Inhibition of O-GlcNAc transferase activates type I interferon-dependent antitumor immunity by bridging cGAS-STING pathway.

The O-GlcNAc transferase (OGT) is an essential enzyme that mediates protein O-GlcNAcylation, a unique form of posttranslational modification of many nuclear and cytosolic proteins. Recent studies observed increased OGT and O-GlcNAcylation levels in a broad range of human cancer tissues compared to adjacent normal tissues, indicating a universal effect of OGT in promoting tumorigenesis. Here, we show that OGT is essential for tumor growth in immunocompetent mice by repressing the cyclic GMP-AMP synthase (cGAS)-dependent DNA sensing pathway. We found that deletion of OGT (Ogt-/-) caused a marked reduction in tumor growth in both syngeneic mice tumor models and a genetic mice colorectal cancer (CRC) model induced by mutation of the Apc gene (Apcmin). Pharmacological inhibition or genetic deletion of OGT induced a robust genomic instability (GIN), leading to cGAS-dependent production of the type I interferon (IFN-I) and IFN-stimulated genes (ISGs). As a result, deletion of Cgas or Sting from Ogt-/- cancer cells restored tumor growth, and this correlated with impaired CD8+ T-cell-mediated antitumor immunity. Mechanistically, we found that OGT-dependent cleavage of host cell factor C1 (HCF-1) is required for the avoidance of GIN and IFN-I production in tumors. In summary, our results identify OGT-mediated genomic stability and activate cGAS-STING pathway as an important tumor-cell-intrinsic mechanism to repress antitumor immunity.

Genomic analysis of Enterobacteriaceae from colorectal cancer patients at a tertiary hospital in Ghana: a case-control study.

Colorectal cancer (CRC) is a severe gastrointestinal cancer and a leading cause of cancer-related deaths in Ghana. The potential role of gut Enterobacteriaceae in the increasing incidence of CRC in Ghana is yet to be thoroughly investigated. In this study, Enterobacteriaceae from CRC patients and healthy control participants were analyzed by whole genome sequencing to identify genomic features that are associated with CRC. Socio-demographic data showed a significant association between age and alcohol consumption and CRC. Escherichia coli was the most abundant Enterobacteriaceae isolated from the study participants and they were predominantly intestinal commensals. Escherichia coli isolates belonging to phylogroup D encoded the highest number of virulence genes. The agn43 and int genes were widespread in Escherichia coli isolates from the CRC patients. Multilocus sequence types of potentially pathogenic Escherichia coli from the CRC patients also encoded genes involved in aggregation, adherence and biofilm formation. The ampC2 and ampH antimicrobial resistance genes were also widespread in the genome of the Escherichia coli isolates. This study highlights the virulence tendencies of Escherichia coli from CRC patients and their ability to transfer virulence determinants to other Enterobacteriaceae residing in the gut.

Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy.

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Its complexity is influenced by various signal transduction networks that govern cellular proliferation, survival, differentiation, and apoptosis. The pathogenesis of CRC is a testament to the dysregulation of these signaling cascades, which culminates in the malignant transformation of colonic epithelium. This review aims to dissect the foundational signaling mechanisms implicated in CRC, to elucidate the generalized principles underpinning neoplastic evolution and progression. We discuss the molecular hallmarks of CRC, including the genomic, epigenomic and microbial features of CRC to highlight the role of signal transduction in the orchestration of the tumorigenic process. Concurrently, we review the advent of targeted and immune therapies in CRC, assessing their impact on the current clinical landscape. The development of these therapies has been informed by a deepening understanding of oncogenic signaling, leading to the identification of key nodes within these networks that can be exploited pharmacologically. Furthermore, we explore the potential of integrating AI to enhance the precision of therapeutic targeting and patient stratification, emphasizing their role in personalized medicine. In summary, our review captures the dynamic interplay between aberrant signaling in CRC pathogenesis and the concerted efforts to counteract these changes through targeted therapeutic strategies, ultimately aiming to pave the way for improved prognosis and personalized treatment modalities in colorectal cancer.

