Signature In Colorectal Cancer Research Articles

E-selectin affinity glycoproteomics reveals neuroendocrine proteins and the secretin receptor as a poor-prognosis signature in colorectal cancer.

Colorectal cancer (CRC) cells express sialylated Lewis antigens (sLe), crucial for metastasis via E-selectin binding. However, these glycoepitopes lack cancer specificity, and E-selectin-targeted glycoproteins remain largely unknown. Here, we established a framework for identifying metastasis-linked glycoproteoforms. More than 70% of CRC tumors exhibited overexpression of sLeA/X, yet without discernible associations with metastasis or survival. However, The Cancer Genome Atlas (TCGA) analysis unveiled differing expression patterns of sLeA/X-related glycogenes correlating with disease severity, indicating context-dependent regulation by distinct glycosyltransferases. Deeper exploration of metastatic tumor sialoglycoproteome identified nearly 600 glycoproteins, greatly expanding our understanding of the metastasis-related glycoproteome. These glycoproteins were linked to cell adhesion, oncogenic pathways, and neuroendocrine functions. Using an in-house algorithm, the secretin receptor (SCTR) emerged as a top-ranked targetable glycoprotein. Tumor screening confirmed SCTR's association with poor prognosis and metastasis, with N-glycosylation adding cancer specificity to this glycoprotein. Prognostic links were reinforced by TCGA-based investigations. In summary, SCTR, a relatively unknown CRC glycoprotein, holds potential as a biomarker of poor prognosis and as an E-selectin ligand, suggesting an unforeseen role in disease dissemination. Future investigations should focus on this glycoprotein's biological implications for clinical applications.

Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer

BackgroundThis study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs. MethodsUtilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group. ResultsIn our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy. ConclusionA prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.

Integrated single-cell and bulk RNA-seq analysis identifies a prognostic T-cell signature in colorectal cancer

Colorectal cancer (CRC) is a major contributor to global morbidity and mortality, necessitating more effective therapeutic approaches. T cells, prominent in the tumor microenvironment, exert a crucial role in modulating immunotherapeutic responses and clinical outcomes in CRC. This study introduces a pioneering method for characterizing the CRC immune microenvironment using single-cell sequencing data. Unlike previous approaches, which focused on individual T-cell signature genes, we utilized overall infiltration levels of colorectal cancer signature T-cells. Through weighted gene co-expression network analysis, Lasso regression, and StepCox analysis, we developed a prognostic risk model, TRGS (T-cell related genes signatures), based on six T cell-related genes. Multivariate Cox analysis identified TRGS as an independent prognostic factor for CRC, showcasing its superior predictive efficacy compared to existing immune-related prognostic models. Immunoreactivity analysis revealed higher Immunophenoscore and lower Tumor Immune Dysfunction and Exclusion scores in the low-risk group, indicating potential responsiveness to immune checkpoint inhibitor therapy. Additionally, patients in the low-risk group demonstrated heightened sensitivity to 5-fluorouracil-based chemotherapy regimens. In summary, TRGS emerges as a standalone prognostic biomarker for CRC, offering insights to optimize patient responses to immunotherapy and chemotherapy, thereby laying the groundwork for personalized tumor management strategies.

Mutational Signatures in Colorectal Cancer: Translational Insights, Clinical Applications, and Limitations.

A multitude of exogenous and endogenous processes have the potential to result in DNA damage. While the repair mechanisms are typically capable of correcting this damage, errors in the repair process can result in mutations. The findings of research conducted in 2012 indicate that mutations do not occur randomly but rather follow specific patterns that can be attributed to known or inferred mutational processes. The process of mutational signature analysis allows for the inference of the predominant mutational process for a given cancer sample, with significant potential for clinical applications. A deeper comprehension of these mutational signatures in CRC could facilitate enhanced prevention strategies, facilitate the comprehension of genotoxic drug activity, predict responses to personalized treatments, and, in the future, inform the development of targeted therapies in the context of precision oncology. The efforts of numerous researchers have led to the identification of several mutational signatures, which can be categorized into different mutational signature references. In CRC, distinct mutational signatures are identified as correlating with mismatch repair deficiency, polymerase mutations, and chemotherapy treatment. In this context, a mutational signature analysis offers considerable potential for enhancing minimal residual disease (MRD) tests in stage II (high-risk) and stage III CRC post-surgery, stratifying CRC based on the impacts of genetic and epigenetic alterations for precision oncology, identifying potential therapeutic vulnerabilities, and evaluating drug efficacy and guiding therapy, as illustrated in a proof-of-concept clinical trial.

