A Novel Immune and Stroma Related Prognostic Marker for Invasive Breast Cancer in Tumor Microenvironment: A TCGA Based Study

BackgroundBreast cancer (BC) is the most frequent cancer in women. The tumor microenvironment (TME), consisting of blood vessels, immune cells, fibroblasts, and extracellular matrix, plays a pivotal role in tumorigenesis and progression. Increasing evidence has emphasized the importance of TME, especially the immune components, in patients with BC. Nevertheless, we still lack a deep understanding of the correlation between tumor invasion and TME status.MethodsTranscriptome and clinical data were retrieved from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was applied for quantifying stromal and immune scores. Then we screened out the differentially expressed genes (DEGs) through the intersection analysis. Furthermore, the establishment of protein-protein interaction (PPI) network and univariate COX regression analysis were utilized to determine the core genes in DEGs. In addition, we also performed Gene Set Enrichment Analysis (GSEA) and CIBERSORT analysis to distinguish the function of crucial gene expression and the proportion of tumor-infiltrating immune cells (TICs), respectively.ResultsA total of 1178 samples (112 normal samples and 1066 tumor samples) were extracted from TCGA for calculation, and 226 DEGs were obtained from this assessment. Further intersection analysis revealed eight key genes, including ITK, CD3E, CCL19, CD2, SH2D1A, CD5, SLAMF6, SPN, which were proven to correlate with BC status. Moreover, ITK was picked out for further study. The results illustrated that high expression of BC patients had a more prolonged overall survival (OS) time than ITK low expression BC patients (p = 0.009), and ITK expression also presented the statistical significance in age, TNM staging, tumor size classification, and metastasis classification. Additionally, GSEA and CIBERSORT analysis indicated that ITK expression had an association with immune activity in TME.ConclusionITK may be a potential indicator for prognosis prediction in patients with BC, and its biological behavior may promote our understanding of the molecular mechanism of tumor progression and targeted therapy.

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Prognostic Value and Immune Infiltration of Novel Biomarkers in Glioma: Evidence From a Comprehensive Analysis of Tumor Microenvironment

10.21203/rs.3.rs-125889/v1 ◽

2020 ◽

Author(s):

Bolun Zhang ◽

Feng Guan ◽

Bin Dai ◽

Guangtong Zhu ◽

Beibei Mao ◽

...

Keyword(s):

Tumor Microenvironment ◽

Tumor Progression ◽

Immune Cells ◽

Stromal Cells ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Cox Regression Analysis ◽

Intersection Analysis ◽

Genome Atlas

Abstract BackgroundIn the glioma microenvironment, infiltrating immune cells has been shown to possess beneficial effects for tumor progression. Immune cells and stromal cells dominate the tumor microenvironment in glioma. The complex interplay between the tumor progression with immune cells or stromal cells was still unknown. MethodsIn this study, we used Estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) calculations to calculate the proportion of tumour-infiltrating immune cells (TIC) and the number of immune and stromal components in glioma cases from the cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Differentially expressed genes (DEG) were analyzed by COX regression analysis and protein-protein interaction (PPI) network construction. Then, JAK3, IL2RB and CD3E were identified as predictors by the intersection analysis of univariate COX and PPI, and further analysis showed that the expression of them were positively correlated with survival and clinicopathological characteristics of glioma patients. Finally, the Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) deconvolution algorithm was applied to quantify the fraction and infiltration of 22 types of immune cells in glioma. ResultsOur results showed that ESTIMATEScores Were Correlated with the Survival of glioma Patients, DEGs Shared by ImmuneScore and StromalScore were predominantly presented as the enrichment of immune-related genes gene set enrichment analysis (GSEA). The intersection analysis of PPI network and univariate COX regression enabled us to identify three genes (JAK3, IL2RB and CD3E) that had never been reported before, whose expression was correlated with clinical characteristics such as survival and WHO grading of these patients. CITICSORT analysis of TIC ratio showed that B cell memory and CD8 + T cells were positively correlated with JAK3, IL2RB and CD3E expression, suggesting that these genes may be responsible for maintaining the immunodominant state of TME. CIBERSORT analysis for the proportion of TICs revealed that the levels of JAK3, IL2RB and CD3E affected the immune activity of TME.ConclusionOur results confirmed that the JAK3, IL2RB and CD3E can be used as diagnostic and prognostic biomarkers for glioma and may be used as therapeutic targets in the future.

