ZG16 regulates PD-L1 expression and promotes local immunity in colon cancer

Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological images

Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds significant importance in enhancing patient treatment and prognosis. Machine learning technology and bioinformatics have provided novel approaches for cancer diagnosis. This study aims to develop a CRC diagnostic model based on immunohistochemical staining image features using machine learning methods. Initially, CRC disease-specific genes were identified through bioinformatics analysis, SVM-RFE and Random Forest algorithm utilizing RNA-seq data from both GEO and TCGA databases. Subsequently, verification of these genes was performed using proteomics data from CPTAC and HPA database, resulting in identification of target proteins (AKR1B10, CA2, DHRS9, and ZG16) for further investigation. SVM and CNN were then employed to analyze and integrate the characteristics of immunohistochemical images to construct a reliable CRC diagnostic model. During the training and validation process of this model, cross-validation along with external validation methods were implemented to ensure accuracy and reliability. The results demonstrate that the established diagnostic model exhibits excellent performance in distinguishing between CRC and normal controls (accuracy rate: 0.999), thereby presenting potential prospects for clinical application. These findings are expected to provide innovative perspectives as well as methodologies for personalized diagnosis of CRC while offering more precise references for promising treatment.

ZG16 impacts gut microbiota-associated intestinal inflammation and pulmonary mucosal function through bacterial metabolites

Zymogen granule 16 (ZG16) is a secretory glycoprotein found in zymogen granules, which also plays an important role in colorectal inflammation and cancer. Herein, a ZG16 gene knock-out (ZG16-/-) mouse line was established and we found that ZG16 deletion damaged the intestinal mucosal barrier and gut microbiota, which resulted in low-level inflammation and further promoted the development of ulcerative colitis and inflammation-related colorectal cancer. Meanwhile, a metabolomics analysis on mouse feces showed that the metabolites significantly differed between ZG16-/- and WT mice, which were important mediators of host-microbiota communication and may impact the pulmonary inflammation of mice. Indeed, ZG16-/- mice showed more severe inflammation in a bronchial asthma model. Taken together, the results demonstrate that ZG16 plays a pivotal role in inhibiting inflammation and regulating immune responses in colorectum and lung of experimental animals, which may provide a better understanding of the underlying mechanism of human inflammatory diseases associated with ZG16.

Goblet cells: guardians of gut immunity and their role in gastrointestinal diseases

Goblet cells (GCs) are specialised guardians lining the intestine. They play a critical role in gut defence and immune regulation. GCs continuously secrete mucus creating a physical barrier to protect from pathogens while harbouring symbiotic gut bacteria adapted to live within the mucus. GCs also form specialised GC-associated passages in a dynamic and regulated manner to deliver luminal antigens to immune cells, promoting gut tolerance and preventing inflammation. The composition of gut bacteria directly influences GC function, highlighting the intricate interplay between these components of a healthy gut. Indeed, imbalances in the gut microbiome can disrupt GC function, contributing to various gastrointestinal diseases like colorectal cancer, inflammatory bowel disease, cystic fibrosis, pathogen infections and liver diseases. This review explores the interplay between GCs and the immune system. We delve into the underlying mechanisms by which GC dysfunction contributes to the development and progression of gastrointestinal diseases. Finally, we examine current and potential treatments that target GCs and represent promising avenues for further investigation.

ZG16 enhances the maturation of dendritic cells via induction of CD40 and contributes to the antitumor immunity in pancreatic cancer

