The effect of cold ischemia time on hypoxia, EMT, and apoptosis pathways in normal colon mucosa

1 1 The effect of cold ischemia time on hypoxia, EMT, and apoptosis pathways in 2 normal colon mucosa 3 Katarzyna Duzowska 1, Mikołaj Opiełka1,2, Kinga Drężek-Chyła1, Anna Kostecka 1, Monika 4 Horbacz1, Jarosław Skokowski3, Olga Rostkowska4, Jarosław Kobiela4, Leszek Kalinowski5,6, Jan 5 P. Dumanski1,7, Arkadiusz Piotrowski1, Marcin Jąkalski1,8&*, Natalia Filipowicz1&* 6 7 1 3P-Medicine Laboratory, Medical University of Gdańsk, Gdańsk, Poland 8 2 Department of Biochemistry, Medical University of Gdańsk, Gdańsk, Poland 9 3 Academy of Applied Medical and Social Science, Elbląg, Poland 10 4 Department of Oncological, Transplant and General Surgery, Medical University of Gdańsk, 11 Gdańsk, Poland 12 5 Department of Medical Laboratory Diagnostics, Medical University of Gdańsk, Gdańsk, 13 Poland 14 6 BioTechMed Center, Department of Mechanics of Materials and Structures, Gdańsk 15 University of Technology, Gdańsk, Poland 16 7 Department of Immunology, Genetics and Pathology and Science for Life Laboratory, 17 Uppsala University, Uppsala, Sweden 18 8 Center for Applied Genomics and Bioinformatics, Faculty of Biology, University of Gdańsk, 19 Gdańsk, Poland 20 &these authors contributed equally to this work 21 * Corresponding author: 22 Email: [email protected] and [email protected] 23 .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 2 24 Short title 25 Cold ischemia time influences transcriptional landscape of colon mucosa 26 Keywords 27 cold ischemia, hypoxia, post-zygotic mutations, preanalytical variable, epithelial-to- 28 mesenchymal transition (EMT), targeted RNA sequencing, colon mucosa, non-tumorous 29 tissue, differentially expressed genes, weighted gene co-expression network analysis 30 (WGCNA) 31 Abstract 32 Cold ischemia time (CIT), the interval between tissue excision and preservation, is a 33 critical preanalytical variable that profoundly impacts gene expression profiles. Variability in 34 CIT can lead to inconsistent transcriptomic results, making study interpretation challenging 35 and undermining reproducibility in biomedical research. Our study aimed to evaluate the 36 impact of CIT on the expression of cancer-related genes, particularly these involved in 37 hypoxia, apoptosis, and epithelial-to-mesenchymal transition (EMT). We performed RNA 38 sequencing on 54 normal colon mucosa samples from nine patients undergoing colorectal 39 cancer surgeries, freezing samples at predefined intervals ranging from 0 to 60 minutes. A 40 total of 44 differentially expressed genes (DEGs) (p < 0.05) were identified when comparing 41 samples frozen immediately (T0) with those frozen after 60 minutes (T5). These DEGs were 42 further analyzed through functional and pathway enrichment analyses and weighted gene co- 43 expression network analysis (WGCNA). The enrichment analysis revealed significant 44 alterations in pathways associated with apoptosis, hypoxia, EMT, and cancer progression, .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 3 45 including p53 and HIF-1 signaling. WGCNA highlighted two co-expressed gene modules: ME2, 46 which showed downregulation of apoptosis-related genes, and ME4, linked to apoptosis and 47 cellular metabolism. Our findings highlight CIT as a critical preanalytical variable, showing that 48 prolonged ischemia can induce transcriptomic changes that may mimic malignancy, and 49 potentially confound research outcomes. To minimize such effects, we recommend keeping 50 CIT under 45–60 minutes. 51 Introduction 52 The quality and integrity of biological samples are crucial for multi-omic studies, directly 53 impacting the reliability of results. Preanalytical variables, including patient-specific factors 54 and tissue handling protocols, can significantly impact the outcomes (1,2). With biobanking 55 playing a crucial role in biomedical translational research, standardized tissue collection 56 protocols are essential to minimize external variability (3–5). Ischemia, defined as a restriction 57 of blood supply to tissues or organs, results in a shortage of oxygen supply necessary to 58 sustain the cellular metabolism. In the ex vivo context, this process can be classified into 59 warm and cold ischemia. Cold ischemia time (CIT) refers to the condition in which oxygen 60 supply is entirely absent after tissue excision while the sample is maintained at temperatures 61 below body temperature (6). Warm ischemia time (WIT) refers to the period during which the 62 tissue remains in the donor’s body but the oxygen supply is insufficient to meet metabolic 63 demands. This preanalytical variable is challenging to control as it relies on the efficiency of 64 the surgical team, for which the priority is the successful completion of the operation. Reports 65 on the impact of WIT on transcriptomic profiles are scarce, but studies, including those by 66 Pedersen et al. and Ma et al., highlight its effects, identifying numerous differentially 67 expressed genes (DEGs) and alterations in oncogenic, inflammatory, and immunological .