Gene Expression Analysis to Predict Patient Response to Chemotherapy
Article Sidebar
-
Gene Expression, Personalized Treatment, Predictive Biomarkers
Abstract
Chemotherapy is a cornerstone of cancer treatment, yet patient responses vary significantly. Understanding the molecular basis of this variability is crucial for optimizing therapy. To predict patient response to chemotherapy using gene expression analysis, aiming to improve personalized treatment strategies. A prospective cohort study involving high-throughput RNA sequencing and microarray analysis was conducted on tumor biopsies and blood samples from patients undergoing chemotherapy. Differentially expressed genes were identified and correlated with treatment outcomes. Gene expression profiles of ABCB1, TP53, BRCA1, ERBB2, and BCL2 were found to significantly predict chemotherapy response. Patients with high expression of these genes showed better treatment outcomes. In vitro and in vivo models validated these findings, confirming the predictive power of these gene signatures. Gene expression analysis provides valuable insights into predicting chemotherapy response, facilitating personalized cancer treatment. Further clinical trials are necessary to validate these biomarkers and develop accessible diagnostic platforms.
Full text article
References
Abarghouei, S., Hedayati, A., Raeisi, M., Hadavand, B. S., Rezaei, H., & Abed-Elmdoust, A. (2021). Size-dependent effects of microplastic on uptake, immune system, related gene expression and histopathology of goldfish (Carassius auratus). Chemosphere, 276, 129977. https://doi.org/10.1016/j.chemosphere.2021.129977
Alhenawi, E., Al-Sayyed, R., Hudaib, A., & Mirjalili, S. (2022). Feature selection methods on gene expression microarray data for cancer classification: A systematic review. Computers in Biology and Medicine, 140, 105051. https://doi.org/10.1016/j.compbiomed.2021.105051
Alquicira-Hernandez, J., & Powell, J. E. (2021). Nebulosa recovers single-cell gene expression signals by kernel density estimation. Bioinformatics, 37(16), 2485–2487. https://doi.org/10.1093/bioinformatics/btab003
Arnold, P. R., Wells, A. D., & Li, X. C. (2020). Diversity and Emerging Roles of Enhancer RNA in Regulation of Gene Expression and Cell Fate. Frontiers in Cell and Developmental Biology, 7, 377. https://doi.org/10.3389/fcell.2019.00377
Aslam, F., Farooq, F., Amin, M. N., Khan, K., Waheed, A., Akbar, A., Javed, M. F., Alyousef, R., & Alabdulijabbar, H. (2020). Applications of Gene Expression Programming for Estimating Compressive Strength of High?Strength Concrete. Advances in Civil Engineering, 2020(1), 8850535. https://doi.org/10.1155/2020/8850535
Bommert, A., Welchowski, T., Schmid, M., & Rahnenführer, J. (2022). Benchmark of filter methods for feature selection in high-dimensional gene expression survival data. Briefings in Bioinformatics, 23(1), bbab354. https://doi.org/10.1093/bib/bbab354
Cai, G., Bossé, Y., Xiao, F., Kheradmand, F., & Amos, C. I. (2020). Tobacco Smoking Increases the Lung Gene Expression of ACE2, the Receptor of SARS-CoV-2. American Journal of Respiratory and Critical Care Medicine, 201(12), 1557–1559. https://doi.org/10.1164/rccm.202003-0693LE
Chen, J., Jiang, Q., Xia, X., Liu, K., Yu, Z., Tao, W., Gong, W., & Han, J. J. (2020a). Individual variation of the SARS?CoV?2 receptor ACE2 gene expression and regulation. Aging Cell, 19(7), e13168. https://doi.org/10.1111/acel.13168
Chen, J., Jiang, Q., Xia, X., Liu, K., Yu, Z., Tao, W., Gong, W., & Han, J. J. (2020b). Individual variation of the SARS?CoV?2 receptor ACE2 gene expression and regulation. Aging Cell, 19(7), e13168. https://doi.org/10.1111/acel.13168
