Signature In Colorectal Cancer Research Articles

The global landscape of immune-derived lncRNA signature in colorectal cancer

Background: Colorectal cancer (CRC) is a highly heterogeneous cancer. This heterogeneity has an impact on the efficacy of immunotherapy. Long noncoding RNAs (lncRNAs) have been found to play regulatory functions in cancer immunity. However, the global landscape of immune-derived lncRNA signatures has not yet been explored in colorectal cancer.Methods: In this study, we applied DESeq2 to identify differentially expressed lncRNAs in colon cancer. Next, we performed an integrative analysis to globally identify immune-driven lncRNA markers in CRC, including immune-associated pathways, tumor immunogenomic features, tumor-infiltrating immune cells, immune checkpoints, microsatellite instability (MSI) and tumor mutation burden (TMB).Results: We also identified dysregulated lncRNAs, such as LINC01354 and LINC02257, and their clinical relevance in CRC. Our findings revealed that the differentially expressed lncRNAs were closely associated with immune pathways. In addition, we found that RP11-354P11.3 and RP11-545G3.1 had the highest association with the immunogenomic signature. As a result, these signatures could serve as markers to assess immunogenomic activity in CRC. Among the immune cells, resting mast cells and M0 macrophages had the highest association with lncRNAs in CRC. The AC006129.2 gene was significantly associated with several immune checkpoints, for example, programmed cell death protein 1 (PD-1) and B and T lymphocyte attenuator (BTLA). Therefore, the AC006129.2 gene could be targeted to regulate the condition of immune cells or immune checkpoints to enhance the efficacy of immunotherapy in CRC patients. Finally, we identified 15 immune-related lncRNA-generated open reading frames (ORFs) corresponding to 15 cancer immune epitopes.Conclusion: In conclusion, we provided a genome-wide immune-driven lncRNA signature for CRC that might provide new insights into clinical applications and immunotherapy.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

A Treg-related riskscore model may improve the prognosis evaluation of colorectal cancer.

Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)-related gene signature in CRC. We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co-expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg-related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored. Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg-related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples. We established a useful riskscore model in light of seven Treg-related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.

Unraveling a novel hippo-associated immunological prognostic signature: The contribution of SERPINE1 in facilitating colorectal cancer progression via the notch signaling pathway

BackgroundAccumulating evidence demonstrated that Hippo signaling pathway is implicated in tumor initiation and progression. However, there have been limited studies on establishing signatures utilizing genes related to the Hippo pathway to evaluate prognosis and clinical efficacy in colorectal cancer (CRC) patients. MethodsHippo pathway-associated genes with prognostic significance were identified in the TCGA database. Subsequently, a prognostic signature associated with the Hippo pathway was constructed using Cox and LASSO regression analyses. Survival analysis, ROC analysis, and stratified analyses were conducted to appraise the performance effect of our prognostic model. We also explored the relationship between the risk score and tumor immune microenvironment. Furthermore, GO analyses and GSEA were performed for SERPINE1. Additional experiments were conducted to illuminate the underlying function and possible mechanism of SERPINE1 in CRC cell proliferation and migration. ResultsWe identified 58 differentially expressed genes associated with Hippo pathway that have prognostic significance in CRC. Among them, five genes (PPP2CB, SERPINE1, WNT5A, TCF7L1, and LEF1) were selected to establish a prognostic signature for CRC. Multivariate analysis demonstrated that this signature exhibited excellent diagnostic and prognostic performance, providing maximum benefits for CRC patients. In accordance with the prognostic signatures, the cases were divided into low-risk and high-risk groups. Remarkably, the high-risk group displayed lower immune scores, reduced immune cell infiltration, and decreased expression of immune checkpoints. Low-risk group could more possibly benefit from conventional chemotherapeutic and targeted therapies. CRC exhibited significantly elevated expression of SERPINE1, which was linked to worst overall survival. Moreover, inhibition of SERPINE1 suppressed proliferation, invasion, and migration of CRC cells via Notch pathway. ConclusionsTo sum up, we established a novel immunological prognostic signature utilizing genes associated with the Hippo pathway. This signature offers accurate prognostic prediction and can guide individualized therapy for patients with CRC.

