Genetic Links Between Common Lung Diseases and Lung Cancer Progression: Bioinformatics and Machine Learning Insights

Md Ali Hossain; Tania Akter Asa; Md. Zulfiker Mahmud; AKM Azad; Mohammad Zahidur Rahman; Mohammad Ali Moni; Ahmed Moustafa

doi:10.28991/ESJ-2025-09-02-021

Authors

Md Ali Hossain 1) Department of Computer Science & Engineering, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh. 2) Health Informatics Lab, Department of Computer Science & Engineering, Daffodil International University, Dhaka 1216, Bangladesh.
Tania Akter Asa 2) Health Informatics Lab, Department of Computer Science & Engineering, Daffodil International University, Dhaka 1216, Bangladesh. 3) Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh.
Md. Zulfiker Mahmud Department of Computer Science and Engineering, Jagannath University, Dhaka 1100,, Bangladesh
AKM Azad Department of Mathematics & Statistics, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318,, Saudi Arabia
Mohammad Zahidur Rahman
rmzahid@juniv.edu
Department of Computer Science & Engineering, Jahangirnagar University, Savar, Dhaka 1342,, Bangladesh
Mohammad Ali Moni 5) Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst 2795, Australia. 6) Rural Health Research Institute, Charles Sturt University, Orange 2800, Australia.
Ahmed Moustafa 7) Department of Human Anatomy and Physiology, Faculty of Health Sciences, University of Johannesburg, Doornfontein, 2094, South Africa. 8) Centre for Data Analytics and School of Psychology, Bond University, Gold Coast, Queensland, 4229, Australia.

Vol. 9 No. 2 (2025): April

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Lung cancer (LC) is one of the most frequently diagnosed cancers and remains the leading cause of cancer-related mortality worldwide, representing a significant global health challenge. While numerous common lung diseases (CLDs) are implicated in LC development, the underlying causes of LC originating from CLDs remain inadequately elucidated. A thorough exploration of LC's progression from CLDs is essential; our approach integrated bioinformatics and machine learning, utilizing data from GEO and TCGA databases. We began by identifying differentially expressed genes (DEGs) in LC and CLDs, and our gene-disease network revealed for the first time shared DEGs (LC shares significant genes with TB (36), asthma (10), pneumonia (17), COPD (18), and Idiopathic Pulmonary Fibrosis (IPF) (78)), providing insights into potential connections of LC with CLDs. This analysis not only broadened our understanding of their associations but also identified significant pathways and hub proteins (SPTBN1, KCNA4, SCN7A, KCNQ3, GRIA1, and SDC1) through a protein-protein interaction network (PPI). Furthermore, RNA-seq and clinical data were obtained from the cBioPortal portal for shared DEGs of LC and CLDs, assessing their impact on LC patient survival. Integrated mRNA-Seq and clinical data were analyzed via univariate and multivariate Cox Proportional Hazard models to elucidate the influence of significant genes on survival. Furthermore, we developed and deployed a predictive model leveraging the identified hub genes, which demonstrated high accuracy in predicting LC progression. The identified biomarkers and pathways hold promise for further translational research and potential therapeutic targets, advancing understanding of LC development from CLDs. Additionally, co-expression networks among common genes were explored using the Weighted Gene Co-expression Network Analysis (WGCNA). Finally, the hub genes were validated using the Human Protein Atlas (HPA) database and evaluated through various classification algorithms to ascertain their predictive power and diagnostic potential.

Doi: 10.28991/ESJ-2025-09-02-021

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Hossain, M. A., Asa, T. A., Mahmud, M. Z., Azad, A., Rahman, M. Z., Moni, M. A., & Moustafa, A. (2025). Genetic Links Between Common Lung Diseases and Lung Cancer Progression: Bioinformatics and Machine Learning Insights. Emerging Science Journal, 9(2), 916–937. https://doi.org/10.28991/ESJ-2025-09-02-021

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Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68(6), 394–424. doi:10.3322/caac.21492.

