Independent Prognostic Factor In Colorectal Cancer Research Articles

Effect of elevated CEA levels on the outcome of colorectal cancer patients with different histopathologic types: A SEER population-based study.

Limited and contradictory evidence has been reported regarding the prognostic effects of carcinoembryonic antigen (CEA) on the prognosis and metastasis of classical adenocarcinoma (CA), mucinous adenocarcinoma (MA), and signet-ring cell carcinoma (SRCC) in colorectal cancer patients. We investigated the associations between histological subtypes and preoperative serum CEA levels in determining the oncologic outcomes of colorectal cancer (CRC) patients. A total of 47,692 patients with clearly diagnosed CRC were selected from the Surveillance, Epidemiology, and End Results (SEER) database and divided into two cohorts based on serum CEA levels: CEA-normal (C0) and CEA-elevated (C1). Chi-square analysis revealed a correlation between CEA levels and histological classification. We then included a newly defined interaction variable (H&CEA) in the Cox regression analysis, which demonstrated that this variable could serve as an independent prognostic factor (P<0.001). CA, in the context of elevated serum CEA levels, differed from the other two histopathological types, showing unexpectedly higher risks for both OS (HR = 1.70, 95% CI = 1.65-1.75, P<0.001) and CSS (HR = 1.78, 95% CI = 1.72-1.85, P<0.001). Furthermore, elevated CEA levels significantly increased the proportion of liver metastases in the CA group (25.43% vs. 3.95%, P<0.001). The interaction variable H&CEA can be used as an independent prognostic factor for CRC and should be considered in the diagnosis of CRC and the development of personalized treatment plans. Additionally, in the context of elevated CEA levels, CA is associated with poor prognosis and increased liver metastases. This CRC subgroup warrants special clinical attention from oncologists.

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.

Consensus clustering and development of a risk signature based on trajectory differential genes of cancer-associated fibroblast subpopulations in colorectal cancer

BackgroundCancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, the impact of CAF subpopulation trajectory differentiation on CRC remains unclear.MethodsIn this study, we first explored the trajectory differences of CAFs subpopulations using bulk and integrated single-cell sequencing data, and then performed consensus clustering of CRC samples based on the trajectory differential genes of CAFs subpopulations. Subsequently, we analyzed the heterogeneity of CRC subtypes using bioinformatics. Finally, we constructed relevant prognostic signature using machine learning and validated them using spatial transcriptomic data.ResultsBased on the differential genes of CAFs subpopulation trajectory differentiation, we identified two CRC subtypes (C1 and C2) in this study. Compared to C1, C2 exhibited worse prognosis, higher immune evasion microenvironment and high CAF characteristics. C1 was primarily associated with metabolism, while C2 was primarily associated with cell metastasis and immune regulation. By combining 101 combinations of 10 machine learning algorithms, we developed a High-CAF risk signatures (HCAFRS) based on the C2 characteristic gene. HCAFRS was an independent prognostic factor for CRC and, when combined with clinical parameters, significantly predicted the overall survival of CRC patients. HCAFRS was closely associated with epithelial-mesenchymal transition, angiogenesis, and hypoxia. Furthermore, the risk score of HCAFRS was mainly derived from CAFs and was validated in the spatial transcriptomic data.ConclusionIn conclusion, HCAFRS has the potential to serve as a promising prognostic indicator for CRC, improving the quality of life for CRC patients.