Prognosis of colorectal cancer, prognostic index of immunogenic cell death associated genes in response to immunotherapy, and potential therapeutic effects of ferroptosis inducers.

Introduction: This study leverages bioinformatics and medical big data to integrate datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), providing a comprehensive overview of immunogenic cell death (ICD)-related gene expression in colorectal cancer (CRC). The research aims to elucidate the molecular pathways and gene networks associated with ICD in CRC, with a focus on the therapeutic potential of cell death inducers, including ferroptosis agents, and their implications for precision medicine. Methods: We conducted differential expression analysis and utilized advanced bioinformatic techniques to analyze ICD-related gene expression in CRC tissues. Unsupervised consensus clustering was applied to categorize CRC patients into distinct ICD-associated subtypes, followed by an in-depth immune microenvironment analysis and single-cell RNA sequencing to investigate immune responses and cell infiltration patterns. Experimental validation was performed to assess the impact of cell death inducers on ICD gene expression and their interaction with ferroptosis inducers in combination with other clinical drugs. Results: Distinct ICD gene expression profiles were identified in CRC tissues, revealing molecular pathways and intricate gene networks. Unsupervised consensus clustering refined the CRC cohort into unique ICD-associated subtypes, each characterized by distinct clinical and immunological features. Immune microenvironment analysis and single-cell RNA sequencing revealed significant variations in immune responses and cell infiltration patterns across these subtypes. Experimental validation confirmed that cell death inducers directly affect ICD gene expression, highlighting their therapeutic potential. Additionally, combinatorial therapies with ferroptosis inducers and clinical drugs were shown to influence drug sensitivity and resistance in CRC. Discussion: Our findings underscore the importance of ICD-related genes in CRC prognosis and therapeutic targeting. The study provides actionable insights into the efficacy of cell death-inducing therapies, particularly ferroptosis inducers, and their regulatory mechanisms in CRC. These discoveries support the development of precision medicine strategies targeting ICD genes and offer valuable guidance for translating these therapies into clinical practice, with the potential to enhance CRC treatment outcomes and patient survival.

APC mutations dysregulate alternative polyadenylation in cancer.

BACKGROUND: Alternative polyadenylation (APA) affects most human genes and is recurrently dysregulated in all studied cancers. However, the mechanistic origins of this dysregulation are incompletely understood. RESULTS: We describe an unbiased analysis of molecular regulators of poly(A) site selection across The Cancer Genome Atlas and identify that colorectal adenocarcinoma is an outlier relative to all other cancer subtypes. This distinction arises from the frequent presence of loss-of-function APC mutations in colorectal adenocarcinoma, which are strongly associated with long 3' UTR expression relative to tumors lacking APC mutations. APC knockout similarly dysregulates APA in human colon organoids. By mining previously published APC eCLIP data, we show that APC preferentially binds G- and C-rich motifs just upstream of proximal poly(A) sites. Lastly, we find that reduced APC expression is associated with APA dysregulation in tumor types lacking recurrent APC mutations. CONCLUSIONS: As APC has been previously identified as an RNA-binding protein that preferentially binds 3' UTRs during mouse neurogenesis, our results suggest that APC promotes proximal poly(A) site use and that APC loss and altered expression contribute to pervasive APA dysregulation in cancers.

Association of various RAS codon mutations and prognostic outcomes of patients with colorectal liver metastases after hepatectomy.