Lemjabbar-Alaoui, H., Hassan, O. U., Yang, Y.-W., & Buchanan, P. (2015). Lung cancer: Biology and treatment options. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer, 1856(2), 189–210. doi:10.1016/j.bbcan.2015.08.002.

Gridelli, C., Rossi, A., Carbone, D. P., Guarize, J., Karachaliou, N., Mok, T., Petrella, F., Spaggiari, L., & Rosell, R. (2015). Non-small-cell lung cancer. Nature Reviews Disease Primers, 1(1). doi:10.1038/nrdp.2015.9.

Sher, T., Dy, G. K., & Adjei, A. A. (2008). Small Cell Lung Cancer. Mayo Clinic Proceedings, 83(3), 355–367. doi:10.4065/83.3.355.

Navarro-Torné, A., Vidal, M., Trzaska, D. K., Passante, L., Crisafulli, A., Laang, H., van de Loo, J.-W., Berkouk, K., & Draghia-Akli, R. (2015). Chronic respiratory diseases and lung cancer research: a perspective from the European Union. European Respiratory Journal, 46(5), 1270–1280. doi:10.1183/13993003.00395-2015.

Sekine, Y., Katsura, H., Koh, E., Hiroshima, K., & Fujisawa, T. (2012). Early detection of COPD is important for lung cancer surveillance. European Respiratory Journal, 39(5), 1230–1240. doi:10.1183/09031936.00126011.

Young, A.Y., Shannon, V.R. (2020). Acute Respiratory Distress Syndrome in Cancer Patients. Oncologic Critical Care. Springer, Cham, Switzerland. doi:10.1007/978-3-319-74588-6_48.

Zhan, P., Suo, L. J., Qian, Q., Shen, X. K., Qiu, L. X., Yu, L. K., & Song, Y. (2011). Chlamydia pneumoniae infection and lung cancer risk: A meta-analysis. European Journal of Cancer, 47(5), 742–747. doi:10.1016/j.ejca.2010.11.003.

Xu, X., Liu, Z., Xiong, W., Qiu, M., Kang, S., Xu, Q., Cai, L., & He, F. (2020). Combined and interaction effect of chlamydia pneumoniae infection and smoking on lung cancer: A case-control study in Southeast China. BMC Cancer, 20(1), 1–10. doi:10.1186/s12885-020-07418-8.

Zhang, K., Qi, S., Cai, J., Zhao, D., Yu, T., Yue, Y., Yao, Y., & Qian, W. (2022). Content-based image retrieval with a Convolutional Siamese Neural Network: Distinguishing lung cancer and tuberculosis in CT images. Computers in Biology and Medicine, 140, 105096. doi:10.1016/j.compbiomed.2021.105096.

Qu, Y. L., Liu, J., Zhang, L. X., Wu, C. M., Chu, A. J., Wen, B. L., Ma, C., Yan, X. yan, Zhang, X., Wang, D. M., Lv, X., & Hou, S. J. (2017). Asthma and the risk of lung cancer: A meta-analysis. Oncotarget, 8(7), 11614–11620. doi:10.18632/oncotarget.14595.

Wang, Y., Liu, Y., Zhang, Q., Gao, R., & Wang, K. (2017). IPF and lung cancer: Homologous but different endings, the progress in the correlation research. International Journal of Clinical and Experimental Medicine, 10(3), 4319–4329.

Antoniou, K. M., Tomassetti, S., Tsitoura, E., & Vancheri, C. (2015). Idiopathic pulmonary fibrosis and lung cancer. Current Opinion in Pulmonary Medicine, 21(6), 626–633. doi:10.1097/mcp.0000000000000217.

Qubo, A. A., Numan, J., Snijder, J., Padilla, M., Austin, J. H. M., Capaccione, K. M., Pernia, M., Bustamante, J., O'connor, T., & Salvatore, M. M. (2022). Idiopathic pulmonary fibrosis and lung cancer: future directions and challenges. Breathe, 18(4). doi:10.1183/20734735.0147-2022.