Diagnostic and prognostic value of double-negative T cells in colorectal cancer

ObjectiveTo evaluate the T-lymphocyte subset distribution and the diagnostic and prognosis value of double-negative T (DNT) cells in colorectal cancer (CRC). MethodsThis retrospective study compared the T-lymphocyte subsets and DNT of 114 patients with CRC with those of 107 healthy controls (HC). The diagnostic potential of DNT and T-lymphocyte subsets was assessed using the receiver operating characteristic (ROC) curve, and prognostic values were evaluated using the Kaplan–Meier curve and the Cox regression model. ResultsThe percentages of CD8+ T cells and DNT cells, and value of carcinoembryonic antigen (CEA), were remarkably higher in patients with CRC than in those with HC, but the ratio of CD4+/CD8+ was decreased. Using ROC curve analysis, DNT cell percentage, CEA, and CD4+/CD8+ ratio all had good diagnostic efficacy, with areas under the curve (AUCs) of 0.865, 0.786 and 0.624, respectively. The combination of DNT cell percentage and CEA had an AUC of 0.905, which was significantly higher than that of any single biomarker (p < 0.05). In univariate analysis, the Tumor Node Metastasis (TNM) clinical stage, CD4+/CD8+ ratio, and DNT cell percentage were significantly associated with overall survival (OS) (p < 0.05). In multivariate analysis, TNM clinical staging (HR = 2.37, 95 % CI: 1.15–4.90), a decreased CD4+/CD8+ ratio (HR = 0.33, 95 % CI: 0.15–0.74), and an increased DNT cell percentage (HR = 2.29, 95 % CI: 1.11–4.73) were independent prognostic factors for CRC. ConclusionThe percentage of DNT cells may be useful as an evaluation index for CRC diagnosis and prognosis, which was even better when combined with serum CEA.

Integrated analysis reveals a novel 5-fluorouracil resistance-based prognostic signature with promising implications for predicting the efficacy of chemotherapy and immunotherapy in patients with colorectal cancer.

5-Fluorouracil (5-FU) has been used as a standard first-line treatment for colorectal cancer (CRC) patients. Although 5-FU-based chemotherapy and immune checkpoint blockade (ICB) have achieved success in treating CRC, drug resistance and low response rates remain substantial limitations. Thus, it is necessary to construct a 5-FU resistance-related signature (5-FRSig) to predict patient prognosis and identify ideal patients for chemotherapy and immunotherapy. Using bulk and single-cell RNA sequencing data, we established and validated a novel 5-FRSig model using stepwise regression and multiple CRC cohorts and evaluated its associations with the prognosis, clinical features, immune status, immunotherapy, neoadjuvant therapy, and drug sensitivity of CRC patients through various bioinformatics algorithms. Unsupervised consensus clustering was performed to categorize the 5-FU resistance-related molecular subtypes of CRC. The expression levels of 5-FRSig, immune checkpoints, and immunoregulators were determined using quantitative real-time polymerase chain reaction (RT‒qPCR). Potential small-molecule agents were identified via Connectivity Map (CMap) and molecular docking. The 5-FRSig and cluster were confirmed as independent prognostic factors in CRC, as patients in the low-risk group and Cluster 1 had a better prognosis. Notably, 5-FRSig was significantly associated with 5-FU sensitivity, chemotherapy response, immune cell infiltration, immunoreactivity phenotype, immunotherapy efficiency, and drug selection. We predicted 10 potential compounds that bind to the core targets of 5-FRSig with the highest affinity. We developed a valid 5-FRSig to predict the prognosis, chemotherapeutic response, and immune status of CRC patients, thus optimizing the therapeutic benefits of chemotherapy combined with immunotherapy, which can facilitate the development of personalized treatments and novel molecular targeted therapies for patients with CRC.

Abstract 4635: RNF208: A novel diagnostic and prognostic biomarker for colorectal cancer and key regulator of malignant behavior

