Signature In Colorectal Cancer Research Articles

Comprehensive proteomic signature and identification of CDKN2A as a promising prognostic biomarker and therapeutic target of colorectal cancer.

BACKGROUNDThe carcinogenesis of colorectal cancer (CRC) involves many different molecules and multiple pathways, and the specific mechanism has not been elucidated until now. Existing studies on the proteomic signature profiles of CRC are relatively limited. Therefore, we herein aimed to provide a more comprehensive proteomic signature profile and discover new prognostic markers and therapeutic targets by performing proteomic analysis of CRC and paired normal tissues.AIMTo investigate the proteomic signature and identify novel protein prognostic biomarkers of CRC.METHODSCancer tissues and paired normal tissues were collected from 48 patients who underwent surgical removal at the China-Japan Friendship Hospital from January 2020 to June 2021. Data independent acquisition (DIA) quantitative proteomic analysis was performed using high-performance liquid chromatography–mass spectrometry/mass spectrometry (nano-UHPLC–MS/MS) to identify differentially expressed proteins, among which those with a P adj value (t test, BH correction) < 0.05 and an absolute fold change (|log2FC|) > 2 were identified as potential markers. Differentially expressed proteins were selected by bioinformatics analysis and validated by immunohistochemical tissue microarrays, and their association with prognosis was further analyzed with the Gene Expression Profiling Interactive Analysis database to identify prognostic protein biomarkers of CRC.RESULTSSignificantly differential protein expression was observed between cancer tissues and normal tissues. Compared with normal tissues, 1115 proteins were upregulated and 705 proteins were downregulated in CRC based on P adj < 0.05 and |log2FC| > 2, and bioinformatics analysis revealed that the differentially expressed proteins were involved in multiple biological processes associated with tumorigenesis, including ribosome biogenesis in eukaryotes, focal adhesion, extracellular matrix-receptor interactions and other tumor metabolism processes. Moreover, cyclin-dependent kinase inhibitor 2A (CDKN2A) expression was markedly upregulated in CRC, as validated by immunohistochemistry (0.228 vs 0.364, P = 0.0044), and was significantly enriched in tumor proliferation and signal transduction pathways such as the cell cycle and p53 signaling pathways. High CDKN2A expression was significantly correlated with poor prognosis (P = 0.021). These results demonstrated that CDKN2A functions as a driver of CRC.CONCLUSIONOur study provides a comprehensive proteomic signature of CRC and highlights CDKN2A as a potential powerful prognostic marker and precision therapeutic target.

Microarray and bioinformatic analysis reveal the parental genes of m6A modified circRNAs as novel prognostic signatures in colorectal cancer.

BackgroundAccumulating evidences have revealed that the abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer. It is noteworthy that m6A modification is widely existed in circRNAs and found its key biological functions in regulating circRNAs metabolism. However, the role of m6A modified circRNAs in colorectal cancer (CRC) remains unknown. To better understand the role of circRNAs in the pathogenesis of CRC, we focus on the relationship between m6A-modified circRNAs and their parental genes.MethodsArraystar m6A-circRNA epitranscriptomic microarray was used to identify differentially m6A modified circRNAs between CRC and the control group. In addition, TCGA-COAD and GSE106582 cohort were used to identify differentially expressed mRNAs. In this study, we screened the parental genes for which both circRNAs and mRNAs were down-regulated further to analyze, including gene expression, survival prognosis, enrichment analysis. Additionally, Western Blotting was used to further validate the role of the parental gene in CRC.ResultsWe found that 1405 significantly downregulated circRNAs in CRC by our microarray data. Moreover, we obtained 113 parental genes for which both circRNAs and mRNAs were down-regulated to analyze the relationship with the prognosis of CRC based on TCGA-COAD cohort. And we identified nine potential prognostic genes, including ABCD3, ABHD6, GAB1, MIER1, MYOCD, PDE8A, RPS6KA5, TPM1 and WDR78. And low expression of these genes was associated with poor survival prognosis of the patients with CRC. In addition, we found that TPM1 is downregulated in CRC by western blotting experiment. And the calcium-signaling pathway may involve the process of the CRC progression.ConclusionsWe identified nine potential prognostic genes, after analyzed the relationship between the parental genes of m6A modified circRNAs and the progression of CRC. Above all, our study further validated TPM1 can serve as a potentail signature for CRC patients.