PURPOSE: The prognostic and predictive value of RAS mutations in patients with colorectal liver metastases (CRLM) who have undergone hepatectomy holds substantial importance. The present study aimed to investigate the impact of different RAS codon mutations on long-term survival in CRLM patients. METHODS: A retrospective analysis was conducted on clinicopathological data from 399 CRLM patients with RAS mutations who underwent hepatectomy between January 2000 and December 2020. The RAS mutation gene status was assessed in KRAS codons (G12, G13, Q61, and A146) and NRAS codons (G12, G13, and Q61). Survival curves were generated using the Kaplan-Meier plotter and compared using the log-rank test. Univariate and multivariate analyses were performed to analyze the clinicopathological data. RESULTS: In the entire cohort, patients with KRAS G12 mutations exhibited the most favorable prognosis (p = 0.018). Comparatively, patients harboring KRAS Q61 mutations experienced poorer overall survival (OS) with a median of 15 months versus 33 months (p = 0.011) when compared to those with KRAS G12 mutations. Moreover, patients with NRAS Q61 mutations also showed decreased OS with a median of 26 months versus 33 months (p = 0.020) in comparison to KRAS G12 mutation patients. The results of multivariate analysis showed that both KRAS Q61 mutation (HR 2.130; 95% CI 1.088-4.168; p = 0.027) and NRAS Q61 mutation (HR 2.877; 95% CI 1.398-5.922; p = 0.004) were independent influencing factors of OS. Based on all identified risk factors, patients with RAS mutation were divided into high-risk and low-risk groups. Notably, in the high-risk group, the incorporation of postoperative chemotherapy was associated with longer OS, while it did not improve the survival of patients in the low-risk group. CONCLUSIONS: KRAS Q61 and NRAS Q61 mutations are promising predictors for OS in CRLM patients after hepatectomy. Postoperative chemotherapy may significantly benefit CRLM patients with RAS mutations, particularly those identified as high-risk.

Oncogenic KRAS drives immunosuppression of colorectal cancer by impairing DDX60-mediated dsRNA accumulation and viral mimicry.

The interferon (IFN) response is vital for the effectiveness of immune checkpoint inhibition (ICI) therapy. Our previous research showed that KRAS (Kirsten rat sarcoma viral) mutation impairs the IFN response in colorectal cancer (CRC), with an unclear mechanism. Here, we demonstrate that KRAS accelerates double-stranded RNA (dsRNA) degradation, impairing dsRNA sensing and IFN response by down-regulating DExD/H-box helicase 6 (DDX60). DDX60 was identified as a KRAS target here and could bind to dsRNAs to protect against RNA-induced silencing complex (RISC)-mediated degradation. Overexpressing DDX60 induced dsRNA accumulation, reactivated IFN signaling, and increased CRC sensitivity to ICI therapy. Mechanistically, KRAS engaged the AKT (also known as protein kinase B)-GSK3ß (glycogen synthase kinase-3 beta) pathway to suppress STAT3 phosphorylation, thereby inhibiting STAT3-driven DDX60 transcription. Our findings reveal a role for KRAS in dsRNA homeostasis, suggesting potential strategies to convert "cold" tumors to "hot" and to overcome ICI resistance in CRC with KRAS mutations.

Revealing the association between East Asian oral microbiome and colorectal cancer through Mendelian randomization and multi-omics analysis.

Background: Colorectal cancer (CRC) poses a global health threat, with the oral microbiome increasingly implicated in its pathogenesis. This study leverages Mendelian Randomization (MR) to explore causal links between oral microbiota and CRC using data from the China National GeneBank and Biobank Japan. By integrating multi-omics approaches, we aim to uncover mechanisms by which the microbiome influences cellular metabolism and cancer development. Methods: We analyzed microbiome profiles from 2017 tongue and 1915 saliva samples, and GWAS data for 6692 CRC cases and 27178 controls. Significant bacterial taxa were identified via MR analysis. Single-cell RNA sequencing and enrichment analyses elucidated underlying pathways, and drug predictions identified potential therapeutics. Results: MR identified 19 bacterial taxa significantly associated with CRC. Protective effects were observed in taxa like RUG343 and Streptococcus_umgs_2425, while HOT-345_umgs_976 and W5053_sp000467935_mgs_712 increased CRC risk. Single-cell RNA sequencing revealed key pathways, including JAK-STAT signaling and tyrosine metabolism. Drug prediction highlighted potential therapeutics like Menadione Sodium Bisulfite and Raloxifene. Conclusion: This study establishes the critical role of the oral microbiome in colorectal cancer development, identifying specific microbial taxa linked to CRC risk. Single-cell RNA sequencing and drug prediction analyses further elucidate key pathways and potential therapeutics, providing novel insights and personalized treatment strategies for CRC.