Abstract Background: RNF208, a member of the RING finger protein family, has been implicated in ubiquitin-mediated degradation of Vimentin, regulating invasion in breast cancer, and serves as a prognostic factor in uveal melanoma. However, its diagnostic and prognostic value in colorectal cancer (CRC), as well as its role in regulating malignant behavior in CRC, remains unclear. This study aims to reveal the potential of RNF208 as a diagnostic and prognostic marker for CRC and its function in regulating malignant behavior in CRC through bioinformatics analysis and experimental validation. Methods: In this study, based on the ExoRbase2.0 human plasma exosome database (CRC=35; Healthy=118) and TCGA-COAD dataset (CRC=478; Healthy=41), we conducted bioinformatics analysis of the transcriptomic differences in plasma exosomes between CRC patients and normal individuals using ExoRbase2.0. Survival data from the TCGA-COAD dataset were integrated to select key molecules for CRC diagnosis and prognosis. Plasma samples from 39 CRC patients and 16 normal individuals were collected from hospital, and exosomes were isolated and purified using a reagent kit. qPCR was employed to validate the levels of RNF208 in plasma exosomes, confirming the results of bioinformatics analysis. Biological behavior was assessed through CCK-8 proliferation assays, flow cytometry experiments, and Transwell migration assays to investigate the relationship between RNF208 and malignant behavior in the HCT116 colorectal cancer cell line. Results: Bioinformatics analysis revealed a significant correlation between elevated RNF208 expression in plasma exosomes and the incidence of CRC, as well as poor prognosis. The qPCR results from clinical samples further confirmed this correlation, and ROC curves demonstrated high sensitivity and accuracy, suggesting RNF208's potential as a diagnostic and independent prognostic factor for CRC. CCK-8 experiments showed that RNF208 knockdown suppressed proliferation activity in HCT116 cells, flow cytometry indicated that RNF208 knockdown promoted apoptosis in HCT116 cells, and Transwell migration assays demonstrated that RNF208 knockdown inhibited the migration of HCT116 cells. Conclusion: RNF208 serves as a novel diagnostic and prognostic biomarker for colorectal cancer, promoting the proliferation and migration of colorectal cancer cells and contributing to the further deterioration of the disease. Citation Format: Fangyue Guo, Shanfeng Zhang, Pei Li. RNF208: A novel diagnostic and prognostic biomarker for colorectal cancer and key regulator of malignant behavior [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 4635.

High patatin like phospholipase domain containing 8 expression as a biomarker for poor prognosis of colorectal cancer.

Patatin like phospholipase domain containing 8 (PNPLA8) has been shown to play a significant role in various cancer entities. Previous studies have focused on its roles as an antioxidant and in lipid peroxidation. However, the role of PNPLA8 in colorectal cancer (CRC) progression is unclear. To explore the prognostic effects of PNPLA8 expression in CRC. A retrospective cohort containing 751 consecutive CRC patients was enrolled. PNPLA8 expression in tumor samples was evaluated by immunohistochemistry staining and semi-quantitated with immunoreactive scores. CRC patients were divided into high and low PNPLA8 expression groups based on the cut-off values, which were calculated by X-tile software. The prognostic value of PNPLA8 was identified using univariate and multivariate Cox regression analysis. The overall survival (OS) rates of CRC patients in the study cohort were compared with Kaplan-Meier analysis and Log-rank test. PNPLA8 expression was significantly associated with distant metastases in our cohort (P = 0.048). CRC patients with high PNPLA8 expression indicated poor OS (median OS = 35.3, P = 0.005). CRC patients with a higher PNPLA8 expression at either stage I and II or stage III and IV had statistically significant shorter OS. For patients with left-sided colon and rectal cancer, the survival curves of two PNPLA8-expression groups showed statistically significant differences. Multivariate analysis also confirmed that high PNPLA8 expression was an independent prognostic factor for overall survival (hazard ratio HR = 1.328, 95%CI: 1.016-1.734, P = 0.038). PNPLA8 is a novel independent prognostic factor for CRC. These findings suggest that PNPLA8 is a potential target in clinical CRC management.

The activation of HAMP promotes colorectal cancer cell proliferation through zinc-mediated upregulation of SMAD4 expression.

