Time course-dependent changes in the transcriptome of human skeletal muscle during recovery from endurance exercise: from inflammation to adaptive remodeling

Reprogramming of gene expression is fundamental for skeletal muscle adaptations in response to endurance exercise. This study investigated the time course-dependent changes in the muscular transcriptome after an endurance exercise trial consisting of 1 h of intense cycling immediately followed by 1 h of intense running. Skeletal muscle samples were taken at baseline, 3 h, 48 h, and 96 h postexercise from eight healthy, endurance-trained men. RNA was extracted from muscle. Differential gene expression was evaluated using Illumina microarrays and validated with qPCR. Gene set enrichment analysis identified enriched molecular signatures chosen from the Molecular Signatures Database. Three hours postexercise, 102 gene sets were upregulated [family wise error rate (FWER), P < 0.05], including groups of genes related with leukocyte migration, immune and chaperone activation, and cyclic AMP responsive element binding protein (CREB) 1 signaling. Forty-eight hours postexercise, among 19 enriched gene sets (FWER, P < 0.05), two gene sets related to actin cytoskeleton remodeling were upregulated. Ninety-six hours postexercise, 83 gene sets were enriched (FWER, P < 0.05), 80 of which were upregulated, including gene groups related to chemokine signaling, cell stress management, and extracellular matrix remodeling. These data provide comprehensive insights into the molecular pathways involved in acute stress, recovery, and adaptive muscular responses to endurance exercise. The novel 96 h postexercise transcriptome indicates substantial transcriptional activity potentially associated with the prolonged presence of leukocytes in the muscles. This suggests that muscular recovery, from a transcriptional perspective, is incomplete 96 h after endurance exercise involving muscle damage.

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Differential gene expression in prostate tissue according to vasectomy.

Journal of Clinical Oncology ◽

10.1200/jco.2016.34.2_suppl.298 ◽

2016 ◽

Vol 34 (2_suppl) ◽

pp. 298-298

Author(s):

Kathryn M Wilson ◽

Travis Gerke ◽

Ericka Ebot ◽

Jennifer A Sinnott ◽

Jennifer R. Rider ◽

...

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Cancer Diagnosis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Prostate Tissue ◽

Gene Set Enrichment ◽

Normal Prostate ◽

Gene Sets ◽

Normal Prostate Tissue

298 Background: We previously found that vasectomy was associated with an increased risk of prostate cancer, and particularly, risk of lethal prostate cancer in the Health Professionals Follow-up Study (HPFS). However, the possible biological basis for this finding is unclear. In this study, we explored possible biological mechanisms by assessing differences in gene expression in the prostate tissue of men with and without a history of vasectomy prostate cancer diagnosis. Methods: Within the HPFS, vasectomy data and gene expression data (20,254 genes) was available from archival tumor tissue from 263 cases, 124 of whom also had data for adjacent normal tissue. To relate expression of individual genes to vasectomy we used linear regression adjusting for age and year at diagnosis. We ran gene set enrichment analysis to identify pathways of genes associated with vasectomy. Results: Among 263 cases, 67 (25%) reported a vasectomy prior to cancer diagnosis. Mean age at diagnosis was 66 years among men without and 65 years among men with vasectomy. Median time between vasectomy and prostate cancer diagnosis was 25 years. Gene expression in tumor tissue was not associated with vasectomy status. In adjacent normal tissue, three individual genes were associated with vasectomy with Bonferroni-corrected p-values of < 0.10: RAPGEF6, OR4C3, and SLC35F4. Gene set enrichment analysis found five pathways upregulated and seven pathways downregulated in men with vasectomy compared to those without in normal prostate tissue with a FDR < 0.05. Upregulated pathways included several immune-related gene sets and G-protein-coupled receptor gene sets. Conclusions: We identified significant differences in gene expression profiles in normal prostate tissue according to vasectomy status among men treated for prostate cancer. The fact that such differences existed several decades after vasectomy provides support for the idea that vasectomy may play a role in the etiology of prostate cancer.

