Potential Immune Biomarker Candidates and Immune Subtypes of Lung Adenocarcinoma for Developing mRNA Vaccines

Yang, Wang; Tan, Huaicheng; Takata, Yasuyuki; Chen, Xiaoxuan; Jing, Fangqi; Shi, Huashan

doi:10.3389/fimmu.2021.755401

Cited by 15 publications

(16 citation statements)

References 54 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Induction of cell death mechanisms rather than apoptosis has emerged as a new cancer treatment strategy, as most tumors are innately resistant to apoptosis (25). The development of bioinformatics technologies has taken a leading edge in analyzing complex genomic changes, and there have been numerous studies demonstrating the potential of transcriptome analysis in predicting lung cancer patient prognosis (26)(27)(28). We focused for the first time on CRGs in LUAD patients after radiotherapy in an attempt to provide a prognostic prediction for radiotherapy-resistant LUAD patients, and we found that CRGs were significant prognostic factors in LUAD, and advanced ANN models based on CRGs showed excellent predictive efficacy in both the training and external validation datasets, with OS reduced greatly in highrisk patients.…”

Section: Discussionmentioning

confidence: 99%

Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma

Luo²,

Wang³

et al. 2022

Front. Endocrinol.

View full text Add to dashboard Cite

BackgroundCuproptosis is a recently found non-apoptotic cell death type that holds promise as an emerging therapeutic modality in lung adenocarcinoma (LUAD) patients who develop resistance to radiotherapy and chemotherapy. However, the Cuproptosis’ role in the onset and progression of LUAD remains unclear.MethodsCuproptosis-related genes (CRGs) were identified by a co-expression network approach based on LUAD cell line data from radiotherapy, and a robust risk model was developed using deep learning techniques based on prognostic CRGs and explored the value of deep learning models systematically for clinical applications, functional enrichment analysis, immune infiltration analysis, and genomic variation analysis.ResultsA three-layer artificial neural network risk model was constructed based on 15 independent prognostic radiotherapy-related CRGs. The risk model was observed as a robust independent prognostic factor for LUAD in the training as well as three external validation cohorts. The patients present in the low-risk group were found to have immune “hot” tumors exhibiting anticancer activity, whereas the high-risk group patients had immune “cold” tumors with active metabolism and proliferation. The high-risk group patients were more sensitive to chemotherapy whereas the low-risk group patients were more sensitive to immunotherapy. Genomic variants did not vary considerably among both groups of patients.ConclusionOur findings advance the understanding of cuproptosis and offer fresh perspectives on the clinical management and precision therapy of LUAD.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma

Luo²,

Wang³

et al. 2022

Front. Endocrinol.

View full text Add to dashboard Cite

show abstract

“…In LUAD, GPRIN1 expression is associated with overall survival and poor prognosis [ 22 , 23 ]. Moreover, GPRIN1 may be a LUAD-related immune biomarker candidate to activate the immune response [ 37 ]. Conversely, Zhou et al .…”

Section: Discussionmentioning

confidence: 99%

Circular RNA circ_0002360 regulates the Taxol resistance and malignant behaviors of Taxol-resistant non-small cell lung cancer cells by microRNA-585-3p-dependent modulation of G protein regulated inducer of neurite outgrowth 1