Colorectal cancer (CRC) poses a significant challenge in digestive system diseases, and emerging evidence underscores the critical role of zinc metabolism in its progression. This study aimed to investigate the clinical implications of genes at the intersection of zinc metabolism and CRC. We downloaded CRC prognosis-related genes and zinc metabolism-related genes from public databases. Then, the overlapping genes were screened out, and bioinformatics analysis was performed to obtain the hub gene associated with CRC prognosis. Subsequently, in vitro assays were carried out to investigate the expression of this hub gene and its exact mechanism between zinc metabolism and CRC. HAMP was identified as the hub CRC prognostic gene from overlapping zinc metabolism-related and CRC prognostic genes. In vitro analysis showed HAMP was over-expressed in CRC, and its knockdown inhibited RKO and HCT-116 cell invasion and migration significantly. ZnSO4 induced HAMP up-regulation to promote cell proliferation, while TPEN decreased HAMP expression to inhibit cell proliferation. Importantly, we further found that ZnSO4 enhanced SMAD4 expression to augment HAMP promoter activity and promote cell proliferation in CRC. HAMP stands out as an independent prognostic factor in CRC, representing a potential therapeutic target. Its intricate regulation by zinc, particularly through the modulation of SMAD4, unveils a novel avenue for understanding CRC biology. This study provides valuable insights into the interplay between zinc metabolism, HAMP, and CRC, offering promising clinical indicators for CRC patients. The findings present a compelling case for further exploration and development of targeted therapeutic strategies in CRC management.

Identification of glutamine metabolism-related gene signature to predict colorectal cancer prognosis.

Backgrounds: Colorectal cancer (CRC) is a highly malignant gastrointestinal malignancy with a poor prognosis, which imposes a significant burden on patients and healthcare providers globally. Previous studies have established that genes related to glutamine metabolism play a crucial role in the development of CRC. However, no studies have yet explored the prognostic significance of these genes in CRC. Methods: CRC patient data were downloaded from The Cancer Genome Atlas (TCGA), while glutamine metabolism-related genes were obtained from the Molecular Signatures Database (MSigDB) database. Univariate COX regression analysis and LASSO Cox regression were utilized to identify 15 glutamine metabolism-related genes associated with CRC prognosis. The risk scores were calculated and stratified into high-risk and low-risk groups based on the median risk score. The model's efficacy was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. Cox regression analysis was employed to determine the risk score as an independent prognostic factor for CRC. Differential immune cell infiltration between the high-risk and low-risk groups was assessed using the ssGSEA method. The clinical applicability of the model was validated by constructing nomograms based on age, gender, clinical staging, and risk scores. Immunohistochemistry (IHC) was used to detect the expression levels of core genes. Results: We identified 15 genes related to glutamine metabolism in CRC: NLGN1, RIMKLB, UCN, CALB1, SYT4, WNT3A, NRCAM, LRFN4, PHGDH, GRM1, CBLN1, NRG1, GLYATL1, CBLN2, and VWC2. Compared to the high-risk group, the low-risk group demonstrated longer overall survival (OS) for CRC. Clinical correlation analysis revealed a positive correlation between the risk score and the clinical stage and TNM stage of CRC. Immune correlation analysis indicated a predominance of Th2 cells in the low-risk group. The nomogram exhibited excellent discriminatory ability for OS in CRC. Immunohistochemistry revealed that the core gene CBLN1 was expressed at a lower level in CRC, while GLYATL1 was expressed at a higher level. Conclusions: In summary, we have successfully identified and comprehensively analyzed a gene signature associated with glutamine metabolism in CRC for the first time. This gene signature consistently and reliably predicts the prognosis of CRC patients, indicating its potential as a metabolic target for individuals with CRC.

CBX2 Deletion Suppresses Growth and Metastasis of Colorectal Cancer by Mettl3-p38/ERK MAPK Signalling Pathway.

