{"paper_id":"2978b3c9-9f2d-4def-9aba-41ccf46ce561","body_text":"1\n1 The effect of cold ischemia time on hypoxia, EMT, and apoptosis pathways in\n2 normal colon mucosa\n3 Katarzyna Duzowska 1, Mikołaj Opiełka1,2, Kinga Drężek-Chyła1, Anna Kostecka 1, Monika \n4 Horbacz1, Jarosław Skokowski3, Olga Rostkowska4, Jarosław Kobiela4, Leszek Kalinowski5,6, Jan \n5 P. Dumanski1,7, Arkadiusz Piotrowski1, Marcin Jąkalski1,8&*, Natalia Filipowicz1&*\n6\n7 1 3P-Medicine Laboratory, Medical University of Gdańsk, Gdańsk, Poland\n8 2 Department of Biochemistry, Medical University of Gdańsk, Gdańsk, Poland\n9 3 Academy of Applied Medical and Social Science, Elbląg, Poland\n10 4 Department of Oncological, Transplant and General Surgery, Medical University of Gdańsk, \n11 Gdańsk, Poland\n12 5 Department of Medical Laboratory Diagnostics, Medical University of Gdańsk, Gdańsk, \n13 Poland\n14 6 BioTechMed Center, Department of Mechanics of Materials and Structures, Gdańsk \n15 University of Technology, Gdańsk, Poland\n16 7 Department of Immunology, Genetics and Pathology and Science for Life Laboratory, \n17 Uppsala University, Uppsala, Sweden\n18 8 Center for Applied Genomics and Bioinformatics, Faculty of Biology, University of Gdańsk, \n19 Gdańsk, Poland\n20 &these authors contributed equally to this work\n21 *  Corresponding author:   \n22 Email: natalia.filipowicz@gumed.edu.pl  and  marcin.jakalski@ug.edu.pl \n23\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n2\n24 Short title\n25 Cold ischemia time influences transcriptional landscape of colon mucosa\n26 Keywords\n27 cold ischemia,  hypoxia, post-zygotic mutations, preanalytical variable, epithelial-to-\n28 mesenchymal transition (EMT), targeted RNA sequencing, colon mucosa, non-tumorous \n29 tissue, differentially expressed genes, weighted gene co-expression network analysis \n30 (WGCNA)\n31 Abstract\n32 Cold ischemia time (CIT), the interval between tissue excision and preservation, is a \n33 critical preanalytical variable that profoundly impacts gene expression profiles. Variability in \n34 CIT can lead to inconsistent transcriptomic results, making study interpretation challenging \n35 and undermining reproducibility in biomedical research. Our study aimed to evaluate the \n36 impact of CIT on the expression of cancer-related genes, particularly these involved in \n37 hypoxia, apoptosis, and epithelial-to-mesenchymal transition (EMT). We performed RNA \n38 sequencing on 54 normal colon mucosa samples from nine patients undergoing colorectal \n39 cancer surgeries, freezing samples at predefined intervals ranging from 0 to 60 minutes. A \n40 total of 44 differentially expressed genes (DEGs) (p < 0.05) were identified when comparing \n41 samples frozen immediately (T0) with those frozen after 60 minutes (T5). These DEGs were \n42 further analyzed through functional and pathway enrichment analyses and weighted gene co-\n43 expression network analysis (WGCNA). The enrichment analysis revealed significant \n44 alterations in pathways associated with apoptosis, hypoxia, EMT, and cancer progression, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n3\n45 including p53 and HIF-1 signaling. WGCNA highlighted two co-expressed gene modules: ME2, \n46 which showed downregulation of apoptosis-related genes, and ME4, linked to apoptosis and \n47 cellular metabolism. Our findings highlight CIT as a critical preanalytical variable, showing that \n48 prolonged ischemia can induce transcriptomic changes that may mimic malignancy, and \n49 potentially confound research outcomes. To minimize such effects, we recommend keeping \n50 CIT under 45–60 minutes.\n51 Introduction\n52 The quality and integrity of biological samples are crucial for multi-omic studies, directly \n53 impacting the reliability of results. Preanalytical variables, including patient-specific factors \n54 and tissue handling protocols, can significantly impact the outcomes (1,2). With biobanking \n55 playing a crucial role in biomedical translational research, standardized tissue collection \n56 protocols are essential to minimize external variability (3–5). Ischemia, defined as a restriction \n57 of blood supply to tissues or organs, results in a shortage of oxygen supply necessary to \n58 sustain the cellular metabolism.  In the ex vivo context, this process can be classified into \n59 warm and cold ischemia. Cold ischemia time (CIT) refers to the condition in which oxygen \n60 supply is entirely absent after tissue excision while the sample is maintained at temperatures \n61 below body temperature (6). Warm ischemia time (WIT) refers to the period during which the \n62 tissue remains in the donor’s body but the oxygen supply is insufficient to meet metabolic \n63 demands. This preanalytical variable is challenging to control as it relies on the efficiency of \n64 the surgical team, for which the priority is the successful completion of the operation. Reports \n65 on the impact of WIT on transcriptomic profiles are scarce, but studies, including those by \n66 Pedersen et al. and Ma et al., highlight its effects, identifying numerous differentially \n67 expressed genes (DEGs) and alterations in oncogenic, inflammatory, and immunological \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n4\n68 pathways (7,8). In contrast, CIT is determined by the biobanking procedure and can be \n69 effectively managed by the tissue collector (9). Prolonged CIT, rather than malignancy, can \n70 lead to specific, even subtle, changes in gene expression profiles, complicating the \n71 interpretation of tissue samples.