A genomic mutation signature predicts the clinical outcomes of immunotherapy and characterizes immunophenotypes in gastrointestinal cancer

AbstractThe association between genetic variations and immunotherapy benefit has been widely recognized, while such evidence in gastrointestinal cancer remains limited. We analyzed the genomic profile of 227 immunotherapeutic gastrointestinal cancer patients treated with immunotherapy, from the Memorial Sloan Kettering (MSK) Cancer Center cohort. A gastrointestinal immune prognostic signature (GIPS) was constructed using LASSO Cox regression. Based on this signature, patients were classified into two subgroups with distinctive prognoses (p < 0.001). The prognostic value of the GIPS was consistently validated in the Janjigian and Pender cohort (N = 54) and Peking University Cancer Hospital cohort (N = 92). Multivariate analysis revealed that the GIPS was an independent prognostic biomarker. Notably, the GIPS-high tumor was indicative of a T-cell-inflamed phenotype and immune activation. The findings demonstrated that GIPS was a powerful predictor of immunotherapeutic survival in gastrointestinal cancer and may serve as a potential biomarker guiding immunotherapy treatment decisions.

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HER2-low and gastric cancer: A prognostic biomarker?

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e16086 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e16086-e16086

Author(s):

Bruno Cezar de Mendonça Uchôa ◽

Rafaela Pirolli ◽

Luciana Beatriz Mendes Gomes Siqueira ◽

Francisca Giselle Rocha Moura ◽

Ana Paula Rondina Correa ◽

...

Keyword(s):

Gastric Cancer ◽

Multivariate Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Prognostic Biomarker ◽

Statistical Significance ◽

Gastroesophageal Junction ◽

Rank Test ◽

Cancer Center ◽

First Line

e16086 Background: The role of HER2 positive (HER2+) as a prognostic biomarker for gastric/gastroesophageal junction cancer (G-GEJC) is controversial. Recently, the HER2-low (HER2l) concept has emerged and proved to predict response to trastuzumab deruxtecan in metastatic scenario. Data on HER2l prognostic value are missing. Methods: All consecutive patients with metastatic G-GEJC, tested for HER2 in the primary tumor or in the metastatic tissue before initiating first-line therapy at A.C. Camargo Cancer Center, were retrospectively recruited. The primary objective was to compare the overall survival (OS: from the metastasis diagnosis to death by any cause) between HER2l and HER2 negative (HER2-) populations. Secondarily, we aimed to compare the first-line progression-free survival (PFS) between HER2l and HER2-, to analyze prognostic factors associated with OS and to compare the OS between HER2+ and HER2l/HER2-. The HER2 immunohistochemistry (IHC) tests were performed with the Ventana anti-HER2/neu kit, by specialized gastrointestinal pathologists of the study center, using the AJCC HER2 scoring criteria for gastric cancer. In situ hybridization (ISH) was done when IHC 2+ was detected. HER2+ were IHC 3+ or 2+ amplified by ISH; HER2l, 1+ or 2+ non-amplified; HER-, 0+. Kaplan-Meier curves, Log-Rank test and Cox regression were used for survival analysis. Cox regression was used for uni and multivariate analysis. Results: From June, 2008 to July, 2020, 398 patients were included (48 HER2+; 103 HER2l; 247 HER2-). The median follow-up was 31 months (m). Median age at diagnosis was 58 years; the majority were men (62.8%), caucasian (50.8%), with gastric (81% vs 19% GEJ), diffuse (50.3%), de novo metastatic (57.0%) tumors. In comparison to HER2l/HER2-, HER2+ group had superior rates of men, GEJC, intestinal subtype and non-visceral metastasis. Central nervous system metastases were uncommon, and proportionally higher in HER2+ tumors (HER2+: 6.2%; HER2l: 2.9%; HER2-: 2.0%; p = 0.27). There were no imbalances between HER2l and HER2- groups. The median OS was similar for HER2l and HER2- (13m for both; HR 1.0, 95%CI 0.76-1.31; p = 1.0), as it was the PFS (5m for both; HR 0.84, 95%CI 0.65-1.08; p = 0.18). These results did not vary on dependence of IHC + (0 vs 1 + vs 2+). HER2+ tumors had a superior median OS (17m vs 13m for HER2l/HER2-; HR 0.70, 95%CI 0.49-0.99; p = 0.046). When ungrouping HER2l/HER2-, this numerical difference remains, with a loss of statistical significance (17m vs 13m vs 13m; HR 0.87, 95%CI 0.74-1.02; p = 0.12). HER2+, > 1 line of treatment and metastasectomy were predictive for improved OS in multivariate analysis. HER2l was neither predictive for OS nor PFS. Conclusions: Although HER2-low emerged as a new predictive biomarker in metastatic gastric cancer, its prognostic value could not be proved in this study, with an absence of impact in OS. HER2+, however, was associated with improved survival.