Dendritic cells (DCs) are critical mediators of antigen priming and T-cell activation. Zymogen granule protein 16 (ZG16) is demonstrated as an anti-oncogene in T-cell mediated antitumor immunity, but its effect on DCs is largely unknown. Herein, we wonder whether ZG16 affects the activation of DCs in pancreatic cancer. Firstly, the increased ZG16 expression was observed during the maturation of DCs derived from mouse bone marrow or human peripheral blood. Then, overexpression of ZG16 or exogenous introduction of recombinant ZG16 protein induced the expression of MHC II, CD86, CD84, and CCR7 on the surface of DCs, thereby facilitating the secretion of proinflammatory mediators IL-1β, IL-6, TNF-α, and IL-12/p70, supporting the promoting effect of ZG16 on DC maturation. By establishing the subcutaneous and orthotopic mouse models of pancreatic cancer, we confirmed that intraperitoneal injection of recombinant ZG16 protein (Re-mZG16) could induce tumor regression by stimulating DC maturation and enhancing antitumor responses of CD4 + , CD8 + , PD-1 + , and Ctla4+ cells. Besides, Re-mZG16 in combination with gemcitabine showed a synergistic effect in the treatment of pancreatic cancer. Mechanistically, we demonstrated that ZG16 inhibited the ubiquitination and degradation of CD40, which depended on the lectin domain of ZG16. In conclusion, this study provided a novel insight into the role of ZG16-CD40 axis in DC-based immunotherapy for pancreatic cancer.

LncRNA HAND2-AS1 Inhibited Colon Cancer Progression By Regulating miR-3118/ZG16 Axis

LncRNA HAND2-AS1 is a novel cancer regulator, but the role and mechanisms of HAND2-AS1 involved with colon cancer (CC) progression remains unknown. The purpose of this research was to figure out how HAND2-AS1 regulates the progression of CC. Using qRT-PCR, we studied expression levels of miR-3118, HAND2-AS1, and ZG16 in CC tissues and cells. Protein levels of apoptosis-related proteins (Bax and Bcl-2) and ZG16 were quantified by western blotting. In vitro function analysis referred to western blotting, wound healing assay and CCK-8. The binding association among miR-3118, HAND2-AS1, and ZG16 was investigated using luciferase reporter and RIP assays. The functional role of HAND2-AS1 was analyzed using xenograft tumor models in vivo. In tissues and cells of CC, HAND2-AS1 was downregulated. We observed that HAND2-AS1 overexpression declined CC cell proliferation and migration while facilitating apoptosis. We further verified that when HAND2-AS1 is overexpressed it reduced CC tumor development in vivo. In CC cells and tissues, miR-3118 competed with HAND2-AS1 and was elevated. Further it was noted that the HAND2-AS1 when overexpressed, lessened the survival of CC cells, however overexpression of miR-3118 restored these changes. ZG16 was shown to be a target of miR-3118, it was found that ZG16 was downregulated in CC tissue and cells. We observed, high expression of ZG16 partially restored the enhanced malignant phenotype caused by miR-3118 overexpression. HAND2-AS1 inhibited CC progression by upregulating ZG16 expression through sponging miR-3118. Hence, HAND2-AS1/miR-3118/ZG16 axis could be a possible new target for CC treatment.

Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer

Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal cancer. Limited research has been conducted on how CRLM develops. RNA sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Four machine learning algorithms were used to screen the hub CRLM-specific genes, including Least Absolute Shrinkage and Selection Operator (Lasso), Random forest, SVM-RFE, and XGboost. The model for identifying CRLM was developed using stepwise logistic regression and was validated using internal and independent datasets. The prognostic value of hub CRLM-specific genes was assessed using the Lasso-Cox method. The in vitro experiments were performed using SW620 cells. The CRLM identification model was developed based on four CRLM-specific genes (SPP1, ZG16, P2RY14, and PRKAR2B), and the model efficacy was validated using GSE41258 and three external cohorts. Five CRLM-specific prognostic hub genes, SPP1, ZG16, P2RY14, CYP2E1, and C5, were identified using the Lasso-Cox algorithm, and a risk score was constructed. The risk score was validated using the GSE39582 cohort. Three genes have both efficacy in identifying CRLM and prognostic value: ZG16, P2RY14, and SPP1. Immune infiltration and enrichment analyses demonstrated that SPP1 was associated with M2 macrophage polarization and extracellular matrix remodeling. In vitro experiments indicated that SPP1 may act as a cancer-promoting factor. The hub CRLM-specific gene SPP1 can help determine the diagnosis, prognosis, and immune infiltration of patients with CRLM.

Identification and validation of metastasis-related gene ZG16 in the prognosis and progression in colorectal cancer

Background

Metastasis remains the leading cause of mortality among colorectal cancer (CRC) patients. Identification of new metastasis-related genes are critical to improve colorectal cancer prognosis.