Identification of a novel lymphangiogenesis signature associated with immune cell infiltration in colorectal cancer based on bioinformatics analysis

BackgroundLymphangiogenesis plays an important role in tumor progression and is significantly associated with tumor immune infiltration. However, the role and mechanisms of lymphangiogenesis in colorectal cancer (CRC) are still unknown. Thus, the objective is to identify the lymphangiogenesis-related genes associated with immune infiltration and investigation of their prognosis value.MethodsmRNA expression profiles and corresponding clinical information of CRC samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The lymphangiogenesis-related genes (LymRGs) were collected from the Molecular Signatures database (MSigDB). Lymphangiogenesis score (LymScore) and immune cell infiltrating levels were quantified using ssGSEA. LymScore) and immune cell infiltrating levels-related hub genes were identified using weighted gene co-expression network analysis (WGCNA). Univariate Cox and LASSO regression analyses were performed to identify the prognostic gene signature and construct a risk model. Furthermore, a predictive nomogram was constructed based on the independent risk factor generated from a multivariate Cox model.ResultsA total of 1076 LymScore and immune cell infiltrating levels-related hub genes from three key modules were identified by WGCNA. Lymscore is positively associated with natural killer cells as well as regulator T cells infiltrating. These modular genes were enriched in extracellular matrix and structure, collagen fibril organization, cell-substrate adhesion, etc. NUMBL, TSPAN11, PHF21A, PDGFRA, ZNF385A, and RIMKLB were eventually identified as the prognostic gene signature in CRC. And patients were divided into high-risk and low-risk groups based on the median risk score, the patients in the high-risk group indicated poor survival and were predisposed to metastasis and advanced stages. NUMBL and PHF21A were upregulated but PDGFRA was downregulated in tumor samples compared with normal samples in the Human Protein Atlas (HPA) database.ConclusionOur finding highlights the critical role of lymphangiogenesis in CRC progression and metastasis and provides a novel gene signature for CRC and novel therapeutic strategies for anti-lymphangiogenic therapies in CRC.

Novel DNA methylation-based epigenetic signatures in colorectal cancer from peripheral blood leukocytes.

Colorectal cancer (CRC) is a multifactorial disease characterized by accumulation of multiple genetic and epigenetic alterations, transforming colonic epithelial cells into adenocarcinomas. Alteration of DNA methylation (DNAm) is a promising biomarker for predicting cancer risk and prognosis, but its role in CRC tumorigenesis is inconclusive. Notably, few DNAm studies have used pre-diagnostic peripheral blood (PB) DNA, causing difficulty in postulating the underlying biologic mechanism of CRC initiation. We conducted epigenome-wide association (EWA) scans in postmenopausal women from Women's Health Initiative (WHI) with their pre-diagnostic DNAm in PB leukocytes (PBLs) to prospectively evaluate CRC development. Our site-specific DNAm analyses across the genome adjusted for DNAm-age, leukocyte heterogeneities, as well as body mass index, diabetes, and insulin resistance. We validated 20 top EWA-CpGs in 2 independent CRC tissue datasets. Also, we detected differentially methylated regions (DMRs) associated with CRC, further mapped to transcriptomic profile, and finally conducted a Gene Set Enrichment Analysis. We detected multiple novel CpGs validated across WHI and tissue datasets. In particular, 2 CpGs (B4GALNT4cg10321339, SV2Bcg18144285) had the strongest effect on CRC risk. Results from our DMR scans contained MIR663cg06007966, which was also validated in EWA analyses. Also, we detected 1 methylome region in PEG10 of Chr7 shared across datasets. Our findings reflect both novel and well-established epigenomic and transcriptomic sites in CRC, warranting further functional validations. Our study contributes to better understanding of the complex interrelated mechanisms on the methylome underlying CRC tumorigenesis and suggests novel preventive DNAm-targets in PBLs for detecting at-risk individuals for CRC development.

A pyroptosis-related signature in colorectal cancer: exploring its prognostic value and immunological characteristics.

