Top differentially expressed genes between patients with progressive disease and those with complete remission following the first course of treatment in different cancer types

Different treatment modalities might lead to partial or complete remission or progressive disease in patients with cancer. In the present study, based on transcriptomics data analysis, top 10 genes whose expression most significantly differ between patients with complete remission/response and patients with progressive disease, following the first course of treatment, were identified in different cancer types, namely, bladder urothelial carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, brain lower grade glioma, lung adenocarcinoma, prostate adenocarcinoma and stomach adenocarcinoma. The majority of the identified genes in different cancer types were found to be highly understudied in the context of those cancer types. Besides, there does not seem to be any overlap in the top differentially expressed genes between different cancers, suggesting that genes/proteins associated with disease progression might be highly cancer type-dependent. Combined, findings from this study indicate the need for further research on the identified genes in given cancer types to better understand if or how their differential expression between cancer patients with complete response and patients with progressive disease clinically influence disease outcomes, with the ultimate aim of finding actionable targets to improve treatment success for patients with given malignancies. Top differentially expressed genes between patients with progressive disease and those with complete remission following the first course of treatment in different cancer types Running head: DE genes based on chemotherapy response Caglar BERKEL 1 1 Department of Molecular Biology and Genetics, Tokat Gaziosmanpasa University, TÜRKİYE correspondence to: Caglar BERKEL; [email protected]; 0000-0003-4787-5157

Different treatment modalities might lead to partial or complete remission or progressive disease in patients with cancer. In the present study, based on transcriptomics data analysis, top 10 genes whose expression most significantly differ between patients with complete remission/response and patients with progressive disease, following the first course of treatment, were identified in different cancer types, namely, bladder urothelial carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, brain lower grade glioma, lung adenocarcinoma, prostate adenocarcinoma and stomach adenocarcinoma. The majority of the identified genes in different cancer types were found to be highly understudied in the context of those cancer types. Besides, there does not seem to be any overlap in the top differentially expressed genes between different cancers, suggesting that genes/proteins associated with disease progression might be highly cancer type-dependent. Combined, findings from this study indicate the need for further research on the identified genes in given cancer types to better understand if or how their differential expression between cancer patients with complete response and patients with progressive disease clinically influence disease outcomes, with the ultimate aim of finding actionable targets to improve treatment success for patients with given malignancies. Main text Cancer remission, which indicates that the signs and symptoms of a specific cancer are reduced in a patient, can be partial or complete. When all signs and symptoms of a cancer have disappeared, it is called complete remission. In contrast, progressive disease indicates an increase in the size of pre-existing lesions and/or the appearance of new lesions [1]. Mostly, current treatment option is changed when the efficacy is evaluated as progressive disease [2]. In the present analysis, using TCGA (The Cancer Genome Atlas) transcriptomics datasets [3, 4], I identified top 10 genes whose expression most significantly differ between patients with complete