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CLEC10A is a Potential Prognostic Biomarker and Immune Regulator of Tumor Microenvironment in Breast Cancer

10.21203/rs.3.rs-295872/v1 ◽

2021 ◽

Author(s):

Jincheng He ◽

Lei Jiang ◽

Jun Wang ◽

Guangtao Min ◽

Xiangwen Wang ◽

...

Keyword(s):

Breast Cancer ◽

T Cells ◽

B Cells ◽

Tumor Microenvironment ◽

Immune Cells ◽

Cox Regression ◽

Clinical Stage ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis

Abstract The communication between tumor cells and immune cells influences the ecology of the tumor microenvironment in breast cancer, as well as the disease progression and clinical outcome. The aim of this study was to investigate the prognostic value of the immunomodulatory factor CLEC10A in breast cancer. We applied the CIBERSORT and ESTIMATE calculation methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 1053 BRCA cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein-protein interaction (PPI) network construction. Then, CLEC10A was identified as a prognostic factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that CLEC10A expression was negatively correlated with the clinical pathologic characteristics (age, clinical stage) and positively correlated with survival of BRCA patients. Gene set enrichment analysis (GSEA) showed that genes in the high CLEC10A expression group were mainly enriched in immune-related activities. Genes in the low CLEC10A expression group were enriched in biochemical functions. CIBERSORT analysis of the proportion of TICs revealed that Macrophages M1, B cells memory, B cells naive, T cells CD4+ memory activated, T cells CD8+, and T cells gamma delta were positively correlated with CLEC10A expression, and Macrophages M0, Macrophages M2, Neutrophils, and NK cells resting were positively correlated with CLEC10A expression was negatively correlated, suggesting that CLEC10A may be an important factor in the immune regulation of the tumor microenvironment, especially in mediating the anti-tumor immune response of tumor-infiltrating immune cells at the tumor initiation stage. Therefore, CLEC10A expression may contribute to the prognosis of BRCA patients and provide a new idea for the immunotherapy of BRCA.

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TEAD4 Serves as a Prognostic Biomarker and Correlates With Immune Phenotype in Lower-Grade Gliomas

10.21203/rs.3.rs-957415/v1 ◽

2021 ◽

Author(s):

Mu Chen ◽

Bingsong Huang ◽

Lei Zhu ◽

Kui Chen ◽

Hao Lian ◽

...

Keyword(s):

Tumor Microenvironment ◽

Clinical Outcome ◽

Immune Cells ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Ppi Network ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Genome Atlas

Abstract Background: Tumor-infiltrating immune cells (TIICs), which play a pivotal role in the tumor microenvironment, are intimately related to tumor progression and clinical outcome. It remains unclear which factors influence tumor immune infiltration in lower-grade gliomas (LGGs). TEAD4 (TEA Domain Transcription Factor 4) is an essential member of the Hippo pathway that is involved in cancer progression, epithelial-mesenchymal transition, metastasis, and cancer stem cell function across multiple types of cancers. However, the prognostic value of TEAD4 and its association with TIICs in LGG have been hardly studied. Methods: LGG data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). TEAD4 expression between different groups was compared by R and survival analysis was implemented by Kaplan–Meier curves. In Virto experiments were conducted to investigate the role of TEAD4 in glioma cells. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network were used to investigate the differential biological processes and signaling pathways. Multiple computational methods were employed to estimate the association between TEAD4 expression and tumor microenvironment in LGG. Correlations were analyzed by Spearman correlationResults: TEAD4 expression was up-regulated in higher-grade gliomas and correlated with a poorer clinical outcome. Glioma cell proliferation and migration were promoted by TEAD4 overexpression. GSEA and PPI network indicated that multiple immune-related pathways and hub genes were closely associated with TEAD4 expression in LGG specimens. TEAD4 expression was negatively associated with glioma purity. Multivariate Cox regression analysis indicated that TEAD4 expression and tumor purity were independent prognostic factors in LGG. TEAD4 expression was positively correlated with the infiltration of multiple immune cells, including plasma cells, CD8+ T cells, and macrophages M1 and M2. Correlation analysis showed that the TEAD4 level can predict the efficacy of immune checkpoint blockade therapy. Conclusions: TEAD4 is highly related to glioma malignancy grades and multiple immune cell infiltration, suggesting TEAD4 can serve as a new biomarker for anti-cancer therapies in LGG.