Choudhary, S., Sreenivasulu, K., Mitra, P., Misra, S., & Sharma, P. (2021). Role of Genetic Variants and Gene Expression in the Susceptibility and Severity of COVID-19. Annals of Laboratory Medicine, 41(2), 129–138. https://doi.org/10.3343/alm.2021.41.2.129
Chu, H.-H., Khan, M. A., Javed, M., Zafar, A., Ijaz Khan, M., Alabduljabbar, H., & Qayyum, S. (2021). Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete. Ain Shams Engineering Journal, 12(4), 3603–3617. https://doi.org/10.1016/j.asej.2021.03.018
Dawood, M. A. O., Metwally, A. E.-S., El-Sharawy, M. E., Atta, A. M., Elbialy, Z. I., Abdel-Latif, H. M. R., & Paray, B. A. (2020). The role of ?-glucan in the growth, intestinal morphometry, and immune-related gene and heat shock protein expressions of Nile tilapia (Oreochromis niloticus) under different stocking densities. Aquaculture, 523, 735205. https://doi.org/10.1016/j.aquaculture.2020.735205
Ding, T., Xu, N., Liu, Y., Du, J., Xiang, X., Xu, D., Liu, Q., Yin, Z., Li, J., Mai, K., & Ai, Q. (2020). Effect of dietary bile acid (BA) on the growth performance, body composition, antioxidant responses and expression of lipid metabolism-related genes of juvenile large yellow croaker (Larimichthys crocea) fed high-lipid diets. Aquaculture, 518, 734768. https://doi.org/10.1016/j.aquaculture.2019.734768
Dong, M., Thennavan, A., Urrutia, E., Li, Y., Perou, C. M., Zou, F., & Jiang, Y. (2021). SCDC: Bulk gene expression deconvolution by multiple single-cell RNA sequencing references. Briefings in Bioinformatics, 22(1), 416–427. https://doi.org/10.1093/bib/bbz166
El-Esawi, M. A., Elkelish, A., Soliman, M., Elansary, H. O., Zaid, A., & Wani, S. H. (2020). Serratia marcescens BM1 Enhances Cadmium Stress Tolerance and Phytoremediation Potential of Soybean Through Modulation of Osmolytes, Leaf Gas Exchange, Antioxidant Machinery, and Stress-Responsive Genes Expression. Antioxidants, 9(1), 43. https://doi.org/10.3390/antiox9010043
Grønbech, C. H., Vording, M. F., Timshel, P. N., Sønderby, C. K., Pers, T. H., & Winther, O. (2020). scVAE: Variational auto-encoders for single-cell gene expression data. Bioinformatics, 36(16), 4415–4422. https://doi.org/10.1093/bioinformatics/btaa293
Hahn, V. S., Knutsdottir, H., Luo, X., Bedi, K., Margulies, K. B., Haldar, S. M., Stolina, M., Yin, J., Khakoo, A. Y., Vaishnav, J., Bader, J. S., Kass, D. A., & Sharma, K. (2021). Myocardial Gene Expression Signatures in Human Heart Failure With Preserved Ejection Fraction. Circulation, 143(2), 120–134. https://doi.org/10.1161/CIRCULATIONAHA.120.050498
Heshmati, J., Golab, F., Morvaridzadeh, M., Potter, E., Akbari-Fakhrabadi, M., Farsi, F., Tanbakooei, S., & Shidfar, F. (2020). The effects of curcumin supplementation on oxidative stress, Sirtuin-1 and peroxisome proliferator activated receptor ? coactivator 1? gene expression in polycystic ovarian syndrome (PCOS) patients: A randomized placebo-controlled clinical trial. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(2), 77–82. https://doi.org/10.1016/j.dsx.2020.01.002
Hodi, F. S., Wolchok, J. D., Schadendorf, D., Larkin, J., Long, G. V., Qian, X., Saci, A., Young, T. C., Srinivasan, S., Chang, H., Tang, H., Wind-Rotolo, M., Rizzo, J. I., Jackson, D. G., & Ascierto, P. A. (2021). TMB and Inflammatory Gene Expression Associated with Clinical Outcomes following Immunotherapy in Advanced Melanoma. Cancer Immunology Research, 9(10), 1202–1213. https://doi.org/10.1158/2326-6066.CIR-20-0983