Integrated Analysis of Colorectal Cancer Reveals Cross-Cohort Gut Microbial Signatures and Associated Serum Metabolites

Studies have reported abnormal gut microbiota or circulating metabolome associated with colorectal cancer (CRC), but it remains a challenge to capture the CRC-relevant features consistent across geographic regions. This is particularly the problem for metabolic traits of CRC because the analyses generally use different platforms and laboratory methods, which poses a barrier to cross-dataset examination. In light of this, we sought to elucidate the microbial and metabolic signatures of CRC with broad population relevance. In this integrated metagenomic (healthy controls [HC], n= 91; colorectal adenoma [CRA], n= 63; CRC, n= 71) and metabolomic (HC, n= 34; CRA, n= 31; CRC, n= 35) analysis, CRC-associated features and microbe-metabolite correlations were first identified from a Shanghai cohort. A gut microbial panel was trained in the in-house cohort and cross-validated in 7 published metagenomic datasets of CRC. The in-house metabolic connections to the cross-cohort microbial signatures were used as evidence to infer serum metabolites with potentially external relevance. In addition, a combined microbe-metabolite panel was produced for diagnosing CRC or adenoma. CRC-associated alterations were identified in the gut microbiome and serum metabolome. A composite microbe-metabolite diagnostic panel was developed and yielded an area under the curve of 0.912 for adenoma and 0.994 for CRC. We showed that many CRC-associated metabolites were linked to cross-cohort gut microbiome signatures of the disease, including CRC-enriched leucylalanine, serotonin, and imidazole propionate; and CRC-depleted perfluorooctane sulfonate, 2-linoleoylglycerol (18:2), and sphingadienine. We generated cross-cohort metagenomic signatures of CRC, some of which linked to in-house CRC-associated serum metabolites. The microbial and metabolic shifts may have wide population relevance.

Microbiome modulation in the prevention and management of colorectal cancer: a systematic review of clinical interventions.

The role of probiotics/prebiotics in modulating the procarcinogenic effects of microbiota have been studied with inconclusive results. This systematic review aimed to identify the role of several studied interventions on the gut microbiota modulation in humans for the prevention and management of colorectal cancer (CRC). We conducted a systematic search using PubMed and Cochrane Central electronic databases, identifying clinical studies published within the last 20 years. We performed a qualitative analysis of eligible studies included in our review on each of the 4 investigated topics: CRC potential biomarkers, dietary interventions, probiotic administration in non-surgical and surgical patients, respectively. A total of 54 studies involving healthy volunteers, in addition to colorectal adenoma and CRC patients were included in our qualitative synthesis. We were able to identify bacterial signatures of CRC including Fusobacterium nucleatum and Clostridium butyricum. Moreover, dietary supplementation with oligosaccharides or fibers increased short chain fatty acid-producing bacteria levels, thus inhibiting tumorigenesis. Furthermore, we have confirmed that Lactobacilli and Bifidobacterium intake modulates gut microbiota towards tumor suppression. We have also showed that probiotic intake around colectomy significantly reduces complications. Bacterial metabolism is strongly linked with colonic carcinogenesis and influenced by diet. Probiotics and prebiotics can act as microbiota modulators, suppressing epithelial proliferation and reversing DNA toxicity. As adjuvants to surgery or chemotherapy, Lactobacilli and Bifidobacteria decrease complications. Improved outcomes in CRC patients can possibly be achieved through future research directed towards the benefits of bacterial agents as tumor suppressors or as treatment of oncological therapy resistance.

Metagenomic Analyses of Multiple Gut Datasets Revealed the Association of Phage Signatures in Colorectal Cancer.