Gut Microbiota Signatures in Colorectal Cancer as a Potential Diagnostic Biomarker in the Future: A Systematic Review.

The gut microbiota has acquired significant attention in recent years for its potential as a diagnostic biomarker for colorectal cancer (CRC). In this literature review, we looked at the studies exploring alterations in gut microbiota composition associated with CRC, the potential mechanisms linking gut dysbiosis to CRC development, and the diagnostic approaches utilizing gut microbiota analysis. Our research has led to the conclusion that individuals with CRC often display alterations in their gut microbiota composition compared to healthy individuals. These alterations can include changes in the diversity, abundance, and type of bacteria present in the gut. While the use of gut microbiota as a diagnostic biomarker for CRC holds promise, further research is needed to validate its effectiveness and standardize testing protocols. Additionally, considerations such as variability in the microbiota composition among individuals and potential factors must be addressed before microbiota-based tests can be widely implemented in clinical practice.

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response. Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified. This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients. Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.

Altered expression of cytokines, chemokines, growth factors, and soluble receptors in patients with colorectal cancer, and correlation with treatment outcome

Insofar as they play an important role in the pathogenesis of colorectal cancer (CRC), this study analyzes the serum profile of cytokines, chemokines, growth factors, and soluble receptors in patients with CRC and cancer-free controls as possible CRC signatures. Serum levels of 65 analytes were measured in patients with CRC and age- and sex-matched cancer-free controls using the ProcartaPlex Human Immune Monitoring 65-Plex Panel. Of the 65 tested analytes, 8 cytokines (CSF-3, IFN-γ, IL-12p70, IL-18, IL-20, MIF, TNF-α and TSLP), 8 chemokines (fractalkine, MIP-1β, BLC, Eotaxin-1, Eotaxin-2, IP-10, MIP-1a, MIP-3a), 2 growth factors (FGF-2, MMP-1), and 4 soluble receptors (APRIL, CD30, TNFRII, and TWEAK), were differentially expressed in CRC. ROC analysis confirmed the high association of TNF-α, BLC, Eotaxin-1, APRIL, and Tweak with AUC > 0.70, suggesting theranostic application. The expression of IFN-γ, IL-18, MIF, BLC, Eotaxin-1, Eotaxin-2, IP-10, and MMP1 was lower in metastatic compared to non-metastatic CRC; only AUC of MIF and MIP-1β were > 0.7. Moreover, MDC, IL-7, MIF, IL-21, and TNF-α are positively associated with tolerance to CRC chemotherapy (CT) (AUC > 0.7), whereas IL-31, Fractalkine, Eotaxin-1, and Eotaxin-2 were positively associated with resistance to CT. TNF-α, BLC, Eotaxin-1, APRIL, and Tweak may be used as first-line early detection of CRC. The variable levels of MIF and MIP-1β between metastatic and non-metastatic cases assign prognostic nature to these factors in CRC progression. Regarding tolerance to CT, MDC, IL-7, MIF, IL-21, and TNF-α are key when down-regulated or resistant to treatment is observed.

Exploiting tertiary lymphoid structures gene signature to evaluate tumor microenvironment infiltration and immunotherapy response in colorectal cancer