\n72 Several studies have assessed the impact of CIT on RNA quality, measured as RNA integrity \n73 number (RIN), reporting minimal changes up to 4 hours (10–12) and even up to 16 hours at \n74 room temperature (13). RNA quality also varies by tissue type, with thyroid and colorectal \n75 tissues being more sensitive to cold ischemia compared to stomach and lung tissues (14). \n76 Gastrointestinal samples, in general, tend to exhibit lower RNA quality than those from other \n77 organs (2,15), with tumor tissues displaying significantly higher RINs than normal tissues, \n78 though CIT has little effect on this observed difference (2).\n79 While most reports focus on RNA integrity, few have explored the impact of CIT on gene \n80 expression, particularly in cancer-related pathways (11,16,17). Transcriptomic analyses \n81 suggest that CIT exceeding 60 minutes can significantly alter gene expression. Aktas et al. \n82 identified 41 transcripts affected by prolonged CIT, including apoptosis- and cell cycle-related \n83 genes, with over 3% showing significant changes (10). Other study reported transcriptomic \n84 alterations as early as 30 minutes post-excision, for instance delayed freezing of colorectal \n85 carcinoma samples lead to increased KLF6 expression, potentially misrepresenting its role in \n86 tumor pathogenesis (16). A study of transcriptomic profiles in renal carcinoma showed over \n87 4,000 genes were affected at longer CIT durations and higher temperatures, emphasizing the \n88 need for immediate snap-freezing to preserve gene expression profiles relevant to cancer \n89 research (11).\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n5\n90 Despite these findings, the existing literature remains limited, particularly regarding the effect \n91 of cold ischemia time (CIT) on the transcription of cancer-associated genes, including those \n92 involved in epithelial-to-mesenchymal transition (EMT), hypoxia, apoptosis, and cell death. \n93 These genes are crucial for understanding cancer biology and the complex interactions within \n94 the tumor microenvironment (18). Moreover, their expression can directly influence \n95 treatment decisions and patient outcomes (16,18–20). Some of these genes may be first \n96 responders to cold ischemia, making them potential markers of CIT. Examining these \n97 preanalytical factors is essential not only to enhance the reliability and reproducibility of \n98 experimental findings but also to ensure that scientific conclusions translate effectively into \n99 clinical practice.\n100 The primary aim of this research is to investigate the influence of CIT on the transcription \n101 profile of macro- and microscopically normal colon mucosa collected from patients with \n102 colorectal cancer. The focus on normal colon mucosa aligns with the design of ongoing \n103 research in our laboratory, which investigates genetic mosaicism focusing on post-zygotic \n104 mutations accumulating in morphologically normal tissues proximal and distal to tumors \n105 (4,21). Furthermore, the majority of the publicly available studies focused on tumor tissue, \n106 with limited insights into healthy tissue (2,13,17). Here we employed targeted RNA \n107 sequencing (NGS RNAseq), including, among the others, genes associated with hypoxia, \n108 apoptosis, and cancer, thus addressing a critical gap in the literature on preanalytical variables \n109 for tissue collection and subsequent analysis for cancer research. \n110 Materials and methods\n111 Study Design and Sample Handling\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n6\n112 The healthy colon mucosa samples were collected from 9 consecutive patients (2 females; 7 \n113 males; mean age 68 years ± 10.9) diagnosed with colorectal cancer who underwent surgical \n114 resection from 21 st of April to 24 th of August 2021 in the Department of Oncological, \n115 Transplant, and General Surgery; University Clinical Center in Gdansk. Written informed \n116 consent was obtained from all the patients prior to surgery. The summary of patients’ data is \n117 presented in Table 1. After the surgical resection, a larger, full-walled specimen of the colon \n118 was immediately excised approximately 10 - 15 cm away from the primary tumor, rinsed in \n119 saline and left at room temperature until the mucosa collection procedure at a particular time \n120 point was