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 4 68 pathways (7,8). In contrast, CIT is determined by the biobanking procedure and can be 69 effectively managed by the tissue collector (9). Prolonged CIT, rather than malignancy, can 70 lead to specific, even subtle, changes in gene expression profiles, complicating the 71 interpretation of tissue samples. 72 Several studies have assessed the impact of CIT on RNA quality, measured as RNA integrity 73 number (RIN), reporting minimal changes up to 4 hours (10–12) and even up to 16 hours at 74 room temperature (13). RNA quality also varies by tissue type, with thyroid and colorectal 75 tissues being more sensitive to cold ischemia compared to stomach and lung tissues (14). 76 Gastrointestinal samples, in general, tend to exhibit lower RNA quality than those from other 77 organs (2,15), with tumor tissues displaying significantly higher RINs than normal tissues, 78 though CIT has little effect on this observed difference (2). 79 While most reports focus on RNA integrity, few have explored the impact of CIT on gene 80 expression, particularly in cancer-related pathways (11,16,17). Transcriptomic analyses 81 suggest that CIT exceeding 60 minutes can significantly alter gene expression. Aktas et al. 82 identified 41 transcripts affected by prolonged CIT, including apoptosis- and cell cycle-related 83 genes, with over 3% showing significant changes (10). Other study reported transcriptomic 84 alterations as early as 30 minutes post-excision, for instance delayed freezing of colorectal 85 carcinoma samples lead to increased KLF6 expression, potentially misrepresenting its role in 86 tumor pathogenesis (16). A study of transcriptomic profiles in renal carcinoma showed over 87 4,000 genes were affected at longer CIT durations and higher temperatures, emphasizing the 88 need for immediate snap-freezing to preserve gene expression profiles relevant to cancer 89 research (11). .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 5 90 Despite these findings, the existing literature remains limited, particularly regarding the effect 91 of cold ischemia time (CIT) on the transcription of cancer-associated genes, including those 92 involved in epithelial-to-mesenchymal transition (EMT), hypoxia, apoptosis, and cell death. 93 These genes are crucial for understanding cancer biology and the complex interactions within 94 the tumor microenvironment (18). Moreover, their expression can directly influence 95 treatment decisions and patient outcomes (16,18–20). Some of these genes may be first 96 responders to cold ischemia, making them potential markers of CIT. Examining these 97 preanalytical factors is essential not only to enhance the reliability and reproducibility of 98 experimental findings but also to ensure that scientific conclusions translate effectively into 99 clinical practice. 100 The primary aim of this research is to investigate the influence of CIT on the transcription 101 profile of macro- and microscopically normal colon mucosa collected from patients with 102 colorectal cancer. The focus on normal colon mucosa aligns with the design of ongoing 103 research in our laboratory, which investigates genetic mosaicism focusing on post-zygotic 104 mutations accumulating in morphologically normal tissues proximal and distal to tumors 105 (4,21). Furthermore, the majority of the publicly available studies focused on tumor tissue, 106 with limited insights into healthy tissue (2,13,17). Here we employed targeted RNA 107 sequencing (NGS RNAseq), including, among the others, genes associated with hypoxia, 108 apoptosis, and cancer, thus addressing a critical gap in the literature on preanalytical variables 109 for tissue collection and subsequent analysis for cancer research. 110 Materials and methods 111 Study Design and Sample Handling .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 6 112 The healthy colon mucosa samples were collected from 9 consecutive patients (2 females; 7 113 males; mean age 68 years ± 10.9) diagnosed with colorectal cancer who underwent surgical 114 resection from 21 st of April to 24 th of August 2021 in the Department of Oncological, 115 Transplant, and General Surgery; University Clinical Center in Gdansk. Written informed 116 consent was obtained from all the patients prior to surgery. The summary of patients’ data is 117 presented in Table 1. After the surgical resection, a larger, full-walled specimen of the colon 118 was immediately excised approximately 10 - 15 cm away from the primary tumor, rinsed in 119 saline and left at room temperature until the mucosa collection procedure at a particular time 120 point was completed. At each time point, a sample of macroscopically unaffected mucosa was 121 excised by dissecting from the colon specimen and detaching gently from the muscular layer. 122 Subsequent washing steps were implemented, including: saline solution (twice), antibiotic 123 solution (Penicillin - Streptomycin 5000 U/ml), and saline solution, followed by snap freezing 124 in liquid nitrogen. Tissue fragments were collected at six time points - 0 (T0), 10 (T1), 20 (T2), 125 30 (T3), 45 (T4), and 60 (T5) minutes after the surgical resection, estimated as the time of cold 126 ischemia. Sample collected at T0 was processed immediately after organ resection. First time 127 point (T0) was used as the reference and the starting point for all the subsequent analyses. In 128 total 54 samples were collected and subjected to further analysis. 129 RNA Extraction and Quality Control 130 Colon mucosa samples were stored at -80°C until RNA isolation. Total RNA was extracted from 131 10 - 30 mg of tissue samples that were mechanically homogenized using T10 Basic ULTRA - 132 TURRAX disperser (IKA) in the presence of QIAzol Lysis reagent (Qiagen). RNA isolation, 133 purification, and DNase digestion were performed using the RNeasy Mini kit (Qiagen) 134 according to the original protocol with two modifications as described in the previous paper .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 7 135 (22) RNA quality and quantity were assessed using TapeStation (Agilent Technologies) using 136 RNA ScreenTape kit according to the manufacturer's protocol (Table 2). RNA integrity number 137 (RIN) was calculated using TapeStation analysis software (Agilent Technologies). Additionally, 138 the DV200 index, indicating the percentage of RNA fragments > 200 nt, was calculated and 139 used as a quality assessment standard (23). Samples with a DV200 index > 75% were used for 140 further NGS analysis. 141 Targeted RNA sequencing 142 The targeted RNA sequencing panel was designed with the Roche NimbleDesign online tool 143 (Roche, now HyperDesign, https://hyperdesign.com/#/) and covered 7229 regions with a 144 total length of 1,243,523 bp. It included 634 genes based on literature research and covered 145 genes associated with apoptosis, cell death, hypoxia, epithelial-to-mesenchymal transition, 146 and other cancer-related genes (the full list of transcripts is given in S2 Table). NGS libraries 147 were prepared with the Kapa RNA HyperPrep Kit (Roche) using Automated Liquid Handling 148 Bravo NGS workstation (Agilent Technologies) according to the manufacturer manual with 149 100 ng RNA used as an input and enzymatic fragmentation at 94°C for 6 minutes, with the 150 addition of ERCC RNA Spike-In Mix (Invitrogen) as an external RNA control. All the single 151 libraries were multiplexed and hybridized with SeqCap EZ Choice Probes (Roche) designed by 152 our group and KAPA HyperCapture Reagent and Bead kit (v2, Roche Sequencing Solutions, 153 Inc.) according to SeqCap RNA Enrichment System User's Guide (v.1.0) with slight 154 modifications. Component A was replaced with formaldehyde, and the Multiplex 155 Hybridization Enhancing Oligo Pool was replaced with Universal Blocking Oligos (UBO).The 156 hybridization was run for 18h, the library was cleaned up, post-captured, amplified, and 157 purified, followed by inspection of fragment distribution using TapeStation with High .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 8 158 Sensitivity D1000 Screen Tape kit (Agilent Technologies) and qPCR quantitation in a Roche 159 LightCycler 480 with KAPA Library Quantification Kit (Roche). Paired-end reads of 150 bp were 160 generated using TruSeq RNA Access sequencing chemistry by an external service provider 161 (Macrogen Europe, Amsterdam, The Netherlands). 162 Transcriptomic data analysis 163 The RNA-seq data were processed as described in Andreou et al. 2024 (24). Briefly, after the 164 quality filtering and trimming of the raw FASTQ files with BBDuk 165 (https://sourceforge.net/projects/bbmap/, version 38.36), the resulting reads were mapped 166 to the reference human genome (hg38, GENCODE version 39) using STAR version 2.7.3a (25). 167 Raw read counts assigned to the annotated genes obtained in the above process were 168 collated into a single gene expression matrix and processed further in R programming 169 language (https://www.r-project.org, version 4.1.2). Lowly expressed genes were filtered out 170 based on a minimal required CPM expression threshold. The filtered gene expression matrix 171 was normalized using the TMM method in edgeR (26). Principal Component Analysis (PCA) 172 was performed to investigate sample grouping and identify potential outliers using 173 FactoMineR, version 2.4 (27). Batch effect correction was performed using ComBat-seq (28). 174 Significantly differentially expressed genes (DEGs) were identified with EdgeR using the 175 glmLRT function (likelihood ratio test) with a significance threshold set to 0.05 (False 176 Discovery Rate, FDR). 177 Weighted gene co-expression network analysis (WGCNA) was performed on the normalized 178 and batch-adjusted gene expression values using the WGCNA R library WGCNA (29). Soft 179 threshold value for building the correlation network was selected empirically based on 180 diagnostic plots (pickSoftThreshold function). The final network was built using the .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 9 181 blockwiseModules function, where the TOMType parameter was set to “signed”. 182 Subsequently, a linear model (lmFit, from the limma package, (30)) was run on all modules to 183 identify those associated with the tested trait (time point). 184 Overrepresented terms / GO / pathways 185 Overrepresentation analysis (ORA) was used to perform functional enrichment analysis of 186 significantly differentially expressed genes, utilizing the Gene Ontology (GO) database and the 187 genome reference set in WebGestalt (31). Pathway enrichment analysis was similarly 188 conducted with ORA, focusing on KEGG pathways and the genome reference set in 189 WebGestalt. In addition, functional and pathway enrichment analyses were separately 190 applied to genes from WGCNA modules ME2 and ME4, using WebGestalt for the analysis. 191 Bioethics Committee Approval 192 All procedures for sample collection were approved by the Independent Bioethics Committee 193 for Research at the Medical University of Gdansk (approval number NKBBN/564/2018 with 194 multiple amendments). Written informed consent was obtained from all the patients prior to 195 surgery. All procedures were performed in accordance with the relevant national and 196 international laws and guidelines as well as in compliance with European Union General Data 197 Protection Regulation (EU GDPR). 198 Table 1. A summary of donors and diagnoses included in the study Patient ID Sex Age ICD-10 Classification ES31C Male 67 C18.7 - Malignant neoplasm of sigmoid colon 5IDSR Female 70 C19 - Malignant neoplasm of rectosigmoid junction OV1IW Male 67 C20 - Malignant neoplasm of rectum .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 10 NYYCH Male 48 C20 - Malignant neoplasm of rectum LZZOP Female 72 C19 - Malignant neoplasm of rectosigmoid junction JSDHP Male 68 C19 - Malignant neoplasm of rectosigmoid junction PZ94B Male 83 C18.7 - Malignant neoplasm of sigmoid colon FFMB1 Male 59 C20 - Malignant neoplasm of rectum ABCD Male 83 C18.5 - Malignant neoplasm of splenic flexure 199 200 Results 201 Quality of total RNA within the studied time frame 202 Tissues from nine patients were collected and snap-frozen at six time points: 203 immediately post-resection (T0), 10 minutes (T1), 20 minutes (T2), 30 minutes (T3), 45 204 minutes (T4), and 60 minutes (T5) post-resection (see Materials and Methods). As many prior 205 studies have focused on RNA quality as a consequence of CIT, we performed quality control 206 by measuring both RIN and DV200 values. Notably, RIN values remained consistent across all 207 analyzed time points, with a median value of 4.3. Furthermore, all samples had DV200 above 208 75%, indicating that RNA was of sufficient quality for subsequent analysis (32). The 209 consistency of these results across all time points suggest that RNA integrity was preserved 210 throughout the collection and processing protocol. Detailed RIN measurements for each 211 sample are provided in S1 Table. 212 Differential Expression Analysis highlights genes associated with hypoxia, EMT, and 213 apoptosis .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 11 214 Differential expression analysis revealed 44 significantly differentially expressed genes 215 (DEGs) between the two extreme time points set for our study, T0 and T5 (60 min) (S3 Table). 216 Principal component analysis (PCA) demonstrated a noticeable separation between samples 217 at T0 and T5 after eliminating patient batch effect, as illustrated in Fig. 1. Comparisons 218 involving T0 and other time points (T1–T4) showed fewer statistically significant DEGs: zero 219 between T0 vs T3, and six for T0 vs T4, namely ABCB1, ABCG2, EPAS1, FZD2, LAMA3, and 220 TMPRSS2. Our results show that the initial gene expression changes occur between 30 221 minutes (T3) and 45 minutes (T4) post-excision; with 44 genes displaying significant changes 222 between 45 minutes and 60 minutes (T5) after excision (S3 Table). Thus, our further analyses 223 focused on the T0 versus T5 comparison. 224 Applying an absolute logFC cutoff of ≥1, we identified 15 upregulated genes and 29 225 downregulated genes in T5 compared to T0. Notably, DEGs included hypoxia- and EMT- 226 associated genes such as SNAI, CDH1, ADM, MMP9, EGLN3, IL-6, AKT3, TSPAN1, and DSC2, as 227 well as apoptosis-related genes: CASP7, TNFSF10, DEFB1, AKT3, and JPH3. Among the 44 DEGs 228 identified between T0 and T5 (S3 Table), 5 were linked to hypoxia, 8 to apoptosis or cell death, 229 and 10 to epithelial-to-mesenchymal transition (EMT) determined using EMTome (33). The 230 remaining 19 were classified as 'other'. It is important to stress that none of 22 housekeeping 231 genes used as a reference set included in the panel showed significant expression changes. 232 S3 Table provides the complete DEG list with the logFC, p-values, and FDR values. 233 Functional enrichment of DEGs using overrepresentation analysis (ORA) identified 234 enrichment of Gene Ontology (GO) terms associated with processes such as apoptosis, cell- 235 cell adhesion, tissue migration, and others. TNF signaling, ABC transporters, gastric cancer, 236 and cancer pathways were among the enriched pathways shown by ORA KEGG analysis (S4 237 Table). .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 12 238 Fig. 1: Principal component analysis (PCA) of all 54 samples using the normalized batch- 239 corrected expression profiles of 1419 genes (both targeted and non-targeted). Each point 240 represents the orientation of a sample projected into the transcriptional space, color and 241 symbol refer to its group membership. The analysis demonstrated clearly separated clusters 242 of T0 and T5 samples (marked in green and yellow circles), a pattern not pronounced among 243 the intermediate time points (T1-T4). T0 - time point 0, frozen immediately after resection; 244 T1 - time point 1, frozen 10 minutes after resection; T2 - time point 2, frozen 20 minutes after 245 resection; T3 - time point 3, frozen 30 minutes after resection; T4 - time point 4, frozen 45 246 minutes after resection; T5 - time point 5, frozen 60 minutes after resection. 247 Weighted Gene Co-Expression Network Analysis (WGCNA) confirms two gene modules 248 associated with apoptosis, EMT and hypoxia 249 To find the clusters of highly correlated genes in our dataset, we applied the WGCNA 250 analysis that identified two significant gene modules, ME2 and ME4 (adjusted p-values 0.037 251 and 0.018, respectively), which demonstrate distinct patterns during the time course of our 252 study. Notably, the genes within these modules were associated with key biological 253 processes, including apoptosis, EMT, and hypoxia-related pathways (Fig. 2). 254 ME2 consisted of 145 genes, primarily exhibiting a downregulation pattern in T5 in relation 255 to T0 (Fig. 2A, 2B). Among these, 26.2% were related to apoptosis or cell death, 18.6% to EMT, 256 11.7% to hypoxia, and 0.69% (one gene) was annotated as a reference gene in our targeted 257 panel (Fig. 2C). The remaining 42.8% were categorized as 'other’ (see details in S5 Table). 258 Functional enrichment analysis of ME2 genes using ORA GO (FDR < 0.05) identified several 259 pathways, including those related to the execution phase of apoptosis and the extrinsic 260 apoptotic signaling pathway. KEGG pathway analysis further highlighted nine pathways, .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 13 261 including cancer-related and apoptosis-related processes such as p53 signaling, HIF-1 262 signaling, and FoxO signaling (S7 Table). 263 ME4, a smaller gene module consisting of 56 genes, also displayed a downregulation pattern 264 (Fig. 2A, 2B). Genes comprised in this module showed a faster response to CIT, reaching a 265 stable point at the T4 time point (Fig. 2A). In ME2, however, the difference between T4 and 266 T5 is visibly larger (Fig. 2A). Within the ME4 cluster, 25% of genes were identified as apoptosis- 267 related, while 16.1% were associated with EMT and hypoxia, respectively. Furthermore, 268 8.93% were classified as reference genes and 33.9% as ‘other’ (Fig. 2A, 2B). ORA GO analysis 269 (FDR < 0.05) in ME4 identified pathways connected to numerous metabolic processes, cell- 270 cell signaling and homeostasis. Similarly, KEGG pathway analysis revealed significant 271 metabolic and cancer-related processes, including glycolysis/gluconeogenesis, HIF-1 signaling 272 pathway, and pathways related to gastric cancer (S7 Table). 273 Fig. 2: Weighted Gene Co-Expression Network Analysis (WGCNA) of the expression profiles 274 of all 54 samples using the normalized batch-corrected expression profiles of 1419 genes. 275 A. Two WGCNA modules significantly associated with the time course phenotype (ME2 and 276 ME4, adjusted p-value 0.037 and 0.018, respectively) The y-axis on the boxplots represents 277 module eigengene expression levels. B. Heatmaps of gene expression of ME2 and ME4 278 module genes; module eigengene provide summarized representation of the expression 279 pattern of all genes in a given module and sample type. C. Pie charts displaying the percentage 280 of transcripts associated with particular biological processes or functions: apoptosis/cell 281 death, hypoxia, epithelial-mesenchymal transition, reference genes, and ‘other’ in each 282 module, ME2 and ME4, respectively. T0 - time point 0, frozen immediately after resection; T1 283 - time point 1, frozen 10 minutes after resection; T2 - time point 2, frozen 20 minutes after .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 14 284 resection; T3 - time point 3, frozen 30 minutes after resection; T4 - time point 4, frozen 45 285 minutes after resection; T5 - time point 5, frozen 60 minutes after resection. 286 Discussion 287 We examined the impact of up to 60 minutes of CIT on the transcription of cancer- 288 related genes, with samples snap-frozen at defined time points. RNA integrity (RIN) varied 289 across samples without a clear correlation to CIT (S1 Table), consistent with studies showing 290 minimal RNA degradation after 3–4 hours (12,13,34,35). Despite applying four rinsing steps, 291 colon mucosa samples showed relatively low RNA quality (mean RIN = 4.4, range 2.7 – 7.7), 292 aligning with findings from other tissue comparisons (14,15). This may stem from enzymatic 293 activity, bacterial influence, or tissue compartment differences (36). To mitigate this, RNA 294 fragmentation conditions were optimized during the process of NGS library preparation. 295 Although utilizing a comprehensive transcriptomic approach covering over 600 genes, we 296 detected no DEGs within the first 30 minutes post-resection, indicating this timeframe is likely 297 safe for tissue excision and snap-freezing. Only six DEGs were identified between the 0 and 298 45 minute time points (T4), while 44 DEGs emerged between the 0 and 60 minute time points 299 (T5), highlighting this period as critical for tissue freezing. Notably, five of the six DEGs in T4 300 and 29 of the 44 DEGs in T5 were downregulated. These results align with findings from Aktas 301 et al., who reported 41 transcripts affected by prolonged CIT, including genes involved in cell 302 cycle regulation, apoptosis, stress responses, and cancer progression (10). Although this 303 number seems modest, it might have significant implications for researchers focusing on 304 specific genes, gene signatures, or pathways, particularly because these DEGs are closely 305 linked to cancer development and progression. The broader impact of sample handling on the 306 transcriptome varies widely across studies. von der Heyde et al . demonstrated that cold .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 15 307 ischemia times exceeding 15 minutes significantly impact the expression of mRNAs, proteins, 308 and phosphosites in tumor and normal tissues across colorectal cancer, hepatocellular 309 carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. The authors conclude 310 that ischemia time is a crucial quality parameter for tissue collections used for target 311 discovery and validation in cancer research (37). Using microarrays, Musella et al . found 312 minimal effects, detecting only 0.2% DEGs in normal colon tissue and colorectal carcinoma 313 within 6 hours, though two identified DEGs were oncogenes (17). In contrast, Spruessel et al. 314 applying the same genotyping technique, reported significant changes, with 20% of 315 detectable genes and proteins altered within 30 minutes post-excision (1). Grizzle et al. noted 316 that while most studies found only 1–3% of transcripts affected, they often overlooked the 317 functional impact of these genes (35). Our findings underscore that while RNA integrity 318 remains stable within the first 30 minutes, transcriptional changes begin to emerge beyond 319 this point. Even within a 60-minute CIT window, the occurring transcriptional changes can 320 potentially alter the results of studies exploring cancer and adjacent tissue genetics. These 321 observations highlight the importance of rapid tissue processing for accurate transcriptomic 322 analyses in cancer research. 323 Our analysis focused on genes involved in cancer development and progression, as they are 324 crucial for the studies of tumor margins and tissues adjacent to the tumor, where changes in 325 the transcriptome may be subtle (21). The results demonstrate the impact of hypoxia on EMT, 326 a process crucial for embryonic development and tissue regeneration, which is aberrantly 327 reactivated in cancer to drive progression and metastasis through enhanced migration, 328 invasiveness, stemness, and resistance to therapies (38). Genes such as SNAI, CDH1, MMP9, 329 and S100A8, which play critical roles in the EMT in cancer, exhibited significantly altered .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 16 330 expression levels (adjusted p-value < 0.05) in our analysis. Snail (SNAI1) and Slug (SNAI2) are 331 transcription factors known to bind to the promoter of CDH1, encoding E-cadherin, to repress 332 its transcription (39). The suppression of CDH1, regulated by SNAI, is a key driver of EMT, 333 contributing to cancer progression by promoting invasion and metastasis, and is recognized 334 as a marker of malignancy and poor clinical prognosis (40). In our study, prolonged CIT caused 335 significant alterations in the expression of SNAI and CDH1, with upregulation of SNAI and 336 downregulation of CDH1. Moreover, the observed upregulation of MMP9, likely induced by 337 CIT, may reflect further malignant changes as MMP family members facilitate cancer cell 338 invasion and metastasis (38). These changes, often linked to malignancy, may be 339 misinterpreted as early indicators of tumor progression in research on adjacent tissues. 340 However, our findings suggest that these alterations result from prolonged CIT. 341 To gain an insight into complex biological mechanisms affected by CIT, we performed 342 enrichment analysis of DEGs using two complementary approaches: a direct analysis of all 343 DEGs and a WGCNA-based analysis of the two resulting modules, both evaluated through GO 344 and KEGG. The direct DEG analysis provided a broad view of the pathways enriched across all 345 DEGs, whereas WGCNA highlighted context-specific pathways within co-regulated gene 346 modules. 347 GO and KEGG analyses of all DEGs identified malignancy-associated pathways, including 348 apoptosis, ABC transporters, and TNF signaling, as well as stress response pathways (S4 349 Table). In comparison, other transcriptomic studies identified alteration of pathways linked 350 to apoptosis, stress responses, cell cycle regulation, and cancer progression (10), as well as 351 those associated with stimulus response and signal transduction (34). Bray et al. found no 352 consistent gene ontology or pathway among 168 transcripts altered within 120 minutes of .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 17 353 CIT, highlighting a complex cellular response (16). In our study, despite targeting over 600 354 cancer-related genes, pathway enrichment was broad, supporting Bray et al.'s conclusions (S4 355 Table). 356 WGCNA identified two significantly downregulated co-expressed gene modules, ME2 and 357 ME4, with distinct functional patterns (S7 Table). ME2 was primarily linked to apoptosis, with 358 GO highlighting pathways like the execution phase of apoptosis and extrinsic apoptotic 359 signaling, while KEGG linked it to FoxO, Hippo, and p53 signaling pathways. These findings 360 underscore CIT's significant impact on apoptosis-related pathways within 60 minutes, 361 potentially confounding studies that do not account for its effects. ME4 was enriched for 362 metabolic pathways, including catabolic processes, homeostasis, pyruvate metabolism (GO), 363 glycolysis/gluconeogenesis, and choline metabolism in cancer (KEGG). Both modules featured 364 HIF-1 and cancer-related pathways, illustrating CIT’s dual impact on apoptosis and 365 metabolism, complicating transcriptomic interpretations. These findings align with reports of 366 ischemia-induced changes in CRC, affecting oncogenes and histone-related genes involved in 367 nucleosome organization, cell cycle, DNA replication, and p53 signaling, beginning at 8–10 368 minutes (1) and persisting up to 60 minutes (17). Notably, ME4 exhibited minimal differences 369 between T4 and T5, whereas ME2 showed a more pronounced variation between these time 370 points, suggesting a slower reaction to CIT (Fig. 2A). 371 Our study highlights that beyond RNA degradation, ischemic stress actively alters gene 372 expression, emphasizing the need to standardize CIT in human tissue collection. The 373 consistency of our observations suggests these transcriptomic changes stem from surgical 374 and tissue handling procedures rather than malignancy. A key strength of our study is the use 375 of NGS RNA-seq, which offers greater sensitivity and accuracy than microarrays (11,17), .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 18 376 enabling robust detection of gene expression changes, even at low levels. Unlike previous 377 studies, our approach analyzes over 600 genes, allowing a broader transcriptomic 378 assessment. However, limitations include a small sample size and potential influences from 379 enzymatic activity, warm ischemia, and other preanalytical factors (2,41). Additionally, our 380 analysis used a targeted cancer-related gene panel, which does not capture all transcriptomic 381 changes. 