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The Early-Stage Triple-Negative Breast Cancer Landscape Derives a Novel Prognostic Signature and Therapeutic Target

10.21203/rs.3.rs-1026006/v1 ◽

2021 ◽

Author(s):

Yun-Song Yang ◽

Yi-Xing Ren ◽

Cheng-Lin Liu ◽

Shuang Hao ◽

Xiao-En Xu ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Therapeutic Target ◽

Prognostic Value ◽

Triple Negative ◽

Cox Regression ◽

Early Stage ◽

Cancer Center ◽

Transcriptome Data ◽

Prognostic Signature

Abstract Purpose: Triple-negative breast cancer (TNBC) is a highly heterogeneous disease. Patients with early-stage TNBCs have distinct likelihood of distant recurrence. Current therapeutic guidance is still limited.Methods: We extracted transcriptome data for 189 pathologically confirmed pT1-2N0M0 TNBC patients at Fudan University Shanghai Cancer Center. Candidate mRNAs were filtered, which was followed by differential expressed mRNAs analysis, survival analysis, and LASSO Cox regression model. All-subsets regression program was used for constructing a multi-mRNA signature in the training set (n=159); the accuracy and prognostic value were then validated using an independent validation set (n=158). Results: Here, we profiled the transcriptome data from 189 early-stage TNBC patients along with 50 paired normal tissues. Early-stage TNBCs are featured of basal-like and immune-suppressed subtype and homologous recombination ability deficiency. We developed a prognostic signature contained seven mRNAs from transcriptome data (ACAN, KRT5, TMEM101, LCA5, RPP40, LAGE3, CDKL2). In both the training (n=159) and validation cohorts (n=158), the signature could identify patients with relatively high recurrence risks and serve as an independent prognostic factor. The signature had better prognostic value than traditional clinicopathological features in both sets. Among the seven mRNAs, TMEM101 is highly expressed in TNBC and represents a potential therapeutic target. Inhibition of TMEM101 impaired tumor progression.Conclusions: Our 7-mRNA signature could accurately predict recurrence risks of early-stage TNBCs. Clinical and genomic low risk TNBC patients may have the opportunity to avoid adjuvant chemotherapy

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Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma

International Journal of Ophthalmology ◽

10.18240/ijo.2021.08.03 ◽

2021 ◽

Vol 14 (8) ◽

pp. 1151-1159

Author(s):

Chen-Lu Liao ◽

◽

Xing-Yu Sun ◽

Qi Zhou ◽

Min Tian ◽

...

Keyword(s):

Prognostic Value ◽

Prognostic Markers ◽

Immune Cell ◽

Cox Regression ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Prognostic Signature ◽

Profile Data ◽

Small Molecule Drugs

AIM: To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis. METHODS: UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package ‘edgeR’. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM. RESULTS: A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib. CONCLUSION: The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.

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Development of a Prognostic Signature Based on Hypoxia-related Genes for Thyroid Cancer

10.21203/rs.3.rs-438962/v1 ◽

2021 ◽

Author(s):

HongYang Zhang ◽

Sijia Li ◽

Wei Li

Keyword(s):

Overall Survival ◽

Thyroid Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Risk Scores ◽

Prognostic Signature ◽

Prognosis Model ◽

Cox Analysis

Abstract Background. We aimed to establish a model to predict the prognosis of patients with thyroid cancer based on differentially expressed hypoxia-related genes.Methods. By comparing the genes in TCGA database and hypoxiaDB database, we obtained differentially expressed genes (DEGs) related to hypoxia in thyroid cancer. Gene function enrichment analysis was performed, and a protein-protein interaction network was constructed using the STRING database. Univariate Cox regression were used to screen hypoxia-related genes with prognostic value. Subsequently, multivariate Cox analysis was used to determine prognostic markers based on thyroid cancer, a prognosis model based on these genes was established. The Kaplan-Meier analysis, Receiver operating characteristic (ROC) analysis and The Harrell’s concordance indexes in the training set and the validation set were used to evaluate the performance of the model. Finally, we conducted univariate analyses of the prognostic value of clinical data (including risk scores) of thyroid cancer patients.Results. 326 hypoxia-related thyroid cancer genes were found. Functional enrichment analysis demonstrated they were mainly involved in regulating biological functions. 23 genes have been proved to be associated with the prognosis of thyroid cancer with univariate Cox regression, among them, 11 marker genes were used to construct a new prognosis model by multivariate Cox analysis. Accordingly, the system of risk scores was constructed, patients with high-risk scores (P <0.005) had shorter overall survival than those with low-risk scores. The ROC curve indicated good performance of the eleven-gene signature at predicting overall survival. The Harrell’s concordance indexes in the internally validated for the 11-gene prognostic signature was 0.881. Moreover, univariate analysis showed that the risk score and age were significantly associated with patient overall survival. The model we created was significantly associated with patient overall survival.Conclusions. The model we established had excellent performance in the prognosis of thyroid cancer.