Ang, L., Ghosh, P., & Seow, W. J. (2021). Association between previous lung diseases and lung cancer risk: a systematic review and meta-analysis. Carcinogenesis, 42(12), 1461–1474. doi:10.1093/carcin/bgab082.

Li, J., Zhang, J. T., Jiang, X., Shi, X., Shen, J., Feng, F., Chen, J., Liu, G., He, P., Jiang, J., Tsang, L. L., Wang, Y., Rosell, R., Jiang, L., He, J., & Chan, H. C. (2015). The cystic fibrosis transmembrane conductance regulator as a biomarker in non-small cell lung cancer. International Journal of Oncology, 46(5), 2107–2115. doi:10.3892/ijo.2015.2921.

Miron, O., Afrasanie, V. A., Paduraru, M. I., Trandafir, L. M., & Miron, L. (2020). The relationship between chronic lung diseases and lung cancer-a narrative review. J BUON, 25, 1687-92.

Otálora-Otálora, B. A., Florez, M., López-Kleine, L., Canas Arboleda, A., Grajales Urrego, D. M., & Rojas, A. (2019). Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases. Frontiers in Genetics, 10, 1260. doi:10.3389/fgene.2019.01260.

Yao, Y., Li, Z., & Gao, W. (2022). Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and NSCLC Progression:Evidence From Bioinformatics Analysis. Frontiers in Genetics, 13, 855789. doi:10.3389/fgene.2022.855789.

Dasgupta, S. (2024). Identification of Hub Genes in Interstitial Lung Disease and their Association With Lung Cancer: An In-Silico Analysis. ICCECE 2024 - International Conference on Computer, Electrical and Communication Engineering, 1–8. doi:10.1109/ICCECE58645.2024.10497218.

Hossain, M. A., Rahman, M. Z., Bhuiyan, T., & Moni, M. A. (2024). Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches. International Journal of Environmental Research and Public Health, 21(11), 1392. doi:10.3390/ijerph21111392.

Ding, X., Liu, H., Xu, Q., Ji, T., Chen, R., Liu, Z., & Dai, J. (2024). Shared biomarkers and mechanisms in idiopathic pulmonary fibrosis and non-small cell lung cancer. International Immunopharmacology, 134, 112162. doi:10.1016/j.intimp.2024.112162.

Dasgupta, S. (2024). Thinking Beyond Disease Silos: Dysregulated Genes Common in Tuberculosis and Lung Cancer as Identified by Systems Biology and Machine Learning. OMICS A Journal of Integrative Biology, 28(7), 347–356. doi:10.1089/omi.2024.0116.

Langfelder, P., & Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9, 1–13. doi:10.1186/1471-2105-9-559.

Ali Hossain, M., Asa, T. A., Rabiul Auwul, M., Aktaruzzaman, M., Mahfizur Rahman, M., Rahman, M. Z., & Moni, M. A. (2023). The pathogenetic influence of smoking on SARS-CoV-2 infection: Integrative transcriptome and regulomics analysis of lung epithelial cells. Computers in Biology and Medicine, 159, 106885. doi:10.1016/j.compbiomed.2023.106885.

Hossain, Md. A., Saiful Islam, S. M., Quinn, J. M. W., Huq, F., & Moni, M. A. (2019). Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality. Journal of Biomedical Informatics, 100, 103313. doi:10.1016/j.jbi.2019.103313.

Hossain, M. A., Asa, T. A., Rahman, M. M., Uddin, S., Moustafa, A. A., Quinn, J. M. W., & Moni, M. A. (2020). Network-based genetic profiling reveals cellular pathway differences between follicular thyroid carcinoma and follicular thyroid adenoma. International Journal of Environmental Research and Public Health, 17(4), 1373. doi:10.3390/ijerph17041373.

Xu, H., Moni, M. A., & Liò, P. (2015). Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer. Computational Biology and Chemistry, 59, 15–31. doi:10.1016/j.compbiolchem.2015.08.010.

