Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers

Guo, You; Cheng, Jun; Ao, Lu; Li, Xiangyu; Guan, Qingzhou; Zhang, Juan; Yan, Haidan; Cai, Hao; Gao, Qun; Jiang, Weizhong; Guo, Zheng

doi:10.1038/srep36935

Cited by 5 publications

(4 citation statements)

References 41 publications

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“…Guo et al performed bioinformatic analysis for gene expression data of 34 normal rectal tissue samples from three datasets and 72 rectal cancer samples from two datasets including 34 responders and 38 non-responders, and identified nCRT-response genes which were comprised of two sets of genes. Cancer related nCRT-response genes were involved in pathways including DNA replication, cell cycle, and DNA repair, while non-cancer related nCRT-response genes were involved in pathways of drug metabolism [52]. Another bioinformatic approach taken by Gim et al using pre-therapeutic biopsy specimens from 77 rectal cancer patients resulted in development and validation of a three-class classification model predicting minimal response, moderate response, or complete response to nCRT [53].…”

Section: Molecular Biomarkers In Tumor Tissuesmentioning

confidence: 99%

Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer

Dayde

Tanaka

Jain

et al. 2017

IJMS

150

152

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Abstract:The standard of care in locally advanced rectal cancer is neoadjuvant chemoradiation (nCRT) followed by radical surgery. Response to nCRT varies among patients and pathological complete response is associated with better outcome. However, there is a lack of effective methods to select rectal cancer patients who would or would not have a benefit from nCRT. The utility of clinicopathological and radiological features are limited due to lack of adequate sensitivity and specificity. Molecular biomarkers have the potential to predict response to nCRT at an early time point, but none have currently reached the clinic. Integration of diverse types of biomarkers including clinicopathological and imaging features, identification of mechanistic link to tumor biology, and rigorous validation using samples which represent disease heterogeneity, will allow to develop a sensitive and cost-effective molecular biomarker panel for precision medicine in rectal cancer. Here, we aim to review the recent advance in tissue-and blood-based molecular biomarker research and illustrate their potential in predicting nCRT response in rectal cancer.

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Section: Molecular Biomarkers In Tumor Tissuesmentioning

confidence: 99%

Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer

Dayde

Tanaka

Jain

et al. 2017

IJMS

150

152

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“…The utility of clinicopathological and radiological features is limited due to a lack of adequate sensitivity and specificity. Although the relationship of gene expression profile and response to nCRT has been well studied (27)(28)(29), Predictors of outcome have not been systematically explored in the context of spontaneous tumor immunity. To our knowledge, the predictive ability of the immune gene signature per se is confirmed for the first time.…”

Section: Discussionmentioning

confidence: 99%

An Immune-Related Gene Signature for Predicting Neoadjuvant Chemoradiotherapy Efficacy in Rectal Carcinoma

Qian

Lai

et al. 2022

Front. Immunol.