BackgroundTertiary lymphoid structures (TLS) is a particular component of tumor microenvironment (TME). However, its biological mechanisms in colorectal cancer (CRC) have not yet been understood. We desired to reveal the TLS gene signature in CRC and evaluate its role in prognosis and immunotherapy response.MethodsThe data was sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Based on TLS-related genes (TRGs), the TLS related subclusters were identified through unsupervised clustering. The TME between subclusters were evaluated by CIBERSORT and xCell. Subsequently, developing a risk model and conducting external validation. Integrating risk score and clinical characteristics to create a comprehensive nomogram. Further analyses were conducted to screen TLS-related hub genes and explore the relationship between hub genes, TME, and biological processes, using random forest analysis, enrichment and variation analysis, and competing endogenous RNA (ceRNA) network analysis. Multiple immunofluorescence (mIF) and immunohistochemistry (IHC) were employed to characterize the existence of TLS and the expression of hub gene.ResultsTwo subclusters that enriched or depleted in TLS were identified. The two subclusters had distinct prognoses, clinical characteristics, and tumor immune infiltration. We established a TLS-related prognostic risk model including 14 genes and validated its predictive power in two external datasets. The model’s AUC values for 1-, 3-, and 5-year overall survival (OS) were 0.704, 0.737, and 0.746. The low-risk group had a superior survival rate, more abundant infiltration of immune cells, lower tumor immune dysfunction and exclusion (TIDE) score, and exhibited better immunotherapy efficacy. In addition, we selected the top important features within the model: VSIG4, SELL and PRRX1. Enrichment analysis showed that the hub genes significantly affected signaling pathways related to TLS and tumor progression. The ceRNA network: PRRX1-miRNA (hsa-miR-20a-5p, hsa-miR-485–5p) -lncRNA has been discovered. Finally, IHC and mIF results confirmed that the expression level of PRRX1 was markedly elevated in the TLS- CRC group.ConclusionWe conducted a study to thoroughly describe TLS gene signature in CRC. The TLS-related risk model was applicable for prognostic prediction and assessment of immunotherapy efficacy. The TLS-hub gene PRRX1, which had the potential to function as an immunomodulatory factor of TLS, could be a therapeutic target for CRC.

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction

Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.

Abstract LB134: Mutational signatures in colorectal cancer from 11 countries reveal new insights in early-onset colorectal cancer

Abstract Background: Colorectal cancers (CRC) have an important impact on both incidence and mortality worldwide, with varying rates in different parts of the world. Despite an overall decrease in incidence in the past years, an alarming increase in early onset colorectal cancer (CRC in patients under 50 years of age) rates has been observed. As part of the Cancer Research UK Grand Challenge Mutographs project, we aim to better understand the underlying mutagenic causes contributing to the differences in CRC incidence rates through mutational signatures. Methods: We collected epidemiological data and performed whole genome sequencing and mutational signature analysis on 981 CRC tumor samples from 11 countries with varying incidence rates, including intermediate incidence regions (Iran, Colombia, Thailand, and Brazil) and higher incidence regions (Argentina, Russia, Canada, Poland, Czechia, Serbia, and Japan). Results: The average mutational profiles were similar across the countries. Mutational signatures associated with DNA repair deficiencies, including POLE and POLD1 associated signatures (SBS10a/b/c/d and SBS28), MUTYH (SBS36), NTHL1 (SBS30), homologous recombination deficiency (SBS3), and a plethora of microsatellite instability (MSI) signatures, were found in 177 cancers (18% of all samples) and at comparable levels across all countries. Multiple signatures with known etiologies were found in 802 DNA repair proficient colorectal cancers including clock-like (SBS1 and SBS5), reactive oxygen species (SBS18), APOBEC (SBS2 and SBS13), and the microbiome-product colibactin (SBS88) signatures. The colibactin signature SBS88 was observed in 14% of samples overall, being more prevalent in distal and rectum tumors. SBS88 was significantly enriched in younger patients (33% <40y, 23% 40-49y, 20% 50-59y,10% 60-69y, 10% ≥70y; p-value = 0.0001). Tumors from early onset patients also showed higher prevalence of another mutational signature with unknown etiology (19% <40y, 16% 40-49y, 13% 50-59y, 6% 60-69y, 7% ≥70y, p-value = 0.005). Conclusions: These results indicate a potential association between SBS88 with early onset CRC, suggesting that the recent increase in incidence may be at least partially explained by the genotoxic compound colibactin, and associated bacteria. Citation Format: Wellington dos Santos, Marcos Diaz-Gay, Sarah Moody, Sergey Senkin, Behnoush Abedi-Ardekani, Mariya Kazachkova, Stephen Fitzgerald, Saamin Cheema, Valerie Gaborieau, Jingwei Wang, Christine Carreira, Thomas Cattiaux, Priscilia Chopard, Calli Latimer, David Zaridze, Riley Cox, Reza Malekzadeh, Miodrag Ognjanovic, Suleeporn Sangrajrang, Maria P. Curado, Rui M. Reis, Ivana Holcatova, Carlos Vaccaro, Beata Swiatkowska, Jolanta Lissowska, Carolina Wiesner, Tatsuhiro Shibata, Surasak Sangkhathat, Patricia Ashton-Prolla, Laura Humphreys, Sandra Perdomo, Ana C. de Caravalho, Mike Stratton, Paul Brennan, Ludmil Alexandrov. Mutational signatures in colorectal cancer from 11 countries reveal new insights in early-onset colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB134.