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., & Von Mering, C. (2019). STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613. doi:10.1093/nar/gky1131.

Chen, S. H., Chin, C. H., Wu, H. H., Ho, C. W., Ko, M. T., & Lin, C. Y. (2009). cyto-Hubba: A Cytoscape plug-in for hub object analysis in network biology. 20th international conference on genome informatics. 14-16, December, 2009, Yokohama, Japan.

Ma, Z., Xu, J., Ru, L., & Zhu, W. (2021). Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis. Bioscience Reports, 41(4). doi:10.1042/BSR20203676.

Wei, J., Wang, Y., Shi, K., & Wang, Y. (2020). Identification of Core Prognosis"Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis. BioMed Research International, 8959210. doi:10.1155/2020/8959210.

Gu, F. F., Zhang, Y., Liu, Y. Y., Hong, X. H., Liang, J. Y., Tong, F., Yang, J. S., & Liu, L. (2016). Lung adenocarcinoma harboring concomitant SPTBN1-ALK fusion, c-Met overexpression, and HER-2 amplification with inherent resistance to crizotinib, chemotherapy, and radiotherapy. Journal of Hematology & Oncology, 9(1), 1–3. doi:10.1186/s13045-016-0296-8.

Guo, S., Wu, X. Y., Lei, T., Zhong, R., Wang, Y. R., Zhang, L., Zhao, Q. Y., Huang, Y., Shi, Y., & Wu, L. (2022). The Role and Therapeutic Value of Syndecan-1 in Cancer Metastasis and Drug Resistance. Frontiers in Cell and Developmental Biology, 9, 3663. doi:10.3389/fcell.2021.784983.

Zheng, Y., Chen, L., Li, J., Yu, B., Su, L., Chen, X., Yu, Y., Yan, M., Liu, B., & Zhu, Z. (2011). Hypermethylated DNA as potential biomarkers for gastric cancer diagnosis. Clinical Biochemistry, 44(17–18), 1405–1411. doi:10.1016/j.clinbiochem.2011.09.006.

Angi, B., Muccioli, S., Szabò, I., & Leanza, L. (2023). A Meta-Analysis Study to Infer Voltage-Gated K+ Channels Prognostic Value in Different Cancer Types. Antioxidants, 12(3), 573. doi:10.3390/antiox12030573.

Liu, Y., Li, X., Chang, R., Chen, Y., & Gao, Y. (2020). PLEK2 and SCN7A: novel biomarkers of non-small cell lung cancer. Preprint: Research Square, 1-19. doi:10.21203/rs.3.rs-16005/v1.

Lv, H., Song, H., Qin, Z., Xing, R., & Chen, Y. (2023). Underexpression of SCN7A is associated with poor prognosis in lung adenocarcinoma. Gene & Protein in Disease, 2(1), 363. doi:10.36922/gpd.363.

Xu, C., Song, L., Yang, Y., Liu, Y., Pei, D., Liu, J., Guo, J., Liu, N., Li, X., Liu, Y., Li, X., Yao, L., & Kang, Z. (2022). Clinical M2 Macrophage-Related Genes Can Serve as a Reliable Predictor of Lung Adenocarcinoma. Frontiers in Oncology, 12, 919899. doi:10.3389/fonc.2022.919899.

Al-Dherasi, A., Huang, Q. T., Liao, Y., Al-Mosaib, S., Hua, R., Wang, Y., Yu, Y., Zhang, Y., Zhang, X., Huang, C., Mousa, H., Ge, D., Sufiyan, S., Bai, W., Liu, R., Shao, Y., Li, Y., Zhang, J., Shi, L., ... Liu, Q. (2021). A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD). Cancer Cell International, 21(1), 1–16. doi:10.1186/s12935-021-01975-z.

Kumar-Singh, A., Parniewska, M. M., Giotopoulou, N., Javadi, J., Sun, W., Szatmári, T., Dobra, K., Hjerpe, A., & Fuxe, J. (2021). Nuclear syndecan-1 regulates epithelial-mesenchymal plasticity in tumor cells. Biology, 10(6), 521. doi:10.3390/biology10060521.