remission/response and patients with progressive disease, following the first course of treatment, for certain cancer types for which these type of data is available (These cancer types are BLCA (Bladder Urothelial Carcinoma) (sample size (n) after filtering = 218), CESC (Cervical squamous cell carcinoma and endocervical adenocarcinoma) (n = 113), LGG (Brain Lower Grade Glioma) (n = 212), LUAD (Lung adenocarcinoma) (n = 134), PRAD (Prostate adenocarcinoma) (n = 219) and STAD (Stomach adenocarcinoma) (n = 182)). For all of these 6 cancer types, I compared the expression of each gene (a total of 23368) in patients based on treatment outcome (progressive disease vs complete remission/response), and then ordered genes according to increasing p values and selected only top 10 genes with lowest p values (those with most significant difference in expression between patients with progressive disease and patients with complete remission/response). Data analysis and visualisation was completely performed in R programming environment [5] using ExperimentHub [6], AnnotationHub [7], SummarizedExperiment [8] Bioconductor packages, and tidyverse [9] and ggpubr [10] R packages, as previously reported [11, 12]. Statistical tests used to analyze RNA-Seq data between patient groups were detailed in refs 11, 12. I found that the top 10 genes whose expression most significantly differ between BLCA (bladder urothelial carcinoma) patients with complete remission/response and BLCA patients with progressive disease following the first course of treatment are WSCD2 (p = 3.3e-08), PLCH1, PLIN4, UCP2, C1orf88, IFT140, SHROOM1, LOC100188947, IL9R and PLIN5 (1.5e-06) (Figure 1; genes were ordered based on increasing p values). The expression of all of these 10 genes are higher in BLCA patients with complete remission compared to those with progressive disease (Figure 1). Within these genes, PLIN4 was identified to construct a prognostic model in bladder cancer [14]. The top 10 genes whose expression are most significantly different between patients with complete remission and patients with progressive disease following the first course of treatment in the case of CESC (cervical squamous cell carcinoma and endocervical adenocarcinoma) are LOC647589 (p = 8.2e-08), PRSS48, FTL, C5AR1, CYP3A7−CYP3AP1, CETN2, SLC25A14, MS4A6E, IRF3 and LOC401177 (p = 1.2e-05) (Figure 2). Those that start with “LOC” are uncharacterized genes. Again, the expression of all of these 10 genes are higher in CESC patients with complete remission than those with progressive disease (Figure 2). Within these genes, interaction of CETN2 with a secreted effector protein from an obligate intracellular bacterium Chlamydia trachomatis, was shown to lead to increased risk of cervical cancer upon chlamydial infection [15]. Another identified gene, IRF3, was found to promote regulatory T cell differentiation from human CD4+-naïve T cells in cervical cancer, pointing to its role in immunosuppression [16]. For patients with LGG (brain lower grade glioma), the top 10 genes whose expression most significantly change based on treatment outcome (progressive disease vs complete remission) were NOG (p = 1.59e-08), BMP2, CAMSAP3, LPPR3, MYCBP, TCEAL2, MAGEH1, LOC400043, TBC1D1 and HMGN5 (p = 2.32e-06) (Figure 3). With the exception of MYCBP, LOC400043 and TBC1D1, all genes showed higher expression in LGG patients with complete response than those who have progressive disease (Figure 3). Within the identified genes, NOG expression was found to correlate negatively with immunosuppressive cells and immune checkpoint molecules [17]. Its protein expression was shown to be reduced in glioma cells, and to be lower in high-grade gliomas compared to low-grade gliomas [17]. Another gene, BMP2, was identified to be associated with overall survival in patients with LGG [18]. Another gene, MycBP, was shown to be associated negatively with survival in LGG [19]. MAGEH1 was also identified as an independent prognosticator in LGG [20]. For LUAD (lung