Transcriptomic Differences between Primary Colorectal Adenocarcinomas and Distant Metastases Reveal Metastatic Colorectal Cancer Subtypes

Kamal, Yasmin; Schmit, Stephanie L.; Hoehn, Hannah J.; Amos, Christopher I.; Frost, H. Robert

doi:10.1158/0008-5472.can-18-3945

Cited by 56 publications

(70 citation statements)

References 54 publications

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“…The M1 metastatic group is characterized by strong activation of inflammatory and immune response pathways (including immune evasion pathways, such as those involving PD-1/-L1 signaling) and enrichment in EMT activity. The M2 metastatic group exhibits MYC activation and cell proliferation [ 18 ]. Importantly, treatment modifies the gene expression profile of metastatic lesions: the immune phenotype of M1 metastases is lost in post-treatment metastases; treatment induces an enrichment of EMT activity [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…The M2 metastatic group exhibits MYC activation and cell proliferation [ 18 ]. Importantly, treatment modifies the gene expression profile of metastatic lesions: the immune phenotype of M1 metastases is lost in post-treatment metastases; treatment induces an enrichment of EMT activity [ 18 ]. The analysis of CMS groups in metastases showed the absence of CMS3 and the presence of CMS1 in only few cases; the majority of metastases were classified as CMS2 (37%) or CMS4 (45%); 86% of metastases were CMS4 in the M1 cluster, while 60% of metastases were CMS2 in the M2 cluster [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Genetic Alterations of Metastatic Colorectal Cancer

Testa

Castelli

2020

Biomedicines

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Genome sequencing studies have characterized the genetic alterations of different tumor types, highlighting the diversity of the molecular processes driving tumor development. Comprehensive sequencing studies have defined molecular subtypes of colorectal cancers (CRCs) through the identification of genetic events associated with microsatellite stability (MSS), microsatellite-instability-high (MSI-H), and hypermutation. Most of these studies characterized primary tumors. Only recent studies have addressed the characterization of the genetic and clinical heterogeneity of metastatic CRC. Metastatic CRC genomes were found to be not fundamentally different from primary CRCs in terms of the mutational landscape or of genes that drive tumorigenesis, and a genomic heterogeneity associated with tumor location of primary tumors helps to define different clinical behaviors of metastatic CRCs. Although CRC metastatic spreading was traditionally seen as a late-occurring event, growing evidence suggests that this process can begin early during tumor development and the clonal architecture of these tumors is consistently influenced by cancer treatment. Although the survival rate of patients with metastatic CRC patients improved in the last years, the response to current treatments and prognosis of many of these patients remain still poor, indicating the need to discover new improvements for therapeutic vulnerabilities and to formulate a rational prospective of personalized therapies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic Alterations of Metastatic Colorectal Cancer

Testa

Castelli

2020

Biomedicines

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“…3 It has been found that for more than 20% of patients CAD may metastasize. 4 Liver metastasis is the poorest prognostic factor of CAD, and the resection rate for colorectal liver metastasis remains at less than 25%. 5 Thus, the early diagnosis of liver metastasis is extremely important for enhancing the survival of CAD patients.…”

Section: Introductionmentioning

confidence: 99%

“…A new method for analyzing the transcriptomic differences between primary CAD and a distant metastasis was developed, and FBN2 and MMP3 were identified as CAD metastasis related genes, which may help predict a high-level risk of CAD metastasis. 4 Sayagués et al revealed the existence of several dysregulated genes including APOA1, HRG, UGT2B4, and RBP4 in CAD with liver metastasis samples in comparison to the primary tumor. 6 Qian et al identified higher expressions of THBS2, INHBB, and BGN in CRC patients with liver metastasis.…”

Section: Introductionmentioning

confidence: 99%

Predicting biomarkers from classifier for liver metastasis of colorectal adenocarcinomas using machine learning models

Han

Zhou

et al. 2020

Cancer Medicine

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Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset http://GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset http://GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of http://GSE131418 and http://GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug‐targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug‐target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis.

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“…For the past few years, investigators have carried out a series of explorations in this eld, and several prognostic gene signatures, 2,3 transcriptional signatures and noncoding RNA signatures have been published. [4][5][6] However, there is still no recognized prognostic prediction model, and further research is required.…”

Section: Introductionmentioning

confidence: 99%

A novel seven-microRNA signature predicts prognosis in colorectal adenocarcinoma

Jiang¹,

Xie²,

Jiang³

2020

Preprint

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Background Colorectal carcinoma, primarily colorectal adenocarcinoma (CRA), is one of the most common malignancies, ranking third, while contributing the second cause of cancer death in the world. MicroRNAs, a type of non-coding RNA, play an important role in regulating cancer-related cell biology. To simply and accurately predict survival risk for CRA patients, we identified a novel seven-miRNA signature. The microRNA (miRNA) expression profiles along with clinical data of 512 CRA patients were downloaded from The Cancer Genome Atlas (TCGA) database, and 415 patients with complete clinical information were further divided into training set and test set randomly. To construct the prognostic signature in the training set, a series of Cox regression analyses were performed, including univariate regression, least absolute shrinkage and selectionator operator (LASSO) - Cox regression, and multivariate regression. Results Seven predictive miRNAs (miR-153-2, miR-3199-2, miR-144, miR-887, miR-561, miR-3684, and miR-505) were ultimately screened. The ROC curves for 5-year survival in the training set, test set and entire set were 0.889, 0.742, and 0.816, respectively. Kaplan-Meier analysis results of the three sets all showed p <0.05. Further analyses demonstrated that the signature was an independent prognostic risk factor for CRA patients, and its predictive ability was superior to age and TNM stage. Functional enrichment analysis revealed that p53 and ErbB pathways were related to the prognostic regulation of miRNAs in the signature in CRA patients.Conclusion Our study demonstrated the potential of this novel seven-miRNA signature to independently predict overall survival in patients with CRA. Functional enrichment analysis further revealed the possible regulatory role of miRNAs in the signature in CRA-related cell biological processes and signaling pathways.

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Transcriptomic Differences between Primary Colorectal Adenocarcinomas and Distant Metastases Reveal Metastatic Colorectal Cancer Subtypes

Cited by 56 publications

References 54 publications

Genetic Alterations of Metastatic Colorectal Cancer

Genetic Alterations of Metastatic Colorectal Cancer

Predicting biomarkers from classifier for liver metastasis of colorectal adenocarcinomas using machine learning models

A novel seven-microRNA signature predicts prognosis in colorectal adenocarcinoma

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