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CCR2 and PTPRC are regulators of Tumor Microenvironment and potential prognostic biomarkers of lung adenocarcinoma

10.21203/rs.3.rs-422168/v1 ◽

2021 ◽

Author(s):

Chengbang Wang ◽

Jie Wei ◽

DaLang Fang

Keyword(s):

Tumor Microenvironment ◽

Lung Adenocarcinoma ◽

Immune Cells ◽

Cox Regression ◽

Γδt Cells ◽

Cox Regression Analysis ◽

Immune State ◽

Protein Protein Interaction ◽

Intersection Analysis ◽

Key Genes

Abstract Tumor microenvironment (TME) plays an essential role in lung adenocarcinoma (LUAD) development and metastasis. With the development of TME research, it has been proved that differences in tumor-infiltrating immune cells (TICs) and gene expression profile are related to the prognosis of cancer. Our study aimed to identify key genes affecting immune state in TME of LUAD. We downloaded the RNA-seq data of LUAD cases from the TCGA database. ImmuneScore, StromalScore and ESTIMATEScore of each LUAD sample were calculated using ESTIMATE algorithm. Based on the median of different scores, LUAD samples were divided into high and low score groups. Differentially expressed genes (DEGs) between groups were obtained, and univariate Cox regression analysis and protein-protein interaction (PPI) network were used to screen shared DEGs generating in the intersection analysis. CIBORSORT algorithm was performed to calculate the relative contents of TICs for each LUAD sample, and correlation analysis between TICs and key genes was used to determine the influence of key genes to the TME. Finally, CCR2 and PTPRC, affecting the immune status of TME and the prognosis of LUAD, were acquired. Analysis based on the CIBERSORT algorithm suggested that CCR2 and PTPRC were correlated with a variety of TICs, and closely related to the clinical characteristics of the LUAD patients. Our research showed that CCR2 and PTPRC may be potential prognostic markers in LUAD, which may affect function of γδT cells and other immune cells by participating in the regulation of TME immune state.

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Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽

10.1186/s12885-021-08041-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lin Chen ◽

Yuxiang Dong ◽

Yitong Pan ◽

Yuhan Zhang ◽

Ping Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Validation Dataset ◽

Immune Gene ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dongqing Su ◽

Qianzi Lu ◽

Yi Pan ◽

Yao Yu ◽

Shiyuan Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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Male breast cancer (MBC) in the VA population: A gender issue?

Journal of Clinical Oncology ◽

10.1200/jco.2006.24.18_suppl.587 ◽

2006 ◽

Vol 24 (18_suppl) ◽

pp. 587-587 ◽

Cited By ~ 1

Author(s):