Colorectal cancer (CRC) seriously endangers human health owing to its high morbidity and mortality. Previous studies have suggested that high expression of CBX2 may be associated with poor prognosis in CRC patients. However, its functional role in CRC remains to be elucidated. Herein, we found that CBX2 overexpression in colorectal cancer tissue compared with adjacent tissues. Additionally, forest maps and the nomogram model indicated that elevated CBX2 expression was an independent prognostic factor in CRC. Moreover, we confirmed that the deletion of CBX2 markedly suppressed the proliferation and migration of CRC cells in vitro and in vivo. Furthermore, downregulation of CBX2 promotes CRC cell apoptosis and hinders the cell cycle. Mechanistically, our data demonstrated that deletion of CBX2 inhibited the MAPK signaling pathway by regulating the protein levels of Mettl3. In conclusion, our study demonstrated that CBX2 is a vital tumor suppressor in CRC and could be a promising anti-cancer therapeutic target.

Prognostic evaluation of preoperative systemic immune inflammatory index in patients with colorectal cancer.

To investigate the impact of preoperative systemic immune inflammatory index (SII) on the clinical prognosis of patients undergoing colorectal cancer (CRC) surgery. One hundred and sixty CRC patients who underwent surgical treatment in our gastrointestinal surgery department from January 2019 to May 2023 were collected. ROC curves were applied to determine the sensitivity and specificity of SII, determine the optimal cut-off value into low SII and high SII groups, compare the clinicopathological data of SII patients in the two groups, and analyze the postoperative survival of patients in the two groups using Kaplan-Meier and Log-rank methods. Univariate and multifactor COX proportional risk regression models were used to analyze clinical prognostic factors. The ROC curve showed that the area under the curve of SII for the evaluation of OS in CRC patients was 0.859, and the best cut-off value was 513.53. There was statistical significance (P < 0.05) in terms of tissue grading and diabetes mellitus in both groups. The Kaplan-Meier survival curves showed that the overall survival rates of the SII<513.53 group and the SII≥513.53 group were 50.88% (29/57) and 32.04% (33/103), and the overall survival rate of the SII<513.53 group was significantly higher than that of the SII≥513.53 group, and the difference was statistically significance (χ2=8.375, P=0.004). COX proportional risk regression showed that TNM stage, lymph node metastases, anastomotic fistula and SII were independent risk factors affecting postoperative survival in patients with CRC. Preoperative SII is an independent prognostic factor for CRC, which is simple, convenient, and non-invasive, and can be used to predict the prognosis of CRC patients.

Prognostic impact of colorectal cancer patients with bone metastases: a single-center experience.

The incidence of bone metastasis (BM) in colorectal cancer (CRC) patients is low and the prognosis is poor. There is no clear conclusion on the risk factors affecting the survival of CRC patients with BM. The aim of this study was to investigate the factors that may affect the prognosis of CRC patients with BM. The clinical and pathological data of CRC patients with BM were retrospectively analyzed. The overall survival after BM diagnosis was estimated using the Kaplan-Meier method and Log-rank test, and a multivariable cox regression model was used to identify the prognostic factors of overall survival. This study included 178 CRC patients with BM, of whom 151 had left-sided CRC and 27 had right-sided colon cancer. 1124 CRC patients with BM from the SEER database were included to perform a sensitivity analysis of the primary outcome. Multivariate analysis showed that the N staging, site of BM, and primary tumor sidedness (PTS) were independent prognostic factors for CRC with BM. Among them, right-sided colon cancer patients with BM had a poorer prognosis. Sensitivity analyses showed that PTS was an independent prognostic factor in CRC patients with BM. Primary tumor sidedness and N stage may be potential prognostic markers for BM of CRC. The prognosis of N0 stage CRC with BM is better, while the prognosis of right-sided colon cancer is poor.