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Long non-coding RNA, LINC01614 as a potential biomarker for prognostic prediction in breast cancer

PeerJ ◽

10.7717/peerj.7976 ◽

2019 ◽

Vol 7 ◽

pp. e7976 ◽

Cited By ~ 1

Author(s):

Yaozong Wang ◽

Baorong Song ◽

Leilei Zhu ◽

Xia Zhang

Keyword(s):

Breast Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Lncrna Expression ◽

Normal Tissues ◽

Potential Biomarker

Background Dysregulated long non-coding RNAs (lncRNAs) may serve as potential biomarkers of cancers including breast cancer (BRCA). This study aimed to identify lncRNAs with strong prognostic value for BRCA. Methods LncRNA expression profiles of 929 tissue samples were downloaded from TANRIC database. We performed differential expression analysis between paired BRCA and adjacent normal tissues. Survival analysis was used to identify lncRNAs with prognostic value. Univariate and multivariate Cox regression analyses were performed to confirm the independent prognostic value of potential lncRNAs. Dysregulated signaling pathways associated with lncRNA expression were evaluated using gene set enrichment analysis. Results We found that a total of 398 lncRNAs were significantly differentially expressed between BRCA and adjacent normal tissues (adjusted P value <= 0.0001 and |logFC| >= 1). Additionally, 381 potential lncRNAs were correlated Overall Survival (OS) (P value < 0.05). A total of 48 lncRNAs remained when differentially expressed lncRNAs overlapped with lncRNAs that had prognostic value. Among the 48 lncRNAs, one lncRNA (LINC01614) had stronger prognostic value and was highly expressed in BRCA tissues. LINC01614 expression was validated as an independent prognostic factor using univariate and multivariate analyses. Higher LINC01614 expression was observed in several molecular subgroups including estrogen receptors+, progesterone receptors+ and human epidermal growth factor receptor 2 (HER2)+ subgroup, respectively. Also, BRCA carrying one of four gene mutations had higher expression of LINC01614 including AOAH, CIT, HER2 and ODZ1. Higher expression of LINC01614 was positively correlated with several gene sets including TGF-β1 response, CDH1 signals and cell adhesion pathways. Conclusions A novel lncRNA LINC01614 was identified as a potential biomarker for prognosis prediction of BRCA. This study emphasized the importance of LINC01614 and further research should be focused on it.

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The Prognostic Value of miR-302b in Patients With NSCLC

10.21203/rs.3.rs-47876/v1 ◽

2020 ◽

Author(s):

Yue Zhao ◽

Xiangjun Kong ◽

Hongbing Wang

Keyword(s):

Lung Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Level ◽

Survival Curves ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Human Cancers ◽

Nsclc Patients

Abstract Background: Lung cancer is the most common cancer worldwide. The most frequent type of lung cancer is non-small cell lung cancer (NSCLC). MicroRNAs (miRNAs) have been reported to play important role in human cancers. Studies suggest that the aberrant expression of miRNAs could act as the diagnostic or prognostic biomarker in human cancers, including lung cancer. MicroRNA-302b (miR-302b) has ever been investigated in several human cancers. The aim of this study was to examine the prognostic value of miR-302b in patients with NSCLC.Methods: Quantitative real-time RT-PCR (qRT-PCR) analysis were used to measure the expression level of miR-302b in NSCLC and adjacent noncancerous samples. The relationship of miR-302b with the clinicopathological data of NSCLC was analyzed by Chi-square test. The prognostic value of miR-302b was assessed by using the Kaplan-Meier survival curves and Cox regression analysis.Results: The expression level of miR-302b was downregulated in the NSCLC samples compared with the paired adjacent noncancerous samples (P < 0.05). The decreased miR-302b was found correlated with the differentiation (P = 0.019) and lymph node metastasis (P = 0.019) of NSCLC. The survival curves suggested that patients with lower miR-302b expression had poor overall survival than those with high miR-302b expression. The results of Cox analysis demonstrated that the expression of miR-302b was an independent and effective prognostic factor in NSCLC patients with the P of 0.002 (HR = 2.508, 95% CI = 1.410 - 4.463).Conclusion: In one word, the expression of miR-302b was decreased in NSCLC samples, and the miR-302 expression might act as a prognostic biomarker in NSCLC patients.

