Characterization of lung adenocarcinoma based on immunophenotyping and constructing an immune scoring model to predict prognosis

Liu, Mengfeng; Xiao, Qifan; Yu, Xiran; Zhao, Yujie; Qu, Changfa

doi:10.3389/fphar.2022.1081244

Cited by 2 publications

(2 citation statements)

References 57 publications

(61 reference statements)

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“…CDH1 might have a function in the development and advancement of LUAD and that it may be a potential target for LUAD treatment. [75][76][77] PIK3CA gene mutation has been present in a diverse range of human tumors, including lung adenocarcinoma. According to Wang's systematic review and meta-analysis article, PIK3CA mutation may affect lymph node metastasis and serve as a promising predictive element in patients with nonsmall-cell lung cancer (NSCLC).…”

Section: Introductionmentioning

confidence: 99%

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A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

Sadeghi,

Kiaei,

Boush

et al. 2024

Preprint

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Background: Adenocarcinoma of the lung is the most common type of lung cancer, and it is characterized by distinct cellular and molecular features. It occurs when abnormal lung cells multiply out of control and form a tumor in the outer region of the lungs. Adenocarcinoma of the lung is a serious and life-threatening condition that requires effective and timely management to improve the survival and quality of life of the patients. One of the challenges in this cancer treatment is finding the optimal combination of drugs that can target the genes or proteins that are involved in the disease process. Method: In this article, we propose a novel method to recommend combinations of trending drugs to target its associated proteins/genes, using a Graph Neural Network (GNN) under the RAIN protocol. The RAIN protocol is a three-step framework that consists of: 1) Applying graph neural networks to recommend drug combinations by passing messages between trending drugs for managing disease and genes that act as potential targets for disease; 2) Retrieving relevant articles with clinical trials that include those proposed drugs in previous step using Natural Language Processing (NLP); 3) Analyzing the network meta-analysis to measure the comparative efficacy of the drug combinations. Result: We applied our method to a dataset of nodes and edges that represent the network, where each node is a drug or a gene, and each edge is a p-value between them. We found that the graph neural network recommends combining Gefitinib, Paclitaxel, and Icotinib as the most effective drug combination to target this cancer associated proteins/genes. We reviewed the clinical trials and expert opinions on these medications and found that they support our claim. The network meta-analysis also confirmed the effectiveness of these drugs on associated genes. Conclusion: Our method is a novel and promising approach to recommend trending drugs combination to target cancer associated proteins/genes, using graph neural networks under the RAIN protocol. It can help clinicians and researchers to find the best treatment options for patients, and also provide insights into the underlying mechanisms of the disease.

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Section: Introductionmentioning

confidence: 99%

“…CDH1 might have a function in the development and advancement of LUAD and that it may be a potential target for LUAD treatment. 75–77…”

Section: Introductionmentioning

confidence: 99%

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

Sadeghi,

Kiaei,

Boush

et al. 2024

Preprint

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A Novel Mitochondrial Quality Regulation Gene Signature for Anticipating Prognosis, TME, and Therapeutic Response in LUAD by Multi-Omics Analysis And Experimental Verification

Zeng,

Wu,

et al. 2024

Preprint

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Background Lung adenocarcinoma (LUAD) is the predominant form of non-small cell lung cancer (NSCLC). Mitochondrial quality-related genes (MQRGs) contribute to the genesis and advancement of tumors. Despite advances in LUAD treatment and detection, early diagnostic biomarkers are still lacking, and the roles of MQRGs in LUAD are not well understood. Methods We extensively examined transcriptome and clinical data from TCGA and GEO databases to discover differentially expressed MQRGs. Utilizing the LASSO algorithm and multivariate COX regression, a predictive risk model was created. Kaplan-Meier study and ROC curves were implemented to predict patient prognosis, resulting in a new Mitochondrial Quality Regulation Gene Signature for accurate prognosis forecasting. R software and packages facilitated statistical, consensus cluster, survival, Cox regression, Lasso regression, and tumor microenvironment analyses. Model-related gene expression was measured using RT-qPCR, single-cell sequencing, HPA data, and UNCAN data. Results We created a concise risk model using four MQRGs (STRAP, SHCBP1, PKP2, and CRTAC1) to forecast overall survival in LUAD patients. High-risk patients experienced significantly lower survival rates. Functional analysis linked these MQRGs to alpha-linolenic acid metabolism pathways. Moreover, the tumor immune microenvironment supports previous findings that higher CD8 + T cell infiltration improves LUAD outcomes. Analysis of different risk scores showed increased activated memory T-cell CD4, suggesting its activation is crucial for LUAD prognosis. Nomograms were generated with clinical data and the MQRGscore model. mRNA and IHC analysis manifested significantly upregulated STRAP, SHCBP1, and PKP2 expression and mitigated CRTAC1 expression in the LUAD contrasted with normal lung tissue. qRT-PCR confirmed these findings, aligning with TCGA data. Conclusions We created a succinct MQRGs risk model to ascertain the LUAD patient's prognosis, potentially offering a novel method for diagnosing and treating this condition.

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Characterization of lung adenocarcinoma based on immunophenotyping and constructing an immune scoring model to predict prognosis

Cited by 2 publications

References 57 publications

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

A Novel Mitochondrial Quality Regulation Gene Signature for Anticipating Prognosis, TME, and Therapeutic Response in LUAD by Multi-Omics Analysis And Experimental Verification

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