The association of colorectal cancer (CRC) and the human gut microbiome dysbiosis has been the focus of several studies in the past. Many bacterial taxa have been shown to have differential abundance among CRC patients compared to healthy controls. However, the relationship between CRC and non-bacterial gut microbiome such as the gut virome is under-studied and not well understood. In this study we conducted a comprehensive analysis of the association of viral abundances with CRC using metagenomic shotgun sequencing data of 462 CRC subjects and 449 healthy controls from 7 studies performed in 8 different countries. Despite the high heterogeneity, our results showed that the virome alpha diversity was consistently higher in CRC patients than in healthy controls (p-value <0.001). This finding is in sharp contrast to previous reports of low alpha diversity of prokaryotes in CRC compared to healthy controls. In addition to the previously known association of Podoviridae, Siphoviridae and Myoviridae with CRC, we further demonstrate that Herelleviridae, a newly constructed viral family, is significantly depleted in CRC subjects. Our interkingdom association analysis reveals a less intertwined correlation between the gut virome and bacteriome in CRC compared to healthy controls. Furthermore, we show that the viral abundance profiles can be used to accurately predict CRC disease status (AUROC >0.8) in both within-study and cross-study settings. The combination of training sets resulted in rather generalized and accurate prediction models. Our study clearly shows that subjects with colorectal cancer harbor a distinct human gut virome profile which may have an important role in this disease.

Development and validation of an individualized immune prognostic signature in colorectal cancer.

e15531 Background: Colorectal cancer (CRC) is the third most common cause of cancer mortality worldwide with more than 1.85 million cases and 850,000 deaths annually. Although the outcome of CRC patients has improved significantly with the recent implementation of annual screening programs, reliable prognostic biomarkers are still needed due to the disease heterogeneity. Accumulating evidence revealed an association between immune signature and CRC prognosis. Therefore, we aim to build a robust immune-related gene pairs (IRGPs) signature that can evaluate the prognosis for CRC patients. Methods: Gene expression profiles and complete clinical information of CRC patients were collected from seven public cohorts, divided into the training cohort (TCGA cohort, N = 460) and six independent validation cohorts (GSE17536, GSE39582, GSE30378, GSE41258, GSE71187 and GSE87211, N = 1,151). Gene expression level in a specific sample or profile underwent pairwise comparison to generate a score for each IRGP. An IRGP score of 1 was assigned if IRG 1 was less than IRG 2; otherwise, the IRGP score was 0. Prognostic IRGPs were selected using log-rank test and LASSO Cox proportional hazards regression model. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were used to examine model predictive performance in the validation cohort. Results: Within 942 immune-related genes that were expressed in all the cohorts, a prognostic immune signature of 80 gene pairs consisting of 101 unique genes was constructed which was significantly associated with patients overall survival in the training dataset (HR, 4.53 [2.89-7.09]; P &lt; 0.0001). In the validation cohorts, the IRGPs signature significantly stratified patients into high-risk and low-risk groups in terms of prognosis and was prognostic in multivariate analyses (HR, 3.08 [2.03-4.65]; P &lt; 0.0001). Biological processes that were dramatically involved in cancer immunology, such as cell chemotaxis and leukocyte migration, were enriched among genes in the IRGPs signature. To further improve accuracy, we combined tumor stage and IRGPs score to fit a Cox proportional hazards regression model using the training dataset and derived an immune-clinical prognostic index (ICPI) as (0.53633 × stage) + (1.31369 × IRGPs score). Significantly improved estimation of survival was achieved by ICPI relative to IRGPs score (mean C-index, 0.81 vs 0.79; P = 0 .014). Conclusions: We developed a robust IRGPs signature for estimating prognosis in CRC patients, providing new insights into the identification of CRC patients with a high risk of mortality. Prospective studies are needed to further validate its analytical accuracy and to test its clinical utility in individualized management of CRC patients.

Comprehensive Analysis of a Cancer-Immunity Cycle-Based Signature for Predicting Prognosis and Immunotherapy Response in Patients With Colorectal Cancer.