DNA methylation analysis of the SDC2, SEPT9 and VIM genes in fecal DNA for colorectal cancer diagnosis.

BACKGROUND: Colorectal cancer is one of the most common cancers worldwide. DNA methylation sites may serve as a new gene signature for colorectal cancer diagnosis. The search for representative DNA methylation sites is urgently needed. This study aimed to systematically identify a methylation gene panel for colorectal cancer diagnosis via tissue and fecal samples. METHODS: A total of 181 fecal and 50 tumor tissue samples were collected. They were obtained from 83 colorectal cancer patients and 98 healthy subjects. These samples were evaluated for DNA methylation of 9 target genes via quantitative bisulfite next-generation sequencing. We employed the rank-sum test to screen the colorectal cancer-specific methylation sites in the tissue and fecal cohorts. A data model was subsequently constructed and validated via the dedicated validation dataset. RESULTS: Compared with the fecal and negative control samples, the colorectal cancer tissue samples presented significantly higher methylation rates for all the selected gene sites. The methylation rates of the tissue and preoperative fecal samples showed the same high and low rates at the same sites. After screening, a panel of 29 loci in the SDC2, SEPT9, and VIM genes proved to be reliable biomarkers for colorectal cancer diagnosis in fecal samples. Logistic regression models were then constructed and validated using this panel. The sensitivity of the model was 91.43% (95% CI = [89.69, 93.17]), the specificity was 100% (95% CI = [100,100]), and the AUC value is 99.31% (95% CI = [99,99.62]). The diagnostic accuracy of the model for stage I and stage II colorectal cancer was 100% (11/11) and 91.3% (21/23), respectively. Overall, this study confirms that the gene locus panel and the model can be used to diagnose colorectal cancer effectively through feces. CONCLUSIONS: Our study identified a set of key methylation sites for colorectal cancer diagnosis from fecal samples, highlighting the importance of using tissue and fecal samples to accurately assess DNA methylation levels to screen for methylation sites, and developing an effective diagnostic model for colorectal cancer.

Copy number amplification-induced overexpression of lncRNA LOC101927668 facilitates colorectal cancer progression by recruiting hnRNPD to disrupt RBM47/p53/p21 signaling.

BACKGROUND: Somatic copy number alterations (SCNAs) are pivotal in cancer progression and patient prognosis. Dysregulated long non-coding RNAs (lncRNAs), modulated by SCNAs, significantly impact tumorigenesis, including colorectal cancer (CRC). Nonetheless, the functional significance of lncRNAs induced by SCNAs in CRC remains largely unexplored. METHODS: The dysregulated lncRNA LOC101927668, induced by copy number amplification, was identified through comprehensive bioinformatic analyses utilizing multidimensional data. Subsequent in situ hybridization was employed to ascertain the subcellular localization of LOC101927668, and gain- and loss-of-function experiments were conducted to elucidate its role in CRC progression. The downstream targets and signaling pathway influenced by LOC101927668 were identified and validated through a comprehensive approach, encompassing RNA sequencing, RT-qPCR, Western blot analysis, dual-luciferase reporter assay, evaluation of mRNA and protein degradation, and rescue experiments. Analysis of AU-rich elements (AREs) within the mRNA 3' untranslated region (UTR) of the downstream target, along with exploration of putative ARE-binding proteins, was conducted. RNA pull-down, mass spectrometry, RNA immunoprecipitation, and dual-luciferase reporter assays were employed to elucidate potential interacting proteins of LOC101927668 and further delineate the regulatory mechanism between LOC101927668 and its downstream target. Moreover, subcutaneous xenograft and orthotopic liver xenograft tumor models were utilized to evaluate the in vivo impact of LOC101927668 on CRC cells and investigate its correlation with downstream targets. RESULTS: Significantly overexpressed LOC101927668, driven by chr7p22.3-p14.3 amplification, was markedly correlated with unfavorable clinical outcomes in our CRC patient cohort, as well as in TCGA and GEO datasets. Moreover, we demonstrated that enforced expression of LOC101927668 significantly enhanced cell proliferation, migration, and invasion, while its depletion impeded these processes in a p53-dependent manner. Mechanistically, nucleus-localized LOC101927668 recruited hnRNPD and translocated to the cytoplasm, accelerating the destabilization of RBM47 mRNA, a transcription factor of p53. As a nucleocytoplasmic shuttling protein, hnRNPD mediated RBM47 destabilization by binding to the ARE motif within RBM47 3'UTR, thereby suppressing the p53 signaling pathway and facilitating CRC progression. CONCLUSIONS: The overexpression of LOC101927668, driven by SCNAs, facilitates CRC proliferation and metastasis by recruiting hnRNPD, thus perturbing the RBM47/p53/p21 signaling pathway. These findings underscore the pivotal roles of LOC101927668 and highlight its therapeutic potential in anti-CRC interventions.

scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

Comprehensive evaluation of immunological attributes and immunotherapy responses of positive T cell function regulators in colorectal cancer.

BACKGROUND: Positive regulators of T-cell function (PTFRs), integral to T-cell proliferation and activation, have been identified as potential prognostic markers in colorectal cancer (CRC). Despite this, their role within the tumor microenvironment (TME) and their response to immunotherapy are not yet fully understood. METHODS: This study delved into PTFR-related CRC subtypes by analyzing four independent transcriptome datasets, emphasizing the most significant prognostic PTFRs. We identified differentially expressed genes (DEGs) between two subtypes and developed a PTFR risk model using LASSO and Cox regression methods. The model's associations with survival time, clinical features, TME characteristics, tumor mutation profiles, microsatellite instability (MSI), cancer stem cell (CSC) index, and responses to chemotherapy, targeted therapy, and immunotherapy were subsequently explored. RESULTS: The PTFR risk model demonstrated a strong predictive capacity for CRC. It facilitated the estimation of immune cell composition, HLA expression levels, immune checkpoint expression, mutation burden, CSC index features, and the effectiveness of immunotherapy. CONCLUSIONS: This study enhances our understanding of the role of PTFRs in CRC progression and introduces an innovative assessment framework for CRC immunotherapy. This framework improves the prediction of treatment outcomes and aids in the customization of therapeutic strategies.

Exploration and validation of ceRNA regulatory networks in colorectal cancer based on associations whole transcriptome sequencing.

Colorectal cancer (CRC) is a wide-spread gastrointestinal cancer that is associated with augmented morbidity and mortality, and we do not yet have a deep understanding of its epidemiology and carcinogenicity. The transcriptome can reveal the complexity and heterogeneity of tumors and uncover new biomarkers or treatment options. In this study, we identified messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), round RNAs (circRNAs), and microRNAs (miRNAs) using whole-transcriptome sequencing and generated competing endogenous RNA (ceRNA) modulatory axes. We conducted whole transcriptome sequencing on 10 CRC and para-cancer (CRCP) samples and discovered 2465 differentially expressed (DE) mRNAs (DEmRNAs), 77 DE miRNAs (DEmiRNAs). 2852 DE lncRNAs (DElncRNAs) and 1477 DE circRNAs (DEcircRNAs). In addition, utilizing co-DE analysis, we generated the ceRNA axis. Subsequently, we employed the ceRNA axis to identify essential genes and corresponding associations with lncRNAs, circRNAs, and miRNAs in CRC. ceRNA regulatory network including mRNA-miRNA-lncRNA and mRNA-miRNA-circRNA. These modulatory axes potentially modulate the positive regulation of smooth muscle contraction, melanosome, plasma membrane, integral plasma membrane component and so on. Finally, the results of RNA sequencing (RNA-SEQ) were combined with the TCGA and GEO databases, and the DEGs strongly correlated with the TCGA-COAD overall survival (OS) as estimated by univariate cox and logarithmic rank analyses were cross-analyzed, and the co-upregulated DEGs were screened. Among the many DEs, KPNA2 was chosen for additional analysis. Using invitro experimentations, western blot, CCK8, EdU and other experiments were performed to verify the results. We found siRNA-based KPNA2 depletion reduces bladder cancer cells' viability, migratory, and proliferative activities, which showed that the DEmRNA profiles were comparable to the sequencing information, confirming that the sequencing data were very reliable. These evidences highlight the ceRNA regulatory mechanisms in CRC and will aid future research into the molecular mechanisms behind colorectal cancer prevention and treatment.

Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer.

Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min-Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635-0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC.

[Sanguinarine induces ferroptosis of colorectal cancer cells by upregulating STUB1 and downregulating GPX4].

OBJECTIVE: To investigate the effect of sanguinarine (SAN) on proliferation and ferroptosis of colorectal cancer cells. METHODS: SW620 and HCT-116 cells treated with different concentrations of SAN were examined for cell viability changes using CCK8 assay to determine the IC50 of SAN in the two cells. The inhibitory effects of SAN on proliferation, invasion and migration of the cells were evaluated using colony-forming assay and Transwell assays. ROS production in the treated cells was analyzed with flow cytometry, and lipid peroxide production was assessed by detecting malondialdehyde (MDA) level. Glutathione (GSH) levels in the cells were detected, and Western blotting was used to detect the expressions of ferroptosis-related proteins STUB1 and GPX4. RESULTS: SAN significantly inhibited the proliferation, invasion and migration of SW620 and HCT-116 cells. SAN treatment significantly promoted ROS production, increased intracellular MDA level, and lowered GSH level in the two cells (P<0.05). Western blotting showed that SAN significantly upregulated the expression of STUB1 and down-regulated the expression of its downstream protein GPX4 (P<0.05). CONCLUSION: SAN induces ferroptosis in colorectal cancer cells by regulating STUB1/GPX4, which may serve as a new therapeutic target for colorectal cancer.

TEX264 drives selective autophagy of DNA lesions to promote DNA repair and cell survival.

DNA repair and autophagy are distinct biological processes vital for cell survival. Although autophagy helps maintain genome stability, there is no evidence of its direct role in the repair of DNA lesions. We discovered that lysosomes process topoisomerase 1 cleavage complexes (TOP1cc) DNA lesions in vertebrates. Selective degradation of TOP1cc by autophagy directs DNA damage repair and cell survival at clinically relevant doses of topoisomerase 1 inhibitors. TOP1cc are exported from the nucleus to lysosomes through a transient alteration of the nuclear envelope and independent of the proteasome. Mechanistically, the autophagy receptor TEX264 acts as a TOP1cc sensor at DNA replication forks, triggering TOP1cc processing by the p97 ATPase and mediating the delivery of TOP1cc to lysosomes in an MRE11-nuclease- and ATR-kinase-dependent manner. We found an evolutionarily conserved role for selective autophagy in DNA repair that enables cell survival, protects genome stability, and is clinically relevant for colorectal cancer patients.

Single-cell RNA sequencing reveals the heterogeneity of MYH11+ tumour-associated fibroblasts between left-sided and right-sided colorectal cancer.