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Alterations in the host transcriptome in vitro and in vivo following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection

10.21203/rs.3.rs-37567/v1 ◽

2020 ◽

Author(s):

Xiaomei Lei ◽

Zhijun Feng ◽

Xiaojun Wang ◽

Xiaodong He

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Microarray Data ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set ◽

Gene Sets ◽

Core Genes

Abstract Background. Exploring alterations in the host transcriptome following SARS-CoV-2 infection is not only highly warranted to help us understand molecular mechanisms of the disease, but also provide new prospective for screening effective antiviral drugs, finding new therapeutic targets, and evaluating the risk of systemic inflammatory response syndrome (SIRS) early.Methods. We downloaded three gene expression matrix files from the Gene Expression Omnibus (GEO) database, and extracted the gene expression data of the SARS-CoV-2 infection and non-infection in human samples and different cell line samples, and then performed gene set enrichment analysis (GSEA), respectively. Thereafter, we integrated the results of GSEA and obtained co-enriched gene sets and co-core genes in three various microarray data. Finally, we also constructed a protein-protein interaction (PPI) network and molecular modules for co-core genes and performed Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis for the genes from modules to clarify their possible biological processes and underlying signaling pathway. Results. A total of 11 co-enriched gene sets were identiﬁed from the three various microarray data. Among them, 10 gene sets were activated, and involved in immune response and inflammatory reaction. 1 gene set was suppressed, and participated in cell cycle. The analysis of molecular modules showed that 2 modules might play a vital role in the pathogenic process of SARS-CoV-2 infection. The KEGG enrichment analysis showed that genes from module one enriched in signaling pathways related to inflammation, but genes from module two enriched in signaling of cell cycle and DNA replication. Particularly, necroptosis signaling, a newly identified type of programmed cell death that differed from apoptosis, was also determined in our findings. Additionally, for patients with SARS-CoV-2 infection, genes from module one showed a relatively high-level expression while genes from module two showed low-level. Conclusions. We identified two molecular modules were used to assess severity and predict the prognosis of the patients with SARS-CoV-2 infection. In addition, these results provide a unique opportunity to explore more molecular pathways as new potential targets on therapy in COVID 19.

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Tracking Difference in Gene Expression in a Time-Course Experiment Using Gene Set Enrichment Analysis

PLoS ONE ◽

10.1371/journal.pone.0107629 ◽

2014 ◽

Vol 9 (9) ◽

pp. e107629 ◽

Cited By ~ 2

Author(s):

Pui Shan Wong ◽

Michihiro Tanaka ◽

Yoshihiko Sunaga ◽

Masayoshi Tanaka ◽

Takeaki Taniguchi ◽

...

Keyword(s):

Gene Expression ◽

Time Course ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Time Course Experiment

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Host transcriptome alterations in vitro and in vivo following severe acute respiratory syndrome coronavirus 2 infection

10.21203/rs.3.rs-37567/v2 ◽

2021 ◽

Author(s):

Yannian Luo ◽

Juan Xu ◽

Mingzhen Zhou ◽

Xiaomei Lei ◽

Wen Cao ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Microarray Data ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set ◽

Gene Sets ◽

Core Genes

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Integrative DNA Methylation and Gene Expression Analysis Reveals Candidate Biomarkers Associated with Dichotomized Response to Chemoimmunotherapy in Diffuse Large B-Cell Lymphoma

Blood ◽

10.1182/blood-2020-137180 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 22-22

Author(s):

Ellen K. Kendall ◽

Manishkumar S. Patel ◽

Sarah Ondrejka ◽

Agrima Mian ◽

Yazeed Sawalha ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Research Funding ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