completed. At each time point, a sample of macroscopically unaffected mucosa was \n121 excised by dissecting from the colon specimen and detaching gently from the muscular layer. \n122 Subsequent washing steps were implemented, including: saline solution (twice), antibiotic \n123 solution (Penicillin - Streptomycin 5000 U/ml), and saline solution, followed by snap freezing \n124 in liquid nitrogen. Tissue fragments were collected at six time points - 0 (T0), 10 (T1), 20 (T2), \n125 30 (T3), 45 (T4), and 60 (T5) minutes after the surgical resection, estimated as the time of cold \n126 ischemia. Sample collected at T0 was processed immediately after organ resection. First time \n127 point (T0) was used as the reference and the starting point for all the subsequent analyses. In \n128 total 54 samples were collected and subjected to further analysis. \n129 RNA Extraction and Quality Control\n130 Colon mucosa samples were stored at -80°C until RNA isolation. Total RNA was extracted from \n131 10 - 30 mg of tissue samples that were mechanically homogenized using T10 Basic ULTRA - \n132 TURRAX disperser (IKA) in the presence of QIAzol Lysis reagent (Qiagen). RNA isolation, \n133 purification, and DNase digestion were performed using the RNeasy Mini kit (Qiagen) \n134 according to the original protocol with two modifications as described in the previous paper \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n7\n135 (22) RNA quality and quantity were assessed using TapeStation (Agilent Technologies) using \n136 RNA ScreenTape kit according to the manufacturer's protocol (Table 2). RNA integrity number \n137 (RIN) was calculated using TapeStation analysis software (Agilent Technologies). Additionally, \n138 the DV200 index, indicating the percentage of RNA fragments > 200 nt, was calculated and \n139 used as a quality assessment standard (23). Samples with a DV200 index > 75% were used for \n140 further NGS analysis.\n141 Targeted RNA sequencing\n142 The targeted RNA sequencing panel was designed with the Roche NimbleDesign online tool \n143 (Roche, now HyperDesign, https://hyperdesign.com/#/) and covered 7229 regions with a \n144 total length of 1,243,523 bp. It included 634 genes based on literature research and covered \n145 genes associated with apoptosis, cell death, hypoxia, epithelial-to-mesenchymal transition, \n146 and other cancer-related genes (the full list of transcripts is given in S2 Table). NGS libraries \n147 were prepared with the Kapa RNA HyperPrep Kit (Roche) using Automated Liquid Handling \n148 Bravo NGS workstation (Agilent Technologies) according to the manufacturer manual with \n149 100 ng RNA used as an input and enzymatic fragmentation at 94°C for 6  minutes, with the \n150 addition of ERCC RNA Spike-In Mix (Invitrogen) as an external RNA control. All the single \n151 libraries  were multiplexed and hybridized with SeqCap EZ Choice Probes (Roche) designed by \n152 our group and KAPA HyperCapture Reagent and Bead kit (v2, Roche Sequencing Solutions, \n153 Inc.) according to SeqCap RNA Enrichment System User's Guide (v.1.0) with slight \n154 modifications. Component A was replaced with formaldehyde, and the Multiplex \n155 Hybridization Enhancing Oligo Pool was replaced with Universal Blocking Oligos (UBO).The \n156 hybridization was run for 18h, the library was cleaned up, post-captured, amplified, and \n157 purified, followed by inspection of fragment distribution using TapeStation with High \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n8\n158 Sensitivity D1000 Screen Tape kit (Agilent Technologies) and qPCR quantitation in a Roche \n159 LightCycler 480 with KAPA Library Quantification Kit (Roche). Paired-end reads of 150 bp were \n160 generated using TruSeq RNA Access sequencing chemistry by an external service provider \n161 (Macrogen Europe, Amsterdam, The Netherlands). \n162 Transcriptomic data analysis\n163 The RNA-seq data were processed as described in Andreou et al. 2024 (24). Briefly, after the \n164 quality filtering and trimming of the raw FASTQ files with BBDuk \n165 (https://sourceforge.net/projects/bbmap/, version 38.36), the resulting reads were mapped \n166 to the reference human genome (hg38, GENCODE version 39) using STAR version 2.7.3a (25). \n167 Raw read counts assigned to the annotated genes obtained in the above process were \n168 collated into a single gene expression matrix and processed further in R programming \n169 language (https://www.r-project.org, version 4.1.2). Lowly expressed genes were filtered out \n170 based on a minimal required CPM expression threshold. The filtered gene expression matrix \n171 was normalized using the TMM method in edgeR (26). Principal Component Analysis (PCA) \n172 was performed to investigate sample grouping and identify potential outliers using \n173 FactoMineR, version 2.4 (27). Batch effect correction was performed using ComBat-seq (28). \n174 Significantly differentially expressed genes (DEGs) were identified with EdgeR using the \n175 glmLRT function (likelihood ratio test) with a significance threshold set to 0.05 (False \n176 Discovery Rate, FDR).