Immune checkpoint blockade (ICB) has been recognized as a promising immunotherapy for colorectal cancer (CRC); however, most patients have little or no clinical benefit. This study aimed to develop a novel cancer-immunity cycle–based signature to stratify prognosis of patients with CRC and predict efficacy of immunotherapy. CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the RNA data from Gene Expression Omnibus (GEO) data sets and real-time quantitative PCR (RT-qPCR) data from paired frozen tissues were used for validation. We built a least absolute shrinkage and selection operator (LASSO)-Cox regression model of the cancer-immunity cycle–related gene signature in CRC. Patients who scored low on the risk scale had a better prognosis than those who scored high. Notably, the signature was an independent prognostic factor in multivariate analyses, and to improve prognostic classification and forecast accuracy for individual patients, a scoring nomogram was created. The comprehensive results revealed that the low-risk patients exhibited a higher degree of immune infiltration, a higher immunoreactivity phenotype, stronger expression of immune checkpoint–associated genes, and a superior response to ICB therapy. Furthermore, the risk model was closely related to the response to multiple chemotherapeutic drugs. Overall, we developed a reliable cancer-immunity cycle–based risk model to predict the prognosis, the molecular and immune status, and the immune benefit from ICB therapy, which may contribute greatly to accurate stratification and precise immunotherapy for patients with CRC.

A Novel Pyroptosis-Associated Gene Signature to Predict Prognosis in Patients with Colorectal Cancer.

Background Pyroptosis is a form of cell death characterized by cell swelling and plasma membrane bubbling in association with inflammatory and immune responses. To date, the association between pyroptosis and colorectal cancer remains unclear. We aimed to establish a novel pyroptosis-associated model for the prognosis of colorectal cancer. Methods Pyroptosis-related genes were extracted using Gene Set Enrichment Analysis. A least absolute shrinkage and selection operator regression model was constructed to identify a pyroptosis-related gene signature using the Cancer Genome Atlas and Gene Expression Omnibus databases. Then, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology and GSEA were performed to better understand the potential mechanisms and the functional pathways associated with pyroptosis involved in colorectal cancer. The relationship between the pyroptosis-related signature and immune infiltration was investigated using Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts and MCPcounter. Results A 12 pyroptosis-related gene signature was identified. Then, patients were classified into high- and low-risk groups. Kaplan–Meier and receiver operating characteristic analyses confirmed that the high-risk groups showed worse overall survival, progression-free survival, or relapse-free survival probability. Functional enrichment analysis showed that pyroptosis was associated with extracellular matrix-related pathways. Furthermore, the pyroptosis risk score was associated with immune infiltration. The low-risk group exhibited a higher percentage of plasma cells, CD4 T cells, activated dendritic cells, and activated mast cells. M2 macrophages and M0 macrophages were positively related to the risk score. Conclusion Our research yielded a novel pyroptosis-related prognostic signature for colorectal cancer that was related to immune cell infiltration, and it provided an immunological perspective for developing personalized therapies.

Identification and validation of prognosis-associated DNA repair gene signatures in colorectal cancer

Colorectal cancer (CRC) is the third most common malignant tumor. DNA damage plays a crucial role in tumorigenesis, and abnormal DNA repair pathways affect the occurrence and progression of CRC. In the current study, we aimed to construct a DNA repair-related gene (DRG) signature to predict the overall survival (OS) of patients with CRC patients. The differentially expressed DRGs (DE-DRGs) were analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The prognostic gene signature was identified by univariate Cox regression and least absolute shrinkage and selection operator (LASSO)-penalized Cox proportional hazards regression analysis. The predictive ability of the model was evaluated using the Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves. The gene set enrichment analysis (GSEA) was performed to explore the underlying biological processes and signaling pathways. ESTIMATE and CIBERSORT were implemented to estimate the tumor immune score and immune cell infiltration status between the different risk group. The half-maximal inhibitory concentration (IC50) was evaluated to representing the drug response of this signature. Nine DE-DRGs (ESCO2, AXIN2, PLK1, CDC25C, IGF1, TREX2, ALKBH2, ESR1 and MC1R) signatures was constructed to classify patients into high- and low-risk groups. The risk score was an independent prognostic indicator of OS (hazard ratio > 1, P < 0.001). The genetic alteration analysis indicated that the nine DE-DRGs in the signature were changed in 63 required samples (100%), and the major alteration was missense mutation. Function enrichment analysis revealed that the immune response and mtotic sister chromatid segregation were the main biological processes. The high-risk group had higher immune score than the low-risk group. What’s more, low-risk patients were more sensitive to selumetinib and dasatinib. The nine DE-DRGs signature was significantly associated with OS and provided a new insight for the diagnosis and treatment of CRC.