The heterogeneity of colorectal cancer (CRC) is the main cause of the disparity of drug sensitivity and the variability of prognosis. Pyroptosis is closely associated with the development and prognosis of various tumors, including CRC. Dividing CRC into distinct subgroups based on pyroptosis is a worthwhile topic for improving the precision treatment and prognosis prediction of CRC. We classified patients into two clusters using the consensus clustering based on the pyroptosis-related genes (PRGs). Next, the prognostic signature was developed with LASSO regression analysis using the screened genes from differentially expressed genes (DEGs) by univariate and multivariate Cox analyses. According to the pyroptosis-related score (PR score) calculated with the signature, patients belonged to two groups with distinct prognosis. Moreover, we assessed the immune profile to explore the relationship between the signature and immunological characteristics. Two single cell sequencing databases were adopted for further exploration of tumor immune microenvironment (TME). In addition, we applied our own cohort and Drugbank to explore the correlation of the signature and clinical therapies. We also studied the expression of key genes by immunohistochemistry. The signature performed well in predicting the prognosis of CRC as the high area under curve (AUC) value demonstrated. Patients with a higher PR score had poorer prognosis and higher expression of immune checkpoints but more abundant infiltration of immune cells. Combining with the indicator of therapeutic analysis, they might benefit more from immune checkpoint blockade (ICB) and neo-adjuvant chemoradiotherapy (nCRT). In conclusion, our study is based on genomics and transcriptomics to investigate the role of PRGs in CRC. We have established a prognostic signature and integrated single-cell data to study the relationship between the signature with the TME in CRC. Its clinical application in reliable prediction of prognosis and personalized treatment was validated by public and own sequencing cohort. It provided a new insight for the personalized treatment of CRC.

USP47 inhibits m6A-dependent c-Myc translation to maintain regulatory T cell metabolic and functional homeostasis.

The functional integrity of Tregs is interwoven with cellular metabolism; however, the mechanisms governing Treg metabolic programs remain elusive. Here, we identified that the deubiquitinase USP47 inhibited c-Myc translation mediated by the RNA N6-methyladenosine (m6A) reader YTHDF1 to maintain Treg metabolic and functional homeostasis. USP47 positively correlated with the tumor-infiltrating Treg signature in samples from patients with colorectal cancer and gastric cancer. USP47 ablation compromised Treg homeostasis and function in vivo, resulting in the development of inflammatory disorders, and boosted antitumor immune responses. USP47 deficiency in Tregs triggered the accumulation of the c-Myc protein and in turn exacerbated hyperglycolysis. Mechanistically, USP47 prevented YTHDF1 ubiquitination to attenuate the association of YTHDF1 with translation initiation machinery, thereby decreasing m6A-based c-Myc translation efficiency. Our findings reveal that USP47 directs m6A-dependent metabolic programs to orchestrate Treg homeostasis and suggest novel approaches for selective immune modulation in cancer and autoimmune diseases by targeting of USP47.

Construction of immunogenic cell death-related molecular subtypes and prognostic signature in colorectal cancer.

Immunotherapy is a promising treatment for advanced colorectal cancers (CRCs). However, immunotherapy resistance remains a common problem. Immunogenic cell death (ICD), a form of regulated cell death, induces adaptive immunity, thereby enhancing anti-tumor immunity. Research increasingly suggests that inducing ICD is a promising avenue for cancer immunotherapy and identifying ICD-related biomarkers for CRCs would create a new direction for targeted therapies. Thus, this study used bioinformatics to address these questions and create a prognostic signature, aiming to improve individualized CRC treatment. We identified two ICD -related molecular subtypes of CRCs. The high subtype showed pronounced immune cell infiltration, high immune activity, and high expression of human leukocyte antigen and immune checkpoints genes. Subsequently, we constructed and validated a prognostic signature comprising six genes (CD1A, TSLP, CD36, TIMP1, MC1R, and NRG1) using random survival forest analyses. Further analysis using this prediction model indicated that patients with CRCs in the low-risk group exhibited favorable clinical outcomes and better immunotherapy responses than those in the high-risk group. Our findings provide novel insights into determining the prognosis and design of personalized immunotherapeutic strategies for patients with CRCs.

A WGCNA-based cancer-associated fibroblast risk signature in colorectal cancer for prognosis and immunotherapy response.