ZNF26-Associated Genes as Prognostic Signatures in Colorectal Cancer with Broad Therapeutic Implications.

The identification of biomarkers correlated with colorectal cancer (CRC) prognosis holds substantial importance from both clinical and scientific perspectives. Zinc finger protein 26 (ZNF26) has not been previously investigated or documented in solid tumors; thus, further research is necessary to ascertain its prognostic value in CRC. Gene expression profiles and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA) database. Subsequently, expression correlation was assessed utilizing the TCGA CRC cohort. The prognostic value of ZNF26 was evaluated through Kaplan-Meier (KM) and ROC curve analyses. Following this, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to perform enrichment analysis between high- and low-ZNF26 expression groups. The association between immune cells, immune checkpoint genes, and ZNF26 expression levels was examined. Lastly, the research findings were further validated using CRC tissue samples. The results revealed that, in comparison to healthy controls, CRC significantly reduced ZNF26 expression. Elevated ZNF26 expression was associated with poorer overall survival in CRC patients. Additionally, high ZNF26 expression exhibited an inverse relationship with the immunological score and immune checkpoint gene expression in CRC patients. The findings from the TCGA data analysis were corroborated by the PCR results obtained from CRC tissue samples. ZNF26 is markedly upregulated in colorectal cancer tissues, potentially serving as a biomarker for CRC.

Abstract 6211: Clinical and prognostic assessment of the lncRNA ZFAS1-related gene signature in colorectal cancer

Abstract Colorectal cancer (CRC) is one of the leading causes of cancer death with a rapidly rising incidence rate in younger adults. It is projected to be the leading cause of death in adults under 50 years old by 2030. Although progress over the last couple of decades has improved the clinical management of patients affected by CRC, there is a lack of accurate biomarkers to stratify CRC patients. Among several other biomolecules, lncRNAs (long non-coding RNAs) are non-coding transcripts under 200 nucleotides that can regulate gene expression. Furthermore, lncRNAs are important players in oncogenesis and cancer progression and have the potential to be used as reliable biomarkers. Through this study, we explored the clinical relevance of lncRNAs in colorectal cancer. To analyze the distribution of lncRNAs, we utilized de-identified FFPE (formalin-fixed paraffin-embedded) blocks from Augusta University (n = 88). FFPE blocks were sectioned to extract tissue segments and were subsequently stained using H&E (hematoxylin and eosin dyes) to identify the tumor and adjacent normal regions. The quantification of the gene expression was performed using a medium-throughput NanoString platform. Further analyses were performed using clinicopathological features of the institutional cohort and an independent Cancer Genome Atlas (TCGA) dataset. TCGA contains genomic data along with clinical and molecular characterization of over 11,000 samples of various lineages. In the institutional cohort, ZFAS1 showed higher gene expression in the low survival group for stage II and III colorectal cancer patients, while no significant difference was observed in stage I and IV patients. ZFAS1 expression did not significantly correlate with other clinicopathological parameters, including race, alcohol consumption, tobacco consumption, age, grade, and family history of cancer. In an orthogonal TCGA analysis, higher ZFAS1 expression was associated with genomic perturbations, including significantly altered tumor mutational burden (TMB), aneuploidy, fractional genome alteration, and microsatellite instability. Further, a 21-gene signature was developed for prognostication using gene co-expression network analysis. The gene signature was significantly associated with overall and disease-free survival in the TCGA-CRC cohort. Immune deconvolution analysis of the RNA sequencing data revealed that higher expression of the 21-gene signature was associated with increased infiltration of regulatory T cells while being inversely correlated with the overall level of T cells. In conclusion, our study identified the clinicopathological role of ZFAS1 lncRNA and its interaction genes with prognostic significance in colorectal cancer. Further investigation into the therapeutic relevance of the ZFAS1-related gene signature may prove invaluable in improving the clinical outcomes of colorectal cancer patients. Citation Format: Pankaj Kumar Ahluwalia, Tiffanie Leeman, Ashis Mondal, Ashutosh Vashisht, Harmanpreet Singh, Nivin Omar, Kimya Jones, Ravindra Kolhe. Clinical and prognostic assessment of the lncRNA ZFAS1-related gene signature in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6211.