Methods

Data on mRNA expression in metastatic and primary CRC was obtained from the Gene Expression Omnibus (GEO) database, including GSE81986, GSE41568, GSE71222, GSE21510, and GSE14333. Additionally, data concerning mRNA expression in colon cancer (COAD) and adjacent normal tissues were acquired from The Cancer Genome Atlas (TCGA) database. Hub genes were identified by weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Moreover, we assessed the impact of hub gene expression on both overall survival (OS) and disease-free survival (DFS) in patients and identified ZG16 as a potential target. We generated CRC cell lines transfected with lentivirus OE-ZG16 to investigate proliferation, invasion, and migration in vitro. To further elucidate the involvement of ZG16, we utilized gene set enrichment analysis (GSEA) to identify enriched pathways, which were subsequently validated via Western blot analysis.

Results

Five datasets containing primary and metastatic CRC samples from GEO database and CRC samples from TCGA database were included in this study and 29 hub genes were identified by WGCNA and differentially expressed gene (DEG) analysis. Low expression of the hub genes (CLCA1 and ZG16) was associated with poor DFS and OS. We confirmed the low expression of ZG16 in CRC using external database and IHC analysis at both transcriptional and protein levels. In addition, the expression of ZG16 was notably elevated in NCM460 cells in comparison to CRC cell lines. The overexpression of ZG16 in CRC cells has been shown to inhibit the proliferation, invasion, and migration of CRC cells. Furthermore, the overexpression of ZG16 has been found to suppress the activation of the epithelial-mesenchymal transition (EMT) and Wnt/β-catenin signaling pathways in CRC.

Conclusion

ZG16 may serve as a promising therapeutic target for metastatic CRC treatment.

Multi-omics cluster defines the subtypes of CRC with distinct prognosis and tumor microenvironment

BACKGROUND

Colorectal cancer (CRC) is a complex malignancy characterized by diverse molecular profiles, clinical outcomes, and limited precision in prognostic markers. Addressing these challenges, this study utilized multi-omics data to define consensus molecular subtypes in CRC and elucidate their association with clinical outcomes and underlying biological processes.

METHODS

Consensus molecular subtypes were obtained by applying ten integrated multi-omics clustering algorithms to analyze TCGA-CRC multi-omics data, including mRNA, lncRNA, miRNA, DNA methylation CpG sites, and somatic mutation data. The association of subtypes with prognoses, enrichment functions, immune status, and genomic alterations were further analyzed. Next, we conducted univariate Cox and Lasso regression analyses to investigate the potential prognostic application of biomarkers associated with multi-omics subtypes derived from weighted gene co-expression network analysis (WGCNA). The function of one of the biomarkers MID2 was validated in CRC cell lines.

RESULTS

Two CRC subtypes linked to distinct clinical outcomes were identified in TCGA-CRC cohort and validated with three external datasets. The CS1 subtype exhibited a poor prognosis and was characterized by higher tumor-related Hallmark pathway activity and lower metabolism pathway activity. In addition, the CS1 was predicted to have less immunotherapy responder and exhibited more genomic alteration compared to CS2. Then a prognostic model comprising five genes was established, with patients in the high-risk group showing substantial concordance with the CS1 subtype, and those in the low-risk group with the CS2 subtype. The gene MID2, included in the prognostic model, was found to be correlated with epithelial-mesenchymal transition (EMT) pathway and distinct DNA methylation patterns. Knockdown of MID2 in CRC cells resulted in reduced colony formation, migration, and invasion capacities.

CONCLUSION

The integrative multi-omics subtypes proposed potential biomarkers for CRC and provided valuable knowledge for precision oncology.