Cancer-associated fibroblasts (CAFs) are notably involved in colorectal cancer (CRC) tumorigenesis, progression, and treatment failure. In this article, we report the in silico development of a CAF-related prognostic signature for CRC. We separately downloaded CRC transcription data from The Cancer Genome Atlas and the Gene Expression Omnibus database. Deconvolution algorithms, including Estimating the Proportions of Immune and Cancer Cells and the Microenvironment Cell Population-counter, were used to calculate CAF abundance, while the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression algorithm was used to calculate the stromal score. Weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator algorithm were used to identify CAF-related genes and prognostic signatures. We identified a three-gene, prognostic, CAF-related signature and defined risk groups based on the Riskscores. Multidimensional validations were applied to evaluate the robustness of the signature and its correlation with clinical parameters. We utilized Tumor Immune Dysfunction and Exclusion (TIDE) and oncoPredict algorithms to predict therapy responses and found that patients in low-risk groups are more sensitive to immunotherapy and chemotherapy drugs such as 5-fluorouracil and oxaliplatin. Finally, we used the Cancer Cell Line Encyclopedia and Human Protein Atlas databases to evaluate the mRNA and protein levels encoded by the signature genes. This novel CAF-related three-gene signature is expected to become a potential prognostic biomarker in CRC and predict chemotherapy and immunotherapy responses. It may be of considerable value for studying the tumor microenvironment in CRC.

The predictive significance of a 5-m6A RNA methylation regulator signature in colorectal cancer

Colorectal cancer attacks the colon or rectum, with increasing morbidity and mortality globally. The RNA modification 6-methyladenine (m6A) is related to RNA modifications, playing a critical role in colorectal cancer. We aimed to identify prognostic signatures for colorectal cancer using risk prediction algorithms, and to validate these signatures using independent datasets and clinical samples. In this study, 175 cases in GSE17536 were assigned into two clusters using consistent clustering and PCA analysis. A multivariate Cox risk regression model revealed that among 21 m6A RNA methylation regulators, RBM15B, FTO, IGF2BP2, ZCCHC4, and KIAA1429 were remarkably associated with colorectal cancer patients' overall survival (OS); however, Kaplan–Meier (KM) survival assessment showed no significant association between these five regulators and colorectal cancer patients' prognosis. A 5-m6A RNA methylation regulator signature was established using LASSO algorithm. Risk scores of cases in GSE17536, GSE17537 and GSE75500 were calculated, and lower risk scores were associated with better DSS/OS. receiver operating characteristic (ROC) curve and the nomogram revealed the satisfactory predictive efficiency of the risk score model. The risk score could distinguish cases in Cluster1 and Cluster2 and normal and tumor tissues based on GSE37182. The prognostic variables for colorectal cancer patients were assessed using both univariate and multivariate Cox's proportional hazard regression models, which revealed that the stage and risk score were significant risk factors. In this study, a comprehensive set of integrative bioinformatics analyses was conducted to investigate the prognostic and diagnostic potential of a panel of 5 m6A RNA methylated regulators in colorectal cancer patients. The conducted studies included the use of several statistical methods, such as the LASSO regression model, KM survival evaluation, ROC curve, and univariate and multivariate Cox's proportional hazard regression analyses. The findings from these analyses collectively established the prognostic marker, highlighting its significance in predicting patient outcomes and diagnosing colorectal cancer.

Identification of a seven autophagy-related gene pairs signature for the diagnosis of colorectal cancer using the RankComp algorithm.

Based on the colorectal cancer microarray sets gene expression data series (GSE) GSE10972 and GSE74602 in colon cancer and 222 autophagy-related genes, the differential signature in colorectal cancer and paracancerous tissues was analyzed by RankComp algorithm, and a signature consisting of seven autophagy-related reversal gene pairs with stable relative expression orderings (REOs) was obtained. Scoring based on these gene pairs could significantly distinguish colorectal cancer samples from adjacent noncancerous samples, with an average accuracy of 97.5% in two training sets and 90.25% in four independent validation GSE21510, GSE37182, GSE33126, and GSE18105. Scoring based on these gene pairs also accurately identifies 99.85% of colorectal cancer samples in seven other independent datasets containing a total of 1406 colorectal cancer samples.

