Prognostic value of tumour microenvironment‐related genes by TCGA database in rectal cancer

Li, Chao; Liu, Tao; Liu, Yi; Zhang, Jiantao; Zuo, Didi

doi:10.1111/jcmm.16547

Cited by 12 publications

(9 citation statements)

References 53 publications

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“…Immune-related pathways, such as cytokine–cytokine receptor interactions, are also abnormally activated in tumors under hypoxia ( 26 ). Moreover, studies on CRC have shown that cytokine–cytokine receptor interaction is involved in changes in the tumor microenvironment and has a good guidance value in the prognosis and immunotherapy prediction of CRC patients ( 27 , 28 ). KEGG pathways and GO term enrichment of differentially expressed genes in patients between the high- and low-immune groups were also analyzed.…”

Section: Discussionmentioning

confidence: 99%

Development of a novel hypoxia-immune–related LncRNA risk signature for predicting the prognosis and immunotherapy response of colorectal cancer

et al. 2022

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BackgroundColorectal cancer (CRC) is one of the most common digestive system tumors worldwide. Hypoxia and immunity are closely related in CRC; however, the role of hypoxia-immune–related lncRNAs in CRC prognosis is unknown.MethodsData used in the current study were sourced from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) databases. CRC patients were divided into low- and high-hypoxia groups using the single-sample gene set enrichment analysis (ssGSEA) algorithm and into low- and high-immune groups using the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm. Differentially expressed lncRNAs (DElncRNAs) between low- and high-hypoxia groups, low- and high-immune groups, and tumor and control samples were identified using the limma package. Hypoxia-immune–related lncRNAs were obtained by intersecting these DElncRNAs. A hypoxia-immune–related lncRNA risk signature was developed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. The tumor microenvironments in the low- and high-risk groups were evaluated using ssGSEA, ESTIMATE, and the expression of immune checkpoints. The therapeutic response in the two groups was assessed using TIDE, IPS, and IC50. A ceRNA network based on signature lncRNAs was constructed. Finally, we used RT-qPCR to verify the expression of hypoxia-immune–related lncRNA signatures in normal and cancer tissues.ResultsUsing differential expression analysis, and univariate Cox and LASSO regression analyses, ZNF667-AS1, LINC01354, LINC00996, DANCR, CECR7, and LINC01116 were selected to construct a hypoxia-immune–related lncRNA signature. The performance of the risk signature in predicting CRC prognosis was validated in internal and external datasets, as evidenced by receiver operating characteristic curves. In addition, we observed significant differences in the tumor microenvironment and immunotherapy response between low- and high-risk groups and constructed a CECR7–miRNA–mRNA regulatory network in CRC. Furthermore, RT-qPCR results confirmed that the expression patterns of the six lncRNA signatures were consistent with those in TCGA-CRC cohort.ConclusionOur study identified six hypoxia-immune–related lncRNAs for predicting CRC survival and sensitivity to immunotherapy. These findings may enrich our understanding of CRC and help improve CRC treatment. However, large-scale long-term follow-up studies are required for verification.

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Section: Discussionmentioning

confidence: 99%

Development of a novel hypoxia-immune–related LncRNA risk signature for predicting the prognosis and immunotherapy response of colorectal cancer

et al. 2022

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“…It is a potential cancer biomarker in cervical cancer [ 25 ] and in paediatric acute myeloid leukaemia [ 24 ], where its expression is also positively correlated with the expression of the actin-binding protein Plastin 3 [ 127 ]. In rectal cancer, MMRN1 is one of five hub genes that act as prognostic biomarkers and elevated MMRN1 expression is associated with poor prognosis [ 109 ]. MMRN1 downregulation is associated with chemoresistance in rectal cancer [ 111 ] and radiosensitivity and associated clinical outcome in gastric cancer [ 128 ].…”

Section: Mmrn1 Differential Expression In Cancermentioning

confidence: 99%

Multimerin-1 and cancer: a review

Posner

2022

Bioscience Reports

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Multimerin-1 (MMRN1) is a platelet protein with a role in haemostasis and coagulation. It is also present in endothelial cells and the extracellular matrix, where it may be involved in cell adhesion, but its molecular functions and protein-protein interactions in these cellular locations have not been studied in detail yet. In recent years, MMRN1 has been identified as a differentially expressed gene in various cancers and it has been proposed as a possible cancer biomarker. Some evidence suggests that MMRN1 expression is regulated by methylation, protein interactions, and non-coding RNAs in different cancers. This raises the questions if a functional role of MMRN1 is being targeted during cancer development, and if MMRN1’s differential expression pattern correlates with cancer progression. As a result, it is timely to review the current state of what is known about MMRN1 to help inform future research into MMRN1’s molecular mechanisms in cancer.

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“…We used Human Protein Atlas (HPA) (https://www.pr oteinatlas.org/) tool to validate the mRNA and protein expression levels of the hub genes in all tissues. The Single Cell Type Atlas part in HPA as used to illustrate the expression of hub genes in single specific cell types [31].…”

Section: Specific Expression Analysis Of Hub Genes In Tissue and Cellsmentioning

confidence: 99%

Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning

Li¹,

Lei²,

Xue-shan³

et al. 2022

Rev. Cardiovasc. Med.

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Background: Valvular heart disease (VHD) is a major precipitating factor of atrial fibrillation (AF) that contributes to decreased cardiac function, heart failure, and stroke. Stroke induced by VHD combined with atrial fibrillation (AF-VHD) is a much more serious condition in comparison to VHD alone. The aim of this study was to explore the molecular mechanism governing VHD progression and to provide candidate treatment targets for AF-VHD. Methods: Four public mRNA microarray datasets were downloaded and differentially expressed genes (DEGs) screening was performed. Weighted gene correlation network analysis was carried out to detect key modules and explore their relationships and disease status. Candidate hub signature genes were then screened within the key module using machine learning methods. The receiver operating characteristic curve and nomogram model analysis were used to determine the potential clinical significance of the hub genes. Subsequently, target gene protein levels in independent human atrial tissue samples were detected using western blotting. Specific expression analysis of the hub genes in the tissue and cell samples was performed using single-cell sequencing analysis in the Human Protein Atlas tool. Results: A total of 819 common DEGs in combined datasets were screened. Fourteen modules were identified using the cut tree dynamic function. The cyan and purple modules were considered the most clinically significant for AF-VHD. Then, 25 hub genes in the cyan and purple modules were selected for further analysis. The pathways related to dilated cardiomyopathy, hypertrophic cardiomyopathy, and heart contraction were concentrated in the purple and cyan modules of the AF-VHD. Genes of importance (CSRP3, MCOLN3, SLC25A5, and FIBP) were then identified based on machine learning. Of these, CSRP3 had a potential clinical significance and was specifically expressed in the heart tissue. Conclusions: The identified genes may play critical roles in the pathophysiological process of AF-VHD, providing new insights into VHD development to AF and helping to determine potential biomarkers and therapeutic targets for treating AF-VHD.

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Prognostic value of tumour microenvironment‐related genes by TCGA database in rectal cancer

Cited by 12 publications

References 53 publications

Development of a novel hypoxia-immune–related LncRNA risk signature for predicting the prognosis and immunotherapy response of colorectal cancer

Development of a novel hypoxia-immune–related LncRNA risk signature for predicting the prognosis and immunotherapy response of colorectal cancer

Multimerin-1 and cancer: a review

Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning

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