Learning models for colorectal cancer signature reconstruction and classification in patients with chronic inflammatory bowel disease

Learning models for colorectal cancer signature reconstruction and classification in patients with chronic inflammatory bowel disease

Ketogenesis Attenuates KLF5-Dependent Production of CXCL12 to Overcome the Immunosuppressive Tumor Microenvironment in Colorectal Cancer.

This study identifies ketogenesis as a critical regulator of the tumor microenvironment in colorectal cancer and suggests the potential for ketogenic diets as a metabolic strategy to overcome immunosuppression and prolong survival. See related commentary by Montrose and Galluzzi, p. 1464.

Stage-differentiated ensemble modeling of DNA methylation landscapes uncovers salient biomarkers and prognostic signatures in colorectal cancer progression

Aberrant DNA methylation acts epigenetically to skew the gene transcription rate up or down, contributing to cancer etiology. A gap in our understanding concerns the epigenomics of stagewise cancer progression. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer (CRC). The methylation β-matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with AJCC stages, and analysed for stage-salient genes using an ensemble of approaches involving stage-differentiated modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against controls (adjusted p-value <0.001 and |log fold-change of M-value| >2), and then filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by matching with clinical data and performing Kaplan-Meier survival analysis to evaluate the impact of methylation patterns of consensus stage-salient biomarkers on disease prognosis. We found significant genome-wide changes in methylation patterns in cancer cases relative to controls agnostic of stage. The stage-differentiated models yielded the following consensus salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were significantly hypermethylated in the promoter regions, indicating down-regulation of expression and implying a putative CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1 survived all the analytical filters, and represents a novel early-stage epigenetic biomarker / target. We have designed and executed a workflow for stage-differentiated epigenomic analysis of colorectal cancer progression, and identified several stage-salient diagnostic biomarkers, and an early-stage prognostic biomarker panel. The study has led to the discovery of an alternative CIMP-like signature in colorectal cancer, reinforcing the role of CIMP drivers in tumor pathophysiology.

Detection of minimal residual disease (MRD) in colorectal cancer (CRC) patients UICC stage II/III by ultra-deep sequencing of cfDNA from post-surgery plasma.

26 Background: Detection of primary tumor mutations in cell-free DNA (cfDNA) of post-surgery plasma of patients with R0-resected not-metastasized solid tumors is a strong indicator of recurrence of disease. We explored whether ultra-deep sequencing of cfDNA could improve sensitivity and specificity with respect to time-to-progression. Methods: 84 CRC patients UICC stage II/III were recruited into the prospective, observational study “Molecular Signatures in Colorectal Cancer”. Matched tumor tissue samples, plasma depleted blood cells (PDBC), and cfDNA (drawn 1 to 34 days after R0-resection, median 7 days) were processed with the Roche AVENIO Tumor Tissue and ctDNA Surveillance Kits*. Samples of 79 patients passed all quality controls, in particular cfDNA was sequenced ultra-deep with a median of 180 Mio. instead of 50 Mio. reads/sample. Somatic variants were identified with AVENIO Oncology Analysis software 2.0*. PDBC informed germline variants were removed. If a tissue baseline variant was detected in cfDNA with a significant adjusted p-value, the patient was defined ctDNA+, and ctDNA- otherwise. Results: 8 ctDNA+ patients (28 variants, median AF = 0.15%) were identified of which 4 had a progression of disease at two years. Sensitivity was 44% (95% CI [0.137, 0.788]), specificity was 94% (95% CI [0.86, 0.984]), positive predictive value was 50% (95% CI [0.157, 0.843]), and negative predictive value was 93% (95% CI [0.843, 0.977]). Comparison of time-to-progression of ctDNA+ and ctDNA- patients using the log-rank test resulted in a p-value of 0.0058. Comparison of survival times of ctDNA+ and ctDNA- patients resulted in a p-value of 0.0333. Multivariate analyses of times-to-progression resulted in ctDNA-status (p = 0.0022, hazard ratio (HR) = 7.098) and neoadjuvant therapy (p = 0.0010, HR = 6.618) as significant parameters. Conclusions: Even in this small cohort of CRC UICC stage II/III patients, MRD detection in post-surgery plasma is the strongest predictor of shorter time to progression. Ultra-deep sequencing of cfDNA samples did not influence MRD detection on a patient-level. *for Research Use Only; not for use in diagnostic procedures.