Abstract 2807: Distinctive microbiome signatures in colorectal cancer: A comparative metagenomic analysis

Abstract The human gut microbiome is increasingly recognized for its role in colorectal cancer (CRC) pathogenesis. This study aims to delineate the microbiome characteristics of CRC patients compared to healthy individuals, using a comprehensive metagenomic approach. We utilized a cohort of 80 CRC patients and an equal number of matched healthy controls. Fecal samples were collected and subjected to in-depth metagenomic sequencing, targeting both structural and functional profiles of the microbiome. Data analysis involved advanced bioinformatics techniques. Key observations were subsequently validated in an independent cohort to bolster the findings. Our analysis revealed significant alterations in the microbiome composition of CRC patients versus healthy individuals. Notably, there was an increased prevalence of pathogenic bacteria, such as Bacteroides Vulgatus in CRC patients. These bacteria are implicated in promoting inflammation and carcinogenesis. Conversely, there was a noticeable depletion of beneficial microbes like Lactobacillus in the CRC group. Functionally, the microbiome in CRC patients was characterized by enriched pathways related to DNA repair, cell cycle control, and metabolism pathways like Citrate cycle and Galactose metabolism. Validation in an independent cohort confirmed these distinctive microbiomic patterns. Our study provides compelling evidence of altered microbiome landscapes in CRC patients, highlighting potential roles in disease development and progression. These findings pave the way for further investigation into microbiome-based diagnostics and therapeutic interventions in colorectal cancer. Citation Format: Ziqi Chen, Bei Ning Nie, Bin Luo, Ziliang Wang, Xinyi Wang. Distinctive microbiome signatures in colorectal cancer: A comparative metagenomic analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2807.

Abstract 1039: A novel serum lipidomics signature validated in a prospective multi-center trial enables accurate and low-cost CRC screening

Abstract Background: Blood-based colorectal cancer (CRC) testing is expected to increase compliance to recommended screening guidelines. However, existing methods based on cfDNA typically suffer from low performance in detecting early-stage cancer and pre-malignant advanced adenomas (AA); and from high-cost, making routine screening financially impractical. Attempts to identify other blood biomarkers suffered from low reproducibility across studies, due to confounding factors related to utilizing samples from diagnosed and potentially treated patients, and artifacts related to differential handling of cases and control samples. Method: Serum samples were obtained from the Israeli Multi-OMICS screening study (IMOSS-500K), in which ~500,000 serum samples were collected as part of standard clinical routine by Maccabi Health between July 2021 and January 2023. De-identified electronic health records were used to identify 152 primary CRC patient, diagnosed within 1 year after serum collection; and another 270 age- and gender-matched controls. The cohort was divided to train and test sub-cohorts, comprising of patients diagnosed within 2-12 and 0-2 months after sample collection, respectively. A 2nd test cohort was obtained from Rambam Hospital, included 97 samples from treatment-native CRC patients, and 24 age- and gender-matched controls. A 3rd test cohort, obtained from IMOSS-500K, included 139 patients that were diagnosed with AA within 2 months from serum collection; and 139 age- and gender-matched controls. All samples were analyzed with a proprietary high-throughput LC-MS based multi-OMICS platform detecting 10,000’s of metabolites, lipids, and protein biomarker ions per sample. A diagnosis score was defined via a logistic regression model in the train sub-cohort and the diagnostic performance for was evaluated in the 3 test cohorts. Results: We identified a novel signature for CRC based on the concentration of 4 structurally related serum lipids, whose structure was not previously reported in literature. Evaluating the performance of the score in the test sub-cohort, showed a remarkable AUC of 0.96 for the detection of CRC, with a high sensitivity of 89% at 90% specificity; with 90% sensitivity achieved already at stage I. The high diagnostic performance for detecting CRC was reproducible in the 2nd test cohort, with an AUC of 0.97, and sensitivity of 94% at 90% specificity. Analyzing the 3nd test cohort, our signature achieved a sensitivity of 37% at 90% specificity for the detection of AA. Conclusion: We developed a novel low-cost blood-lipidomics based method for CRC detection, suitable for routine screening. Comprehensive validation through a multi-center prospective study showed similar performance to that published for the stool-cfDNA test ColoGuard, and higher performance than other published blood-cfDNA methods in the detection of early-stage CRC and AA. Citation Format: Boris Sarvin, Avi Shoshan, Eldad Kepten, Mark Vernik, Ilya Podolsky, Carmel Shor, Shira Shaham-Niv, Tomer Shlomi. A novel serum lipidomics signature validated in a prospective multi-center trial enables accurate and low-cost CRC screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1039.