Shah, L., Walter, K. L., Borczuk, A. C., Kawut, S. M., Sonett, J. R., Gorenstein, L. A., Ginsburg, M. E., Steinglass, K. M., & Powell, C. A. (2004). Expression of syndecan-1 and expression of epidermal growth factor receptor are associated with survival in patients with nonsmall cell lung carcinoma. Cancer, 101(7), 1632–1638. doi:10.1002/cncr.20542.

Anttonen, A., Heikkilä, P., Kajanti, M., Jalkanen, M., & Joensuu, H. (2001). High syndecan-1 expression is associated with favourable outcome in squamous cell lung carcinoma treated with radical surgery. Lung Cancer, 32(3), 297–305. doi:10.1016/S0169-5002(00)00230-0.

Parimon, T., Brauer, R., Schlesinger, S. Y., Xie, T., Jiang, D., Ge, L., Huang, Y., Birkland, T. P., Parks, W. C., Habiel, D. M., Hogaboam, C. M., Gharib, S. A., Deng, N., Liu, Z., & Chen, P. (2018). Syndecan-1 Controls Lung Tumorigenesis by Regulating miRNAs Packaged in Exosomes. The American Journal of Pathology, 188(4), 1094–1103. doi:10.1016/j.ajpath.2017.12.009.

Zhai, Y., Chen, Y., Jiang, Y., & Li, Q. (2019). Weighted Gene Co-expression Network Analysis of Gene Modules for Lung Adenocarcinoma. 2018 5th International Conference on Systems and Informatics, ICSAI 2018, 473–477. doi:10.1109/ICSAI.2018.8599411.

Chen, M., Zeng, J., Chen, S., Li, J., Wu, H., Dong, X., Lei, Y., Zhi, X., & Yao, L. (2020). SPTBN1 suppresses the progression of epithelial ovarian cancer via SOCS3-mediated blockade of the JAK/STAT3 signaling pathway. Aging, 12(11), 10896–10911. doi:10.18632/aging.103303.

Ying, J., Lin, C., Wu, J., Guo, L., Qiu, T., Ling, Y., Shan, L., Zhou, H., Zhao, D., Wang, J., Liang, J., Zhao, J., Jiao, Y., Lu, N., & Zhao, H. (2015). Anaplastic lymphoma kinase rearrangement in digestive tract cancer: Implication for targeted therapy in Chinese population. PLoS ONE, 10(12), 144731. doi:10.1371/journal.pone.0144731.

Yang, P., Yang, Y., Sun, P., Tian, Y., Gao, F., Wang, C., Zong, T., Li, M., Zhang, Y., Yu, T., & Jiang, Z. (2020). βII spectrin (SPTBN1): Biological function and clinical potential in cancer and other diseases. International Journal of Biological Sciences, 17(1), 32–49. doi:10.7150/ijbs.52375.

Li, A., Zhang, W., Xia, H., Miao, Y., Zhou, H., Zhang, X., Dong, Q., Li, Q., Qiu, X., & Wang, E. (2016). Overexpression of CASS4 promotes invasion in non-small cell lung cancer by activating the AKT signaling pathway and inhibiting E-cadherin expression. Tumor Biology, 37(11), 15157–15164. doi:10.1007/s13277-016-5411-5.

Kang, J. Y., Yang, J., Lee, H., Park, S., Gil, M., & Kim, K. E. (2024). Systematic Multiomic Analysis of PKHD1L1 Gene Expression and Its Role as a Predicting Biomarker for Immune Cell Infiltration in Skin Cutaneous Melanoma and Lung Adenocarcinoma. International Journal of Molecular Sciences, 25(1), 359. doi:10.3390/ijms25010359.