Cui

Zhang

et al. 2022

Bioengineered

View full text Add to dashboard Cite

Drug resistance has become the major obstacle for the treatment of non-small cell lung cancer (NSCLC). Circular RNAs (circRNAs) are tightly linked to the development of drug resistance of NSCLC. Herein, we tested the function of circ_0002360 in the Taxol resistance of NSCLC. Circ_0002360, microRNA (miR)-585-3p and G protein regulated inducer of neurite outgrowth 1 (GPRIN1) were quantified by quantitative real-time PCR (qRT-PCR). To identify the circular structure of circ_0002360, RNase R digestion was applied. To detect cell proliferation, colony formation and 5-ethynyl-2’-deoxyuridine (EdU) assays were used. For assessment of cell apoptosis, flow cytometry was adopted. For motility and invasion analyses, transwell assay was employed. Our data showed that circ_0002360 was mainly located in the cytoplasm and was highly expressed in the Taxol-resistant NSCLC. Silencing of circ_0002360 inhibited cell Taxol resistance, proliferation, motility, and invasiveness and induced apoptosis in vitro . MiR-585-3p was underexpressed in Taxol-resistant NSCLC and was targeted by circ_0002360. MiR-585-3p knockdown alleviated the influence of circ_0002360 silence on Taxol-resistant cells. GPRIN1 was directly targeted by miR-585-3p. The influence of miR-585-3p on cell Taxol resistance and functional behaviors was reversed by GPRIN1 overexpression. Moreover, circ_0002360 modulated GPRIN1 through miR-585-3p. Additionally, silencing of circ_0002360 weakened the growth of xenografts in vivo . Our study demonstrated that silencing of circ_0002360 enhanced the Taxol sensitivity and suppressed the malignant behaviors of Taxol-resistant NSCLC cells by miR-585-3p/GPRIN1 axis, providing novel targets for improving the anti-tumor efficacy of Taxol in NSCLC.

show abstract

“…Tumor mutational burden (TMB) was a novel therapeutic metric for determining immunotherapy sensitivity. The R package “maftools” [ 31 ] were used to handle somatic mutation data, which includes somatic coding, base replacement, and insert-deletion mutations. The median TMB value was utilized as the cutoff point for classifying BCa patients as high-TMB or low-TMB.…”

Section: Methodsmentioning

confidence: 99%

A Novel Risk Model for lncRNAs Associated with Oxidative Stress Predicts Prognosis of Bladder Cancer

Feng

Yang

Kuang

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

Background. Oxidative stress (OS) reactions are closely related to the development and progression of bladder cancer (BCa). This project aimed to identify new potential biomarkers to predict the prognosis of BCa and improve immunotherapy. Methods. We downloaded transcriptomic information and clinical data on BCa from The Cancer Genome Atlas (TCGA). Screening for OS genes was statistically different between tumor and adjacent normal tissue. A coexpression analysis between lncRNAs and differentially expressed OS genes was performed to identify OS-related lncRNAs. Then, differentially expressed oxidative stress lncRNAs (DEOSlncRNAs) between tumors and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select the lncRNAs for risk assessment. LASSO analysis was conducted to establish a prognostic model. The prognostic risk model could accurately predict BCa patient prognosis and reveal a close correlation with clinicopathological features. We analyzed the principal component analysis (PCA), immune microenvironment, and half-maximal inhibitory concentration (IC50) in the risk groups. Results. We constructed a model containing eight DEOSlncRNAs (AC021321.1, AC068196.1, AC008750.1, SETBP1-DT, AL590617.2, THUMPD3-AS1, AC112721.1, and NR4A1AS). The prognostic risk model showed better results in predicting the prognosis of BCa patients and was strongly correlated with clinicopathological characteristics. We found great agreement between the calibration plots and prognostic predictions in this model. The areas under the receiver operating characteristic (ROC) curve (AUCs) at 1, 3, and 5 years were 0.792, 0.804, and 0.843, respectively. This model also showed good predictive ability regarding the tumor microenvironment and tumor mutation burden. In addition, the high-risk group was more sensitive to eight therapeutic agents, and the low-risk group was more responsive to five therapeutic agents. Sixteen immune checkpoints were significantly different between the two risk groups. Conclusion. Our eight DEOSlncRNA risk models provide new insights into predicting prognosis and clinical progression in BCa patients.

show abstract

Potential Immune Biomarker Candidates and Immune Subtypes of Lung Adenocarcinoma for Developing mRNA Vaccines

Cited by 15 publications

References 54 publications

Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma

Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma

Circular RNA circ_0002360 regulates the Taxol resistance and malignant behaviors of Taxol-resistant non-small cell lung cancer cells by microRNA-585-3p-dependent modulation of G protein regulated inducer of neurite outgrowth 1

A Novel Risk Model for lncRNAs Associated with Oxidative Stress Predicts Prognosis of Bladder Cancer

Contact Info

Product

Resources

About