Identification of a prognostic signature in colorectal cancer using combinatorial algorithm-driven analysis.

Colorectal carcinoma is one of the most common types of malignancy and a leading cause of cancer‐related death. Although clinicopathological parameters provide invaluable prognostic information, the accuracy of prognosis can be improved by using molecular biomarker signatures. Using a large dataset of immunohistochemistry‐based biomarkers (n = 66), this study has developed an effective methodology for identifying optimal biomarker combinations as a prognostic tool. Biomarkers were screened and assigned to related subsets before being analysed using an iterative algorithm customised for evaluating combinatorial interactions between biomarkers based on their combined statistical power. A signature consisting of six biomarkers was identified as the best combination in terms of prognostic power. The combination of biomarkers (STAT1, UCP1, p‐cofilin, LIMK2, FOXP3, and ICOS) was significantly associated with overall survival when computed as a linear variable (χ 2 = 53.183, p < 0.001) and as a cluster variable (χ 2 = 67.625, p < 0.001). This signature was also significantly independent of age, extramural vascular invasion, tumour stage, and lymph node metastasis (Wald = 32.898, p < 0.001). Assessment of the results in an external cohort showed that the signature was significantly associated with prognosis (χ 2 = 14.217, p = 0.007). This study developed and optimised an innovative discovery approach which could be adapted for the discovery of biomarkers and molecular interactions in a range of biological and clinical studies. Furthermore, this study identified a protein signature that can be utilised as an independent prognostic method and for potential therapeutic interventions.

A prognostic fibroblast-related risk signature in colorectal cancer.

Colorectal cancer (CRC) is the third most common cancer in the world. The accessibility of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus data allows the prognostic evaluation of CRC. Fibroblasts play a key role in the development and progression of tumors while fibroblast-related risk signature in CRC patients has rarely been mentioned. In this study, TCGA data was classified into high-fibroblast and low-fibroblast groups according to the median of fibroblast content. Among 3845 differentially expressed genes between two groups, 14 prognostic genes commonly expressed in GSE39582 and TCGA were identified by LASSO-COX analysis. Then we established a fibroblast-related risk signature in TCGA training group and validated in the GSE39582 testing group. The risk score was significantly associated with the overall survival (OS), and the poor prognosis of patients in high-risk group might relate to the immune cell infiltration in the tumor microenvironment, epithelial-mesenchymal transition, and extracellular matrix related processes. Overall, we proved that the fibroblast-related signature could predict the prognosis of patients which might shed light on the treatment of CRC.

Derivation and Clinical Validation of a Redox-Driven Prognostic Signature for Colorectal Cancer.

Colorectal cancer (CRC), a seriously threat that endangers public health, has a striking tendency to relapse and metastasize. Redox-related signaling pathways have recently been extensively studied in cancers. However, the study and potential role of redox in CRC remain unelucidated. We developed and validated a risk model for prognosis and recurrence prediction in CRC patients via identifying gene signatures driven by redox-related signaling pathways. The redox-driven prognostic signature (RDPS) was demonstrated to be an independent risk factor for patient survival (including OS and RFS) in four public cohorts and one clinical in-house cohort. Additionally, there was an intimate association between the risk score and tumor immune infiltration, with higher risk score accompanied with less immune cell infiltration. In this study, we used redox-related factors as an entry point, which may provide a broader perspective for prognosis prediction in CRC and have the potential to provide more promising evidence for immunotherapy.

Evaluation of intestinal cancer by 1H HR-MAS NMR spectroscopy: A new diagnostic tool?