\n177 Weighted gene co-expression network analysis (WGCNA) was performed on the normalized \n178 and batch-adjusted gene expression values using the WGCNA R library WGCNA (29). Soft \n179 threshold value for building the correlation network was selected empirically based on \n180 diagnostic plots (pickSoftThreshold function). The final network was built using the \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n9\n181 blockwiseModules function, where the TOMType parameter was set to “signed”. \n182 Subsequently, a linear model (lmFit, from the limma package, (30)) was run on all modules to \n183 identify those associated with the tested trait (time point).\n184 Overrepresented terms / GO / pathways\n185 Overrepresentation analysis (ORA) was used to perform functional enrichment analysis of \n186 significantly differentially expressed genes, utilizing the Gene Ontology (GO) database and the \n187 genome reference set in WebGestalt (31). Pathway enrichment analysis was similarly \n188 conducted with ORA, focusing on KEGG pathways and the genome reference set in \n189 WebGestalt. In addition, functional and pathway enrichment analyses were separately \n190 applied to genes from WGCNA modules ME2 and ME4, using WebGestalt for the analysis.\n191 Bioethics Committee Approval\n192 All procedures for sample collection were approved by the Independent Bioethics Committee \n193 for Research at the Medical University of Gdansk (approval number NKBBN/564/2018 with \n194 multiple amendments). Written informed consent was obtained from all the patients prior to \n195 surgery. All procedures were performed in accordance with the relevant national and \n196 international laws and guidelines as well as in compliance with European Union General Data \n197 Protection Regulation (EU GDPR).\n198 Table 1. A summary of donors and diagnoses included in the study\nPatient ID Sex Age ICD-10 Classification\nES31C Male 67 C18.7 - Malignant neoplasm of sigmoid colon\n5IDSR Female 70 C19 - Malignant neoplasm of rectosigmoid junction\nOV1IW Male 67 C20 - Malignant neoplasm of rectum\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n10\nNYYCH Male 48 C20 - Malignant neoplasm of rectum\nLZZOP Female 72 C19 - Malignant neoplasm of rectosigmoid junction\nJSDHP Male 68 C19 - Malignant neoplasm of rectosigmoid junction\nPZ94B Male 83 C18.7 - Malignant neoplasm of sigmoid colon\nFFMB1 Male 59 C20 - Malignant neoplasm of rectum\nABCD Male 83 C18.5 - Malignant neoplasm of splenic flexure \n199\n200 Results\n201 Quality of total RNA within the studied time frame\n202 Tissues from nine patients were collected and snap-frozen at six time points: \n203 immediately post-resection (T0), 10 minutes (T1), 20 minutes (T2), 30 minutes (T3), 45 \n204 minutes (T4), and 60 minutes (T5) post-resection (see Materials and Methods). As many prior \n205 studies have focused on RNA quality as a consequence of CIT, we performed quality control \n206 by measuring both  RIN and DV200 values. Notably, RIN values remained consistent across all \n207 analyzed time points, with a median value of 4.3. Furthermore, all samples had DV200 above \n208 75%, indicating that RNA was of sufficient quality for subsequent analysis (32). The \n209 consistency of these results across all time points suggest that RNA integrity was preserved \n210 throughout the collection and processing protocol.  Detailed RIN measurements for each \n211 sample are provided in S1 Table.\n212 Differential Expression Analysis highlights genes associated with hypoxia, EMT, and \n213 apoptosis \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n11\n214 Differential expression analysis revealed 44 significantly differentially expressed genes \n215 (DEGs) between the two extreme time points set for our study, T0 and T5 (60 min) (S3 Table). \n216 Principal component analysis (PCA) demonstrated a noticeable separation between samples \n217 at T0 and T5 after eliminating patient batch effect, as illustrated in Fig. 1. Comparisons \n218 involving T0 and other time points (T1–T4) showed fewer statistically significant DEGs: zero \n219 between T0 vs T3, and six for T0 vs T4, namely ABCB1, ABCG2, EPAS1, FZD2, LAMA3, and \n220 TMPRSS2. Our results show that the initial gene expression changes occur between 30 \n221 minutes (T3) and 45 minutes (T4) post-excision; with 44 genes displaying significant changes \n222 between 45 minutes and 60 minutes (T5) after excision (S3 Table). Thus, our further analyses \n223 focused on the T0 versus T5 comparison.