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Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

10.21203/rs.3.rs-50552/v1 ◽

2020 ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Cancer Genome ◽

Regression Analyses ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Tcga Dataset

Abstract Background: Given that metabolic reprogramming has been recognized as an essential hallmark of cancer cells, this study sought to investigate the potential prognostic values of metabolism-related genes(MRGs) for hepatocellular carcinoma (HCC) diagnosis and treatment. Methods: The metabolism-related genes sequencing data of HCC samples with clinical information were obtained from the International Cancer Genome Consortium(ICGC) and The Cancer Genome Atlas (TCGA). The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify metabolism-related DEGs that related to overall survival(OS). A novel metabolism-related prognostic signature was developed using the least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analyses . Furthermore, the signature was validated in the TCGA dataset. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in HCC. Results: A total of 178 differentially expressed MRGs were detected between the ICGA dataset and the TCGA dataset. We found that 17 MRGs were most significantly associated with OS by using the univariate Cox proportional hazards regression analysis in HCC. Then, the Lasso and multivariate Cox regression analyses were applied to construct the novel metabolism-relevant prognostic signature, which consisted of six MRGs. The prognostic value of this prognostic model was further successfully validated in the TCGA dataset. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. Six MRGs (FLVCR1, MOGAT2, SLC5A11, RRM2, COX7B2, and SCN4A) showed high prognostic performance in predicting HCC outcomes, and were further associated with tumor TNM stage, gender, age, and pathological stage. Finally, the signature was found to be associated with various clinicopathological features. Conclusions: In summary, our data provided evidence that the metabolism-based signature could serve as a reliable prognostic and predictive tool for overall survival in patients with HCC.

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Analysis of the Expression and Prognostic Value of MSH2 in Pan-Cancer Based on Bioinformatics

BioMed Research International ◽

10.1155/2021/9485273 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Wenli Qiu ◽

Ke Ding ◽

Lusheng Liao ◽

Yongchang Ling ◽

Xiaoqiong Luo ◽

...

Keyword(s):

T Cells ◽

Prognostic Value ◽

Cox Regression ◽

Cancer Susceptibility ◽

Prognostic Significance ◽

Housekeeping Gene ◽

Significant Negative Correlation ◽

Immune Infiltration ◽

Potential Biomarker ◽

Pan Cancer

Background. MutS homolog 2 (MSH2), with the function of identifying mismatches and participating in DNA repair, is the “housekeeping gene” in the mismatch repair (MMR) system. MSH2 deficiency has been reported to enhance cancer susceptibility for the association of hereditary nonpolyposis colorectal cancer. However, the expression and prognostic significance of MSH2 have not been studied from the perspective of pan-cancer. Methods. The GTEx database was used to analyze the expression of MSH2 in normal tissues. The TCGA database was used to analyze the differential expression of MSH2 in pan-cancers. The prognostic value of MSH2 in pan-cancer was assessed using Cox regression and Kaplan-Meier analysis. Spearman correlations were used to measure the relationship between the expression level of MSH2 in pan-cancer and the level of immune infiltration, tumor mutational burden (TMB), and microsatellite instability (MSI). Results. MSH2 is highly expressed in most type of cancers and significantly correlated with prognosis. In COAD, KIRC, LIHC, and SKCM, the expression of MSH2 was significantly positively correlated with the abundance of B cells, CD4+ T cells, CD8+ T cells, dendritic cells, macrophages, and neutrophils. In THCA, MSH2 expression correlated with CD8+T Cell showed a significant negative correlation. MSH2 had significantly negative correlations with stromal score and immune score in a variety of cancers and significantly correlated with TMB and MSI of a variety of tumors. Conclusions. MSH2 may play an important role in the occurrence, development, and immune infiltration of cancer. MSH2 can emerge as a potential biomarker for cancer diagnosis and prognosis.