The cancer inflammation prognostic index is a valuable biomarker for predicting the survival of patients with stage I–III colorectal cancer

This study aimed to assess the relationship between the Cancer-Inflammation Prognostic Index (CIPI) and disease-free survival (DFS) and overall survival (OS) in patients with stage I–III colorectal cancer (CRC). The relationship between the CIPI and survival was evaluated using restricted cubic splines. Survival curves were established using the Kaplan–Meier method and the log-rank test. Cox proportional hazards models were used to explore independent prognostic factors for CRC. Meaningful variables from the multivariate analysis were used to construct prognostic nomograms. The relationship between the CIPI values on a continuous scale and the risk of DFS/OS mortality was an inverted L-shape. Patients with a high CIPI had significantly lower DFS (53.0% vs. 68.5%, p < 0.001) and OS (55.5% vs. 71.7%, p < 0.001) than those with a low CIPI. The CIPI can also serve as an effective auxiliary tool to further distinguish the prognosis of patients with CRC at the same pathological stage, especially for stages II and III. After multivariate adjustment, a high CIPI was found to be an independent risk factor for DFS (HR 1.443, 95% CI 1.203–1.730, p < 0.001) and OS (HR 1.442, 95% CI 1.189–1.749, p < 0.001) in CRC patients. These nomograms have the advantage of integrating individual profiles, tumour characteristics, and serum inflammatory markers, providing favourable discrimination and calibration values. Compared with traditional TNM staging, nomograms have a better predictive performance. The CIPI is an effective and easy-to-use clinical tool for predicting the recurrence and overall mortality of patients with stage I–III CRC.

High expression of SHP2 predicts a promising prognosis in colorectal cancer.

Src homology 2 domain-containing phosphatase 2 (SHP2) is hyper-activated in some solid tumors. Previous findings suggest that the expression of SHP2 in colorectal cancer (CRC) may be associated with prognosis. However, validation with large sample data is lacking. Materials and Methods: Tissue microarrays containing 860 CRCs and 197 mucosal tissues adjacent to the tumors were constructed. Immunohistochemistry was used to evaluate the expression of SHP2. Differences between SHP2 expression and clinicopathological parameters were evaluated. Kaplan-Meier survival curves and log-rank tests were used to analyze the relationships between SHP2 expression and the overall survival of patients. A Cox proportional hazard regression model was used for univariate and multivariate analyses of prognostic factors. SHP2 expression in CRCs tissues was significantly higher than those in adjacent mucosal tissues (P < 0.001). SHP2 expression was related to tumor differentiation, depth of invasion, distant metastasis, vascular tumor thrombus, lymph node metastasis, and TNM classification (P < 0.05). The prognosis of the high-expression group of SHP2 was significantly better than that of the low-expression group (P = 0.008). Univariate analysis showed that the expression of SHP2 was a prognostic factor for CRC (P = 0.008). Multivariate analysis demonstrated that SHP2 remained an independent prognostic factor for CRC (P = 0.033). The expression of SHP2 was significantly higher in CRC tissues than in adjacent normal tissues. High expression of SHP2 was associated with a promising outcome, suggesting that SHP2 may be a favorable prognostic indicator of CRC.

A novel anoikis-related risk model predicts prognosis in patients with colorectal cancer and responses to different immunotherapy strategies.

We aimed to study the role of anoikis-related genes (ARGs) in colorectal cancer (CRC) using bioinformatics. GSE39582 and GSE39084, which collectively contain 363 CRC samples, were downloaded from the NCBI Gene Expression Omnibus (GEO) database as a test set. TCGA-COADREAD, with 376 CRC samples, was downloaded from the UCSC database as a validation set. Univariate Cox regression analysis was used to screen for ARGs that were significantly associated with prognosis. The top 10 ARGs were used to classify the samples into different subtypes based on unsupervised cluster analysis. The immune environments of the different subtypes were analyzed. ARGs that were significantly associated with CRC prognosis were used to construct a risk model. Univariate and multivariate Cox regression analyses were used to screen independent prognostic factors and construct a nomogram. Four anoikis-related subtypes (ARSs) with differential prognoses and immune microenvironments were identified. KRAS and epithelial-mesenchymal transition pathways were enriched in subtype B, which had the worst prognosis. Three ARGs (DLG1, AKT3, and LPAR1) were used to construct the risk model. Both the test and validation sets showed worse outcomes for patients in the high-risk group than those in the low-risk group. Risk score was found to be an independent prognostic factor for CRC. Moreover, there was a difference in drug sensitivity between the high- and low-risk groups. The identified ARGs and risk scores were associated with CRC prognosis and could predict the responses of patients with CRC to immunotherapy strategies.