\n224 Applying an absolute logFC cutoff of ≥1, we identified 15 upregulated genes and 29 \n225 downregulated genes in T5 compared to T0. Notably, DEGs included hypoxia- and EMT-\n226 associated genes such as SNAI, CDH1, ADM, MMP9, EGLN3, IL-6, AKT3, TSPAN1, and DSC2, as \n227 well as apoptosis-related genes: CASP7, TNFSF10, DEFB1, AKT3, and JPH3. Among the 44 DEGs \n228 identified between T0 and T5 (S3 Table), 5 were linked to hypoxia, 8 to apoptosis or cell death, \n229 and 10 to epithelial-to-mesenchymal transition (EMT) determined using EMTome (33). The \n230 remaining 19 were classified as 'other'. It is important to stress that none of 22 housekeeping \n231 genes used as a reference set included in the panel showed significant expression changes. \n232 S3 Table provides the complete DEG list with the logFC, p-values, and FDR values. \n233 Functional enrichment of DEGs using overrepresentation analysis (ORA) identified \n234 enrichment of Gene Ontology (GO) terms associated with processes such as apoptosis, cell-\n235 cell adhesion, tissue migration, and others. TNF signaling, ABC transporters, gastric cancer, \n236 and cancer pathways were among the enriched pathways shown by ORA KEGG analysis (S4 \n237 Table).\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n12\n238 Fig. 1: Principal component analysis (PCA) of all 54 samples using the normalized batch-\n239 corrected expression profiles of 1419 genes (both targeted and non-targeted).  Each point \n240 represents the orientation of a sample projected into the transcriptional space, color and \n241 symbol refer to its group membership. The analysis demonstrated clearly separated clusters \n242 of T0 and T5 samples (marked in green and yellow circles), a pattern not pronounced among \n243 the intermediate time points (T1-T4). T0 - time point 0, frozen immediately after resection; \n244 T1 - time point 1, frozen 10 minutes after resection; T2 - time point 2, frozen 20 minutes after \n245 resection; T3 - time point 3, frozen 30 minutes after resection; T4 - time point 4, frozen 45 \n246 minutes after resection; T5 - time point 5, frozen 60 minutes after resection. \n247 Weighted Gene Co-Expression Network Analysis (WGCNA) confirms two gene modules \n248 associated with apoptosis, EMT and hypoxia\n249 To find the clusters of highly correlated genes in our dataset, we applied the WGCNA \n250 analysis that identified two significant gene modules, ME2 and ME4 (adjusted p-values 0.037 \n251 and 0.018, respectively), which demonstrate distinct patterns during the time course of our \n252 study. Notably, the genes within these modules were associated with key biological \n253 processes, including apoptosis, EMT, and hypoxia-related pathways (Fig. 2). \n254 ME2 consisted of 145 genes, primarily exhibiting a downregulation pattern in T5 in relation \n255 to T0 (Fig. 2A, 2B). Among these, 26.2% were related to apoptosis or cell death, 18.6% to EMT, \n256 11.7% to hypoxia, and 0.69% (one gene) was annotated as a reference gene in our targeted \n257 panel (Fig. 2C). The remaining 42.8% were categorized as 'other’ (see details in S5 Table). \n258 Functional enrichment analysis of ME2 genes using ORA GO (FDR < 0.05) identified several \n259 pathways, including those related to the execution phase of apoptosis and the extrinsic \n260 apoptotic signaling pathway. KEGG pathway analysis further highlighted nine pathways, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n13\n261 including cancer-related and apoptosis-related processes such as p53 signaling, HIF-1 \n262 signaling, and FoxO signaling (S7 Table).\n263 ME4, a smaller gene module consisting of 56 genes, also displayed a downregulation pattern \n264 (Fig. 2A, 2B). Genes comprised in this module showed a faster response to CIT, reaching a \n265 stable point at the T4 time point (Fig. 2A). In ME2, however, the difference between T4 and \n266 T5 is visibly larger (Fig. 2A). Within the ME4 cluster, 25% of genes were identified as apoptosis-\n267 related, while 16.1% were associated with EMT and hypoxia, respectively. Furthermore, \n268 8.93% were classified as reference genes and 33.9% as ‘other’ (Fig. 2A, 2B). ORA GO analysis \n269 (FDR < 0.05) in ME4 identified pathways connected to numerous metabolic processes, cell-\n270 cell signaling and homeostasis. Similarly, KEGG pathway analysis revealed significant \n271 metabolic and cancer-related processes, including glycolysis/gluconeogenesis, HIF-1 signaling \n272 pathway, and pathways related to gastric cancer (S7 Table).\n273 Fig. 2: Weighted Gene Co-Expression Network Analysis (WGCNA) of the expression profiles \n274 of all 54 samples using the normalized batch-corrected expression profiles of 1419 genes. \n275 A. Two WGCNA modules significantly associated with the time course phenotype (ME2 and \n276 ME4, adjusted p-value 0.037 and 0.018, respectively) The y-axis on the boxplots represents \n277 module eigengene expression levels. B. Heatmaps of gene expression of ME2 and ME4 \n278 module genes; module eigengene provide summarized representation of the expression \n279 pattern of all genes in a given module and sample type. C. Pie charts displaying the percentage \n280 of transcripts associated with particular biological processes or functions: apoptosis/cell \n281 death, hypoxia, epithelial-mesenchymal transition, reference genes, and ‘other’ in each \n282 module, ME2 and ME4, respectively. T0 - time point 0, frozen immediately after resection; T1 \n283 - time point 1, frozen 10 minutes after resection; T2 - time point 2, frozen 20 minutes after \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n14\n284 resection; T3 - time point 3, frozen 30 minutes after resection; T4 - time point 4, frozen 45 \n285 minutes after resection; T5 - time point 5, frozen 60 minutes after resection.