B Cell Lymphoma ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Gene Sets

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. While 60% of DLBCL patients achieve complete remission with frontline therapy, relapsed/refractory (R/R) DLBCL patients have a poor prognosis with median overall survival below one year, necessitating investigation into the biological principles that distinguish cured from R/R DLBCL. Recent analyses have identified unfavorable molecular signatures when accounting for gene expression, copy number alterations and mutational profiles in R/R DLBCL. However, an integrative analysis of the relationship between epigenetic and transcriptomic changes has yet to be described. In this study, we compared baseline methylation and gene expression profiles of DLBCL patients with dichotomized clinical outcomes. Methods: Diagnostic DLBCL biopsies were obtained from two patient cohorts: patients who relapsed or were refractory following chemoimmunotherapy ("R/R"), and patients who entered durable clinical remission following therapy ("cured"). The median age for R/R and cured cohorts were 62 (range 35-86) years vs. 64 (range 28-83) years (P= 0.27). High-intermediate or high IPI scores were present in 14 vs. 6 patients (P= 0.08) in the R/R and cured cohorts, respectively. All patients were treated with frontline R-CHOP or R-EPOCH. DNA and RNA were extracted simultaneously from formalin-fixed, paraffin embedded biopsy samples. An Illumina 850k Methylation Array was used to identify DNA methylation levels in 29 R/R patients and 20 cured patients. RNA sequencing was performed on 9 R/R patients and 7 cured patients at diagnosis using Illumina HiSeq4000. Differentially methylated probes were identified using the DMRcate package, and differentially expressed genes were identified using the DESeq2 package. Gene set enrichment analysis was performed using canonical pathway gene sets from MSigDB. Results: At the time of diagnosis, we found significant epigenetic and transcriptomic differences between cured and R/R patients. Comparing cured to R/R samples, there were 8,159 differentially methylated probes (FDR<0.05). Differentially methylated regions between R/R and cured cohorts overlap with genes previously identified as mutation hotspots in DLBCL. Upon comparing transcriptomic profiles between R/R and cured, 267 genes were found to be differentially expressed (Log2FC>|1| and FDR<0.05). Gene set enrichment analysis revealed gene sets related to cell cycle, membrane trafficking, Rho and Rab family GTPase function, and transcriptional regulation were upregulated in the R/R samples. Gene sets related to innate immune signaling, Type I and II interferon signaling, fatty acid and carbohydrate metabolism were upregulated in the cured samples. To identify genes likely to be regulated by specific changes in methylation, we selected genes that were both differentially expressed and differentially methylated between the R/R and cured cohorts. In the R/R samples, 13 genes (ARMC5, ARRDC1, C12orf57, CCSER1, D2HGDH, DUOX2, FAM189B, FKBP2, KLF5, MFSD10, NEK8, NT5C, and WDR18) were significantly hypermethylated and underexpressed when compared to cured specimens, suggesting that epigenetic silencing of these genes is associated with lack of response to chemoimmunotherapy. In contrast, 12 genes (ATP2B1, C15orf41, FAM102B, FAM3C, FHOD3, FYTTD1, GPR180, KIAA1841, LRMP, MEF2A, RRAS2, and TPD52) were significantly hypermethylated and underexpressed in cured patients, suggesting that epigenetic silencing of these genes is favorable for treatment response. Many of these epigenetically modified genes have been previously implicated in cancer biology, including roles in NOTCH signaling, chromosomal instability, and biomarkers of prognosis. Conclusions: This is the first integrative epigenetic and transcriptomic analysis of diagnostic biopsies from cured and R/R DLBCL patients following chemoimmunotherapy. At the time of diagnosis, both the methylation and gene expression profiles significantly differ between patients that enter durable remission as opposed to those who are R/R to therapy. Soon, the hypomethylating agent CC-486 (i.e. oral azacitidine) will be explored in combination with mini-R-CHOP for older DLBCL patients in whom DNA methylation is likely increased. These data support the use of hypomethylating agents to potentially restore sensitivity of DLBCL to chemoimmunotherapy. Disclosures Hsi: Eli Lilly: Research Funding; Abbvie: Research Funding; Miltenyi: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; CytomX: Consultancy, Honoraria. Hill:Celgene: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Takeda: Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding.

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NGSEA: network-based gene set enrichment analysis for interpreting gene expression phenotypes with functional gene sets

10.1101/636498 ◽

2019 ◽

Cited By ~ 1

Author(s):

Heonjong Han ◽

Sangyoung Lee ◽

Insuk Lee

Keyword(s):

Gene Expression ◽

Enrichment Analysis ◽

Functional Gene ◽

Gene Set Enrichment Analysis ◽

Clinical Samples ◽

Functional Genes ◽

Biological Processes ◽

Gene Set Enrichment ◽

Gene Sets ◽

Anti Cancer

ABSTRACTGene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets, however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

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XGSEA: CROSS-species Gene Set Enrichment Analysis via domain adaptation

