Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data

Zhang, Yan; Wu, Yuan; Gong, Zhongmiao; Ye, Hai-Dan; Zhao, Xiaokai; Li, Jieyi; Zhang, Xiaomei; Li, Sheng; Zhu, Wei; Wang, Mei; Liang, Geyu; Liu, Yun; Guan, Xin; Zhang, Daoyun; Shen, Bo

doi:10.1007/s11596-021-2356-8

Cited by 10 publications

(8 citation statements)

References 37 publications

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“…As an important strategy for treating tumors, immunotherapy has a good clinical effect, but the existence of immune tolerance can affect the therapeutic effect of immunotherapy. Clinical investigations have shown that cancer cells protect themselves at the site of cancer by various mechanisms, which are carried out by adjusting the expression of immunosuppressive molecules to evade the body's immune response (11,12). PD-1, an immunosuppressive molecule that has received more attention in recent years, is expressed in most tumor cells as well as in immune cells (13).…”

Section: Discussionmentioning

confidence: 99%

Expression of PD-L1 and PD-1 in stage T4 rectal cancer tissues and surrounding metastatic lymph nodes and correlation with prognosis

Wang¹,

Lu²,

Li³

2022

Cell Mol Biol (Noisy-le-grand)

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Section: Discussionmentioning

confidence: 99%

Expression of PD-L1 and PD-1 in stage T4 rectal cancer tissues and surrounding metastatic lymph nodes and correlation with prognosis

Wang¹,

Lu²,

Li³

2022

Cell Mol Biol (Noisy-le-grand)

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“…AUC, accuracy, F1 score, precision, and recall were compared using four machine learning classifiers. Because all four machine learning classifiers have been used in many gene expression studies [93][94][95][96], it is difficult to know which classifier is suitable. Therefore, we used all four widely used machine learning classifiers.…”

Section: Discussionmentioning

confidence: 99%

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Haam

Han

Lee

et al. 2021

Cancers

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Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.

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“…Zhang et al. recently reported that several genes can be used as biomarkers to identify colon cancer from rectal cancer based on the prediction of vector machine method algorithm and RNA-sequencing data, while additional research is required to support this finding ( 140 ). However, a study conducted by The Cancer Genome Atlas (TCGA) showed similar patterns of genetic alterations in the colon and rectal tissues from non-hypermutated tumors.…”

Section: Opportunities For Crc Treatmentmentioning

confidence: 99%

Molecular Network of Colorectal Cancer and Current Therapeutic Options

Huang

Yang

2022

Front. Oncol.

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Colorectal cancer (CRC), a leading cause of cancer-related mortalities globally, results from the accumulation of multiple genetic and epigenetic alterations in the normal colonic and rectum epithelium, leading to the progression from colorectal adenomas to invasive carcinomas. Almost half of CRC patients will develop metastases in the course of the disease and most patients with metastatic CRC are incurable. Particularly, the 5-year survival rate of patients with stage 4 CRC at diagnosis is less than 10%. Although genetic understanding of these CRC tumors and paired metastases has led to major advances in elucidating early driver genes responsible for carcinogenesis and metastasis, the pathophysiological contribution of transcriptional and epigenetic aberrations in this malignancy which influence many central signaling pathways have attracted attention recently. Therefore, treatments that could affect several different molecular pathways may have pivotal implications for their efficacy. In this review, we summarize our current knowledge on the molecular network of CRC, including cellular signaling pathways, CRC microenvironment modulation, epigenetic changes, and CRC biomarkers for diagnosis and predictive/prognostic use. We also provide an overview of opportunities for the treatment and prevention strategies in this field.

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Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data

Cited by 10 publications

References 37 publications

Expression of PD-L1 and PD-1 in stage T4 rectal cancer tissues and surrounding metastatic lymph nodes and correlation with prognosis

Expression of PD-L1 and PD-1 in stage T4 rectal cancer tissues and surrounding metastatic lymph nodes and correlation with prognosis

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Molecular Network of Colorectal Cancer and Current Therapeutic Options

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