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BackgroundLocally advanced rectal cancers (LARC) show a highly variable response to neoadjuvant chemoradiotherapy (nCRT), and the impact of the tumor immune response in this process is poorly understood. This study aimed to characterize the immune-related gene expression profiles (GEP), pathways, and cell types associated with response or resistance to neoadjuvant chemoradiotherapy.MethodsThe transcriptomic and clinical data of Rectal carcinoma from the Gene Expression Omnibus database and Immune-related genes (IRGs) from ImmPort were downloaded to identify the differentially expressed immune-related genes (DEIRGs) between responder and non-responder to neoadjuvant chemoradiotherapy. Gene set enrichment analyses were performed to uncover significantly enriched GO terms and KEGG pathways. Immune cell infiltration was estimated from RNA-sequencing data using ImmuCellAI. Afterward, we constructed an immune-related gene-based predictive model (IRGPM) by Support Vector Machine and validated it in an external cohort.ResultA 15-gene signature (HLA-DPB1, HLA-DQA1, CXCL9, CXCL10, TAP2, INHBB, BMP2, CD74, IL33, CCL11, CXCL11, DEFB1, HLA-DPA1, CCN3, STAT1) was identified as DEIRGs and found to be significantly associated with nCRT outcomes. Gene set enrichment analyses indicated that the 15 genes play active roles in inflammation-related biological processes. In addition, ImmuCellAI revealed that CD4 naive T cells, Tex, Th1 were significantly up-regulated (p=0.035, p=0.02, p=0.0086, respectively), while Tfh were significantly down-regulated (p=0.015) in responder subgroup. Finally, a novel predictive model was developed by SVM based on DEIRGs with an AUC of 80% (internal validation) and 73.5% (external validation).ConclusionOur team conducted a genomic study of the relationship between gene expression profile and response to nCRT in LARC. Our data suggested that the DEIRGs signature could help predict the efficacy of nCRT. And a DEIRGs‐based SVM model was developed to monitor the outcomes of nCRT in LARC.

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“…Highly heterogeneous forms of osteosarcoma lead to a low frequency of common DEGs. Therefore, genes that were differentially expressed in more than 80% 18,19 of all samples were considered to be universal DEGs. Interestingly, five genes were found to be downregulated in more than 90% of all the osteosarcoma samples.…”

Section: Identification Of Universal Degs In Osteosarcoma Tissuesmentioning

confidence: 99%

Identification of potential key genes associated with osteosarcoma based on integrated bioinformatics analyses

Cheng

et al. 2019

J of Cellular Biochemistry

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Due to high rates of metastasis and poor clinical outcomes for patients, it is important to study the pathomechanisms of osteosarcoma. However, due to the fact that osteosarcoma shows significant interindividual variation and high heterogeneity, the identification of differentially expressed genes (DEGs) at the population level cannot answer many important questions related to osteosarcoma tumorigenesis. Therefore, a new strategy to identify dysregulated genes in osteosarcoma samples is required. The aim of this study was to improve our understanding of osteosarcoma pathogenesis by identifying genes with universal aberrant expression in osteosarcoma samples. Because the relative expression ordering of genes is stable in normal bone tissues but is disrupted in osteosarcoma tissues, we used the RankComp algorithm to identify DEGs in normal and osteosarcoma tissue samples. We then calculated the dysregulation frequency for each gene. Genes with deregulation frequencies above 80% were deemed to be universal DEGs. Next, coexpression, pathway enrichment, and protein-protein interaction network analyses were performed to characterize the functions of these genes. From 188 samples of osteosarcoma obtained from four datasets measured on different platforms, 51 universal DEGs were identified, including 4 universally upregulated genes and 47 universally downregulated genes. Genes that were differentially coexpressed with these universal DEGs were found to be enriched in 46 cancer-related pathways. In addition, functional and network analyses showed that genes with high dysregulation frequencies were involved in cancer-related functions. Thus, the commonly aberrant genes identified in osteosarcoma tissues may be important targets for osteosarcoma diagnosis and therapy. K E Y W O R D S differentially expressed gene (DEGs), individualized analysis, osteosarcoma, relative expression ordering J Cell Biochem. 2019;120:13554-13561. wileyonlinelibrary.com/journal/jcb 13554 |Abbreviations: DEGs, differentially expressed genes; GEO, the Gene Expression Omnibus; GO, gene ontology; KEGG, kyoto encyclopedia of genes and genomes; PPI, protein-protein interaction; REO, relative expression ordering; STRING, search tool for the retrieval of interacting genes.Hu and Cheng have contributed equally to this study.

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Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers

Cited by 5 publications

References 41 publications

Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer

Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer

An Immune-Related Gene Signature for Predicting Neoadjuvant Chemoradiotherapy Efficacy in Rectal Carcinoma

Identification of potential key genes associated with osteosarcoma based on integrated bioinformatics analyses

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