Hör, J., Matera, G., Vogel, J., Gottesman, S., & Storz, G. (2020). Trans-Acting Small RNAs and Their Effects on Gene Expression in Escherichia coli and Salmonella enterica. EcoSal Plus, 9(1), 10.1128/ecosalplus.ESP-0030–2019. https://doi.org/10.1128/ecosalplus.esp-0030-2019
Hoseinifar, S. H., Shakouri, M., Yousefi, S., Van Doan, H., Shafiei, S., Yousefi, M., Mazandarani, M., Torfi Mozanzadeh, M., Tulino, M. G., & Faggio, C. (2020). Humoral and skin mucosal immune parameters, intestinal immune related genes expression and antioxidant defense in rainbow trout (Oncorhynchus mykiss) fed olive (Olea europea L.) waste. Fish & Shellfish Immunology, 100, 171–178. https://doi.org/10.1016/j.fsi.2020.02.067
Hu, F.-F., Liu, C.-J., Liu, L.-L., Zhang, Q., & Guo, A.-Y. (2021). Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response. Briefings in Bioinformatics, 22(3), bbaa176. https://doi.org/10.1093/bib/bbaa176
Hussain, Q., Asim, M., Zhang, R., Khan, R., Farooq, S., & Wu, J. (2021). Transcription Factors Interact with ABA through Gene Expression and Signaling Pathways to Mitigate Drought and Salinity Stress. Biomolecules, 11(8), 1159. https://doi.org/10.3390/biom11081159
Javed, M. F., Amin, M. N., Shah, M. I., Khan, K., Iftikhar, B., Farooq, F., Aslam, F., Alyousef, R., & Alabduljabbar, H. (2020). Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete. Crystals, 10(9), 737. https://doi.org/10.3390/cryst10090737
Jézéquel, P., Gouraud, W., Ben Azzouz, F., Guérin-Charbonnel, C., Juin, P. P., Lasla, H., & Campone, M. (2021). bc-GenExMiner 4.5: New mining module computes breast cancer differential gene expression analyses. Database, 2021, baab007. https://doi.org/10.1093/database/baab007
Karasinska, J. M., Topham, J. T., Kalloger, S. E., Jang, G. H., Denroche, R. E., Culibrk, L., Williamson, L. M., Wong, H.-L., Lee, M. K. C., O’Kane, G. M., Moore, R. A., Mungall, A. J., Moore, M. J., Warren, C., Metcalfe, A., Notta, F., Knox, J. J., Gallinger, S., Laskin, J., … Schaeffer, D. F. (2020). Altered Gene Expression along the Glycolysis–Cholesterol Synthesis Axis Is Associated with Outcome in Pancreatic Cancer. Clinical Cancer Research, 26(1), 135–146. https://doi.org/10.1158/1078-0432.CCR-19-1543
Khan, M. A., Memon, S. A., Farooq, F., Javed, M. F., Aslam, F., & Alyousef, R. (2021a). Compressive Strength of Fly?Ash?Based Geopolymer Concrete by Gene Expression Programming and Random Forest. Advances in Civil Engineering, 2021(1), 6618407. https://doi.org/10.1155/2021/6618407
Khan, M. A., Memon, S. A., Farooq, F., Javed, M. F., Aslam, F., & Alyousef, R. (2021b). Compressive Strength of Fly?Ash?Based Geopolymer Concrete by Gene Expression Programming and Random Forest. Advances in Civil Engineering, 2021(1), 6618407. https://doi.org/10.1155/2021/6618407
Konkle, B. A., Walsh, C. E., Escobar, M. A., Josephson, N. C., Young, G., Von Drygalski, A., McPhee, S. W. J., Samulski, R. J., Bilic, I., De La Rosa, M., Reipert, B. M., Rottensteiner, H., Scheiflinger, F., Chapin, J. C., Ewenstein, B., & Monahan, P. E. (2021). BAX 335 hemophilia B gene therapy clinical trial results: Potential impact of CpG sequences on gene expression. Blood, 137(6), 763–774. https://doi.org/10.1182/blood.2019004625
Kumar, S., & Mohapatra, T. (2021). Deciphering Epitranscriptome: Modification of mRNA Bases Provides a New Perspective for Post-transcriptional Regulation of Gene Expression. Frontiers in Cell and Developmental Biology, 9, 628415. https://doi.org/10.3389/fcell.2021.628415
Liu, Y., Yang, Q., & Zhao, F. (2021). Synonymous but Not Silent: The Codon Usage Code for Gene Expression and Protein Folding. Annual Review of Biochemistry, 90(1), 375–401. https://doi.org/10.1146/annurev-biochem-071320-112701
