CDK1 and HSP90AA1 Appear as the Novel Regulatory Genes in Non-Small Cell Lung Cancer: A Bioinformatics Approach

Bhattacharyya, Nirjhar; Gupta, Samriddhi; Sharma, Shubham; Soni, Aman; Bagabir, Sali Abubaker; Bhattacharyya, Malini; Mukherjee, Atreyee; Almalki, Atiah H.; Alkhanani, Mustfa; Haque, Shafiul; Ray, Ashwini Kumar; Malik, Md. Zubbair

doi:10.3390/jpm12030393

Cited by 24 publications

(11 citation statements)

References 83 publications

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“…ESR1, an estrogen receptor, affects cell proliferation and differentiation in target tissues, participating in the pathological process including breast cancer, endometrial cancer, and osteoporosis [29,30]. HSP90AA1 is a target associated with colorectal cancer, non-small-cell, lung cancer, gastric cancer, breast cancer, and hepatocellular carcinoma [31][32][33][34][35][36]. It not only affects the survival of tumor 11 BioMed Research International cells but also acts on the invasion and migration of cancer cells and is closely related to the poor prognosis of tumors [37].…”

Section: Discussionmentioning

confidence: 99%

Network Pharmacology-Integrated Molecular Docking Reveals the Expected Anticancer Mechanism of Picrorhizae Rhizoma Extract

Zhao

Cai

et al. 2022

BioMed Research International

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This study sought to explore the anticancer mechanism of Picrorhizae Rhizoma (PR) extract based on network pharmacology and molecular docking. The potential chemicals of PR were screened through the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and relevant literatures. Corresponding targets of active ingredients were found with the help of the UniProtKB database, and therapeutic targets for cancer action were screened with the help of the GeneCards database. We used Cytoscape software to construct the compound-target-pathway network of PR extract. We utilized the STRING database to obtain the protein-protein interaction (PPI) network. We used DAVID database combining Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, molecular docking was employed for initial efficacy checking. We have identified 16 potential active components of PR through screening, involving 112 disease action targets. Utilizing the GeneCards database, 112 intersecting targets between PR extract and cancer were found, which mainly exerts anticancer effects by regulating tumor necrosis factor (TNF), recombinant caspase 3 (CASP3), c-Jun NH2-terminal kinase (JNK)/JUN, epidermal growth factor receptor (EGFR), and estrogen receptor-1 (ESR1) with some other target genes and pathways associated with cancer. The major anticancer species are prostate cancer, colorectal cancer, small cell lung cancer, etc. In the molecular docking study, herbactin had a strong affinity for TNF. Based on network pharmacology and molecular docking studies, PR and their compounds have demonstrated potential anticancer activities against several key targets. Our preliminary findings provide a strong foundation for further experiments with PR constituents.

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Section: Discussionmentioning

confidence: 99%

Network Pharmacology-Integrated Molecular Docking Reveals the Expected Anticancer Mechanism of Picrorhizae Rhizoma Extract

Zhao

Cai

et al. 2022

BioMed Research International

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“…Among the high-SHAP oncRNAs for the model, we observed overlap or vicinity of oncRNAs to some of the genes with significance in lung cancer etiology and prognosis. These included SOX2-OT (Dodangeh et al, 2023), HSP90AA1, (Niu et al, 2022; Bhattacharyya et al, 2022), and FZD2 (Tuluhong et al, 2021) (Figure 2e).…”

Section: Resultsmentioning

confidence: 99%

Deep generative AI models analyzing circulating orphan non-coding RNAs enable accurate detection of early-stage non-small cell lung cancer

Karimzadeh,

Momen-Roknabadi,

Cavazos

et al. 2024

Preprint

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Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors, when the disease can be more effectively managed and cured. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the generative capability of variational auto-encoders enables learning a robust and generalizable signature of blood-based biomarkers that capture true biological signals while removing spurious confounders (e.g., library size, zero-inflation, and batch effects). In this study, we analyzed orphan non-coding RNAs (oncRNAs) from serum samples of 1,050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls to evaluate the potential use of deep generative models. We demonstrated that our multi-task generative AI model, Orion, surpassed commonly used methods in both overall performance and generalizability to held-out datasets. Orion achieved an overall sensitivity of 92% (95% CI: 85%–97%) at 90% specificity for cancer detection across all stages, outperforming the sensitivity of other methods such as support vector machine (SVM) classifier, ElasticNet, or XGBoost on held-out validation datasets by more than ∼30%.

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“…CDK1 encodes a serine/threonine kinase that functions as a cell cycle checkpoint protein and plays a key role in controlling eukaryotic cell cycle progression [49]. Binding to different cyclin proteins, CDK1 is considered the most critical cell cycle element and is sufficient to regulate numerous steps in cell proliferation and organ development [50]. Recently, the cell cycle-independent function of CDK1 in enhancing overall protein synthesis has been uncovered [51].…”

Section: Discussionmentioning

confidence: 99%

Construction of a novel cuproptosis-related gene signature for predicting prognosis and estimating tumor immune microenvironment status in papillary thyroid carcinoma

et al. 2022

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Background Cuproptosis, a new form of programmed cell death, has been recently reported to be closely related to tumor progression. However, the significance of cuproptosis-related genes (CRGs) in papillary thyroid carcinoma (PTC) is still unclear. Therefore, this study aimed to investigate the role of the CRG signature in prognosis prediction and immunotherapeutic effect estimation in patients with PTC. Methods RNA-seq data and the corresponding clinical information of patients with PTC were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Comprehensive analyses, namely, consensus clustering, immune analyses, functional enrichment, least absolute shrinkage and selection operator-multivariate Cox regression, and nomogram analysis, were performed to identify new molecular subgroups, determine the tumor immune microenvironment (TIME) status of the identified subgroups, and construct a clinical model. Independent verification cohort data and quantitative real-time polymerase chain reaction (qPCR) was performed to validate the expression of specific prognosis-related and differentially expressed CRGs (P-DECRGs). Results In the TCGA database, 476 patients with PTC who had complete clinical and follow-up information were included. Among 135 CRGs, 21 were identified as P-DECRGs. Two molecular subgroups with significantly different disease-free survival and TIME statuses were identified based on these 21 P-DECRGs. The differentially expressed genes between the two subgroups were mainly associated with immune regulation. The risk model and nomogram were constructed based on four specific P-DECRGs and validated as accurate prognostic predictions and TIME status estimation for PTC by TCGA and GEO verification cohorts. Finally, the qPCR results of 20 PTC and paracancerous thyroid tissues validated those in the TCGA database. Conclusions Four specific P-DECRGs in PTC were identified, and a clinical model based on them was established, which may be helpful for individualized immunotherapeutic strategies and prognostic prediction in patients with PTC.

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CDK1 and HSP90AA1 Appear as the Novel Regulatory Genes in Non-Small Cell Lung Cancer: A Bioinformatics Approach

Cited by 24 publications

References 83 publications

Network Pharmacology-Integrated Molecular Docking Reveals the Expected Anticancer Mechanism of Picrorhizae Rhizoma Extract

Network Pharmacology-Integrated Molecular Docking Reveals the Expected Anticancer Mechanism of Picrorhizae Rhizoma Extract

Deep generative AI models analyzing circulating orphan non-coding RNAs enable accurate detection of early-stage non-small cell lung cancer

Construction of a novel cuproptosis-related gene signature for predicting prognosis and estimating tumor immune microenvironment status in papillary thyroid carcinoma

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