10.1101/2020.07.21.213645 ◽

2020 ◽

Author(s):

Menglan Cai ◽

Canh Hao Nguyen ◽

Hiroshi Mamitsuka ◽

Limin Li

Keyword(s):

Gene Expression ◽

Domain Adaptation ◽

Gene Knockout ◽

Enrichment Analysis ◽

Real Data ◽

Gene Set Enrichment Analysis ◽

Data Sets ◽

Gene Set Enrichment ◽

Gene Set ◽

Gene Sets

AbstractGene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. However, gene expression are assessed more often for model organisms than minor species. More importantly, gene expression could not be measured under specific conditions for human, due to high healthy risk of direct experiments, such as non-approved treatment or gene knockout, and then often substituted by mouse. Thus predicting enrichment significance (on a phenotype) of a given gene set of a species (target, say human), by using gene expression measured under the same phenotype of the other species (source, say mouse) is a vital and challenging problem, which we call CROSS-species Gene Set Enrichment Problem (XGSEP). For XGSEP, we propose XGSEA (Cross-species Gene Set Enrichment Analysis), with three steps of: 1) running GSEA for a source species to obtain enrichment scores and p-values of source gene sets; 2) representing the relation between source and target gene sets by domain adaptation; and 3) using regression to predict p-values of target gene sets, based on the representation in 2). We extensively validated XGSEA by using four real data sets under various settings, proving that XGSEA significantly outperformed three baseline methods. A case study of identifying important human pathways for T cell dysfunction and reprogramming from mouse ATAC-Seq data further confirmed the reliability of XGSEA. Source code is available through https://github.com/LiminLi-xjtu/XGSEAAuthor summaryGene set enrichment analysis (GSEA) is a powerful tool in the gene sets differential analysis given a ranked gene list. GSEA requires complete data, gene expression with phenotype labels. However, gene expression could not be measured under specific conditions for human, due to high risk of direct experiments, such as non-approved treatment or gene knockout, and then often substituted by mouse. Thus no availability of gene expression leads to more challenging problem, CROSS-species Gene Set Enrichment Problem (XGSEP), in which enrichment significance (on a phenotype) of a given gene set of a species (target, say human) is predicted by using gene expression measured under the same phenotype of the other species (source, say mouse). In this work, we propose XGSEA (Cross-species Gene Set Enrichment Analysis) for XGSEP, with three steps of: 1) GSEA; 2) domain adaptation; and 3) regression. The results of four real data sets and a case study indicate that XGSEA significantly outperformed three baseline methods and confirmed the reliability of XGSEA.

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Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

Beilstein Journal of Nanotechnology ◽

10.3762/bjnano.6.252 ◽

2015 ◽

Vol 6 ◽

pp. 2438-2448 ◽

Cited By ~ 14

Author(s):

Andrew Williams ◽

Sabina Halappanavar

Keyword(s):

Gene Expression ◽

Pulmonary Fibrosis ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Data Driven ◽

Gene Set Enrichment ◽

Gene Set ◽

Analysis Methods ◽

Gene Sets

Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.

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Transcriptome analysis of neutrophils after endurance exercise reveals novel signaling mechanisms in the immune response to physiological stress

Journal of Applied Physiology ◽

10.1152/japplphysiol.00143.2013 ◽

2013 ◽

Vol 114 (12) ◽

pp. 1677-1688 ◽

Cited By ~ 35

Author(s):

Oliver Neubauer ◽

Surendran Sabapathy ◽

Ross Lazarus ◽

Jeremy B. M. Jowett ◽

Ben Desbrow ◽

...

Keyword(s):