\n286 Discussion\n287 We examined the impact of up to 60 minutes of CIT on the transcription of cancer-\n288 related genes, with samples snap-frozen at defined time points. RNA integrity (RIN) varied \n289 across samples without a clear correlation to CIT (S1 Table), consistent with studies showing \n290 minimal RNA degradation after 3–4 hours (12,13,34,35). Despite applying four rinsing steps, \n291 colon mucosa samples showed relatively low RNA quality (mean RIN = 4.4, range 2.7 – 7.7), \n292 aligning with findings from other tissue comparisons (14,15). This may stem from enzymatic \n293 activity, bacterial influence, or tissue compartment differences (36). To mitigate this, RNA \n294 fragmentation conditions were optimized during the process of NGS library preparation.\n295 Although utilizing a comprehensive transcriptomic approach covering over 600 genes, we \n296 detected no DEGs within the first 30 minutes post-resection, indicating this timeframe is likely \n297 safe for tissue excision and snap-freezing. Only six DEGs were identified between the 0 and \n298 45 minute time points (T4), while 44 DEGs emerged between the 0 and 60 minute time points \n299 (T5), highlighting this period as critical for tissue freezing. Notably, five of the six DEGs in T4 \n300 and 29 of the 44 DEGs in T5 were downregulated. These results align with findings from Aktas \n301 et al., who reported 41 transcripts affected by prolonged CIT, including genes involved in cell \n302 cycle regulation, apoptosis, stress responses, and cancer progression (10). Although this \n303 number seems modest, it might have significant implications for researchers focusing on \n304 specific genes, gene signatures, or pathways, particularly because these DEGs are closely \n305 linked to cancer development and progression. The broader impact of sample handling on the \n306 transcriptome varies widely across studies. von der Heyde et al . demonstrated that cold \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n15\n307 ischemia times exceeding 15 minutes significantly impact the expression of mRNAs, proteins, \n308 and phosphosites in tumor and normal tissues across colorectal cancer, hepatocellular \n309 carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. The authors conclude \n310 that ischemia time is a crucial quality parameter for tissue collections used for target \n311 discovery and validation in cancer research (37). Using microarrays, Musella et al . found \n312 minimal effects, detecting only 0.2% DEGs in normal colon tissue and colorectal carcinoma \n313 within 6 hours, though two identified DEGs were oncogenes (17). In contrast, Spruessel et al. \n314 applying the same genotyping technique, reported significant changes, with 20% of \n315 detectable genes and proteins altered within 30 minutes post-excision (1). Grizzle et al. noted \n316 that while most studies found only 1–3% of transcripts affected, they often overlooked the \n317 functional impact of these genes (35). Our findings underscore that while RNA integrity \n318 remains stable within the first 30 minutes, transcriptional changes begin to emerge beyond \n319 this point. Even within a 60-minute CIT window, the occurring transcriptional changes can \n320 potentially alter the results of studies exploring cancer and adjacent tissue genetics. These \n321 observations highlight the importance of rapid tissue processing for accurate transcriptomic \n322 analyses in cancer research. \n323 Our analysis focused on genes involved in cancer development and progression, as they are \n324 crucial for the studies of tumor margins and tissues adjacent to the tumor, where changes in \n325 the transcriptome may be subtle (21). The results demonstrate the impact of hypoxia on EMT, \n326 a process crucial for embryonic development and tissue regeneration, which is aberrantly \n327 reactivated in cancer to drive progression and metastasis through enhanced migration, \n328 invasiveness, stemness, and resistance to therapies (38). Genes such as SNAI, CDH1, MMP9, \n329 and S100A8, which play critical roles in the EMT in cancer, exhibited significantly altered \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n16\n330 expression levels (adjusted p-value < 0.05) in our analysis. Snail (SNAI1) and Slug (SNAI2) are \n331 transcription factors known to bind to the promoter of CDH1, encoding E-cadherin, to repress \n332 its transcription (39). The suppression of CDH1, regulated by SNAI, is a key driver of