Proteome analysis of patients with early onset colorectal cancer in Ireland.

e15639 Background: Early onset colorectal cancer (EOCRC) is defined as colorectal cancer (CRC) in those under the age of 50. The incidence of EOCRC is rising at an alarming rate. Unlike in the USA, screening for CRC in Ireland begins at 60 years of age. It has been hypothesized that the signatures of CRC in younger populations could differ from that in elderly patients. Deep characterization of the tissue using omics profiling, including proteomics, could provide the answer to this question. Moreover, omics profiling could identify new biomarkers for patient stratification and earlier diagnosis. Methods: Forty micrograms of healthy and matched frozen tumors were taken from 8 patients with locally advanced EOCRC for whole proteome analysis. The samples were digested with trypsin and mass spectrometry was performed using data dependent analysis parallel accumulation serial fragmentation (DDA-PASEF). The raw data was searched against the Homo sapiens subset of the Uniprot Swissprot database (reviewed) with the proteomics pipeline software Fragpipe (Version 19.1) Statistical analysis was performed using Perseus software. Results: When we compared proteomes of 8 tumor tissues versus matched normal tissues, out of 5,717 protein IDs, there were more than 1,300 differentially expressed proteins. Our pathway enrichment analysis, using differentially expressed proteins, has identified a number of molecular functions altered in EOCRC samples. The most enriched signaling pathways were associated with FAT10 signaling, neutrophil extracellular trap (NET) signaling and oxidative stress response. The upstream regulator analysis (URA) has identified LASP1 and LONP1 proteins. LASP1 is known to promote CRC progression via the Hippo signaling pathway, while LONP1 is known to play a role in mitochondrial functions, metabolism, and epithelial–mesenchymal transition (EMT) in CRC cells. Importantly, the NET signaling activation in all samples suggests unfavorable immunomodulation that could stimulate cell proliferation and invasiveness. The observed activation of oxidative stress signaling components is in line with the inflammatory nature of CRC, which could signify potential response to immune checkpoint inhibitors. Conclusions: Our findings in a small cohort of patients indicate that proteomics can provide a deeper insight into aberrant signalling in EOCRC. We have identified novel vulnerabilities that might be used as therapeutic targets as well as patient stratification biomarkers. Additional proteome studies with corresponding genomic and transcriptomic analysis are needed to fully understand the potential of this approach.

TIGD1 Function as a Potential Cuproptosis Regulator Following a Novel Cuproptosis-Related Gene Risk Signature in Colorectal Cancer

Cuproptosis is a new form of copper-dependent programmed cell death commonly occurring within the body. There is emerging evidence indicating that cuproptosis has a significant regulatory function in the onset and progression of cancer. However, it is still unclear how cuproptosis regulates cancer and whether other genes are involved in the regulation. Using the TCGA-COAD dataset of 512 samples, we found that seven of ten cuproptosis markers showed prognostic value in colorectal cancer (CRC) using Kaplan-Meier survival analysis. Furthermore, 31 prognostic cuproptosis-related genes were identified using weighted gene co-expression network analysis and univariate Cox analysis. Subsequently, we constructed a 7-PCRG signature using least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. The risk score predicting survival in patients with CRC was evaluated. Two risk groups were classified based on their risk scores. The two groups revealed a significant difference in immune cells, such as B and T cells. Furthermore, we identified differences in many immune functions and checkpoints, including CD276 and CD28. In vitro experiments showed that a hub cuproptosis-related gene, TIGD1, could significantly regulate cuproptosis in CRC after exposure to elesclomol. This study validated that cuproptosis was closely related to the progression of CRC. Seven new cuproptosis-related genes were identified, and the function of TIGD1 in cuproptosis was preliminarily understood. Since a certain concentration of copper in CRC cells is important, cuproptosis may provide a new target for cancer therapy. This study may provide novel insights into the treatment of CRC.