CD51 Intracellular Domain Promotes Cancer Cell Neurotropism through Interacting with Transcription Factor NR4A3 in Colorectal Cancer.

The abundant nervous system in intestine provides the basis for perineural invasion (PNI) of colorectal cancer (CRC). PNI is defined as the invasion of the nerves by cancer cells. Although PNI is already known to be an independent prognostic factor in CRC, the molecular mechanism underlying PNI remains obscure. In this study, we first demonstrated that CD51 could promote the neurotropism of tumor cells through cleavage with γ-secretase to generate an intracellular domain (ICD). Mechanistically, ICD of CD51 could bind to the transcription factor NR4A3, and act as a coactivator to promote the expression of downstream effectors, such as NTRK1, NTRK3, and SEMA3E. Pharmacological inhibition of γ-secretase impedes PNI mediated by CD51 in CRC both in vitro and in vivo and may become a potential therapeutic target for PNI in CRC.

A novel inflammation-related signature for predicting prognosis and characterizing the tumor microenvironment in colorectal cancer.

Inflammation is a critical component of tumor progression, and it modifies the tumor microenvironment by various mechanisms. Here, we explore the effect of the inflammatory response on the tumor microenvironment in colorectal cancer (CRC). A prognostic signature consisting of inflammation-related genes (IRGs) was constructed and verified based on the inflammatory response by bioinformatics analysis. IRG risk model was identified as an independent prognostic factor in CRC, and was related to biological processes of extracellular matrix, cell adhesion and angiogenesis. The IRG risk score predicted the clinical benefit of ipilimumab. Weighted correlation network analysis identified TIMP1 as the hub gene of the inflammatory response in the IRG risk model. Coculture experiments with macrophages and CRC cells revealed that TIMP1 promoted macrophage migration, inhibited the expression of M1 markers (CD11C and CD80), and promoted the expression of M2 markers (ARG1 and CD163). TIMP1 promoted the expression of ICAM1 and CCL2 by activating the ERK1/2 signaling pathway to promote macrophage migration and M2-like polarization. These IRGs in the risk model regulated stromal and immune components in the tumor microenvironment and could serve as potential therapeutic targets in CRC. TIMP1 promoted macrophage migration and meditated macrophage M2 polarization by activating ERK1/2/CLAM1 and CCL2.

Overexpression of synaptic vesicle protein Rab GTPase 3C promotes vesicular exocytosis and drug resistance in colorectal cancer cells.

Rab GTPase 3C (RAB3C) is a peripheral membrane protein that is involved in membrane trafficking (vesicle formation) and cell movement. Recently, researchers have noted the exocytosis of RAB proteins, and their dysregulation is correlated with drug resistance and the altered tumor microenvironment in tumorigenesis. However, the molecular mechanisms of exocytotic RABs in the carcinogenicity of colorectal cancer (CRC) remain unknown. Researchers have used various in silico datasets to evaluate the expression profiles of RAB family members. We confirmed that RAB3C plays a key role in CRC progression. Its overexpression promotes exocytosis and is related to the resistance to several chemotherapeutic drugs. We established a proteomic dataset based on RAB3C, and found that dystrophin is one of the proteins that is upregulated with the overexpression of RAB3C. According to our results, RAB3C-induced dystrophin expression promotes vesicle formation and packaging. A connectivity map predicted that the cannabinoid receptor 2 (CB2) agonists reverse RAB3C-associated drug resistance, and that these agonists have synergistic effects when combined with standard chemotherapy regimens. Moreover, we found high dystrophin expression levels in CRC patients with poor survival outcomes. A combination of the dystrophin and RAB3C expression profiles can serve as an independent prognostic factor in CRC and is associated with several clinicopathological parameters. In addition, the RAB3C-dystrophin axis is positively correlated with the phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA) genetic alterations in CRC patients. These findings can be used to provide novel combined therapeutic options for the treatment of CRC.