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Transcriptome Analysis Derives a Novel Prognostic 7-mRNA Signature in Early-Stage Triple-Negative Breast Cancer

10.21203/rs.3.rs-389386/v1 ◽

2021 ◽

Author(s):

Yun-Song Yang ◽

Yi-Xing Ren ◽

Shuang Hao ◽

Xiao-En Xu ◽

Xi Jin ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Prognostic Value ◽

Triple Negative ◽

Cox Regression ◽

Early Stage ◽

Distant Recurrence ◽

Cancer Center ◽

Transcriptome Data ◽

Normal Tissues

Abstract Background: Triple-negative breast cancer (TNBC) is a highly heterogeneous disease and patients with early-stage TNBCs have distinct likelihood of distant recurrence. Methods: In this study, We extracted transcriptome data for 189 pathologically confirmed pT1-2 node-negative TNBC patients at Fudan University Shanghai Cancer Center. Candidate mRNAs were filtered, which was followed by differential expressed mRNAs analysis, survival analysis, and LASSO Cox regression model. All-subsets regression program was used for constructing a multi-mRNA signature in the training set (n=159); the accuracy and prognostic value were then validated using an independent validation set (n=158). Results: Here, we profiled the transcriptome data from 189 early-stage TNBC patients along with 50 paired normal tissues, and developed a prognostic signature based on seven mRNAs (ACAN, KRT5, TMEM101, LCA5, RPP40, LAGE3, CDKL2).In both the training (n=159) and validation cohorts (n=158), the signature could identify patients with relatively high recurrence risks and serve as an independent prognostic factor. Furthermore, the signature had better prognostic value than traditional clinicopathological features in both sets. Among the seven mRNAs, TMEM101 was identified as a prognostic biomarker of early-stage TNBC. Additional cell experiments suggested that TMEM101 could facilitate migration and proliferation of TNBC cells. Conclusions: Our 7-mRNA signature could accurately predict recurrence risks of early-stage TNBCs. Clinical and genomic low risk TNBC patients may safely avoid adjuvant chemotherapy.

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EVA1B to Evaluate the Tumor Immune Microenvironment and Clinical Prognosis in Glioma

Frontiers in Immunology ◽

10.3389/fimmu.2021.648416 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shanqiang Qu ◽

Jin Liu ◽

Huafu Wang

Keyword(s):

T Cells ◽

B Cells ◽

Immune Cells ◽

Cox Regression ◽

Prognostic Biomarker ◽

Area Under The Curve ◽

Biological Databases ◽

Clinical Prognosis ◽

Immune Infiltration ◽

Potential Biomarker

BackgroundPrevious research indicated that the tumor cells and microenvironment interactions are critical for the immunotherapeutic response. However, predicting the clinical response to immunotherapy remains a dilemma for clinicians. Hence, this study aimed to investigate the associations between EVA1B expression and prognosis and tumor-infiltrating immune cells in glioma.MethodsFirstly, we detected the EVA1B expression in glioma tissues through biological databases. The chi-squared test, Kaplan-Meier, and univariate and multivariate Cox regression analyses were used to analyze the clinical significance of EVA1B expression. The correlation between EVA1B expression and levels of tumor-infiltrating immune cells in glioma tissues was investigated. Receiver operating characteristic (ROC) analysis was performed to compare the predictive power between EVA1B and other commonly immune-related markers.ResultsIn the CGGA cohort of 325 glioma patients, we found that EVA1B was upregulated in glioma, and increased with tumor grade. High EVA1B expression was prominently associated with unfavorable clinicopathological features, and poorer survival of patients, which were further confirmed by TCGA (n=609) and GEO (n=74) cohorts. Furthermore, multivariate analysis indicated that EVA1B is an independent prognostic biomarker for glioma. Importantly, EVA1B overexpression was associated with a higher infiltration level of CD4+ T cells, CD8+ T cells, B cells, macrophages, and neutrophils in glioma. ROC curves showed that, compared with PD-L1, CTLA-4, and Siglec15, EVA1B presented a higher area under the curve (AUC) value (AUC=0.824) for predicting high immune infiltration levels in glioma.ConclusionsWe found that EVA1B was upregulated and could act as a poor prognostic biomarker in glioma. Importantly, EVA1B overexpression was associated with the immune infiltration levels of immune cells including B cells, CD4+ T cells, CD8+ T cells, macrophages, and neutrophils, and strongly with the overall immune infiltration levels of glioma. These findings suggested that EVA1B might be a potential biomarker for evaluating prognosis and immune infiltration in glioma.

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