Previous studies have demonstrated that cancer cells harbor unique metabolic characteristics relative to healthy counterparts. The current study is a prospective ex-vivo HR-MAS NMR analysis of malignant colorectal cancer (CRC) tissue specimens and its corresponding benign tissues.To assess the HR-MAS Spectroscope qualitatively &amp; to analyze significant difference between the normal, benign and malignant intestinal mucosa.Between November 2013and January 2016, 36 consecutive patients with confirmed CRC were recruited to a prospective observational study. Fresh tissue samples were obtained from center of tumor and 5 cm from tumor margin from surgical resection specimens. Samples were run in duplicate where tissue volume permitted to compensate for anticipated sample heterogeneity. Typically, the sample was packed into a 4 mm ZrO2 rotor of 50 μl capacity; a volume of 20μl of D2O having 0.03% TSP was used as a chemical shift reference. The sample-rotor-setup was then transferred into the HR-MAS NMR probe for analysis.A total of 36 spectra were acquired (center of tumor, n = 18; 5 cm from tumor margin, n = 18). The malignant clustering occurs due to increased Val (0.90ppm), Lac (1.34ppm), Ala(1.48ppm) levels of acetate (1.90ppm), glutamate (2.35ppm), taurine (3.23 ppm), choline containing compounds (3.20-3.22ppm), glycine (3.56ppm), lactate (4.12ppm) and α-H of Leu, Ileu, Val, Lys, Ala (3.76-3.79ppm . In addition unique metabolic profiles were observed for tumors of differing T-stage. The information gathered from clustering in PCA had highly suggested that malignancy induces metabolic perturbations at cellular levels.HR-MAS NMR profiling demonstrates cancer-specific metabolic signatures in CRC and reveals metabolic differences between benign and malignant tumors. In addition, this approach reveals that tumor metabolism undergoes modification during local tumor advancement, offering potential in future staging and therapeutic approaches.

RNA-Sequencing Based microRNA Expression Signature of Colorectal Cancer: The Impact of Oncogenic Targets Regulated by miR-490-3p.

To elucidate novel aspects of the molecular pathogenesis of colorectal cancer (CRC), we have created a new microRNA (miRNA) expression signature based on RNA-sequencing. Analysis of the signature showed that 84 miRNAs were upregulated, and 70 were downregulated in CRC tissues. Interestingly, our signature indicated that both guide and passenger strands of some miRNAs were significantly dysregulated in CRC tissues. These findings support our earlier data demonstrating the involvement of miRNA passenger strands in cancer pathogenesis. Our study focused on downregulated miR-490-3p and investigated its tumor-suppressive function in CRC cells. We successfully identified a total of 38 putative oncogenic targets regulated by miR-490-3p in CRC cells. Among these targets, the expression of three genes (IRAK1: p = 0.0427, FUT1: p = 0.0468, and GPRIN2: p = 0.0080) significantly predicted 5-year overall survival of CRC patients. Moreover, we analyzed the direct regulation of IRAK1 by miR-490-3p, and its resultant oncogenic function in CRC cells. Thus, we have clarified a part of the molecular pathway of CRC based on the action of tumor-suppressive miR-490-3p. This new miRNA expression signature of CRC will be a useful tool for elucidating new molecular pathogenesis in this disease.

Proteomics analysis of tissue small extracellular vesicles reveals protein panels for the reoccurrence prediction of colorectal cancer