PADI1 and Its Co-Expressed Gene Signature Unveil Colorectal Cancer Prognosis and Immunotherapy Efficacy.

Peptidyl arginine deiminase 1 (PADI1) catalyzes protein citrullination and has a role in regulating immune responses. The tumor immune microenvironment has been reported to be important in colorectal cancer (CRC), which was correlated with the ability of CRC patients to benefit from immunotherapy. However, there is a lack of molecular markers for matching CRC immunotherapy. Previously, single-gene risk models have only considered the effect of individual genes on intrinsic tumor properties, ignoring the role of genes and their co-expressed genes as a whole. In this study, we analyzed the differential expression of PADI1 in colorectal cancer (CRC). We found that PADI1 was highly expressed in CRC. Subgroup survival analysis revealed a prognostic survival difference for PADI1 in CRC patients aged less than 65 years, male, T stage, N0, M0, and stage I-II (p < 0.05). In addition, we analyzed the functions and signaling pathways associated with PADI1 in CRC and found that it was highly enriched in several immune-related functions and pathways. Then, a set of PADI1 co-expressed genes (PCGs) risk-prognosis scores was developed with PADI1 as the core, which could accurately predict the prognosis of CRC (p < 0.05). PCGs risk score can be an independent prognostic factor for CRC. A new set of Norman plot models were developed for clinical characteristics with age, sex, and TNM stage, which can accurately predict CRC 1, 3, and 5 years survival, and calibration curves and decision curve analysis (DCA) validated the accuracy of the models. The risk score assessed the immune microenvironment of CRC and found that the immune score was higher in the low-risk group, and CD4+ T cells, helper T cells, and eosinophils were more infiltrated in the low-risk group (p < 0.05). Immunotherapy efficacy was better in the low-risk group (p < 0.05). The underlying mechanism may be that the high-risk group of PCGs was enriched in some pathways that promote immune escape and immune dysfunction. In conclusion, PCGs may better predict CRC prognosis and immunotherapeutic response.

Construction of a Colorectal Cancer Prognostic Risk Model and Screening of Prognostic Risk Genes Using Machine-Learning Algorithms.

This study is aimed at constructing a prognostic risk model for colorectal cancer (CRC) using machine-learning algorithms to provide accurate staging and screening of credible prognostic risk genes. We extracted CRC data from GSE126092 and GSE156355 of the Gene Expression Omnibus (GEO) database and datasets from TCGA to analyze the differentially expressed genes (DEGs) using bioinformatics analysis. Among the 330 shared DEGs related to CRC prognosis, we divided the analysis period into different phases and applied univariate COX regression, LASSO, and multivariate COX regression analysis. GO analysis and KEGG analysis revealed that the functions of these DEGs were primarily focused on cell cycle, DNA replication, cell mitosis, and other related functions, and this confirmed our results from a biological perspective. Finally, a prognostic risk model for CRC based on the CHGA, CLU, PLK1, AXIN2, NR3C2, IL17RB, GCG, and AJUBA genes was constructed, and the risk score enabled us to predict the prognosis for CRC. To obtain a comprehensive and accurate model, we used both internal and external evaluations, and the model was able to correctly differentiate patients with CRC into a high-risk group with poor prognosis and a low-risk group with good prognosis. The AUC values of the 3-, 5-, and 10-year survival ROC curves were 0.715, 0.721, and 0.777, respectively, according to the internal evaluation, and the AUC values were 0.606, 0.698, and 0.608, respectively, for the external evaluation using GSE39582 from the GEO database. We determined that CLU, PLK1, and IL17RB could be considered to be independent prognostic factors for CRC with significantly different expression (P < 0.05). Using machine-learning methods, a prognostic risk model comprised of eight genes was constructed. Not only does this model provide improved treatment guidance, but it also provides a novel perspective for analyzing survival conditions at a deeper biological level.