Abstract 460: Fecal microRNA and microbiome profiles in colorectal cancer patients: Are they predictor of survival and how do behave in longitudinal samples

Abstract Background: Colorectal cancer (CRC) is characterized by a high complexity that significantly influences treatment response and survival. There is still a lack of prognostic factors after surgery that could help predict postoperative survival. Currently, lymph node metastases are the single most important prognostic factor but it gives no clue on tumor aggressiveness and recurrence. Our group recently contributed to the identification of a fecal microRNA (miRNA) signature that accurately discriminates sporadic CRC and aimed at improving non-invasive diagnosis (Pardini et al, Gastroenterology 2023) as well as altered gut microbiome species associated with CRC (Thomas et al, Nat. Medicine 2019), but there are scanty evidence on the potentiality of such markers for prognosis. Aims: In the same cohort, we investigated if the fecal miRNome and gut microbiome at diagnosis are associated with CRC patients’ prognosis. Moreover, we assessed fecal miRNA in a new set of CRC patients collected longitudinally before and after the surgery to investigate whether such associations reflect miRNA profiles over time. Methods. We performed small RNA and shotgun metagenomic sequencing in stool samples in 3 cohorts from: Italy (IT, 128 CRC), Czech Republic (CZ, 69 CRC), and Turkey (TU, 34 CRC, 40 controls). Stool samples from TU CRC subjects were collected before surgery and after 6 months. Preliminary Results: In the IT and CZ cohorts, an average of 479 fecal miRNAs were detected with 209 differentially expressed (DE) miRNAs significantly associated with survival, including miR-1246 which was present in the CRC-associated signature identified by us. Two miRNAs (miR-197-5p and miR-6081-5p) were significantly down-regulated in patients with the worst prognosis (adj.p<0.01, HR<0.50) in both cohorts. In TU cohort, 494 miRNAs were DE in CRC (pre-surgery) vs controls. Among them, the 25 DE miRNAs detected in our previous work were also significantly altered, including the 5-miRNA CRC signature (miR-149-3p, miR-607-5p, miR-1246, miR-4488, and miR-6777-5p) identified by machine learning. Notably, after 6 months from surgery, levels of such miRNAs reverted to those measured in controls (for miR-1246 p<0.001 in both controls vs pre- and vs post-surgery comparisons). More analyses on the longitudinal fecal miRNA data are ongoing as well as for the metagenome data assessing the abundancies of CRC-associated microbial species before and after tumor resection and their possible association with survival. Conclusions: Fecal miRNAs may serve as biomarkers for CRC prognosis. Following tumor resection, CRC levels of altered miRNAs return in a range comparable with those of healthy controls in a time-dependent manner. These data further support the use of fecal miRNAs as accurate diagnostic CRC biomarkers and provide preliminary information on their possible translation as a prognostic tool. Citation Format: Barbara Pardini, Nazim Arda Keles, Giulio Ferrero, Sonia Tarallo, Giulia Francescato, Amedeo Gagliardi, Virginia Alberini, Carla Di Battista, Veronika Vymetalkova, Fatma Turkoglu, Ozgul Duzgun, Omer Faruk Ozkan, Bilge Guvenc Tuna, Pavel Vodicka, Soner Dogan, Alessio Naccarati. Fecal microRNA and microbiome profiles in colorectal cancer patients: Are they predictor of survival and how do behave in longitudinal samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 460.