Intestinal mucus components and secretion mechanisms: what we do and do not know

Damage to the colon mucus barrier, the first line of defense against microorganisms, is an important determinant of intestinal diseases such as inflammatory bowel disease and colorectal cancer, and disorder in extraintestinal organs. The mucus layer has attracted the attention of the scientific community in recent years, and with the discovery of new mucosal components, it has become increasingly clear that the mucosal barrier is a complex system composed of many components. Moreover, certain components are jointly involved in regulating the structure and function of the mucus barrier. Therefore, a comprehensive and systematic understanding of the functional components of the mucus layer is clearly warranted. In this review, we summarize the various functional components of the mucus layer identified thus far and describe their unique roles in shaping mucosal structure and function. Furthermore, we detail the mechanisms underlying mucus secretion, including baseline and stimulated secretion. In our opinion, baseline secretion can be categorized into spontaneous Ca²⁺ oscillation-mediated slow and continuous secretion and stimulated secretion, which is mediated by massive Ca²⁺ influx induced by exogenous stimuli. This review extends the current understanding of the intestinal mucus barrier, with an emphasis on host defense strategies based on fortification of the mucus layer.

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

BACKGROUND

Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.

METHODS

To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.

RESULTS

We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.

CONCLUSION

The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

ZG16 promotes T-cell mediated immunity through direct binding to PD-L1 in colon cancer

Immunotherapy using programmed cell death 1 (PD1) inhibitors has shown great efficacy in colorectal cancer patients harboring mismatch-repair-deficient (dMMR) and microsatellite instability-high (MSI-H) alterations. We previously showed a negative correlation of zymogen granule protein 16 (ZG16) with programmed death-ligand 1 (PD-L1) expression in patients with colorectal cancer. However, how ZG16 regulates PD-L1 expression is unclear. In this study, we showed that ZG16 can directly bind to glycosylated PD-L1 through its lectin domain, leading to PD-L1 degradation. Mutations on the lectin domain of ZG16 largely inhibit the interaction between ZG16 and PD-L1. Importantly, ZG16 overexpression suppressed tumor growth in two syngeneic mouse models through blockage of PD-L1 expression in cancer cells meanwhile suppression of PD1 expression in T cells. We also showed that ZG16 could improve the effect of chemotherapy and may be delivered as a protein to serve as an immune checkpoint inhibitor to promote T-cell mediated immunity.

Downregulation of LRRC19 Is Associated with Poor Prognosis in Colorectal Cancer

OBJECTIVE

Colorectal cancer (CRC) is globally one of the most often diagnosed cancers with high mortality rates. This study aimed to explore novel biomarkers for the diagnosis and prognosis of CRC.

METHODS

We collected 4 datasets about CRC in GEO and sought differentially expressed genes (DEGs) with GEO2R. Leucine-rich repeat-containing protein 19 (LRRC19) expression was assessed through the Oncomine and TIMER database analyses, which was further confirmed by qRT-PCR of CRC samples. We used online survival analysis tools (GEPIA, PrognoScan, and Kaplan-Meier plotter) to examine the prognostic value of LRRC19 in CRC and other malignancies. GO and KEGG enrichment analyses were employed to explore the biological functions of LRRC19. Finally, we conducted network prediction by STRING and further validation on the GEPIA to discover other molecules that might interact with LRRC19.

RESULTS

A total of 21 upregulated and 46 downregulated DEGs were identified from the 4 datasets. The TIMER and Oncomine online analyses showed lower mRNA of LRRC19 in CRC tissues compared with adjacent normal tissues, which was validated by qRT-PCR in CRC patient samples. The survival analysis through the GEPIA and PrognoScan websites revealed that low LRRC19 expression was significantly correlated with poor prognosis in CRC patients. The Kaplan-Meier plotter survival analysis indicated that low LRRC19 expression was significantly associated with the disease progression of patients with ovarian cancer, gastric cancer, breast cancer, and lung cancer. The enrichment analysis suggested that low expression of LRRC19 could be involved in the retinol metabolism and the zymogen granule membrane. Through STRING and GEPIA, it was found that LRRC19 is clearly associated with ZCCHC10, MOB3B, IMMP2L, and TRMT11.

CONCLUSION

LRRC19 mRNA was prominently decreased in human CRC tissues and was significantly associated with shorter survival in CRC patients. LRRC19 might serve as a possible target for early diagnosis and prognosis assessment in CRC.