Abstract 3338: Targeted serum metabolomics for noninvasive detection of colorectal neoplasia

Abstract Background: Using a shotgun metabolomics approach, we have previously reported distinct features in the circulating metabolome of patients with colorectal cancer (CRC) and adenoma. A clinically useful metabolomic assay requires a targeted approach that detects CRC with high sensitivity, and which also detects pre-malignant precursor lesions. Our objective was to identify a metabolomic signature for CRC, adenomatous polyps (AP), and sessile serrated adenomas (SSA), using a targeted approach. Methods: Serum from patients with all stages of CRC (N=86), AP (N=48), SSA (N=46), as well as age- and sex-matched disease-free controls (DFC; N=120) were analyzed using a targeted metabolomic assay (Biocrates MxP® Quant 500). The assay was performed on liquid chromatography-tandem mass spectrometry (LC-MS/MS), quantifying 630 metabolites across 26 biochemical classes. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to identify metabolite patterns distinguishing each neoplastic condition relative to DFCs. A small validation cohort (N=78) was additionally analyzed to test our meta-biomarkers. Results: In comparisons with DFCs, statistically significant OPLS-DA models were constructed for all three neoplastic conditions. The OPLS-DA models for CRC, AP, and SSA consisted of 39, 14, and 22 metabolites, respectively, with the most frequently represented biochemical classes being triglycerides, phosphatidylcholines, bile acids, and acylcarnitines. Based on 7-fold internal cross validation, receiver operating characteristic (ROC) analysis demonstrated an area under the curve (AUC) of 0.85, 0.89, and 0.82 for CRC, AP, and SSA, respectively. When applied to the independent validation cohort, the sensitivity and specificity of each model were: 87.0% and 87.0% for CRC; 53.3% and 93.3% for AP; and 78.6% and 57.1% for SSA. Conclusion: Metabolomic meta-biomarkers for CRC and its precursors demonstrated excellent performance based on internal validation. Importantly, these lesions were each detected with superior sensitivity compared to readily available stool-tests. A large external validation study in progress will be used to confirm these findings. Citation Format: Liam W. Fitzgerald, Dennis J. Orton, Karen A. Kopciuk, Hans J. Vogel, Robert J. Hilsden, Oliver F. Bathe. Targeted serum metabolomics for noninvasive detection of colorectal neoplasia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3338.

CAF signature predicts the prognosis of colorectal cancer patients: A retrospective study based on bulk RNA sequencing and single-cell RNA sequencing data.

The incidence rate and mortality rate of colorectal cancer (CRC) ranks third and second globally. Cancer-associated fibroblasts (CAFs) are the major constituent of the stromal cells in the tumor microenvironment (TME) and are closely associated with patients' prognoses. Our study intended to establish a prognostic model for CRC using hallmark genes of CAFs. The expression values of genes and clinicopathological characteristics of patients were enrolled from the cancer genome atlas database as well as the gene expression omnibus database. The single-cell RNA sequencing data were collected and analyzed in the deeply integrated human single-cell omics database and cancer single-cell expression map databases. The ESTIMATE algorithm was applied to access the infiltration levels of immune and stromal cells. The prognostic genes were selected by the Cox regression analysis and the prognostic signature was constructed by the least absolute shrinkage and selection operator algorithm. gene set enrichment analysis was used to explore the enriched gene sets. In this study, based on bulk RNA sequencing and single-cell RNA sequencing data, and we found that more CAFs were infiltrated in the tumor microenvironment and consisted of 3 subtypes. Then we constructed a prognostic signature for CRC using hallmark genes of CAFs and proved that this signature exhibited high values to predict the overall survival of CRC patients in independent training and validating cohorts. Besides, function enrichment analysis revealed that our prognostic model was significantly associated with immune regulation. Further analysis showed that the infiltrated levels of tumor-suppressing immune cells and the expression of higher immune checkpoint genes in CRC tissues were higher in patients with high-risk scores. Furthermore, immunohistochemistry analysis exhibited that these genes in our prognostic signature were markedly upregulated in CRC tissues. We first constructed a signature based on CAFs hallmark genes to predict the survival of CRC patients and further revealed that the tumor-suppressing microenvironment and dysregulated immune checkpoint genes in CRC tissues were partly responsible for the poor prognosis of patients.