adenocarcinoma) patients, the top 10 genes whose expression are most significantly different between patients with complete remission and patients with progressive disease following the first course of treatment were C1orf162 (p = 1.83e-22), KCTD12, FGL2, PDZRN3, P2RY14, MOXD1, DNM3OS, DCN, LOC100652846 and PAPLN (p = 5.85e-19); and the expression of all these 10 genes were higher in LUAD patients with complete response compared to those with progressive disease (Figure 4). In one study, KCTD12 was demonstrated to have excellent accuracy in the diagnosis of LUAD [21]. FGL2 expression was associated with a better prognosis in lung adenocarcinoma [22]. Patients with LUAD who have high P2RY14 expression were observed to a better prognosis than those with low expression [23]. The gene expression profile of MOXD1 was shown to be useful in predicting the durable response to anti-PD-1/PD-L1 therapy [24]. The lncRNA DNM3OS was found to be associated with epithelial-mesenchymal transition and prognostic for the clinical outcomes of patients with lung squamous cell carcinoma [25]. In a non-small cell lung cancer (NSCLC) cohort, papilin (PAPLN) was found to be associated with immune checkpoint inhibitor (ICI) sensitivity [26]. Similarly, I found that top 10 genes whose expression most significantly differ between PRAD (prostate adenocarcinoma) patients with complete remission and PRAD patients with progressive disease following the first course of treatment are PIWIL3 (p = 1.48e-10), DEFB136, MT1F, CNTNAP2, CA14, SLC22A3, TPTE2P3, GALNT9, LSAMP and MT1DP (p = 4.97e-8); and that the expression of all these 10 genes were higher in PRAD patients with complete response than those with progressive disease (Figure 5). In one study, MT1F was shown to be useful in a novel prognostic model for prostate cancer [27]. Similarly, CA14 was found to be useful in risk score model in prostate cancer patients [28]. The downregulation of SLC22A3 was found to be associated with drug resistance in prostate cancer [29]. Certain genomic alterations of LSAMP were observed to be associated with aggressive prostate cancer in African American men [30]. Lastly, I studied the expression of genes in STAD (stomach adenocarcinoma) patients in terms of disease outcome (Figure 6). The expression of the following 10 genes were most significantly different between STAD patients with complete remission and STAD patients with progressive disease: FEZF2 (p = 1.1e-09), MIR320A, FOXB2, FLJ46300, C8orf40, SOX1, MIR624, RBMXL2, SCARNA2 and MIR4488 (p = 1.9e-05). Here, it should be noted that some of these genes are genes that encode miRNAs. Aberrant expression of FEZF2 was shown to promote gastric tumorigenesis through epigenetic modification of its promoter region [31]. miR-320a was shown to serve as a negative regulator in the progression of gastric cancer and to modulate cell growth and chemosensitivity in gastric cancer [32,33,34]. It should also be noted here that 3 of 10 identified genes associated with therapy response in stomach adenocarcinoma here is miRNAs. The majority of the identified genes in different cancer types are highly understudied in the context of those cancer types. For instance, in the case of PRAD (since, it has the largest sample size among other cancer types included), 6 out of the top 10 genes which were identified in the present study to show differential expression based on treatment outcome have never been studied in the context of prostate cancer (i.e. 0 search results in PubMed). Besides, the studies on the other 4 genes in prostate cancer are highly limited (equal or less than 10 search results). This indicates the need for further research on the identified genes in given cancer types to better understand if or how their differential expression between cancer patients with complete response and patients with progressive disease clinically influences disease outcomes, with the ultimate aim of finding actionable targets to improve treatment success for patients with given