Identification and Prognostic Value Exploration of Radiotherapy Sensitivity-Associated Genes in Non-Small-Cell Lung Cancer

Ma, Qing; Geng, Kai; Xiao, Ping; Zeng, Lili

doi:10.1155/2021/5963868

Cited by 6 publications

(3 citation statements)

References 54 publications

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“…In order to better understand the differences between different subtypes, we used WGCNA analysis to identify hub genes among each subtype. Extensive analyses showed that WGCNA was an effective method for identifying phenotype-genotype linkages and biomarkers and therapeutic targets ( Niemira et al, 2019 ; Ma et al, 2021 ; Zhang et al, 2022 ). Nine modules were obtained, and pink module was highly associated with Stromal-E subtype.…”

Section: Discussionmentioning

confidence: 99%

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

Liu

Xiao

et al. 2022

Front. Pharmacol.

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Background: Lung cancer poses great threat to human health, and lung adenocarcinoma (LUAD) is the main subtype. Immunotherapy has become first line therapy for LUAD. However, the pathogenic mechanism of LUAD is still unclear.Methods: We scored immune-related pathways in LUAD patients using single sample gene set enrichment analysis (ssGSEA) algorithm, and further identified distinct immune-related subtypes through consistent clustering analysis. Next, immune signatures, Kaplan-Meier survival analysis, copy number variation (CNV) analysis, gene methylation analysis, mutational analysis were used to reveal differences between subtypes. pRRophetic method was used to predict the response to chemotherapeutic drugs (half maximal inhibitory concentration). Then, weighted gene co-expression network analysis (WGCNA) was performed to screen hub genes. Significantly, we built an immune score (IMscore) model to predict prognosis of LUAD.Results: Consensus clustering analysis identified three LUAD subtypes, namely immune-Enrich subtype (Immune-E), stromal-Enrich subtype (Stromal-E) and immune-Deprived subtype (Immune-D). Stromal-E subtype had a better prognosis, as shown by Kaplan-Meier survival analysis. Higher tumor purity and lower immune cell scores were found in the Immune-D subtype. CNV analysis showed that homologous recombination deficiency was lower in Stromal-E and higher in Immune-D. Likewise, mutational analysis found that the Stromal-E subtype had a lower mutation frequency in TP53 mutations. Difference in gene methylation (ZEB2, TWIST1, CDH2, CDH1 and CLDN1) among three subtypes was also observed. Moreover, Immune-E was more sensitive to traditional chemotherapy drugs Cisplatin, Sunitinib, Crizotinib, Dasatinib, Bortezomib, and Midostaurin in both the TCGA and GSE cohorts. Furthermore, a 6-gene signature was constructed to predicting prognosis, which performed better than TIDE score. The performance of IMscore model was successfully validated in three independent datasets and pan-cancer.

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Section: Discussionmentioning

confidence: 99%

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

Liu

Xiao

et al. 2022

Front. Pharmacol.

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“…Radiotherapy is one of the most common treatments for NSCLC, and the tumor sensitivity to radiotherapy may affect individual prognoses of NSCLC. However, predictable signatures related to the radiotherapy response are still limited [ 31 ]. The importance of upregulation, enhanced activation, and critical signature of the PI3K/Akt/mTOR pathway are well documented.…”

Section: Molecular Radioresistance Mechanismsmentioning

confidence: 99%

Radioresistance of Non-Small Cell Lung Cancers and Therapeutic Perspectives

Césaire

Montanari

Curcio

et al. 2022

Cancers

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Survival in unresectable locally advanced stage non-small cell lung cancer (NSCLC) patients remains poor despite chemoradiotherapy. Recently, adjuvant immunotherapy improved survival for these patients but we are still far from curing most of the patients with only a 57% survival remaining at 3 years. This poor survival is due to the resistance to chemoradiotherapy, local relapses, and distant relapses. Several biological mechanisms have been found to be involved in the chemoradioresistance such as cancer stem cells, cancer mutation status, or the immune system. New drugs to overcome this radioresistance in NSCLCs have been investigated such as radiosensitizer treatments or immunotherapies. Different modalities of radiotherapy have also been investigated to improve efficacity such as dose escalation or proton irradiations. In this review, we focused on biological mechanisms such as the cancer stem cells, the cancer mutations, the antitumor immune response in the first part, then we explored some strategies to overcome this radioresistance in stage III NSCLCs with new drugs or radiotherapy modalities.

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“…A large number of regularization regression models (e.g. LASSO regression) have been demonstrated to be effective [9][10][11][12][13][14]. Other genebased machine-learning algorithms such as random forest, partial least squares, and SVM could also successfully predict radiosensitivity and radiocurability [15,16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of radiosensitivity and radiocurability using a novel supervised artificial neural network

Zeng

Luo

et al. 2022

BMC Cancer

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Background Radiotherapy has been widely used to treat various cancers, but its efficacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity. However, there is still a lack of emerging powerful models, artificial neural networks (ANN), in the practice of gene-based radiosensitivity prediction. In addition, ANN may overfit and learn biologically irrelevant features. Methods We developed a novel ANN with Selective Connection based on Gene Patterns (namely ANN-SCGP) to predict radiosensitivity and radiocurability. We creatively used gene patterns (gene similarity or gene interaction information) to control the "on–off" of the first layer of weights, enabling the low-dimensional features to learn the gene pattern information. ANN-SCGP was trained and tested in 82 cell lines and 1,101 patients from the 11 pan-cancer cohorts. Results For survival fraction at 2 Gy, the root mean squared errors (RMSE) of prediction in ANN-SCGP was the smallest among all algorithms (mean RMSE: 0.1587–0.1654). For radiocurability, ANN-SCGP achieved the first and second largest C-index in the 12/20 and 4/20 tests, respectively. The low dimensional output of ANN-SCGP reproduced the patterns of gene similarity. Moreover, the pan-cancer analysis indicated that immune signals and DNA damage responses were associated with radiocurability. Conclusions As a model including gene pattern information, ANN-SCGP had superior prediction abilities than traditional models. Our work provided novel insights into radiosensitivity and radiocurability.

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Identification and Prognostic Value Exploration of Radiotherapy Sensitivity-Associated Genes in Non-Small-Cell Lung Cancer

Cited by 6 publications

References 54 publications

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

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

Radioresistance of Non-Small Cell Lung Cancers and Therapeutic Perspectives

Prediction of radiosensitivity and radiocurability using a novel supervised artificial neural network

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