Identification of a hypoxia-related gene prognostic signature in colorectal cancer based on bulk and single-cell RNA-seq

Colorectal cancer (CRC) is the most common and fatal tumor in the gastrointestinal system. Its incidence and mortality rate have increased in recent years. Hypoxia, a persistent physiological tumor feature, plays a vital role in CRC tumorigenesis, metastasis, and tumor microenvironment (TME). Therefore, we constructed a hypoxia-related gene (HRG) nomogram to predict overall survival (OS) and explored the role of HRGs in the CRC TME. The Cancer Genome Atlas (TCGA) dataset was used as the training set, and two Gene Expression Omnibus datasets (GSE39582 and GSE103479) were used as the testing sets. HRGs were identified using the Gene Set Enrichment Analysis (GSEA) database. An HRG prognostic model was constructed in the training set using the least absolute shrinkage and selection operator regression algorithm and validated in the testing sets. Then, we analyzed tumor-infiltrating cells (TICs) using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. Furthermore, single-cell next-generation RNA sequencing (RNA-seq) was used to investigate HRG expression in different TICs in the GSE139555 dataset. Finally, reverse transcription polymerase chain reactions (RT-PCR) were used to validate HRG mRNA expression in ten pairs of CRC normal and cancer tissue samples. A six HRG prognostic signature was constructed, with a superior OS prediction ability in CRC patients (area under the receiver operating characteristic curve (AUC) at one year: 0.693, AUC at three years: 0.712, and AUC at five years: 0.780). GSEA enrichment analysis identified six pathways enriched in the high-risk group. The TIC analysis indicated that the high-risk group had lower T-cell expression and higher neutrophil expression than the low-risk group. Furthermore, immune-related genes had an inseparable relationship with the HRG prognostic signature. Based on single-cell RNA-seq data, we found elevated hexokinase 1 (HK1) and glucose-6-phosphate isomerase (GPI) gene expression in natural killer (NK) and CD8+ T cells. RT-PCR in ten CRC normal-tumor tissue pairs showed that expression of the signature’s six HRGs varied differently in cancerous and paracancerous tissues. The constructed HRG signature successfully predicted the OS of whole-stage CRC patients. In addition, we showed that the signature’s six HRGs were closely associated with the TME in CRC, where hypoxia inhibits the antitumor function of T cells.

Abstract PR005: Features and susceptibility to dietary-induced alkylating damage in colorectal cancer

Abstract Introduction: Colorectal cancer (CRC) has several established risk factors, including diet. However, their mutagenic effect has not been observed directly in patients’ tumors and the individuals or ethnic groups who are most susceptible to diet-induced carcinogenesis are yet to be identified. We hypothesized that mutational signature analyses in CRC, coupled with epidemiologic, tumor molecular and patient germline data, can be linked to pre-diagnosis diet and specific germline alterations which can further inform cancer prevention efforts. Methods: We analyzed 900 CRCs with whole-exome sequencing (WES) and prospectively collected pre-diagnosis dietary data from the Nurses’ Health Studies I and II (NHS) and the Health Professionals Follow-up Study (HPFS), as well as 540 The Cancer Genome Atlas (TCGA) tumors and 295 non-Western CRCs from the Pan-Cancer Analysis of Whole Genomes. We additionally examined a sequencing dataset of normal colonic crypts. Following variant calling, mutational signature analyses were performed on cancer specimens using Non-negative Matrix Factorization. We leveraged the WES results to identify all patients that harbored the O-6-Methylguanine-DNA Methyltransferase (MGMT) germline rs16906252 single-nucleotide polymorphism (SNP), which is associated with tumor MGMT promoter hypermethylation and impaired alkylating damage repair. Results: We identified a novel alkylating mutational signature in CRC, which was associated with pre-diagnosis intake of red meats (overall red meat P = 0.017, unprocessed red meat P = 7.8×10-3, processed red meat P = 7.1×10-3, Mann-Whitney test), distal tumor location (P = 1.4×10-4, Mann-Whitney test) and worse CRC-specific survival (P trend = 0.036, multivariable Cox regression). We found a similar mutational signature in normal colonic crypts, suggesting an early oncogenic process. We showed that the alkylating signature’s oncogenicity is mainly mediated by KRAS p.G12D and KRAS p.G13D hotspot mutations. In addition, individuals harboring the MGMT rs16906252 germline SNP had a significant enrichment in tumors with alkylating damage (P = 0.015, Mann-Whitney test) and the effect of the SNP was synergistic with pre-diagnosis red-meat intake (P = 0.0099, Wald Test). Notably, we did not observe the alkylating mutational signature in CRCs from African and East-Asian patients, populations in which the MGMT rs16906252 SNP is largely absent. Conclusions: Through the integration of molecular and epidemiologic data, we identified evidence of red meat-associated alkylating damage in CRCs. This alkylating mutational signature was associated with distal colon location, KRAS oncogenic drivers, and poor CRC-specific survival. In addition, tumor alkylating damage was enriched among individuals harboring the MGMT germline rs16906252 polymorphism, absent in East Asian and African CRC patients and was synergistic with pre-diagnosis red-meat intake. Together, these results have significant implications for dietary-induced carcinogenesis and precision prevention in CRC. Citation Format: Carino Gurjao, Rong Zhong, Koichiro Haruki, Chichun Tan, Yvonne Li, Henry Lee-Six, Brendan Reardon, Tomotaka Ugai, Mingyang Song, Eliezer M. Van Allen, Charles S. Fuchs, Jonathan A. Nowak, Kana Wu, Jeffrey A. Meyerhardt, Edward L. Giovannucci, Jeffrey P. Townsend, Shuji Ogino, Marios Giannakis. Features and susceptibility to dietary-induced alkylating damage in colorectal cancer. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr PR005.

