Identification of Endoplasmic Reticulum Stress-Related Subtypes, Infiltration Analysis of Tumor Microenvironment, and Construction of a Prognostic Model in Colorectal Cancer

Liu, Baike; Yin, Xiaonan; Jiang, Guangfu; Li, Yang; Jiang, Zhiyuan; Qiang, Liming; Chen, Na; Fan, Yixuan; Shen, Chaoyong; Dai, Lei; Yin, Yuan; Zhang, Bo

doi:10.3390/cancers14143326

Cited by 5 publications

(3 citation statements)

References 60 publications

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“…Similar to our study, Liu et al [29] established prognostic models of 14 genes, including ASNS, CALR3, and DNAJB2, through bioinformatic analysis and found that ER stress was closely related to the prognosis of CRC. Ryan et al [30] found that the ERS-induced protein calnexin is a marker of poor prognosis in CRC by comparing fresh frozen tumors and matched normal tissue samples from 23 patients with stage II and III colon cancer.…”

Section: Discussionsupporting

confidence: 88%

Classification of Colorectal Cancer Subtypes Based on Endoplasmic Reticulum Stress

Qu,

Chu,

et al. 2023

Digestion

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Introduction: Endoplasmic reticulum stress (ERS) is associated with the occurrence and development of colorectal cancer (CRC). Methods: One thousand nine CRC samples and 3 ERS gene sets from GEO database were used to screen and validate genes related to stage and prognosis of CRC. Twenty thousand five hundred thirty samples from the TCGA database validated the ERS genes related to prognosis. PPI network construction and coexpression analysis were used to investigate the correlation of genes. ConsensusClusterPlus analysis was used to classify CRC subtypes. Cox regression and the LASSO algorithm were used to screen ERS genes related to prognosis. HE staining, immunohistochemical staining and RT‒qPCR of 50 owner-central samples were used to verify the genes. The ERscore model was constructed based on the ERS genes related to prognosis. The nomogram model was used to verify that different subtypes of CRC patients have different prognosis. Results: Fifty ERS differentially expressed genes related to CRC stage and 8 ERS model genes related to prognosis were screened. Three subtypes of CRC were classified based on the former 50 genes. The clinical characteristics were significantly different among the subtypes. The ERscore model was constructed based on the latter 8 genes, and its accuracy was verified by clinical samples. Finally, the nomogram was constructed based on ERscore, age and CRC stage, and the accuracy of the nomogram prediction was verified. Conclusion: ERS-related genes can be used as classification criteria for CRC, and the related clinical characteristics of different CRC subtypes are different.

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Section: Discussionsupporting

confidence: 88%

Classification of Colorectal Cancer Subtypes Based on Endoplasmic Reticulum Stress

Qu,

Chu,

et al. 2023

Digestion

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“…It was found that endoplasmic stress activity was higher in the tumor region compared to adjacent normal tissue. Furthermore, there was an increased association of FOXP3 Treg cells, indicating a correlation between ER stress and immunosuppressive niches in the CRC microenvironment [120]. In another interesting study, spatial analysis was used to analyze host-bacterial interactions in the tumor microenvironment [121].…”

Section: Spatial Biology-understanding Tumor Heterogeneity In Crcmentioning

confidence: 99%

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

Ahluwalia,

Ballur,

Leeman

et al. 2024

Cancers

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Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.

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“…In recent years, ER stress-related signaling pathways have emerged as critical regulators of tumor growth; metastasis; and the response to chemotherapy, targeted therapy, and immunotherapy [ 19 ]. Spatial transcriptomics data revealed that ER stress-related gene patterns in CRC were involved in forming the immunosuppressive TME and predicting patient prognosis [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Lipid Metabolism and Endoplasmic Reticulum Stress-Related Risk Model for Predicting Immune Infiltration and Prognosis in Colorectal Cancer

Jin,

Xia,

Wang

et al. 2023

IJMS

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Lipid metabolism and endoplasmic reticulum stress exhibit crosstalk in various cancer types, which are closely associated with the progression of colorectal cancer (CRC). This study constructs a prognostic signature based on lipid metabolism and endoplasmic reticulum stress-related genes (LERGs) for CRC patients, aiming to predict the prognosis and immune response. RNA sequencing and clinical data from the TCGA and GEO databases were analyzed to identify differentially expressed LERGs with prognostic relevance using univariate Cox regression. Subsequently, a risk model was developed using the LASSO regression. CRC patients were stratified into low-risk and high-risk groups based on risk scores, with the high-risk cohort demonstrating a poorer clinical prognosis in multiple databases. The risk model showed robust correlations with clinical features, gene mutations, and treatment sensitivity. Significant differences in immune cell infiltration and the expression of immune-related factors were also detected between risk groups, and elevated scores of cytokines and failure factors were detected in single-cell RNA sequencing analysis. This research indicates that lipid metabolism and endoplasmic reticulum stress in CRC are correlated with tumor progression, an immunosuppressive landscape, and alterations of drug sensitivity. The developed risk model can serve as a powerful prognostic tool, offering critical insights for refining clinical management and optimizing treatment in CRC patients.

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Identification of Endoplasmic Reticulum Stress-Related Subtypes, Infiltration Analysis of Tumor Microenvironment, and Construction of a Prognostic Model in Colorectal Cancer

Cited by 5 publications

References 60 publications

Classification of Colorectal Cancer Subtypes Based on Endoplasmic Reticulum Stress

Classification of Colorectal Cancer Subtypes Based on Endoplasmic Reticulum Stress

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

A Novel Lipid Metabolism and Endoplasmic Reticulum Stress-Related Risk Model for Predicting Immune Infiltration and Prognosis in Colorectal Cancer

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