Possible pathways used to predict different stages of lung adenocarcinoma

Chen, Xiao; Duan, Qiongyu; Yang, Xuan; Sun, Yunan; Wu, Rong

doi:10.1097/md.0000000000006736

Cited by 5 publications

(6 citation statements)

References 25 publications

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“…Additionally, it has been demonstrated that IFN− inhibits lung cancer by causing the PI3K-Akt pathway to become active in lung adenocarcinoma cells (24). Non-small cell lung cancer, small cell lung cancer, and hepatitis C have all been shown to play an important role in lung adenocarcinoma and prognosis prediction (25). Subsequently, we evaluated eight targets, including BCL2, BIRC5, CCNA2, CDK1, PRKCB, RXRA, RXRB, and STAT1, based on the traditional Chinese medicine drug target pathway network, LUAD target protein interaction network, and PPI network core.…”

Section: Discussionmentioning

confidence: 99%

Exploring the nucleotide molecular mechanism of compound kushen injection for lung adenocarcinoma based on network pharmacology and molecular docking

Yao

Chen

et al. 2022

Front. Oncol.

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Compound kushen injection is an effective traditional Chinese medicine for the treatment of lung cancer. However, its influence on the survival and prognosis of patients with lung adenocarcinoma patients was less studied; especially its pharmacological mechanism remains to be further elucidated. In the present study, we adopted a network pharmacology (NP)-based approach to screening effective compounds, screening and predicting target genes, analyzing biological functions and pathways, constructing a regulatory network and protein interaction network, and screening the key targets. Moreover, mass survival analysis and molecular docking were conducted. In the end, 35 key compounds and four possible central target genes were screened out, which could be used for the treatment of lung adenocarcinoma and affected the survival and prognosis of patients with lung adenocarcinoma. In addition, their key compounds had good docking affinity. Enrichment analysis showed that CKI might affect the treatment and prognosis of lung adenocarcinoma patients by regulating the PI3K–Akt signaling pathway, TNF signaling pathway, non-small cell lung cancer, Hepatitis C, etc. We discussed the pharmacological mechanisms and potential therapeutic targets of CKI in the treatment of lung adenocarcinoma, which verified the effect of CKI on the prognosis and survival of patients. The present study might promote the further clinical application of CKI and provide a theoretical basis for further experimental studies.

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Section: Discussionmentioning

confidence: 99%

Exploring the nucleotide molecular mechanism of compound kushen injection for lung adenocarcinoma based on network pharmacology and molecular docking

Yao

Chen

et al. 2022

Front. Oncol.

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“…Again, the RNASeq data were the most used to address this problem. In Fan et al (2018) , they obtained a signature of 12 genes capable of distinguishing patients with lung cancer with different risks, while in Chen et al (2017) they identified pathways of interest capable of classifying the different stages of lung adenocarcinoma. For example, in Yang, Xu & Zeng (2018) , they used only the features corresponding to lncRNA, obtaining a signature of six lncRNA capable of classifying patients with melanoma according to their stages.…”

Section: Machine Learning As a Source Of New Knowledgementioning

confidence: 99%

Machine learning analysis of TCGA cancer data

Liñares-Blanco

Fernández-Lozano

2021

PeerJ Computer Science

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In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order to study the state of the art, this review is provided to cover the main works that have used ML with TCGA data. Firstly, the principal discoveries made by the TCGA consortium are presented. Once these bases have been established, we begin with the main objective of this study, the identification and discussion of those works that have used the TCGA data for the training of different ML approaches. After a review of more than 100 different papers, it has been possible to make a classification according to following three pillars: the type of tumour, the type of algorithm and the predicted biological problem. One of the conclusions drawn in this work shows a high density of studies based on two major algorithms: Random Forest and Support Vector Machines. We also observe the rise in the use of deep artificial neural networks. It is worth emphasizing, the increase of integrative models of multi-omic data analysis. The different biological conditions are a consequence of molecular homeostasis, driven by both protein coding regions, regulatory elements and the surrounding environment. It is notable that a large number of works make use of genetic expression data, which has been found to be the preferred method by researchers when training the different models. The biological problems addressed have been classified into five types: prognosis prediction, tumour subtypes, microsatellite instability (MSI), immunological aspects and certain pathways of interest. A clear trend was detected in the prediction of these conditions according to the type of tumour. That is the reason for which a greater number of works have focused on the BRCA cohort, while specific works for survival, for example, were centred on the GBM cohort, due to its large number of events. Throughout this review, it will be possible to go in depth into the works and the methodologies used to study TCGA cancer data. Finally, it is intended that this work will serve as a basis for future research in this field of study.

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“…For example, one study aiming to predict response to therapy for patients with severe ulcerative colitis used microRNA profiles, which are not yet routinely performed in many institutions. 20 Another study was able to accomplish the similar task of predicting tumor response to chemoradiotherapy in esophageal cancer using readily available imaging radiomics features, which, in turn, improved the relative translational utility of the model. 21 As genomic analysis of tumors is incorporated into clinical practice, the translational potential of these studies will grow in tandem.…”

Section: Are the Selected Features Clinically Meaningful?mentioning

confidence: 99%

Supervised Machine Learning in Oncology: A Clinician's Guide

Murali

Kucukkaya

Petukhova

et al. 2020

Digestive Disease Interventions

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The widespread adoption of electronic health records has resulted in an abundance of imaging and clinical information. New data-processing technologies have the potential to revolutionize the practice of medicine by deriving clinically meaningful insights from large-volume data. Among those techniques is supervised machine learning, the study of computer algorithms that use self-improving models that learn from labeled data to solve problems. One clinical area of application for supervised machine learning is within oncology, where machine learning has been used for cancer diagnosis, staging, and prognostication. This review describes a framework to aid clinicians in understanding and critically evaluating studies applying supervised machine learning methods. Additionally, we describe current studies applying supervised machine learning techniques to the diagnosis, prognostication, and treatment of cancer, with a focus on gastroenterological cancers and other related pathologies.

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Possible pathways used to predict different stages of lung adenocarcinoma

Abstract: Supplemental Digital Content is available in the text

Cited by 5 publications

References 25 publications

Exploring the nucleotide molecular mechanism of compound kushen injection for lung adenocarcinoma based on network pharmacology and molecular docking

Exploring the nucleotide molecular mechanism of compound kushen injection for lung adenocarcinoma based on network pharmacology and molecular docking

Machine learning analysis of TCGA cancer data

Supervised Machine Learning in Oncology: A Clinician's Guide

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