Identification of macrophage-related molecular subgroups and risk signature in colorectal cancer based on a bioinformatics analysis

Macrophages play a crucial role in tumor initiation and progression, while macrophage-associated gene signature in colorectal cancer (CRC) patients has not been investigated. Our study aimed to identify macrophage-related molecular subgroups and develop a macrophage-related risk model to predict CRC prognosis. The mRNA expression profile and clinical information of CRC patients were obtained from TCGA and GEO databases. CRC patients from TCGA were divided into high and low macrophage subgroups based on the median macrophage score. The ESTIMATE and CIBERSORT algorithms were used to assess immune cell infiltration between subgroups. GSVA and GSEA analyses were performed to investigate differences in enriched pathways between subgroups. Univariate and LASSO Cox regression were used to build a prognostic risk model, which was further validated in the GSE39582 dataset. A high macrophage score subgroup was associated with poor prognosis, highly activated immune-related pathways and an immune-active microenvironment. A total of 547 differentially expressed macrophage-related genes (DEMRGs) were identified, among which seven genes (including RIMKLB, UST, PCOLCE2, ZNF829, TMEM59L, CILP2, DTNA) were identified by COX regression analyses and used to build a risk score model. The risk model shows good predictive and diagnostic values for CRC patients in both TCGA and GSE39852 datasets. Furthermore, multivariate Cox regression analysis showed that the risk score was an independent risk factor for overall survival in CRC patients. Our findings provided a novel insight into macrophage heterogeneity and its immunological role in CRC. This risk score model may serve as an effective prognostic tool and contribute to personalised clinical management of CRC patients.

Comprehensive analysis of basement membrane-related gene based on single-cell and bulk RNA sequencing data to predict prognosis and evaluate immune characteristics in colorectal cancer.

Basement membrane-related genes (BMs) participate in regulating cell polarity, invasion, metastasis, and survival across different tumor types. Nevertheless, the specific functions of BMs in colorectal cancer (CRC) remain uncertain. To investigate the clinical relevance of BMs in CRC, we retrieved both gene expression and clinical data from The Cancer Genome Atlas (TCGA) datasets for subsequent analysis. The Kaplan-Meier (K-M) survival curve was employed to evaluate prognosis in high- and low-risk groups. Furthermore, additional analyses, including nomogram construction, functional enrichment, examination of the tumor immune microenvironment, prediction of small-molecule drugs, and more, were conducted to delve into the significance of BM-related signatures in CRC. Single-cell data from seven CRC patients were obtained from the TISCH2 database, and expression validation and cell source exploration of BM-related signatures were performed. Lastly, the expression and function of TIMP1, a key gene in BMs that may play a role in the progression of CRC, was validated in vitro through a series of basic experiments. We constructed a seven BMs-based model to categorize CRC patients into high-risk and low-risk groups. K-M survival analysis indicated a poorer prognosis for high-risk CRC patients. Cox regression analysis further identified the risk score as an independent prognostic factor for CRC patients. The nomogram model exhibited superior discrimination and calibration abilities of CRC patients. Based on the results from GO/KEGG and GSEA, genes in the high-risk subgroup were implicated in immune-related pathways and exhibited a positive correlation with immune checkpoints. In single-cell data, we found that TIMP1 is highly expressed in many cells, especially in malignant tumor cells. We also observed up-regulation of TIMP1 in CRC cell lines, promoting cancer invasion and migration in vitro. Our study has discovered a novel prognostic index derived from BM-related genes in CRC patients. Specifically, the new model enables patient stratification, improving the selection of individuals likely to benefit from immunotherapy.