EMT, \n333 contributing to cancer progression by promoting invasion and metastasis, and is recognized \n334 as a marker of malignancy and poor clinical prognosis (40). In our study, prolonged CIT caused \n335 significant alterations in the expression of SNAI and CDH1, with upregulation of SNAI and \n336 downregulation of CDH1. Moreover, the observed upregulation of MMP9, likely induced by \n337 CIT, may reflect further malignant changes as MMP family members facilitate cancer cell \n338 invasion and metastasis (38). These changes, often linked to malignancy, may be \n339 misinterpreted as early indicators of tumor progression in research on adjacent tissues. \n340 However, our findings suggest that these alterations result from prolonged CIT.\n341 To gain an insight into complex biological mechanisms affected by CIT, we performed \n342 enrichment analysis of DEGs using two complementary approaches: a direct analysis of all \n343 DEGs and a WGCNA-based analysis of the two resulting modules, both evaluated through GO \n344 and KEGG. The direct DEG analysis provided a broad view of the pathways enriched across all \n345 DEGs, whereas WGCNA highlighted context-specific pathways within co-regulated gene \n346 modules.\n347 GO and KEGG analyses of all DEGs identified malignancy-associated pathways, including \n348 apoptosis, ABC transporters, and TNF signaling, as well as stress response pathways (S4 \n349 Table). In comparison, other transcriptomic studies identified alteration of pathways linked \n350 to apoptosis, stress responses, cell cycle regulation, and cancer progression (10), as well as \n351 those associated with stimulus response and signal transduction (34). Bray et al. found no \n352 consistent gene ontology or pathway among 168 transcripts altered within 120 minutes of \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n17\n353 CIT, highlighting a complex cellular response (16). In our study, despite targeting over 600 \n354 cancer-related genes, pathway enrichment was broad, supporting Bray et al.'s conclusions (S4 \n355 Table).\n356 WGCNA identified two significantly downregulated co-expressed gene modules, ME2 and \n357 ME4, with distinct functional patterns (S7 Table). ME2 was primarily linked to apoptosis, with \n358 GO highlighting pathways like the execution phase of apoptosis and extrinsic apoptotic \n359 signaling, while KEGG linked it to FoxO, Hippo, and p53 signaling pathways. These findings \n360 underscore CIT's significant impact on apoptosis-related pathways within 60 minutes, \n361 potentially confounding studies that do not account for its effects. ME4 was enriched for \n362 metabolic pathways, including catabolic processes, homeostasis, pyruvate metabolism (GO), \n363 glycolysis/gluconeogenesis, and choline metabolism in cancer (KEGG). Both modules featured \n364 HIF-1 and cancer-related pathways, illustrating CIT’s dual impact on apoptosis and \n365 metabolism, complicating transcriptomic interpretations. These findings align with reports of \n366 ischemia-induced changes in CRC, affecting oncogenes and histone-related genes involved in \n367 nucleosome organization, cell cycle, DNA replication, and p53 signaling, beginning at 8–10 \n368 minutes (1) and persisting up to 60 minutes (17). Notably, ME4 exhibited minimal differences \n369 between T4 and T5, whereas ME2 showed a more pronounced variation between these time \n370 points, suggesting a slower reaction to CIT (Fig. 2A). \n371 Our study highlights that beyond RNA degradation, ischemic stress actively alters gene \n372 expression, emphasizing the need to standardize CIT in human tissue collection. The \n373 consistency of our observations suggests these transcriptomic changes stem from surgical \n374 and tissue handling procedures rather than malignancy. A key strength of our study is the use \n375 of NGS RNA-seq, which offers greater sensitivity and accuracy than microarrays (11,17), \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n18\n376 enabling robust detection of gene expression changes, even at low levels. Unlike previous \n377 studies, our approach analyzes over 600 genes, allowing a broader transcriptomic \n378 assessment. However, limitations include a small sample size and potential influences from \n379 enzymatic activity, warm ischemia, and other preanalytical factors (2,41). Additionally, our \n380 analysis used a targeted cancer-related gene panel, which does not capture all transcriptomic \n381 changes.\n382 Conclusion\n383 Our study highlights the significant impact of CIT on the transcriptomic profile of healthy colon \n384 mucosa, with a particular focus on cancer-related genes. Using targeted RNA sequencing and \n385 robust analyses, we identified differentially expressed genes primarily associated with \n386 apoptosis, hypoxia, and EMT, as well as pathways involved in cancer development and \n387 progression. Our findings underscore the importance of considering CIT as a critical \n388 preanalytical variable, as prolonged ischemia can alter gene expression in ways that may \n389 mimic malignant changes, leading to misinterpretations of results and incorrect clinical \n390 conclusions. Importantly, our results suggest that CIT should be kept below 45 minutes to \n391 minimize its impact on transcriptomic profiles. However, as the effects of CIT might be tissue-\n392 dependent, this recommendation should be applied with caution when extrapolating to other \n393 tissues.