Xu, J., Zheng, H., Yuan, S., Zhou, B., Zhao, W., Pan, Y., & Qi, D. (2019). Overexpression of ANLN in lung adenocarcinoma is associated with metastasis. Thoracic Cancer, 10(8), 1702–1709. doi:10.1111/1759-7714.13135.

Ru, M., Liu, Y.-B., Tian, Z.-H., GODJE, I. S. G., & Li, J.-Q. (2021). ANLN and Lung Adenocarcinoma Prognosis and Immune Infiltration Research. Preprint: Research Square, 1-21. doi:10.21203/rs.3.rs-826813/v1.

Kasprowicz, A., Sophie, G. D., Lagadec, C., & Delannoy, P. (2022). Role of GD3 Synthase ST8Sia I in Cancers. Cancers, 14(5), 1299. doi:10.3390/cancers14051299.

Tang, W., Wang, J., Dai, T., Qiu, H., Liu, C., Chen, S., & Hu, Z. (2024). Association of leptin receptor polymorphisms with susceptibility of non-small cell lung cancer: Evidence from 2249 subjects. Cancer Medicine, 13(8), 7178. doi:10.1002/cam4.7178.

Ji, X., Bossé, Y., Landi, M. T., Gui, J., Xiao, X., Qian, D., Joubert, P., Lamontagne, M., Li, Y., Gorlov, I., de Biasi, M., Han, Y., Gorlova, O., Hung, R. J., Wu, X., McKay, J., Zong, X., Carreras-Torres, R., Christiani, D. C., ... Amos, C. I. (2018). Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk. Nature Communications, 9(1). doi:10.1038/s41467-018-05074-y.

Galluzzi, L., Vitale, I., Senovilla, L., Olaussen, K. A., Pinna, G., Eisenberg, T., Goubar, A., Martins, I., Michels, J., Kratassiouk, G., Carmona-Gutierrez, D., Scoazec, M., Vacchelli, E., Schlemmer, F., Kepp, O., Shen, S., Tailler, M., Niso-Santano, M., Morselli, E., ... Kroemer, G. (2012). Prognostic Impact of Vitamin B6 Metabolism in Lung Cancer. Cell Reports, 2(2), 257–269. doi:10.1016/j.celrep.2012.06.017.

Zuo, H., Ueland, P. M., Midttun, í˜., Tell, G. S., Fanidi, A., Zheng, W., Shu, X., Xiang, Y., Wu, J., Prentice, R., Pettinger, M., Thomson, C. A., Giles, G. G., Hodge, A., Cai, Q., Blot, W. J., Johansson, M., Hultdin, J., Grankvist, K., ... Ulvik, A. (2019). Vitamin B6 catabolism and lung cancer risk: results from the Lung Cancer Cohort Consortium (LC3). Annals of Oncology, 30(3), 478–485. doi:10.1093/annonc/mdz002.

Ahmed, M. B., Alghamdi, A. A. A., Islam, S. U., Lee, J.-S., & Lee, Y.-S. (2022). cAMP Signaling in Cancer: A PKA-CREB and EPAC-Centric Approach. Cells, 11(13), 2020. doi:10.3390/cells11132020.

Han, K., Wang, J., Qian, K., Zhao, T., & Zhang, Y. (2021). Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes. Bioscience Reports, 41(10), BSR20211433. doi:10.1042/BSR20211433.

Zhuo, D., Li, X., & Guan, F. (2018). Biological Roles of Aberrantly Expressed Glycosphingolipids and Related Enzymes in Human Cancer Development and Progression. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.00466.

Wei, Z., Liu, X., Cheng, C., Yu, W., & Yi, P. (2021). Metabolism of Amino Acids in Cancer. Frontiers in Cell and Developmental Biology, 8. doi:10.3389/fcell.2020.603837.

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Genetic Links Between Common Lung Diseases and Lung Cancer Progression: Bioinformatics and Machine Learning Insights

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Genetic Links Between Common Lung Diseases and Lung Cancer Progression: Bioinformatics and Machine Learning Insights

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Impediments of Green Finance Adoption System: Linking Economy and Environment

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic