Molecular heterogeneity exists in many biological systems, such as major malignancies or diverse cell populations. Clustering of gene expression profiles has been widely used to dissect molecular heterogeneity. One drawback common to most clustering methods is that they often suffer from high dimensionality and noise, as well as feature redundancy. To address these challenges, we propose Extreme learning machine self-diffusion (ELMSD), an auto-encoder extreme learning machine feature representation method that incorporates a self-diffusion graph denoising framework to effectively dissect molecular heterogeneity. Our method, ELMSD, first learns a compressed representation of gene expression profiles from the hidden layer of the autoencoder extreme learning machine, followed by an iterative graph diffusion process to enhance the sample-to-sample similarity. The enhanced graph can largely facilitate the downstream clustering analysis, making it more efficient to analyze molecular properties. To demonstrate the utility of ELMSD, we applied it on one simulation dataset, five single-cell datasets, and 20 cancer datasets. Experiment results show that the ELMSD approach outperforms several state-of-the-art clustering methods and cancer subtypes, cell types identified by ELMSD reveal strong clinical relevance and biological interpretation. The ELMSD code is available at: https://github.com/DXCODEE/ELMSD.

Emerging evidence suggests that tumour morphological heterogeneity may influence mutational profiles relevant to therapy response. In this pilot study, we aimed to assess whether mutations identified within specific morphological patterns or at the invasion front correlate with shorter time to progression after anti-EGFR therapy, as compared to whole-tissue analysis.

We investigated genetic mutations in 142 samples from primary tumours of 39 KRAS wild-type metastatic colorectal cancer (CRC) patients receiving anti-EGFR therapy. Deep next-generation sequencing was performed on whole-tumour sections and six morphology-defined tumour regions.

Mutations in genes linked to anti-EGFR therapy response (KRAS, BRAF, NRAS, PTEN and PI3KCA) were found uniquely in the non-responder group, with substantial variability across morphological sub-regions. BRAF mutations were aligned with serrated and mucinous morphologies, while KRAS mutations (p.Lys147Glu and p.Ala146Thr) were associated with mucinous and desmoplastic morphologies. In all cases, the cumulative mutational profile from sub-regions provided more details than that of the whole-tumour profile.

Our findings highlight that comprehensive analysis, considering morphological heterogeneity, is crucial for personalised CRC treatment strategies.

Colorectal cancer obstruction is a common problem with distinct symptomatic clues on CT/MR images even under incomplete conditions. The choice of management in the emergency setting has a significant effect on the prognosis of obstructive and nonobstructive colorectal cancer patients. Previous studies have demonstrated that obstruction in colorectal cancer is associated with significantly poorer outcomes, alongside distinct alterations in the composition of the extracellular matrix. Based on accumulating evidence, it is hypothesized that ECM remodeling plays a pivotal role in the development of colorectal cancer obstruction. This review explores the pathological features of obstructive colorectal cancer, emphasizing extracellular matrix remodeling as a central process. Key mechanisms include tumor-stromal cell interactions, tumor cell aggregation and migration mediated by the peripheral nervous system, vascular and lymphatic remodeling within the tumor microenvironment, and microbiota-mediated regulation of cancer progression. These findings demonstrate that further remodeling of the extracellular matrix may be a molecular biological feature of obstructive colorectal cancer with poor prognosis.

The purpose of this study was to evaluate the diagnostic performance of artificial intelligence based on imaging data to predict rectal cancer recurrence using a meta-analysis system.

Medline, Embase, Cochrane Library, Web of Science, and other databases were searched for all articles on artificial intelligence prediction of rectal cancer recurrence based on imaging data published publicly from the establishment of the library to December 31, 2023. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis was performed by the software Revman 5.4 and Statistics data (Stata), and sensitivity analysis was used to detect potential sources of heterogeneity and test to assess the presence of publication bias. We evaluated how well imaging-based data can predict recurrence in patients with rectal cancer by analysing the pooled sensitivity, specificity, and area under the curve.

Ten studies were included. The pooled sensitivity, specificity, and area under the curve of imaging-based data for recurrence in patients with rectal cancer were respectively 0.84 (95 % confidence interval [CI]: 0.74-0.91), 0.87 (95 % CI: 0.82-0.91) and 0.92 (95 % CI: 0.89-0.94). Based on QUADAS-2, the quality of the article is acceptable. We found the causes of heterogeneity through meta-regression: recurrence time predesign Lasso. Based on Deeks' funnel plot, no publication bias was detected.

Artificial intelligence based on imaging data has a high predictive ability for rectal cancer recurrence.

The aim of this study is to investigate the impact of sh-LncRNA ASB16-AS1 on doxorubicin (DOX) resistance in colorectal cancer (CRC). First, an in vitro study was conducted to investigate the effects of LncRNA ASB16-AS1, miR-185-5p, and TEAD1 on drug resistance in CRC cells. Subsequently, utilizing nanotechnology, poly(beta amino esters) (PBAE)/zeolitic imidazolate framework-8 (ZIF-8)@sh-LncRNA ASB16-AS1 nanoparticles (PZSNP) were synthesized and characterized, evaluating their cellular toxicity and hemolytic activity. Finally, a mouse subcutaneous tumor model was established by subcutaneous injection of SW480/DOX cell suspension to investigate the impact of PZSNP on the tumor. Under DOX treatment, downregulation of LncRNA ASB16-AS1, overexpression of miR-185-5p, or downregulation of TEAD1 suppressed the viability and proliferation of drug-resistant CRC cells while promoting apoptosis. Conversely, overexpression of LncRNA ASB16-AS1, inhibition of miR-185-5p, or overexpression of TEAD1 enhanced the viability and proliferation of drug-resistant CRC cells while inhibiting apoptosis. The synthesized PZSNP exhibited a spherical shape with an average particle size of 123.6 nm, possessed positive charge, displayed good stability. It effectively encapsulated shRNA and displayed low cellular toxicity and hemolytic activity. Under DOX treatment, significant tumor necrosis was observed in the PZSNP group, and tumor growth was suppressed without causing weight loss. LncRNA ASB16-AS1, miR-185-5p, and TEAD1 are involved in regulating cell viability, proliferation, and apoptosis, contributing to drug resistance in CRC cells. sh-LncRNA ASB16-AS1 enhances the sensitivity of CRC cells to DOX during treatment, and in vivo delivery of PZSNP may serve as an effective strategy to overcome chemotherapy resistance in CRC.

Introduction  Colorectal carcinoma (CRC) is the second leading cause of cancer-related mortality worldwide. Within this context, a subset of CRCs shows overexpression of the human epidermal growth factor receptor 2 (HER2/neu), a characteristic also seen in other malignancies like breast and gastric cancers. The success of targeting the HER2 pathway in these cancers has prompted investigations into the potential use of similar interventions in CRC. Aims This research aims to assess HER2/neu expression in CRCs and its correlation with the CRC stage and histopathology, as well as to evaluate the demographic characteristics of CRC patients. Materials and methods  This prospective observational study was conducted on a cohort of 40 CRC patients, using histopathology and immunohistochemistry (IHC) sections from the Department of Pathology. Clinical and demographic data were collected over a 24-month period, and routine tissue processing and IHC staining were performed on colectomy tissues. Immunostaining with the HER2/neu marker was conducted for detailed analysis. Data were analyzed using IBM SPSS Statistics for Windows, Version 27.0 (Released 2020; IBM Corp., Armonk, NY, United States). Results  Among the 40 CRC cases studied, six cases (15%) exhibited robust membranous positivity, while eight cases (20%) showed moderate focal membranous positivity. Twenty-six cases (65%), however, demonstrated no HER2/neu staining positivity. A significant correlation was found between the histological grade of the tumor and HER2/neu expression (p = 0.0001). Additionally, HER2/neu expression was significantly correlated with lymphovascular invasion (p < 0.0001) and lymph node status (p < 0.047). Conclusion  This study found a strong correlation between the tumor's stage, grade, lymph node status, and lymphovascular invasion and HER2/neu expression. Therefore, HER2/neu can be a predictive and therapeutic marker in colorectal cancers. This underscores the potential importance of incorporating these parameters in the clinical evaluation and targeted treatment strategies for CRC patients.

Colorectal cancer (CRC) represents a significant cause of cancer-related mortality on a global scale. It is a highly heterogeneous cancer, and the response of patients to homogeneous drug therapy varies considerably. Patient-derived tumor organoids (PDTOs) represent an optimal preclinical model for cancer research. A substantial body of evidence from numerous studies has demonstrated that PDTOs can accurately predict a patient's response to different drug treatments. This article outlines the utilization of PDTOs in the management of CRC across a range of therapeutic contexts, including postoperative adjuvant chemotherapy, palliative chemotherapy, neoadjuvant chemoradiotherapy, targeted therapy, third-line and follow-up treatment, and the treatment of elderly patients. This article delineates the manner in which PDTOs can inform therapeutic decisions at all stages of CRC, thereby assisting clinicians in selecting treatment options and reducing the risk of toxicity and resistance associated with clinical drugs. Moreover, it identifies shortcomings of existing PDTOs, including the absence of consistent criteria for assessing drug sensitivity tests, the lack of vascular and tumor microenvironment models, and the high cost of the technology. In conclusion, despite their inherent limitations, PDTOs offer several advantages, including rapid culture, a high success rate, high consistency, and high throughput, which can be employed as a personalized treatment option for CRC. The use of PDTOs in CRC allows for the prediction of responses to different treatment modalities at various stages of disease progression. This has the potential to reduce adverse drug reactions and the emergence of resistance associated with clinical drugs, facilitate evidence-based clinical decision-making, and guide CRC patients in the selection of personalized medications, thereby advancing the individualized treatment of CRC.

Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.

The behaviour of metastases in patients with liver-metastatic colorectal cancer (CRC) is still not adequately considered during treatment planning. However, studies in large cohorts have shown that the disease course in these patients depends on the histopathological growth pattern (HGP) of the liver metastases, with the desmoplastic (or encapsulated) pattern responsible for a favourable outcome and the replacement pattern for an unfavourable course. To increase our knowledge of cancer biology in general as well as to design clinical trials that take into account the diverse behaviour of liver metastases, it is necessary to know the cellular and molecular determinants of these growth patterns. For that purpose, we compared the transcriptome of tumour tissue (prospective cohort; n = 57) sampled very precisely at the transition of metastasis and adjacent liver, between the desmoplastic and replacement HGP. In addition, the mutational profiles for 46 genes related to CRC were extracted from the RNA sequencing reads. First, we show that the genetic constitution of a liver metastasis from colorectal cancer does not determine its HGP. Second, we show clear differences between HGPs regarding the expression of genes belonging to the Molecular Signatures Database hallmark gene sets. Biological themes of the replacement HGP reflect cancer cell proliferation and glucose metabolism, while the desmoplastic HGP is characterized by inflammation and immune response, and angiogenesis. This study supports the view that HGPs are a reflection of the biology of CRC liver metastases and suggests the HGPs are driven epigenetically rather than by specific gene mutations.

We developed a whole transcriptome sequencing (WTS)-based Consensus Molecular Subtypes (CMS) classifier using FFPE tissue and investigated its prognostic and predictive utility in a large clinico-genomic database of CRC patients (n = 24,939).

The classifier was trained against the original CMS datasets using an SVM model and validated in an independent blinded TCGA dataset (88.0% accuracy). Kaplan-Meier estimates of overall survival (OS) and time-on-treatment (TOT) were calculated for each CMS (p < 0.05 considered significant).

CMS2 tumors were enriched on left-side of colon and conferred the longest median OS. In RAS-wildtype mCRC, left-sided tumors and CMS2 classification were associated with longer TOT with anti-EGFR antibodies (cetuximab and panitumumab). When restricting to only CMS2, there was no significant difference in TOT between right- versus left-sided tumors. CMS1 tumors were associated with a longer median TOT with pembrolizumab relative to other CMS groups, even when analyzing only microsatellite stable (MSS) tumors.

A WTS-based CMS classifier allowed investigation of a large multi-institutional clinico-genomic mCRC cohort, suggesting anti-EGFR therapy benefit for right-sided RAS-WT CMS2 tumors and immune checkpoint inhibitor benefit for MSS CMS1. Routine CMS classification of CRC provides important treatment associations that should be further investigated.

Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad-CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (p < 0.001) and the TCGA cohort (p < 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor-node-metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in APC, SMAD2, EEF1AKMT4, EPG5, and TANC1. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.

Metastasis is the main cause of mortality in colorectal cancer (CRC) patients. Exploring the mechanisms of metastasis is of great importance in both clinical and fundamental CRC research. CRC is a highly heterogeneous disease with variable therapeutic outcomes of treatment. In this study, we applied spatial transcriptomics (ST) to generate a tissue-wide transcriptome from two primary colorectal cancer tissues and their matched liver metastatic tissues. Spatial RNA information showed intratumoral heterogeneity (ITH) of both primary and metastatic tissues. The comparison of gene expressions across tissues revealed an apparent enrichment of cancer stem cells (CSCs) in metastatic tissues and identified FOXD1 as a novel metastatic CSC marker. Trajectory and pseudo-time analyses revealed distinct evolutionary trajectories and a dedifferentiation-differentiation process during metastasis. CellphoneDB analysis suggested a dominant interaction of CD74-MIF with tumor cells in metastatic tissues. Further analysis confirmed FOXD1 as a maker of CSCs and the predictor of patient survival, especially in metastatic diseases. Our study found ITH of primary and metastatic tissues and provides novel insights into the cellular mechanisms underlying liver metastasis of CRC and foundations for therapeutic strategies for CRC metastasis.

Gastrointestinal cancer cells display both morphology and physiology diversity, thus posing a significant challenge for precise representation by a single data model. We conducted an in-depth study of gastrointestinal cancer heterogeneity by integrating and analyzing data from multiple modalities.

We used a modified Canny algorithm to identify edges from tumor images, capturing intricate nonlinear interactions between pixels. These edge features were then combined with differentially expressed mRNA, miRNA, and immune cell data. Before data integration, we used the K-medoids algorithm to pre-cluster individual data types. The results of pre-clustering were used to construct the kernel matrix. Finally, we applied spectral clustering to the fusion matrix to identify different tumor subtypes. Furthermore, we identified hub genes linked to these subtypes and their biological roles through the application of Weighted Gene Co-expression Network Analysis (WGCNA) and Gene Ontology (GO) enrichment analysis.

Our investigation categorized patients into three distinct tumor subtypes and pinpointed hub genes associated with each. Genes MAGI2-AS3, MALAT1, and SPARC were identified as having a differential impact on the metastatic and invasive capabilities of cancer cells.

By harnessing multimodal features, our study enhances the understanding of gastrointestinal tumor heterogeneity and identifies biomarkers for personalized medicine and targeted treatments.

Hydroperoxides (ROOHs) are known as damaging agents capable of mediating mutation, while a role as signaling agents through oxidation of protein sulfhydryls that can alter cancer-related pathways has gained traction. Glutathione peroxidase 2 (GPX2) is an antioxidant enzyme that reduces ROOHs at the expense of glutathione (GSH). GPX2 is noted for a tendency of large increases or decreases in expression levels during tumorigenesis that leads to investigators focusing on its role in cancer. However, GPX2 is only one component of multiple enzyme families that metabolize ROOH, and GPX2 levels are often very low in the context of these other ROOH-reducing activities. Colorectal cancer (CRC) was selected as a case study for examining GPX2 function, as colorectal tissues and cancers are sites where GPX2 is highly expressed. A case can be made for a significant impact of changes in expression levels. There is also a link between GPX2 and NADPH oxidase 1 (NOX1) from earlier studies that is seldom addressed and is discussed, presenting data on a unique association in colon and CRC. Tumor-derived cell lines are quite commonly used for pre-clinical studies involving the role of GPX2 in CRC. Generally, selection for this type of work is limited to identifying cell lines based on high and low GPX2 expression with the standard research scheme of overexpression in low-expressing lines and suppression in high-expressing lines to identify impacted pathways. This overlooks CRC subtypes among cell lines involving a wide range of gene expression profiles and a variety of driver mutation differences, along with a large difference in GPX2 expression levels. A trend for low and high GPX2 expressing cell lines to segregate into different CRC subclasses, indicated in this report, suggests that choices based solely on GPX2 levels may provide misleading and conflicting results by disregarding other properties of cell lines and failing to factor in differences in potential protein targets of ROOHs. CRC and cell line classification schemes are presented here that were intended to assist workers in performing pre-clinical studies but are largely unnoted in studies on GPX2 and CRC. Studies are often initiated on the premise that the transition from normal to CRC is associated with upregulation of GPX2. This is probably correct. However, the source normal cells for CRC could be almost any colon cell type, some with very high GPX2 levels. These factors are addressed in this study.

Gastrointestinal (GI) cancers are highly heterogeneous at multiple levels. Tumor heterogeneity can be captured by molecular profiling, such as genetic, epigenetic, proteomic, and transcriptomic classification. Transcriptomic subtyping has the advantage of combining genetic and epigenetic information, cancer cell-intrinsic properties, and the tumor microenvironment (TME). Unsupervised transcriptomic subtyping systems of different GI malignancies have gained interest because they reveal shared biological features across cancers and bear prognostic and predictive value. Importantly, transcriptomic subtypes accurately reflect complex phenotypic states varying not only per tumor region, but also throughout disease progression, with consequences for clinical management. Here, we discuss methodologies of transcriptomic subtyping, proposed taxonomies for GI malignancies, and the challenges posed to clinical implementation, highlighting opportunities for future transcriptomic profiling efforts to optimize clinical impact.

Cancer metastasis and therapy resistance are intricately linked with the dynamics of Epithelial-Mesenchymal Transition (EMT) and Circulating Tumor Cells (CTCs). EMT hybrid cells, characterized by a blend of epithelial and mesenchymal traits, have emerged as pivotal in metastasis and demonstrate remarkable plasticity, enabling transitions across cellular states crucial for intravasation, survival in circulation, and extravasation at distal sites. Concurrently, CTCs, which are detached from primary tumors and travel through the bloodstream, are crucial as potential biomarkers for cancer prognosis and therapeutic response. There is a significant interplay between EMT hybrid cells and CTCs, revealing a complex, bidirectional relationship that significantly influences metastatic progression and has a critical role in cancer drug resistance. This resistance is further influenced by the tumor microenvironment, with factors such as tumor-associated macrophages, cancer-associated fibroblasts, and hypoxic conditions driving EMT and contributing to therapeutic resistance. It is important to understand the molecular mechanisms of EMT, characteristics of EMT hybrid cells and CTCs, and their roles in both metastasis and drug resistance. This comprehensive understanding sheds light on the complexities of cancer metastasis and opens avenues for novel diagnostic approaches and targeted therapies and has significant advancements in combating cancer metastasis and overcoming drug resistance.

Consensus Molecular Subtype (CMS) classification of colorectal cancer (CRC) tissues is complicated by RNA degradation upon formalin-fixed paraffin-embedded (FFPE) preservation. Here, we present an FFPE-curated CMS classifier. The CMSFFPE classifier was developed using genes with a high transcript integrity in FFPE-derived RNA. We evaluated the classification accuracy in two FFPE-RNA datasets with matched fresh-frozen (FF) RNA data, and an FF-derived RNA set. An FFPE-RNA application cohort of metastatic CRC patients was established, partly treated with anti-EGFR therapy. Key characteristics per CMS were assessed. Cross-referenced with matched benchmark FF CMS calls, the CMSFFPE classifier strongly improved classification accuracy in two FFPE datasets compared with the original CMSClassifier (63.6% versus 40.9% and 83.3% versus 66.7%, respectively). We recovered CMS-specific recurrence-free survival patterns (CMS4 versus CMS2: hazard ratio 1.75, 95% CI 1.24-2.46). Key molecular and clinical associations of the CMSs were confirmed. In particular, we demonstrated the predictive value of CMS2 and CMS3 for anti-EGFR therapy response (CMS2&3: odds ratio 5.48, 95% CI 1.10-27.27). The CMSFFPE classifier is an optimized FFPE-curated research tool for CMS classification of clinical CRC samples.

The presence of molecular mutations in colorectal cancer (CRC) is a decisive factor in selecting the most effective first-line therapy. However, molecular analysis is routinely performed only in a limited number of patients with remote metastases. We propose to use tissue stiffness as a marker of the presence of molecular mutations in CRC samples. For this purpose, we applied compression optical coherence elastography (C-OCE) to calculate stiffness values in regions corresponding to specific CRC morphological patterns (n = 54). In parallel to estimating stiffness, molecular analysis from the same zones was performed to establish their relationships. As a result, a high correlation between the presence of KRAS/NRAS/BRAF driver mutations and high stiffness values was revealed regardless of CRC morphological pattern type. Further, we proposed threshold stiffness values for label-free targeted detection of molecular alterations in CRC tissues: for KRAS, NRAS, or BRAF driver mutation-above 803 kPa (sensitivity-91%; specificity-80%; diagnostic accuracy-85%), and only for KRAS driver mutation-above 850 kPa (sensitivity-90%; specificity-88%; diagnostic accuracy-89%). To conclude, C-OCE estimation of tissue stiffness can be used as a clinical diagnostic tool for preliminary screening of genetic burden in CRC tissues.

Bulk transcriptional profiles of early colorectal cancer (CRC) can fail to detect biological processes associated with disease-free survival (DFS) if the transcriptional patterns are subtle and/or obscured by other processes' patterns. Consensus-independent component analysis (c-ICA) can dissect such transcriptomes into statistically independent transcriptional components (TCs), capturing both pronounced and subtle biological processes.

In this study we (1) integrated transcriptomes (n = 4228) from multiple early CRC studies, (2) performed c-ICA to define the TC landscape within this integrated data set, 3) determined the biological processes captured by these TCs, (4) performed Cox regression to identify DFS-associated TCs, (5) performed random survival forest (RSF) analyses with activity of DFS-associated TCs as classifiers to identify subgroups of patients, and 6) performed a sensitivity analysis to determine the robustness of our results RESULTS: We identify 191 TCs, 43 of which are associated with DFS, revealing transcriptional diversity among DFS-associated biological processes. A prominent example is the epithelial-mesenchymal transition (EMT), for which we identify an association with nine independent DFS-associated TCs, each with coordinated upregulation or downregulation of various sets of genes.

This finding indicates that early CRC may have nine distinct routes to achieve EMT, each requiring a specific peri-operative treatment strategy. Finally, we stratify patients into DFS patient subgroups with distinct transcriptional patterns associated with stage 2 and stage 3 CRC.

Existing colorectal cancer subtyping methods were generated without much consideration of potential differences in expression profiles between colon and rectal tissues. Moreover, locally advanced rectal cancers at resection often have received neoadjuvant chemoradiotherapy which likely has a significant impact on gene expression.

We collected mRNA expression profiles for rectal and colon cancer samples (n = 2121). We observed that (i) Consensus Molecular Subtyping (CMS) had a different prognosis in treatment-naïve rectal vs. colon cancers, and (ii) that neoadjuvant chemoradiotherapy exposure produced a strong shift in CMS subtypes in rectal cancers. We therefore clustered 182 untreated rectal cancers to find rectal cancer-specific subtypes (RSSs).

We identified three robust subtypes. We observed that RSS1 had better, and RSS2 had worse disease-free survival. RSS1 showed high expression of MYC target genes and low activity of angiogenesis genes. RSS2 exhibited low regulatory T cell abundance, strong EMT and angiogenesis signalling, and high activation of TGF-β, NF-κB, and TNF-α signalling. RSS3 was characterised by the deactivation of EGFR, MAPK and WNT pathways.

We conclude that RSS subtyping allows for more accurate prognosis predictions in rectal cancers than CMS subtyping and provides new insight into targetable disease pathways within these subtypes.

Microsatellite instability-high (MSI-H) colorectal cancer (CRC) is known for its heightened responsiveness to immunotherapy. However, establishing robust predictive markers for immunotherapy efficacy remains imperative. This retrospective study aimed to elucidate the genetic landscape of MSI-H CRC and correlate these genetic alterations with immunotherapy outcomes in a cohort of 121 patients.

We analyzed clinical and molecular data from 121 patients with MSI-H CRC. We conducted a thorough genetic analysis of MSI-H CRC patients, with a specific emphasis on the APC, TP53, RAS, and MMR genes. We further analyzed the relationship between gene mutations and immunotherapy efficacy. The primary endpoints analyzed were objective response rate (ORR) and progression-free survival (PFS). All statistical analysis was conducted using SPSS26.0 and R 4.2.0 software.

Our findings underscored the complexity of the genetic landscape in MSI-H CRC, shedding light on the intricate interplay of these genes in CRC development. Notably, mutations in MMR genes exhibited a distinctive pattern, providing insights into the underlying mechanisms of MSI-H. Furthermore, our results revealed correlations between specific genetic alterations and immunotherapy outcomes, with a particular focus on treatment response rates and progression-free survival.

This study represents a significant step toward unraveling the genetic nuances of MSI-H CRC. The distinctive pattern of MMR gene mutations not only adds depth to our understanding of MSI-H CRC but also hints at potential avenues for targeted therapies. This research sets the stage for future investigations aimed at refining therapeutic strategies and improving outcomes for patients with MSI-H CRC.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or mesenchymality of tumors based on bulk tumor expression profiling. However, recent studies suggested that noncancerous cells from the microenvironment or macroenvironment heavily influence such signature profiles. Here, we strengthen these findings by investigating 11 published and frequently referenced gene expression signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. By analyses of bulk, single-cell, and pseudobulk expression data, we show that the cell type composition of a tumor sample frequently dominates scores of these EMT-related signatures. A comprehensive, integrated analysis of bulk RNA sequencing (RNA-seq) and single-cell RNA-seq data shows that stromal cells, most often fibroblasts, are the main drivers of EMT-related signature scores. We call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of "stemness subtypes" often result from low cancer cell content in tumor biopsies rather than cancer cell-specific stemness or mesenchymal/EMT characteristics.

Cancer self-renewal and migratory abilities are often characterized via gene module expression profiles, also called EMT or stemness gene expression signatures. Using published clinical tumor samples, cancer cell lines, and single cancer cells, we highlight the dominating influence of noncancer cells in low cancer cell content biopsies on their scores. We caution on their application for low cancer cell content clinical cancer samples with the intent to assign such characteristics or subtypes.

The goal of this study was to use a new interpretable machine-learning framework based on max-logistic competing risk factor models to identify a parsimonious set of differentially expressed genes (DEGs) that play a pivotal role in the development of colorectal cancer (CRC). Transcriptome data from nine public datasets were analyzed, and a new Chinese cohort was collected to validate the findings. The study discovered a set of four critical DEGs - CXCL8, PSMC2, APP, and SLC20A1 - that exhibit the highest accuracy in detecting CRC in diverse populations and ethnicities. Notably, PSMC2 and CXCL8 appear to play a central role in CRC, and CXCL8 alone could potentially serve as an early-stage marker for CRC. This work represents a pioneering effort in applying the max-logistic competing risk factor model to identify critical genes for human malignancies, and the interpretability and reproducibility of the results across diverse populations suggests that the four DEGs identified can provide a comprehensive description of the transcriptomic features of CRC. The practical implications of this research include the potential for personalized risk assessment and precision diagnosis and tailored treatment plans for patients.

Colorectal cancer (CRC) is a major global health concern, resulting in numerous cancer-related deaths. CRC detection, treatment, and prevention can be improved by identifying genes and biomarkers. Despite extensive research, the underlying mechanisms of CRC remain elusive, and previously identified biomarkers have not yielded satisfactory insights. This shortfall may be attributed to the predominance of univariate analysis methods, which overlook potential combinations of variants and genes contributing to disease development. Here, we address this knowledge gap by presenting a novel multivariate machine-learning strategy to pinpoint genes associated with CRC. Additionally, we applied our analysis pipeline to Inflammatory Bowel Disease (IBD), as IBD patients face substantial CRC risk. The importance of the identified genes was substantiated by rigorous validation across numerous independent datasets. Several of the discovered genes have been previously linked to CRC, while others represent novel findings warranting further investigation. A Python implementation of our pipeline can be accessed publicly at https://github.com/AriaSar/CRCIBD-ML.

Heterogeneity of colorectal carcinoma (CRC) represents a major hurdle towards personalized medicine. Efforts based on whole tumor profiling demonstrated that the CRC molecular subtypes were associated with specific tumor morphological patterns representing tumor subregions. We hypothesize that whole-tumor molecular descriptors depend on the morphological heterogeneity with significant impact on current molecular predictors. We investigated intra-tumor heterogeneity by morphology-guided transcriptomics to better understand the links between gene expression and tumor morphology represented by six morphological patterns (morphotypes): complex tubular, desmoplastic, mucinous, papillary, serrated, and solid/trabecular. Whole-transcriptome profiling by microarrays of 202 tumor regions (morphotypes, tumor-adjacent normal tissue, supportive stroma, and matched whole tumors) from 111 stage II-IV CRCs identified morphotype-specific gene expression profiles and molecular programs and differences in their cellular buildup. The proportion of cell types (fibroblasts, epithelial and immune cells) and differentiation of epithelial cells were the main drivers of the observed disparities with activation of EMT and TNF-α signaling in contrast to MYC and E2F targets signaling, defining major gradients of changes at molecular level. Several gene expression-based (including single-cell) classifiers, prognostic and predictive signatures were examined to study their behavior across morphotypes. Most exhibited important morphotype-dependent variability within same tumor sections, with regional predictions often contradicting the whole-tumor classification. The results show that morphotype-based tumor sampling allows the detection of molecular features that would otherwise be distilled in whole tumor profile, while maintaining histopathology context for their interpretation. This represents a practical approach at improving the reproducibility of expression profiling and, by consequence, of gene-based classifiers.

Colorectal cancer (CRC) is a heterogeneous disease. More insight into the biological diversity of CRC is needed to improve therapeutic outcomes. Established CRC cell lines are frequently used and were shown to be representative models of the main subtypes of CRC at the genomic and transcriptomic level. In the present work, we established stable, luciferase expressing derivatives from 10 well-established CRC cell lines, generated spheroids and subcutaneous xenograft tumors in nude mice, and performed comparative characterization of these model systems. Transcriptomic analyses revealed the close relation of cell lines with their derived spheroids and xenograft tumors. The preclinical model systems clustered with patient tumor samples when compared to normal tissue thereby confirming that cell-line-based tumor models retain specific characteristics of primary tumors. Xenografts showed different differentiation patterns and bioluminescence imaging revealed metastatic spread to the lungs. In addition, the models were classified according to the CMS classification system, with further sub-classification according to the recently identified two intrinsic epithelial tumor cell states of CRC, iCMS2 and iCMS3. The combined data showed that regarding primary tumor characteristics, 3D-spheroid cultures resemble xenografts more closely than 2D-cultured cells do. Furthermore, we set up a bioluminescence-based spheroid cytotoxicity assay in order to be able to perform dose-response relationship studies in analogy to typical monolayer assays. Applying the established assay, we studied the efficacy of oxaliplatin. Seven of the ten used cell lines showed a significant reduction in the response to oxaliplatin in the 3D-spheroid model compared to the 2D-monolayer model. Therapy studies in selected xenograft models confirmed the response or lack of response to oxaliplatin treatment. Analyses of differentially expressed genes in these models identified CAV1 as a possible marker of oxaliplatin resistance. In conclusion, we established a combined 2D/3D, in vitro/in vivo model system representing the heterogeneity of CRC, which can be used in preclinical research applications.

Colorectal cancer (CRC) is the third most common and second most lethal cancer globally. It is highly heterogeneous with different clinical-pathological characteristics, prognostic status, and therapy responses. Thus, the precise diagnosis of CRC subtypes is of great significance for improving the prognosis and survival of CRC patients. Nowadays, the most commonly used molecular-level CRC classification system is the Consensus Molecular Subtypes (CMSs). In this study, we applied a weakly supervised deep learning method, named attention-based multi-instance learning (MIL), on formalin-fixed paraffin-embedded (FFPE) whole-slide images (WSIs) to distinguish CMS1 subtype from CMS2, CMS3, and CMS4 subtypes, as well as distinguish CMS4 from CMS1, CMS2, and CMS3 subtypes. The advantage of MIL is training a bag of the tiled instance with bag-level labels only. Our experiment was performed on 1218 WSIs obtained from The Cancer Genome Atlas (TCGA). We constructed three convolutional neural network-based structures for model training and evaluated the ability of the max-pooling operator and mean-pooling operator on aggregating bag-level scores. The results showed that the 3-layer model achieved the best performance in both comparison groups. When compared CMS1 with CMS234, max-pooling reached the ACC of 83.86 % and the mean-pooling operator reached the AUC of 0.731. While comparing CMS4 with CMS123, mean-pooling reached the ACC of 74.26 % and max-pooling reached the AUC of 0.609. Our results implied that WSIs could be utilized to classify CMSs, and manual pixel-level annotation is not a necessity for computational pathology imaging analysis.

We classified colorectal cancer (CRC) patients into four phenotypic subgroups and investigated the prognostic value of angiogenic growth factors across subgroups. Preoperative serum concentrations and tissue expressions of VEGF, bFGF, and PDGF-bb were determined among 322 CRC patients. We classified patients into phenotypic subgroups (immune, canonical, metabolic, and mesenchymal) according to a method described in our earlier work. Among the metabolic subgroup, patients with high serum concentrations of VEGF, bFGF, or PDGF-bb exhibited a significantly improved prognosis. Moreover, those with high VEGF tissue expressions exhibited a significantly improved prognosis among patients in the metabolic subgroup. Among immune patients, a high VEGF serum expression is associated with a worse prognosis. A high serum bFGF concentration is associated with a favorable prognostic factor among patients with a canonical tumor phenotype. A high PDGF-bb tissue expression is associated with non-metastasized disease and with the immune, canonical, and metabolic subtypes. To our knowledge, this is the first study to show that the prognostic value of angiogenic growth factors differs between phenotypic subtypes.

Colorectal cancer (CRC) is the second most common cause of cancer-related mortality in the United States. Among newly diagnosed patients with CRC, 20% will present with metastatic disease and another 25% will develop metastases. The surgical resection of the primary tumor and metastatic disease sites confers the best chance at long-term survival. Unfortunately, many patients will recur after resection or present with unresectable disease. As such, metastatic CRC is commonly treated with a combination of surgery, systemic therapy, and/or liver-directed therapies. Despite best efforts, 5-year survival for unresectable metastatic CRC is only about 20%. CRC is a heterogeneous disease and the underlying genetic differences inform behavior, treatment strategy, and prognosis. Given the limitations of cytotoxic chemotherapy and the growing role of molecular profiling, research has focused on identifying and developing targeted therapies. We herein review how genetic profiling informs prognosis, crucial cell-signaling pathways that play a role in CRC carcinogenesis, and currently approved targeted therapies for metastatic CRC.

Colorectal adenocarcinoma (COREAD) is the second most deadly cancer and third most frequently encountered malignancy worldwide. Despite efforts in molecular subtyping and subsequent personalized COREAD treatments, multidisciplinary evidence suggests separating COREAD into colon cancer (COAD) and rectal cancer (READ). This new perspective could improve diagnosis and treatment of both carcinomas. RNA-binding proteins (RBPs), as critical regulators of every hallmark of cancer, could fulfill the need to identify sensitive biomarkers for COAD and READ separately. To detect new RBPs involved in COAD and READ progression, here we used a multidata integration strategy to prioritize tumorigenic RBPs. We analyzed and integrated 1) RBPs genomic and transcriptomic alterations from 488 COAD and 155 READ patients, 2) ∼ 10,000 raw associations between RBPs and cancer genes, 3) ∼ 15,000 immunostainings, and 4) loss-of-function screens performed in 102 COREAD cell lines. Thus, we unraveled new putative roles of NOP56, RBM12, NAT10, FKBP1A, EMG1, and CSE1L in COAD and READ progression. Interestingly, FKBP1A and EMG1 have never been related with any of these carcinomas but presented tumorigenic features in other cancer types. Subsequent survival analyses highlighted the clinical relevance of FKBP1A, NOP56, and NAT10 mRNA expression to predict poor prognosis in COREAD and COAD patients. Further research should be performed to validate their clinical potential and to elucidate their molecular mechanisms underlying these malignancies.

Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. An alternative and easy to apply approach is quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest by modeling conditional quantiles within a regression framework. Quantile regression can be applied efficiently at biobank scale using standard statistical packages in much the same way as linear regression, while having some unique advantages such as identifying variants with heterogeneous effects across different quantiles, including non-additive effects and variants involved in gene-environment interactions; accommodating a wide range of phenotype distributions with invariance to trait transformation; and overall providing more detailed information about the underlying genotype-phenotype associations. Here, we demonstrate the value of quantile regression in the context of GWAS by applying it to 39 quantitative traits in the UK Biobank (n > 300 , 000 individuals). Across these 39 traits we identify 7,297 significant loci, including 259 loci only detected by quantile regression. We show that quantile regression can help uncover replicable but unmodelled gene-environment interactions, and can provide additional key insights into poorly understood genotype-phenotype correlations for clinically relevant biomarkers at minimal additional cost.

Colorectal cancer (CRC) remains a challenging and deadly disease with high tumor microenvironment (TME) heterogeneity. Using an integrative multi-omics analysis and artificial intelligence-enabled spatial analysis of whole-slide images, we performed a comprehensive characterization of TME in colorectal cancer (CCCRC). CRC samples were classified into four CCCRC subtypes with distinct TME features, namely, C1 as the proliferative subtype with low immunogenicity; C2 as the immunosuppressed subtype with the terminally exhausted immune characteristics; C3 as the immune-excluded subtype with the distinct upregulation of stromal components and a lack of T cell infiltration in the tumor core; and C4 as the immunomodulatory subtype with the remarkable upregulation of anti-tumor immune components. The four CCCRC subtypes had distinct histopathologic and molecular characteristics, therapeutic efficacy, and prognosis. We found that the C1 subtype may be suitable for chemotherapy and cetuximab, the C2 subtype may benefit from a combination of chemotherapy and bevacizumab, the C3 subtype has increased sensitivity to the WNT pathway inhibitor WIKI4, and the C4 subtype is a potential candidate for immune checkpoint blockade treatment. Importantly, we established a simple gene classifier for accurate identification of each CCCRC subtype. Collectively our integrative analysis ultimately established a holistic framework to thoroughly dissect the TME of CRC, and the CCCRC classification system with high biological interpretability may contribute to biomarker discovery and future clinical trial design.

Transcriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information.

A multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS) was developed for single-sample classification.

Surprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification.

These results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.

With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models.

A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation.

The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction.

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths globally. The Colorectal Cancer Subtyping Consortium used the transcriptome-based method to classify CRC according to four molecular subtypes, each showing different genomic alterations and prognoses: CMS1 (microsatellite instable [MSI] immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal). To expedite the clinical implementation of such methods, easier and preferably tumor phenotype-based methods are needed. In this study, we describe a method to divide patients into four phenotypic subgroups using immunohistochemistry. Moreover, we analyze disease-specific survival (DSS) among different phenotypic subtypes and the associations between the phenotypic subtypes and clinicopathological variables.

We categorized 480 surgically treated CRC patients into four phenotypic subtypes (immune, canonical, metabolic, and mesenchymal) using the immunohistochemically determined CD3-CD8 tumor-stroma index, proliferation index, and tumor-stroma percentage. We analyzed survival rates for the phenotypic subtypes in different clinical patient subgroups using the Kaplan-Meier method and Cox regression analysis. Associations between phenotypic subtypes and clinicopathological variables were examined using the chi-square test.

Patients with immune subtype tumors exhibited the best 5-year DSS, while mesenchymal subtype tumors accompanied the worst prognosis. The prognostic value of the canonical subtype showed wide variation among different clinical subgroups. Immune subtype tumors were associated with being female, stage I disease, and a right-side colon location. Metabolic tumors, however, were associated with pT3 and pT4 tumors, and being male. Finally, a mesenchymal subtype associated with stage IV disease, a mucinous histology, and a rectal tumor location.

Phenotypic subtype predicts patient outcome in CRC. Associations and prognostic values for subtypes resemble the transcriptome-based consensus molecular subtypes (CMS) classification. In our study, the immune subtype stood out with its exceptionally good prognosis. Moreover, the canonical subtype showed wide variability among clinical subgroups. Further studies are needed to investigate the concordance between transcriptome-based classification systems and the phenotypic subtypes.

The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).

Background: Colorectal cancer (CRC) is a common and lethal cancer worldwide. Extraction of high-quality RNA from CRC samples plays a key role in scientific research and translational medicine. Specimen collection and washing methods that do not compromise RNA quality or quantity are needed to ensure high quality specimens for gene expression analysis and other RNA-based downstream applications. We investigated the effect of tissue specimen collection and different preparation processes on the quality and quantity of RNA extracted from surgical CRC tissues. Materials and Methods: After surgical resection, tissues were harvested and prepared with various washing processes in a room adjacent to the operating room. One hundred fourteen tissues from 36 CRC patients were separately washed in either cold phosphate-buffered saline reagent (n = 34) or Dulbecco's modified Eagle's medium (DMEM; n = 34) for 2-3 minutes until the stool was removed, and unwashed specimens served as controls (n = 34). Six tissue specimens were washed and immersed in DMEM for up to 1 hour at 4°C. Before RNA extraction, all specimens were kept in the stabilizing reagent for 3 months at -80°C. RNA was extracted, and the concentration per milligram of tissue was measured. RNA quality was assessed using the RNA integrity number (RIN) value. Results: Different washing processes did not result in significant differences in RNA quantity or RIN values. In the six tissues that were washed and immersed in DMEM for 1 hour, RIN values significantly decreased. The quality of the extracted RNA from most specimens was excellent with the average RIN greater than 7. Conclusions: RNA is stable in specimens washed in different processes for short periods, but RIN values may decrease with prolonged wash times.