Molecular subtype identification and prognosis stratification by a immunogenic cell death-related gene expression signature in colorectal cancer

ABSTRACT Objectives This study intended to develop a new immunogenic cell death (ICD)-related prognostic signature for colorectal cancer (CRC) patients. Research design and methods The Non-Negative Matrix Factorization (NMF) algorithm was adopted to cluster tumor samples based on ICD gene expression to obtain ICD-related subtypes. Survival analysis and immune microenvironment analysis were conducted among different subtypes. Regression analysis was used to construct the model. Based on riskscore median, cancer patients were classified into high and low risk groups, and independent prognostic ability of the model was analyzed. The CIBERSORT algorithm was adopted to determine the immune infiltration level of both groups. Results We analyzed the differential genes between cluster 4 and cluster 1–3 and obtained 12 genes with the best prognostic features finally (NLGN1, SLC30A3, C3orf20, ADAD2, ATOH1, ATP6V1B1, KCNQ2, MUCL3, RGCC, CLEC17A, COL6A5, and INSL4). In addition, patients with lower risk had higher levels of infiltration of most immune cells, lower Tumor Immune Dysfunction and Exclusion (TIDE) level and higher immunophenscore (IPS) level than those with higher risk. Conclusions This study constructed and validated the ICD feature signature predicting CRC prognosis and provide a reference criteria for guiding the prognosis and immunotherapy of CRC cancer patients.

Construction of a New Ferroptosis-related Prognosis Model for Survival Prediction in Colorectal Cancer.

This study was designed to develop a ferroptosis-related gene signature for guiding the prognostic prediction in colorectal cancer (CRC) and to explore the potential in the molecular functions of the gene signature. Ferroptosis is mainly characterized by lipid peroxide accumulation on the cell membranes in an iron-dependent manner, resulting in cellular oxidative stress, metabolic disorders, and, ultimately, cell death. This study aimed to develop a prognostic ferroptosis signature in CRC and explore its potential molecular function. The present work was designed to devise a ferroptosis signature for CRC prognosis and explore its potential molecular function. Single-cell RNA sequencing data GSE161277 and transcriptome sequencing data GSE17537 and TCGA-CRC from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases were downloaded, respectively. Quality control, dimension reduction, clustering, and clustering of single-cell RNA sequencing (scRNA- seq) data were performed using the Seurat package. A total of 259 ferroptosis-correlated genes from the FerrDb database were acquired. The single sample gene set enrichment analysis (ssGSEA) was performed to calculate the scores of genes related to ferroptosis. ESTIMATE was used to calculate immune infiltration. Independent prognostic factors were determined by performing Weighted Gene Co-Expression Network Analysis (WGCNA), univariate and Cox analyses, and Lasso analyses were used to search for independent prognostic factors. From the scRNA-seq (GSE161277) dataset, 22 cell clusters were initially identified, and according to immune cell markers, only 8 types of cells (Follicular B, central memory T cell, Epithelial, Natural killer T cell, Plasma B, M1 macrophage, Fibroblasts, and Mast cell) were finally determined to be related to CRC prognosis. The results of the scRNA-seq analysis showed that the score of ferroptosis-related genes was higher in tumour tissues and in 8 types of cells in tumour samples. In the TCGA dataset, CRC samples were divided into ferroptosis-related high scores, ferroptosis-related median scores, and ferroptosis-related low scores. Immune cell analysis revealed that ferroptosis- related high scores had the highest abundance of immune cells. An 11-gene signature was developed by WGCNA, univariate Cox, and Lasso Cox regression. The prediction ability of the signature was successfully validated in the GSE17537 dataset. A comprehensive nomogram combining the 11 signature genes and clinical parameters could effectively predict the overall survival of CRC patients. The present molecular signature established based on the 11 ferroptosis-related genes performed well in assessing CRC prognosis. The present discoveries could inspire further research on ferroptosis, providing a new direction for CRC management.

Identification of a novel macrophage-related prognostic signature in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent and deadliest illnesses all around the world. Growing proofs demonstrate that tumor-associated macrophages (TAMs) are of critical importance in CRC pathogenesis, but their mechanisms remain yet unknown. The current research was designed to recognize underlying biomarkers associated with TAMs in CRC. We screened macrophage-related gene modules through WGCNA, selected hub genes utilizing the LASSO algorithm and COX regression, and established a model. External validation was performed by expression analysis using datasets GSE14333, GSE74602, and GSE87211. After validating the bioinformatics results using real-time quantitative reverse transcription PCR, we identified SPP1, C5AR1, MMP3, TIMP1, ADAM8 as potential biomarkers associated with macrophages in CRC.