Programmed Cell Death Ligand 1-Transfected Mouse Bone Marrow Mesenchymal Stem Cells as Targeted Therapy for Rheumatoid Arthritis

Programmed cell death 1 ligand (PD-L1) and its receptor (PD-1) are key molecules for immunoregulation and immunotherapy. PD-L1 binding PD-1 is an effective way to regulate T or B cell immunity in autoimmune diseases such as rheumatoid arthritis (RA). In our study, we overexpressed PD-L1 by constructing a recombinant of PD-L1-lentiviral vector, which was subsequently used to transfect mouse bone marrow mesenchymal stem cells (MBMMSCs) and significantly suppressed the development of collagen-induced arthritis (CIA) in DBA/1j mice. In addition, PD-L1-transfected MBMMSCs (PD-L1-MBMMSCs) ameliorated joint damage, reduced proinflammatory cytokine expression, and inhibited T and B cell activation. Furthermore, PD-L1-MBMMSCs decreased the number of dendritic cells and increased the numbers of regulatory T cells and regulatory B cells in joints of CIA mice. In conclusion, our results provided a potential therapeutic strategy for RA treatment with PD-L1-MBMMSC-targeted therapy.

Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients

Colon adenocarcinoma (COAD) is one of the most common malignant tumors and has high migration and invasion capacity. In this study, we attempted to establish a multigene signature for predicting the prognosis of COAD patients. Weighted gene co-expression network analysis and differential gene expression analysis methods were first applied to identify differentially co-expressed genes between COAD tissues and normal tissues from the Cancer Genome Atlas (TCGA)-COAD dataset and GSE39582 dataset, and a total of 309 overlapping genes were screened out. Then, our study employed TCGA-COAD cohort as the training dataset and an independent cohort by merging the GES39582 and GSE17536 datasets as the testing dataset. After univariate and multivariate Cox regression analyses were performed for these overlapping genes and overall survival (OS) of COAD patients in the training dataset, a 13-gene signature was constructed to divide COAD patients into high- and low-risk subgroups with significantly different OS. The testing dataset exhibited the same results utilizing the same predictive signature. The area under the curve of receiver operating characteristic analysis for predicting OS in the training and testing datasets were 0.789 and 0.868, respectively, which revealed the enhanced predictive power of the signature. Multivariate Cox regression analysis further suggested that the 13-gene signature could independently predict OS. Among the 13 prognostic genes, NAT1 and NAT2 were downregulated with deep deletions in tumor tissues in multiple COAD cohorts and exhibited significant correlations with poorer OS based on the GEPIA database. Notably, NAT1 and NAT2 expression levels were positively correlated with infiltrating levels of CD8+ T cells and dendritic cells, exhibiting a foundation for further research investigating the antitumor immune roles played by NAT1 and NAT2 in COAD. Taken together, the results of our study showed that the 13-gene signature could efficiently predict OS and that NAT1 and NAT2 could function as biomarkers for prognosis and the immune response in COAD.

Glycosyltransferase B4GALNT2 as a Predictor of Good Prognosis in Colon Cancer: Lessons from Databases

BACKGROUND

glycosyltransferase B4GALNT2 and its cognate carbohydrate antigen Sd^a are highly expressed in normal colon but strongly downregulated in colorectal carcinoma (CRC). We previously showed that CRC patients expressing higher B4GALNT2 mRNA levels displayed longer survival. Forced B4GALNT2 expression reduced the malignancy and stemness of colon cancer cells.

METHODS

Kaplan-Meier survival curves were determined in "The Cancer Genome Atlas" (TCGA) COAD cohort for several glycosyltransferases, oncogenes, and tumor suppressor genes. Whole expression data of coding genes as well as miRNA and methylation data for B4GALNT2 were downloaded from TCGA.

RESULTS

the prognostic potential of B4GALNT2 was the best among the glycosyltransferases tested and better than that of many oncogenes and tumor suppressor genes; high B4GALNT2 expression was associated with a lower malignancy gene expression profile; differential methylation of an intronic B4GALNT2 gene position and miR-204-5p expression play major roles in B4GALNT2 regulation.

CONCLUSIONS

high B4GALNT2 expression is a strong predictor of good prognosis in CRC as a part of a wider molecular signature that includes ZG16, ITLN1, BEST2, and GUCA2B. Differential DNA methylation and miRNA expression contribute to regulating B4GALNT2 expression during colorectal carcinogenesis.