Colorectal cancer (CRC) exhibits considerable heterogeneity on tumour location. However, there is still a lack of comprehensive annotation regarding the characteristics and differences between the left-sided (L-CRC) and right-sided (R-CRC) CRC. Here, we performed single-cell RNA sequencing (scRNA-seq) on immune and stromal cells from 12 L-CRC and 10 R-CRC patients. We found that L-CRC exhibited stronger tumour invasion and poor prognosis compared with R-CRC. In addition, functional enrichment analysis of a normal cohort showed that fibroblasts of left colon are associated with tumour-related pathways. This suggested that the heterogeneity observed in both L-CRC and R-CRC may be influenced by the specific location within the colon itself. Further, we identified a potentially novel MYH11+ cancer-associated fibroblast (CAF) subset predominantly enriched in L-CRC. Moreover, we found that MYH11+ CAFs may promote tumour migration via interacting with macrophages, and was associated with poor prognosis in CRC. In summary, our study revealed the crucial role of MYH11+ CAFs in predicting a poor prognosis, thereby contributing valuable insights to the exploration of heterogeneity in L-CRC and R-CRC.

c-MET is an important marker for acid-driven metastasis and anti-immune in colorectal cancer.

BACKGROUND: The tumor microenvironment plays an important role in cancer progression, especially acidic microenvironment which distinguish cancer from normal tissues and immune microenvironment. This study was the first to investigate whether acidic microenvironment affects colorectal metastasis through MET and the relationship between MET and immune microenvironment. METHODS: We used TCGA and GEO databases to predict MET expression, its relationship with clinical features, and biological function it mediated, and validated its expression with clinical data, as well as to verify that MET mediates acidic microenvironment-induced colorectal cancer metastasis by inducing EMT at the cellular and animal levels. The TCGA database was also used to analyze the relationship between MET and immune cells, immune checkpoints and TMB in colorectal cancer, and to predict its value in prognosis and immunological treatment and targeted therapy in pan-cancer. RESULTS: MET is highly expressed in colorectal cancer and is associated with metastasis and prognosis. Its biological function is mainly related to adhesion, cell cycle and fatty acid metabolism, and it can mediate acidic microenvironment to induce EMT and promote metastasis. According to immunoinfiltration analysis, MET expression is correlated with CD8 + T cells, DC, macrophages, Tregs, and TMB in CRC and were associated with the prognosis, immune checkpoint, and TMB of ACC, PRAD, LUAD respectively, in pan-cancer. CONCLUSIONS: MET is an important contributor to acid-driven colorectal cancer metastasis and participates in immune escape of colorectal cancer. It is of great significance for the prognosis and immunotherapy of colorectal cancer and some other cancers.

Be bold, start cold! cold formalin fixation of colorectal cancer specimens granted superior DNA and RNA quality for downstream molecular analysis.

The use of cold formalin fixation (CFF; i.e., fixating tissue samples with 4 °C precooled formalin) recently attracted further attention owing to its putative improved ability to preserve nucleic acid compared with standard room temperature formalin (SFF). In this study, we aimed to assess the effect of four formalin-based fixation protocols (SFF, CFF, delayed formalin fixation-DFF, and cold formalin hyperfixation; CFH) on both DNA and RNA quality. We collected 97 colorectal cancer (CRC) and analyzed 23 metrics of nucleic acid quantity and quality yield using a multiplatform approach by combining spectrophotometric, fluorimetric, electrophoretic, and polymerase chain reaction (PCR) assays. Following confirmation of fixation-protocol-related different effects via clustering analysis, CFF presented best metrics compared with all protocols, specifically positive coefficients of DV1000-60000, DV2/DV1, DNA λ ratio 260/230, and ABL gene expression absolute copies, and negative coefficient of DV150-1000. The SFF subgroup presented a positive coefficient of DV150-1000 and negative coefficients for DV1000-60000, DV2/DV1, RNA λ ratio 260/230, RNA QuBit concentration, DV100/200, RNA electrophoresis concentration and absolute quantity, and ABL copies. Overall, we confirmed the superior yield performances of CFF preservation for both DNA and RNA compared with the other protocols in our series of CRC samples. Pending further validations and clarification of the specific mechanisms behind these findings, our study supports the implementation of CFF in the pathology unit routine specimen management for tumor tissue molecular profiling.