Many stage II/III colorectal cancer (CRC) patients might relapse after routine treatment and there is a great need of reliable biomarkers for predicting its reoccurrence risk. Small extracellular vesicles (sEVs) could regulate many pathophysiological processes of diseases, which are promising source for biomarker discovery. In this study, we implemented a MS-based workflow that utilizes data-dependent acquisition (DDA) for discovery and parallel reaction monitoring (PRM) for validation of high relapse risk related biomarkers. We compared the protein profiling of sEVs from CRC tissues and paired adjacent tissues in relapsed group (n = 5) and non-relapsed group (n = 5). 417 and 1140 proteins were differentially expressed between the tumor tissues and adjacent tissues in relapsed group and non-relapsed group, respectively. Bioinformatics analysis showed that immunity of the relapsed patients (Z-score − 0.69) was relatively poorer than the non-relapsed patients (Z-score 2.59), while chronic inflammatory response was activated (Z-score 3.0), which might enhance the reoccurrence risk. Four proteins (HLA-DPA1, S100P, NUP205, PCNA) showed significant expressions in the adjacent tissues of the relapsed group by PRM validation. ROC analysis of HLA-DPA1 (AUC = 0.96) achieved the best classification accuracy in separating the relapsed group and the non-relapsed group. Our data demonstrate that tissue-derived sEVs harbor prognostic proteomic signatures of CRC. SignificanceIn this research, our proteomics analysis of tissue sEVs revealed that poor immunity as well as chronic inflammatory of the CRC relapsed patient likely lead to poor prognosis and high risk of reoccurrence. The significant expression levels of four proteins (HLA-DPA1, S100P, NUP205, PCNA) in the adjacent tissues of the relapsed group might be used to predict the risk of relapse in postoperative follow-ups.

Abstract 2309: Predicting molecular networks mediating colorectal cancer neoplastic progression by integrative transcriptome-wide meta-analysis

Abstract Introduction: Colorectal cancer (CRC) is the third most common cancer in the United States. While survival rates have increased in part due to enhanced detection and targeted therapeutics, mortality remains high, highlighting a need to further investigate novel targetable pathways along the neoplastic axis. To explore potential molecular networks involved in pre- and post-malignant processes, we implemented a bioinformatics-based pipeline to meta-analyze multiple microarray datasets for predicting differentially active/inhibited pathways. Methods: We constructed a curated, batch-corrected Meta-dataset comprising twelve microarray datasets including GSE4183, GSE8671, GSE9348, GSE15960, GSE20916, GSE21510, GSE22598, GSE23194, GSE23878, GSE32323, GSE33113, and GSE37364, harboring normal (n = 221), adenoma (n = 137), and CRC (n = 452) samples. The Meta-dataset expression set was validated against the TCGA-COAD dataset with functional adenoma and CRC signatures confirmed by Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment Analysis (GSEA). Differentially expressed genes (DEGs) were identified for Adenoma:Normal (A v N), CRC:Adenoma (C v A), and CRC:Normal (C v N) comparison groups and submitted to IPA. To identify relevant gene clusters between DEGs, a heatmap was constructed and arranged by Euclidean distance-weighted unsupervised hierarchal clustering. The same was done regarding IPA-predicted mediators with analysis being restricted to upstream regulators with activation Z-scores &amp;gt; I 2 I in at least two of the three comparison groups. Results: Using ComBat, inter-study batch effects were removed from the Meta-dataset whilst preserving normal, adenoma, and CRC signatures. We found a high degree of correlation between the Meta- and the TCGA-COAD dataset (rho = 0.89, p = 2.2e-16) with functional validation of both adenoma (FDR &amp;lt; 0.0001) and CRC (FDR &amp;lt; 0.0001) signatures using IPA and GSEA. Following, three-way analysis of A v N, C v A, and C v N DEGs revealed four distinct clusters associated with adenoma formation and malignant transformation. Clusters were then cross-reference against established networks regulated by KRAS, EGFR, ERBB2, and TP53 in IPA. Predicted networks associated with adenoma formation were enriched in epithelial-mesenchymal transition pathways (AREG, SNAI1, FOXM1, TCF4) while those associated with malignant transformation were inflammatory based (IL17, TNF, CD24, TLR4). Moreover, early and late downregulated networks were primarily controlled by tumor suppressors (CDKN1A, CST5, TRPS1 and NR1H family members). Conclusion: We identified both known and novel pathways involved in the neoplastic progression of CRC through transcriptome-wide pooled analysis. These pathways may be attractive candidates for future investigation of tumorigenic mechanisms, prognostication, and therapeutics. Citation Format: Michael W. Rohr, Deborah Altomare. Predicting molecular networks mediating colorectal cancer neoplastic progression by integrative transcriptome-wide meta-analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2309.