malignancies. Besides, there does not seem to be any overlap in the top differentially expressed genes between different cancers, suggesting that genes/proteins associated with disease progression might be highly cancer type-dependent. The most commonly prescribed treatment, for each tumor type studied here, is as following: BLCA (Cisplatin+Gemcitabine), CESC (Cisplatin), LGG (Temozolomide), LUAD (Carboplatin+Paclitaxel), PRAD (Leuprolide) and STAD (Fluorouracil) [13]. In this study, minor differences in the first course of therapy between patients with the same cancer type were not taken into account, due to the unavailability of this type of data.

Ethical Statements: Not applicable. Patient Consent: Not applicable. Data availability statement The data used in the present study is publicly available. Abbreviations BLCA: Bladder Urothelial Carcinoma CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma LGG: Brain Lower Grade Glioma LUAD: Lung Adenocarcinoma PRAD: Prostate Adenocarcinoma STAD: Stomach Adenocarcinoma TCGA: The Cancer Genome Atlas Figure legends Figure 1. The top 10 genes whose expression most significantly differ between BLCA (Bladder Urothelial Carcinoma) patients with complete remission/response and BLCA patients with progressive disease following the first course of treatment ns (non-significant): p > 0.05; *: p <= 0.05; **: p <= 0.01; ***: p <= 0.001 and ****: p <= 0.0001. Figure 2. The top 10 genes whose expression most significantly differ between CESC (Cervical squamous cell carcinoma and endocervical adenocarcinoma) patients with complete remission/response and CESC patients with progressive disease following the first course of treatment ns (non-significant): p > 0.05; *: p <= 0.05; **: p <= 0.01; ***: p <= 0.001 and ****: p <= 0.0001. Figure 3. The top 10 genes whose expression most significantly differ between LGG (Brain Lower Grade Glioma) patients with complete remission/response and LGG patients with progressive disease following the first course of treatment ns (non-significant): p > 0.05; *: p <= 0.05; **: p <= 0.01; ***: p <= 0.001 and ****: p <= 0.0001. Figure 4. The top 10 genes whose expression most significantly differ between LUAD (Lung adenocarcinoma) patients with complete remission/response and LUAD patients with progressive disease following the first course of treatment ns (non-significant): p > 0.05; *: p <= 0.05; **: p <= 0.01; ***: p <= 0.001 and ****: p <= 0.0001. Figure 5. The top 10 genes whose expression most significantly differ between PRAD (Prostate adenocarcinoma) patients with complete remission/response and PRAD patients with progressive disease following the first course of treatment ns (non-significant): p > 0.05; *: p <= 0.05; **: p <= 0.01; ***: p <= 0.001 and ****: p <= 0.0001. Figure 6. The top 10 genes whose expression most significantly differ between STAD (Prostate adenocarcinoma) patients with complete remission/response and STAD patients with progressive disease following the first course of treatment ns (non-significant): p > 0.05; *: p <= 0.05; **: p <= 0.01; ***: p <= 0.001 and ****: p <= 0.0001. Compliance with Ethical Standards: Not applicable. Funding: No funding has been received for this study. Conflict of Interest: The authors declare no conflicts of interest. Contributions: CB conceptualized the study, CB and SYA performed the data analysis and visualization; CB wrote the paper.

1. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45:228–47. 2. Mori R, Futamura M, Morimitsu K, Asano Y, Tokumaru Y, Kitazawa M, Yoshida K. The mode of progressive disease affects the prognosis of patients with metastatic breast cancer. World J Surg Oncol. 2018 Aug 14;16(1):169. doi: 10.1186/s12957-018-1472-9. PMID: 30107807; PMCID: PMC6092765. 3. Cancer Genome Atlas Research Network; Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013 Oct;45(10):1113-20. doi: 10.1038/ng.2764. PMID: 24071849; PMCID: PMC3919969. 4. Rahman M, Jackson LK, Johnson WE, Li DY et al. Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results. Bioinformatics 2015 Nov 15;31(22):3666-72. PMID: 26209429 5. R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 6. Morgan M, Shepherd L (2022). _ExperimentHub: Client to access ExperimentHub resources_. R package version 2.4.0. 7. Morgan M, Shepherd L (2022). _AnnotationHub: Client to access AnnotationHub resources_. R package version 3.4.0. 8. Morgan M, Obenchain V, Hester J, Pagès H (2022). version 1.26.1, . 9. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” _Journal of Open Source Software_, *4*(43), 1686. doi:10.21105/joss.01686 . 10. Kassambara A (2023). _ggpubr: ’ggplot2’ Based Publication Ready Plots_. R package version 0.6.0, . 11. Berkel, C. An analysis of gasdermin family of genes in UCEC with respect to malignancy status, mutation percentages and histologic diagnosis. Genome Instab. Dis. 5, 105–115 (2024). https://doi.org/10.1007/s42764-024-00128-6 12. Berkel C, Cacan E. Copy number and expression of CEP89, a protein required for ciliogenesis, are increased and predict poor prognosis in patients with ovarian cancer. Cell Biochem Funct. 2022 Apr;40(3):298-309. doi: 10.1002/cbf.3694. Epub 2022 Mar 14. PMID: 35285957. 13. Moiso E. Manual curation of TCGA treatment data and identification of potential markers of therapy response. medRxiv 2021.04.30.21251941; doi: https://doi.org/10.1101/2021.04.30.21251941 14. Yang L, Li C, Qin Y, Zhang G, Zhao B, Wang Z, Huang Y, Yang Y. A Novel Prognostic Model Based on Ferroptosis-Related Gene Signature for Bladder Cancer. Front Oncol. 2021 Aug 6;11:686044. doi: 10.3389/fonc.2021.686044. PMID: 34422642; PMCID: PMC8378228. 15. Steiert B, Icardi CM, Faris R, McCaslin PN, Smith P, Klingelhutz AJ, Yau PM, Weber MM. The Chlamydia trachomatis type III-secreted effector protein CteG induces centrosome amplification through interactions with centrin-2. Proc Natl Acad Sci U S A. 2023 May 16;120(20):e2303487120. doi: 10.1073/pnas.2303487120. Epub 2023 May 8. PMID: 37155906; PMCID: PMC10193975. 16. Ni H, Zhang H, Li L, Huang H, Guo H, Zhang L, Li C, Xu JX, Nie CP, Li K, Zhang X, Xia X, Li J. T cell-intrinsic STING signaling promotes regulatory T cell induction and immunosuppression by upregulating FOXP3 transcription in cervical cancer. J Immunother Cancer. 2022 Sep;10(9):e005151. doi: 10.1136/jitc-2022-005151. PMID: 36126994; PMCID: PMC9490630. 17. Wang J, Zhou Q, Sun E, Li G, Li Z, Wang Z. The predictive value of a prognostic risk model constructed for three aging-associated genes in glioma. Technol Health Care. 2025 Apr 30:9287329251333904. doi: 10.1177/09287329251333904. Epub ahead of print. PMID: 40302497. 18. Wu Y, Peng Z, Gu S, Wang H, Xiang W. A Risk Score Signature Consisting of Six Immune Genes Predicts Overall Survival in Patients with Lower-Grade Gliomas. Comput Math Methods Med. 2022 Feb 11;2022:2558548. doi: 10.1155/2022/2558548. PMID: 35186111; PMCID: PMC8856808. 19. Lehrer S, Rheinstein PH, Rosenzweig KE. Loss of MycBP may be associated with the improved survival in 1P co-deletion of lower grade glioma patients. Clin Neurol Neurosurg. 2018 Sep;172:112-115. doi: 10.1016/j.clineuro.2018.07.003. Epub 2018 Jul 5. PMID: 29986195. 20. Arora M, Kumari S, Singh J, Chopra A, Chauhan SS. Downregulation of Brain Enriched Type 2 MAGEs Is Associated With Immune Infiltration and Poor Prognosis in Glioma. Front Oncol. 2020 Dec 23;10:573378. doi: 10.3389/fonc.2020.573378. PMID: 33425727; PMCID: PMC7787151. 21. Li H, Yang L, Yang Q, Liang Z, Su W, Yu L. Integration of osimertinib-targeted EGFR gene-associated differential gene expression in constructing a prognostic model for lung adenocarcinoma. Funct Integr Genomics. 2024 Dec 11;24(6):226. doi: 10.1007/s10142-024-01499-5. PMID: 39661202; PMCID: PMC11634928. 22. Li Z, Zhou B, Zhu X, Yang F, Jin K, Dai J, Zhu Y, Song X, Jiang G. Differentiation-related genes in tumor-associated macrophages as potential prognostic biomarkers in non-small cell lung cancer. Front Immunol. 2023 Mar 9;14:1123840. doi: 10.3389/fimmu.2023.1123840. PMID: 36969247; PMCID: PMC10033599. 