Gene Expression ◽

Endurance Exercise ◽

Calcium Binding ◽

Physiological Stress ◽

Gene Set Enrichment Analysis ◽

P Value ◽

Molecular Signatures ◽

Proinflammatory Response ◽

Blood Concentrations ◽

Signaling Mechanisms

Neutrophils serve as an intriguing model for the study of innate immune cellular activity induced by physiological stress. We measured changes in the transcriptome of circulating neutrophils following an experimental exercise trial (EXTRI) consisting of 1 h of intense cycling immediately followed by 1 h of intense running. Blood samples were taken at baseline, 3 h, 48 h, and 96 h post-EXTRI from eight healthy, endurance-trained, male subjects. RNA was extracted from isolated neutrophils. Differential gene expression was evaluated using Illumina microarrays and validated with quantitative PCR. Gene set enrichment analysis identified enriched molecular signatures chosen from the Molecular Signatures Database. Blood concentrations of muscle damage indexes, neutrophils, interleukin (IL)-6 and IL-10 were increased ( P < 0.05) 3 h post-EXTRI. Upregulated groups of functionally related genes 3 h post-EXTRI included gene sets associated with the recognition of tissue damage, the IL-1 receptor, and Toll-like receptor (TLR) pathways (familywise error rate, P value < 0.05). The core enrichment for these pathways included TLRs, low-affinity immunoglobulin receptors, S100 calcium binding protein A12, and negative regulators of innate immunity, e.g., IL-1 receptor antagonist, and IL-1 receptor associated kinase-3. Plasma myoglobin changes correlated with neutrophil TLR4 gene expression ( r = 0.74; P < 0.05). Neutrophils had returned to their nonactivated state 48 h post-EXTRI, indicating that their initial proinflammatory response was transient and rapidly counterregulated. This study provides novel insight into the signaling mechanisms underlying the neutrophil responses to endurance exercise, suggesting that their transcriptional activity was particularly induced by damage-associated molecule patterns, hypothetically originating from the leakage of muscle components into the circulation.

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LGG-04. MULTIOMIC ANALYSIS OF MAPK PATHWAY ACTIVITY IN PEDIATRIC PILOCYTIC ASTROCYTOMA

Neuro-Oncology ◽

10.1093/neuonc/noab090.128 ◽

2021 ◽

Vol 23 (Supplement_1) ◽

pp. i31-i32

Author(s):

Romain Sigaud ◽

Florian Selt ◽

Thomas Hielscher ◽

Anja Stefanski ◽

Nina Overbeck ◽

...

Keyword(s):

Gene Expression ◽

Time Course ◽

Mapk Pathway ◽

Phase 1 ◽

Enrichment Analysis ◽

Pediatric Brain Tumors ◽

Gene Set Enrichment Analysis ◽

Proteomic Data ◽

Pilocytic Astrocytomas ◽

Differential Gene

Abstract Introduction Pilocytic astrocytomas (PA) are the most common pediatric brain tumors. They are characterized by MAPK pathway alterations, leading to its constitutive activation and modulating the balance between cell proliferation and the oncogene-induced senescence (OIS) sustained by senescence-associated secretory phenotype (SASP) factors. This makes PA suitable for MAPK inhibitor (MAPKi) therapies, showing encouraging results in phase 1/2 clinical trials. Little is known about the molecular implications of MAPK downregulation in the proliferating and senescent compartments. Methods DKFZ-BT66 PA cells derived from a primary KIAA:BRAF-fusion positive PA cell line, were used as model system. Gene expression and phospho-proteomic datasets were generated from DKFZ-BT66 cells, in both the proliferative and senescent states, and treated with the MEKi trametinib for different time-spans. A time course analysis based on differentially expressed genes was performed, followed by a single-sample gene set enrichment analysis (ssGSEA). Analysis of the phospho-proteomic data is ongoing. Results Differential gene expression analysis revealed that MEK inhibition leads to the inhibition of the OIS-SASP gene program in senescent DKFZ-BT66. ssGSEA showed that most MAPK-related signatures were downregulated upon MEKi treatment, while pathways related to upstream MAPK activators (including FGFR, NTRK and TGFB pathways) were upregulated, in both proliferating and senescent DKFZ-BT66. Genes regulated by the MAPK pathway and involved in OIS-SASP were identified by analyzing genes differentially regulated between proliferating and senescent DKFZ-BT66, and modulated upon MEKi treatment. Conclusion This data suggests that MAPKi reverses OIS in senescent PA cells, while inducing the activation of MAPK upstream regulators in proliferating and senescent PA cells, identifying putative co-targets that could help prevent growth rebound upon MAPKi withdrawal. Furthermore, the identification of the MAPK-related OIS-SASP genes provide insight about the regulation of OIS-SASP by the MAPK pathway. Validation of this data with the ongoing phospho-proteomic analysis and in primary samples is needed.

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