Ming, J., Ye, J., Zhang, Y., Xu, Q., Yang, X., Shao, X., Qiang, J., & Xu, P. (2020). Optimal dietary curcumin improved growth performance, and modulated innate immunity, antioxidant capacity and related genes expression of NF-?B and Nrf2 signaling pathways in grass carp (Ctenopharyngodon idella) after infection with Aeromonas hydrophila. Fish & Shellfish Immunology, 97, 540–553. https://doi.org/10.1016/j.fsi.2019.12.074
Miranda, S., Vilches, P., Suazo, M., Pavez, L., García, K., Méndez, M. A., González, M., Meisel, L. A., Defilippi, B. G., & Del Pozo, T. (2020). Melatonin triggers metabolic and gene expression changes leading to improved quality traits of two sweet cherry cultivars during cold storage. Food Chemistry, 319, 126360. https://doi.org/10.1016/j.foodchem.2020.126360
Mohamed, A. A.-R., El-Houseiny, W., EL-Murr, A. E., Ebraheim, L. L. M., Ahmed, A. I., & El-Hakim, Y. M. A. (2020). Effect of hexavalent chromium exposure on the liver and kidney tissues related to the expression of CYP450 and GST genes of Oreochromis niloticus fish: Role of curcumin supplemented diet. Ecotoxicology and Environmental Safety, 188, 109890. https://doi.org/10.1016/j.ecoenv.2019.109890
Moustafa, E. M., Dawood, M. A. O., Assar, D. H., Omar, A. A., Elbialy, Z. I., Farrag, F. A., Shukry, M., & Zayed, M. M. (2020). Modulatory effects of fenugreek seeds powder on the histopathology, oxidative status, and immune related gene expression in Nile tilapia (Oreochromis niloticus) infected with Aeromonas hydrophila. Aquaculture, 515, 734589. https://doi.org/10.1016/j.aquaculture.2019.734589
Ouyang, J. F., Kamaraj, U. S., Cao, E. Y., & Rackham, O. J. L. (2021). ShinyCell: Simple and sharable visualization of single-cell gene expression data. Bioinformatics, 37(19), 3374–3376. https://doi.org/10.1093/bioinformatics/btab209
Ouyang, Y., Yin, J., Wang, W., Shi, H., Shi, Y., Xu, B., Qiao, L., Feng, Y., Pang, L., Wei, F., Guo, X., Jin, R., & Chen, D. (2020). Downregulated Gene Expression Spectrum and Immune Responses Changed During the Disease Progression in Patients With COVID-19. Clinical Infectious Diseases, 71(16), 2052–2060. https://doi.org/10.1093/cid/ciaa462
Park, C., Ha, J., & Park, S. (2020). Prediction of Alzheimer’s disease based on deep neural network by integrating gene expression and DNA methylation dataset. Expert Systems with Applications, 140, 112873. https://doi.org/10.1016/j.eswa.2019.112873
Pinal-Fernandez, I., Casal-Dominguez, M., Derfoul, A., Pak, K., Miller, F. W., Milisenda, J. C., Grau-Junyent, J. M., Selva-O’Callaghan, A., Carrion-Ribas, C., Paik, J. J., Albayda, J., Christopher-Stine, L., Lloyd, T. E., Corse, A. M., & Mammen, A. L. (2020). Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis. Annals of the Rheumatic Diseases, 79(9), 1234–1242. https://doi.org/10.1136/annrheumdis-2019-216599
Reis, A. C., & Salis, H. M. (2020). An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models. ACS Synthetic Biology, 9(11), 3145–3156. https://doi.org/10.1021/acssynbio.0c00394
Unger, M. S., Li, E., Scharnagl, L., Poupardin, R., Altendorfer, B., Mrowetz, H., Hutter-Paier, B., Weiger, T. M., Heneka, M. T., Attems, J., & Aigner, L. (2020). CD8+ T-cells infiltrate Alzheimer’s disease brains and regulate neuronal- and synapse-related gene expression in APP-PS1 transgenic mice. Brain, Behavior, and Immunity, 89, 67–86. https://doi.org/10.1016/j.bbi.2020.05.070
Authors
Copyright (c) 2025 Loso Judijanto, Rafiullah Amin, Roya Zahir
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