23. Xu T, Xu S, Yao Y, Chen X, Zhang Q, Zhao X, Wang X, Zhu J, Liu N, Zhang J, Lin Y, Zou J. P2RY14 downregulation in lung adenocarcinoma: a potential therapeutic target associated with immune infiltration. J Thorac Dis. 2022 Feb;14(2):515-535. doi: 10.21037/jtd-22-115. PMID: 35280459; PMCID: PMC8902120. 24. Zhang X, Gao G, Zhang Q, Zhao S, Li X, Cao W, Luo H, Zhou C. In-depth proteomic analysis identifies key gene signatures predicting therapeutic efficacy of anti-PD-1/PD-L1 monotherapy in non-small cell lung cancer. Transl Lung Cancer Res. 2024 Jan 31;13(1):34-45. doi: 10.21037/tlcr-23-713. Epub 2024 Jan 29. PMID: 38405006; PMCID: PMC10891414. 25. Sun Q, Gao Y, Zhang Y, Cao H, Liu J, Neo SY, Chen K, Bi Y, Wu J. Prognostic Profiling of the EMT-Associated and Immunity-Related LncRNAs in Lung Squamous Cell Carcinomas. Cells. 2022 Sep 15;11(18):2881. doi: 10.3390/cells11182881. PMID: 36139456; PMCID: PMC9497331. 26. Zhang X, Yang W, Chen K, Zheng T, Guo Z, Peng Y, Yang Z. The potential prognostic values of the ADAMTS-like protein family: an integrative pan-cancer analysis. Ann Transl Med. 2021 Oct;9(20):1562. doi: 10.21037/atm-21-4946. PMID: 34790768; PMCID: PMC8576672. 27. Fan A, Zhang Y, Cheng J, Li Y, Chen W. A novel prognostic model for prostate cancer based on androgen biosynthetic and catabolic pathways. Front Oncol. 2022 Nov 10;12:950094. doi: 10.3389/fonc.2022.950094. PMID: 36439479; PMCID: PMC9685527. 28. Zhao Y, Tao Z, Li L, Zheng J, Chen X. Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients. BMC Cancer. 2022 Mar 4;22(1):239. doi: 10.1186/s12885-022-09331-8. PMID: 35246070; PMCID: PMC8896158. 29. Yan Y, Shi L, Ma T, Wang L, Huang H. SNP rs9364554 Modulates Androgen Receptor Binding and Drug Response in Prostate Cancer. Biomolecules. 2025 Jan 4;15(1):64. doi: 10.3390/biom15010064. PMID: 39858458; PMCID: PMC11763896. 30. Petrovics G, Li H, Stümpel T, Tan SH, Young D, Katta S, Li Q, Ying K, Klocke B, Ravindranath L, Kohaar I, Chen Y, Ribli D, Grote K, Zou H, Cheng J, Dalgard CL, Zhang S, Csabai I, Kagan J, Takeda D, Loda M, Srivastava S, Scherf M, Seifert M, Gaiser T, McLeod DG, Szallasi Z, Ebner R, Werner T, Sesterhenn IA, Freedman M, Dobi A, Srivastava S. A novel genomic alteration of LSAMP associates with aggressive prostate cancer in African American men. EBioMedicine. 2015 Oct 31;2(12):1957-64. doi: 10.1016/j.ebiom.2015.10.028. PMID: 26844274; PMCID: PMC4703707. 31. Song IS, Ha GH, Kim JM, Jeong SY, Lee HC, Kim YS, Kim YJ, Kwon TK, Kim NS. Human ZNF312b oncogene is regulated by Sp1 binding to its promoter region through DNA demethylation and histone acetylation in gastric cancer. Int J Cancer. 2011 Nov 1;129(9):2124-33. doi: 10.1002/ijc.25871. Epub 2011 Mar 4. PMID: 21170990. 32. Li Y, Liu H, Shao J, Xing G. miR-320a serves as a negative regulator in the progression of gastric cancer by targeting RAB14. Mol Med Rep. 2017 Sep;16(3):2652-2658. doi: 10.3892/mmr.2017.6937. Epub 2017 Jul 6. PMID: 28713899; PMCID: PMC5547933. 33. Ge X, Cui H, Zhou Y, Yin D, Feng Y, Xin Q, Xu X, Liu W, Liu S, Zhang Q. miR-320a modulates cell growth and chemosensitivity via regulating ADAM10 in gastric cancer. Mol Med Rep. 2017 Dec;16(6):9664-9670. doi: 10.3892/mmr.2017.7819. Epub 2017 Oct 19. PMID: 29152656. 34. Wang Y, Zeng J, Pan J, Geng X, Li L, Wu J, Song P, Wang Y, Liu J, Wang L. MiR-320a inhibits gastric carcinoma by targeting activity in the FoxM1-P27KIP1 axis. Oncotarget. 2016 May 17;7(20):29275-86. doi: 10.18632/oncotarget.8676. PMID: 27086911; PMCID: PMC5045395. Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 260views 129downloads Citations Download citation Caglar Berkel. Top differentially expressed genes between patients with progressive disease and those with complete remission following the first course of treatment in different cancer types. Authorea. 22 May 2025. DOI: https://doi.org/10.22541/au.174792078.86225913/v1 DOI: https://doi.org/10.22541/au.174792078.86225913/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.