\n394 Acknowledgements\n395 We thank all the anonymous patients for acceptance to participate, sample contribution and \n396 information provided in the questionnaire. We also thank surgeons, and nurses involved in \n397 the patient recruitment process, collaborating technicians, diagnosticians and pathologists \n398 from University Clinical Centre in Gdańsk, University Hospital in Cracow and Specialist \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n19\n399 Hospital in Kościerzyna. This study was sponsored by the Foundation for Polish Science (FNP) \n400 under the International Research Agendas Program to J.P.D. and A.P., co-financed by the \n401 European Union under the European Regional Development Fund.\n402 References\n403 1. Spruessel A, Steimann G, Jung M, Lee SA, Carr T, Fentz AK, et al. 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Tissue Handling for Genome-\n515 Wide Expression Analysis: A Review of the Issues, Evidence, and Opportunities. Arch \n516 Pathol Lab Med. 2007 Dec 1;131(12):1805–16. \n517  \n518 Supporting information \n519 S1 Table. Pseudonymized list of patients that were included in the study with the \n520 information on gender, ICD10 (International Statistical Classification of Diseases and \n521 Related Health Problems, 10th Revision), sample time points, type of tissue, and RNA \n522 Integrity Number (RIN) values. (XLSX)\n523 S2 Table. Target genes included in the designed panel and functional classification of \n524 particular transcripts. (a) Gene symbols of targets included in the panel, (b) Ensembl ID, (c) \n525 Ensembl ID as in Gencode (version 35), (d) Gene symbol according to the HUGO Gene \n526 Nomenclature Committee, (e) EntrezID, (f) Full name of the gene, (g) - (p) Functional \n527 classification. (XLSX)\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n22\n528 S3 Table. Differentially Expressed Genes (DEGs) between time points T5 (60 minutes) and \n529 T0 (0 minutes). (a) Ensembl ID as in Gencode (version 35), (b) Gene symbol, (c) Direction of \n530 the expression change: up - upregulation, down - downregulation, (d) logFoldChange (logFC), \n531 (e) p-value, (f) False Discovery Rate (FDR), (g) Indication if the gene was included in our panel: \n532 N - no, Y - yes, (h) Information regarding the comparison between different groups; T5 (time \n533 point 5, 60 minutes after resection), T4 (time point 4, 45 minutes after resection) and T0 (time \n534 point 0, 0 minutes after resection). (XLSX)\n535 S4 Table. The results of enrichment analysis for differentially expressed genes (DEGs) \n536 between T5 (time point 5, 60 minutes after resection) and T0 (time point 0, 0 minutes after \n537 resection). (a) Gene Ontology Term ID and Kyoto Encyclopedia of Genes and Genomes (KEGG) \n538 ID, (b) short description of identified Gene Ontology (GO) terms, (c) p-value, (d) False \n539 Discovery Rate (FDR), (e) ratio of genes from our targeted panel vs. all genes associated with \n540 the particular GO term, (f) genes from our analysis associated with the particular GO term, (g) \n541 information regarding the comparison between different groups; T5 (time point 5, 60 minutes \n542 after resection) and T0 (time point 0, 0 minutes after resection). (XLSX)\n543 S5 Table. Genes included in weighted gene co-expression network analysis (WCGNA) \n544 module eigengene 2 (ME2). (a) gene symbol, (b) description, (c) functional classification. \n545 (XLSX)\n546 S6 Table. Genes included in weighted gene co-expression network analysis (WCGNA) \n547 module eigengene 4 (ME4). (a) gene symbol, (b) description, (c) functional classification. \n548 (XLSX)\n549 S7 Table. Gene Ontology (GO) terms and KEGG pathways identified via enrichment \n550 analyses, including cluster membership information. Enrichment analysis was conducted \n551 for genes from weighted gene co-expression network analysis (WCGNA) from statistically \n552 significant modules - ME2 and ME4. (a) Gene Ontology Term ID and  Kyoto Encyclopedia of \n553 Genes and Genomes (KEGG) ID, (b) short description of identified Gene Ontology (GO) terms, \n554 (c) p-value, (d) False Discovery Rate (FDR), (e) ratio of genes included in the panel, associated \n555 with the particular GO term, (f) Genes associated with the particular GO term, (g) Information \n556 regarding the comparison between different groups: T5 (time point 5, 60 minutes after \n557 resection) and T0 (time point 0, 0 minutes after resection). (XLSX)\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}