Z. Nahleh ◽

R. Srikantiah ◽

R. Komrokji ◽

M. Safa ◽

J. Pancoast ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Ductal Carcinoma ◽

Early Stage ◽

Statistical Significance ◽

Clinical Stage ◽

Hormonal Treatment ◽

Cancer Site ◽

Early Stage Disease ◽

Cox Regression Analysis

587 Background: The incidence of MBC continues to rise. Few studies have addressed the differences between MBC and female breast cancer (FBC). Treatment for MBC has ben extrapolated from FBC regimens. The VA cancer registry (VACCR) provides a unique source to study MBC. This retrospective analysis aims at comparing the characteristics and outcome of MBC and FBC in the VA population. Methods: We reviewed the VACCR database between 1995 and 2005, for 120 VA medical centers. Primary breast cancer site codes were identified (500–508). Data was entered and analyzed using bio-statistical software SPSS. Results: A total of 3025 patients :612 MBC and 2413 FBC were compared. Mean age at diagnosis was 67 for MBC and 57 for FBC (p <0.005). More MBC patients were black. MBC patients presented with a significantly higher stage of disease, more node positive(N+) and larger tumor size. In MBC, ductal histology was more common while lobular and ductal carcinoma in situ were less common than in FBC. ER + and PR + tumors were significantly more common in MBC (60% vs 52% and 53% vs 47%, P< 0.005). MBC patients received less chemotherapy while no statistical difference in hormonal treatment was observed. The median overall survival (OS) was lower for MBC (7 years vs 9.8 years, p<0.005). OS was not significantly different for stage III and IV while OS was inferior for MBC in stage I (7 yr vs not reached, p 0.005) and stage II (6 vs 8.6yr, p 0.001). In N- tumors, OS was inferior in MBC (6.1 vs 14.6 yr, p<0.005) but not statistically different for N+ tumors . In ER + and PR + tumors, OS was inferior in MBC (7yr vs 8yr and 7.3 yr vs 9.8 yr p<0.005); however, no statistical significance was observed in ER - or PR - tumors. Using Cox regression analysis age, sex, clinical stage, nodal status were statistically independent prognostic factors while race, histology and grade were not. Conclusion: This study suggests differences in the biology, pathology, presentation, and survival between male and female VA breast cancer patients. Survival of MBC patients appears inferior in early stage disease and N- tumors suggesting gender differences in the tumor pathogenesis and biology. In hormone receptor + MBC, survival was also inferior despite similar hormonal treatment practices. This observational study calls for different approach and treatment strategies in MBC. No significant financial relationships to disclose.

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Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

10.21203/rs.3.rs-524410/v1 ◽

2021 ◽

Author(s):

Jixiang Cao ◽

Xi Chen ◽

Guang Lu ◽

Haowei Wang ◽

Xinyu Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Tumor Microenvironment ◽

Immune Cells ◽

Prognostic Model ◽

Cox Regression ◽

Gene Signature ◽

Risk Scores ◽

Tumor Infiltration ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.569318 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiaoping Li ◽

Jishang Chen ◽

Qihe Yu ◽

Hui Huang ◽

Zhuangsheng Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

High Risk ◽

Cox Regression ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

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FABP4 Might be an Indicator of the Status of Tumor Microenvironment in Colorectal Cancer

10.21203/rs.3.rs-498848/v1 ◽

2021 ◽

Author(s):

Lijun Ning ◽

Yuqing Yan ◽

Tianying Tong ◽

Ziyun Gao ◽

Zhe Cui ◽

...

Keyword(s):

Colorectal Cancer ◽

T Cells ◽

Tumor Microenvironment ◽

Cox Regression ◽

Nk Cell ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Fatty Acid Binding ◽

Cox Regression Analysis ◽

The Status

Abstract Background: As tumor microenvironment (TME) play an indispensable role in tumorigenesis of colorectal cancer, this study performs a bunch of bioinformatics analysis to identify the indicator of the status of TME in Colorectal cancer (CRC). Results: In the presented study, we applied CIBERSORT and ESTIMATE computational methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 444 COAD-READ cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein–protein interaction (PPI) network construction. Then, fatty acid-binding protein four ( FABP4 ) was determined as a predictive factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that FABP4 expression was positively correlated with the clinical pathologic characteristics (clinical stage, distant metastasis) and negatively correlated with the survival of CRC patients. Gene Set Enrichment Analysis (GSEA) showed that the genes in the high-expression FABP4 group were mainly enriched in immune-related activities. In the low-expression FABP4 group, the genes were enriched in metabolic pathways. CIBERSORT analysis for the proportion of TICs revealed that NK cell, CD4 + T cells and CD8 + T cells were negatively correlated with FABP4 expression, suggesting that FABP4 might be a potential prognostic factor of CRC patients. Conclusion: Our study has developed a new biomarker (FABP4) that can predict the status of tumor microenvironment in Colorectal cancer. Keywords: FABP4, tumor microenvironment, ESTIMATE, CIBERSORT, colorectal cancer

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