Untangling determinants of gut microbiota and tumor immunologic status through a multi-omics approach in colorectal cancer

The changes in gut microbiota have been implicated in colorectal cancer (CRC). The interplays between the host and gut microbiota remain largely unclear, and few studies have investigated these interplays using integrative multi-omics data. In this study, large-scale multi-comic datasets, including microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing of CRC patients, were analyzed individually and integrated through advanced bioinformatics methods. We further examined the clinical relevance of these findings in the mice recolonized with microbiota from human. We found that CRC patients had distinct microbiota compositions compared to healthy controls. A machine-learning model was developed with 28 biomarkers for detection of CRC, which had high accuracy and clinical applicability. We identified multiple significant correlations between genera and well-characterized genes, suggesting the potential role of gut microbiota in tumor immunity. Further analysis showed that specific metabolites worked as profound communicators between these genera and tumor immunity. Integrating microbiota and metabolome perspectives, we cataloged gut taxonomic and metabolomic features that represented the key multi-omics signature of CRC. Furthermore, gut microbiota transplanted from CRC patients compromised the response of CRC to immunotherapy. These phenotypes were strongly associated with the alterations in gut microbiota, immune cell infiltration as well as multiple metabolic pathways. The comprehensive interplays across multi-comic data of CRC might explain how gut microbiota influenced tumor immunity. Hence, we proposed that modifying the CRC microbiota using healthy donors might serve as a promising strategy to improve response to immunotherapy.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer.

In this study, we explored the pyroptosis-related biomarkers and signatures of colorectal cancer (CRC). Gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA)-COADREAD and were analyzed for differentially expressed genes (DEGs). DEGs in CRC‒pyroptosis-related genes (CRC‒PRGs) were obtained by intersecting DEGs associated with CRC and PRGs. The CRC‒PRGs were verified; functional enrichment analysis was performed with Gene Ontology (GO) followed by cluster analysis. Cox analyses and LASSO regression were used in TCGA dataset to construct a prognostic model for patients with CRC. A prognostic risk assessment model was constructed and efficacy was evaluated. Decision curve analysis was utilized to assess the role of the Lasso-Cox regression prognostic model for clinical utility at 1, 3, and 5years. Twelve CRC‒PRGs were identified as prognostic pyroptosis-related DEGs. CXCL8, IL13RA2, MELK, and POP1 were selected as prognostic genes to construct features with a good prognostic performance in GEO and TCGA. Functional enrichment indicated that the 4-gene signature might be involved in CRC tumorigenesis and development through various pathways by playing a prognostic role in CRC. Furthermore, the results of the immune landscape analysis showed that the expression of CXCL8 and IL13RA2 in TCGA-COADREAD dataset was positively correlated with significant differential enrichment of most immune cells. A novel prognostic model consisting of four key genes, CXCL8, IL13RA2, MELK, and POP1, can accurately predict the survival of patients with CRC. This finding may provide a new perspective for the treatment of pyroptosis-related CRC.