Dataset Information

Decreased m6A Modification of CD34/CD276(B7-H3) Leads to Immune Escape in Colon Cancer.

ABSTRACT: Previous studies have reported that m6a modification promotes tumor immune escape by affecting tumor microenvironment (TME). Due to the complexity of TME, a single biomarker is insufficient to describe the complex biological characteristics of tumor and its microenvironment. Therefore, it is more meaningful to explore a group of effective biomarkers reflecting different characteristics of cancer to evaluate the biological characteristics of solid tumors. Here, the immune gene CD34/CD276 with different m6A peak was obtained by m6A sequencing (MeRIP-seq) of colon cancer (CRC)clinical samples and combined with MsIgDB database, which was used to perform cluster analysis on TCGA-COAD level 3 data. The CD34/CD276 as a molecular marker for CRC prognosis was confirmed by survival analysis and immunohistochemical assay. Further bioinformatics analysis was carried out to analyze the molecular mechanism of CD34/CD276 affecting the TME through m6a-dependent down-regulation and ultimately promoting immune escape of CRC.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC8297592 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Decreased m6A Modification of CD34/CD276(B7-H3) Leads to Immune Escape in Colon Cancer.

Zhou Yiran Y Zhou Haodong H Shi Jianlin J Guan Aoran A Zhu Yankun Y Hou Zongliu Z Li Ruhong R

Frontiers in cell and developmental biology 20210708

Previous studies have reported that m6a modification promotes tumor immune escape by affecting tumor microenvironment (TME). Due to the complexity of TME, a single biomarker is insufficient to describe the complex biological characteristics of tumor and its microenvironment. Therefore, it is more meaningful to explore a group of effective biomarkers reflecting different characteristics of cancer to evaluate the biological characteristics of solid tumors. Here, the immune gene CD34/CD276 with dif ...[more]

PMID: 34307389

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Project description:CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.

Dataset Information

Decreased m6A Modification of CD34/CD276(B7-H3) Leads to Immune Escape in Colon Cancer.

Publications

Decreased m6A Modification of CD34/CD276(B7-H3) Leads to Immune Escape in Colon Cancer.

Similar Datasets

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