with sarcoma.

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