382 Conclusion 383 Our study highlights the significant impact of CIT on the transcriptomic profile of healthy colon 384 mucosa, with a particular focus on cancer-related genes. Using targeted RNA sequencing and 385 robust analyses, we identified differentially expressed genes primarily associated with 386 apoptosis, hypoxia, and EMT, as well as pathways involved in cancer development and 387 progression. Our findings underscore the importance of considering CIT as a critical 388 preanalytical variable, as prolonged ischemia can alter gene expression in ways that may 389 mimic malignant changes, leading to misinterpretations of results and incorrect clinical 390 conclusions. Importantly, our results suggest that CIT should be kept below 45 minutes to 391 minimize its impact on transcriptomic profiles. However, as the effects of CIT might be tissue- 392 dependent, this recommendation should be applied with caution when extrapolating to other 393 tissues. 394 Acknowledgements 395 We thank all the anonymous patients for acceptance to participate, sample contribution and 396 information provided in the questionnaire. We also thank surgeons, and nurses involved in 397 the patient recruitment process, collaborating technicians, diagnosticians and pathologists 398 from University Clinical Centre in Gdańsk, University Hospital in Cracow and Specialist .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 19 399 Hospital in Kościerzyna. This study was sponsored by the Foundation for Polish Science (FNP) 400 under the International Research Agendas Program to J.P.D. and A.P., co-financed by the 401 European Union under the European Regional Development Fund. 402 References 403 1. Spruessel A, Steimann G, Jung M, Lee SA, Carr T, Fentz AK, et al. 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Pseudonymized list of patients that were included in the study with the 520 information on gender, ICD10 (International Statistical Classification of Diseases and 521 Related Health Problems, 10th Revision), sample time points, type of tissue, and RNA 522 Integrity Number (RIN) values. (XLSX) 523 S2 Table. Target genes included in the designed panel and functional classification of 524 particular transcripts. (a) Gene symbols of targets included in the panel, (b) Ensembl ID, (c) 525 Ensembl ID as in Gencode (version 35), (d) Gene symbol according to the HUGO Gene 526 Nomenclature Committee, (e) EntrezID, (f) Full name of the gene, (g) - (p) Functional 527 classification. (XLSX) .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint 22 528 S3 Table. Differentially Expressed Genes (DEGs) between time points T5 (60 minutes) and 529 T0 (0 minutes). (a) Ensembl ID as in Gencode (version 35), (b) Gene symbol, (c) Direction of 530 the expression change: up - upregulation, down - downregulation, (d) logFoldChange (logFC), 531 (e) p-value, (f) False Discovery Rate (FDR), (g) Indication if the gene was included in our panel: 532 N - no, Y - yes, (h) Information regarding the comparison between different groups; T5 (time 533 point 5, 60 minutes after resection), T4 (time point 4, 45 minutes after resection) and T0 (time 534 point 0, 0 minutes after resection). (XLSX) 535 S4 Table. The results of enrichment analysis for differentially expressed genes (DEGs) 536 between T5 (time point 5, 60 minutes after resection) and T0 (time point 0, 0 minutes after 537 resection). (a) Gene Ontology Term ID and Kyoto Encyclopedia of Genes and Genomes (KEGG) 538 ID, (b) short description of identified Gene Ontology (GO) terms, (c) p-value, (d) False 539 Discovery Rate (FDR), (e) ratio of genes from our targeted panel vs. all genes associated with 540 the particular GO term, (f) genes from our analysis associated with the particular GO term, (g) 541 information regarding the comparison between different groups; T5 (time point 5, 60 minutes 542 after resection) and T0 (time point 0, 0 minutes after resection). (XLSX) 543 S5 Table. Genes included in weighted gene co-expression network analysis (WCGNA) 544 module eigengene 2 (ME2). (a) gene symbol, (b) description, (c) functional classification. 545 (XLSX) 546 S6 Table. Genes included in weighted gene co-expression network analysis (WCGNA) 547 module eigengene 4 (ME4). (a) gene symbol, (b) description, (c) functional classification. 548 (XLSX) 549 S7 Table. Gene Ontology (GO) terms and KEGG pathways identified via enrichment 550 analyses, including cluster membership information. Enrichment analysis was conducted 551 for genes from weighted gene co-expression network analysis (WCGNA) from statistically 552 significant modules - ME2 and ME4. (a) Gene Ontology Term ID and Kyoto Encyclopedia of 553 Genes and Genomes (KEGG) ID, (b) short description of identified Gene Ontology (GO) terms, 554 (c) p-value, (d) False Discovery Rate (FDR), (e) ratio of genes included in the panel, associated 555 with the particular GO term, (f) Genes associated with the particular GO term, (g) Information 556 regarding the comparison between different groups: T5 (time point 5, 60 minutes after 557 resection) and T0 (time point 0, 0 minutes after resection). (XLSX) .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted March 13, 2025. ; https://doi.org/10.1101/2025.03.09.642255doi: bioRxiv preprint