The identification of a common different gene expression signature in patients with colorectal cancer

The impact of disease and species differences on the intestinal CLCA4 gene expression

The human chloride channel regulator, calcium-activated (CLCA) 4 is discussed as a driver of epithelial-to-mesenchymal transition as well as a biomarker for colorectal cancer (CRC) and ulcerative colitis. In contrast to humans, the Clca4 gene is duplicated in the mouse, a common model species to study gene functions. However, the relevance of the functional murine Clca4 variants in healthy and diseased intestine is largely unknown. Here, we characterized the spatiotemporal expression patterns of the murine Clca4a and Clca4b genes in the healthy intestinal tract as well as in dextran sulfate sodium (DSS)-induced colitis and colitis-associated colon cancer (CAC) mouse model using RT-qPCR and in situ-hybridization. Similarly, we analyzed expression of the human CLCA4 in healthy, inflamed and cancerous intestinal tracts at single cell level. Murine Clca4a and -4b but not the human CLCA4 were detected in small intestine enterocytes of the respective species. Conversely, healthy colonocytes expressed the human CLCA4 and its murine ortholog Clca4a but not the murine Clca4b. Under inflammatory conditions, de novo expression of Clca4b was observed with both murine homologs abundantly expressed in enterocytes adjacent to ulcerations. Neoplastic colonocytes expressed none or only minimal amounts of the CLCA4 homologs both in humans and mice, whereas adjacent non-neoplastic colonocytes strongly up-regulated the human or both murine homologs, respectively. Our results suggest marked species- and homolog-specific differences in the expression patterns of the three CLCA4 homologs. Moreover, all three seem to play a role in reactive, non-neoplastic colonocytes adjacent to ulcerated and neoplastic lesions. KEY MESSAGES: Human CLCA4 and murine Clca4a, but not Clca4b, are expressed in healthy colonocytes. Inflammation leads to a de novo expression of the murine Clca4b in colonocytes. Human and murine CLCA4 homologs are absent from neoplastic enterocytes. Human and murine CLCA4s are highly expressed in tumor-adjacent, reactive colonocytes.

Identification of Novel lncRNAs Related to Colorectal Cancer Through Bioinformatics Analysis

Long noncoding RNA (lncRNA) plays a critical role in cancer cell proliferation, invasion, metastasis, and chemoresistance. The current study introduces novel lncRNAs in colorectal cancer (CRC) through bioinformatics analysis. GSE134834 CRC-related microarray of Gene Expression Omnibus (GEO) was analyzed to identify differentially expressed genes (DEGs) in CRC samples against normal samples. Analysis revealed 6763 DEGs (p < 0.05 and |log fold change (FC)| ≥ 0.5) that include differentially expressed mRNA (DEmRNA) and differentially expressed long noncoding RNA (DElncRNA). Novel lncRNAs were identified, and to better understand the biological function of the identified lncRNAs, gene modules were constructed using weighted gene coexpression network analysis (WGCNA), and finally, two modules for lncRNAs were obtained. The coexpression modules with these lncRNAs were subjected to enrichment analysis in FunRich software to predict their functions through their coexpressed genes. Gene ontology results of modules related to novel lncRNA revealed they significantly enriched the cellular pathways regulation in cancer. The protein-protein interaction (PPI) network of novel lncRNAs-related modules was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and visualized using the Cytoscape software. Hub genes were screened from the PPI network by the CytoHubba plug-in of Cytoscape. The hub genes were MRTO4, CDK1, CDC20, RPF2, NOP58, NIFK, GTPBP4, BUB1, BUB1B, and BOP1 for the lightpink4 module and BYSL, RPS23 (ribosomal protein S23), RSL1D1 (ribosomal L1 domain containing 1), NAT10, NOP14, GNL2, MRPS12, NOL6 (nucleolar protein 6), IMP4, and RRP12 (ribosomal RNA processing 12 homolog) for the pink module. The expression levels of the top DEmRNA and module hub genes in CRC were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) database. Generally, our findings offer crucial insight into the hub genes and novel lncRNAs in the development of CRC by bioinformatics analysis, information that may prove useful in the identification of new biomarkers and treatment targets in CRC; however, more experimental investigation is required to validate the findings of the present study.

SNP rs6543176 is associated with extreme human longevity but increased risk for cancer

Using whole-genome sequencing (WGS) might offer insights into rare genetic variants associated with healthy aging and extreme longevity (EL), potentially pointing to useful therapeutic targets. In this study, we conducted a genome-wide association study using WGS data from the Long Life Family Study and identified a novel longevity-associated variant rs6543176 in the SLC9A2 gene. This SNP also showed a significant association with reduced hypertension risk and an increased, though not statistically significant, cancer risk. The association with cancer risk was replicated in the UK Biobank and FinnGen. Metabolomic analyses linked the rs6543176 longevity allele to higher serine levels, potentially associated with delayed mortality. Our findings warrant further investigation of SLC9A2's role in both longevity and cancer susceptibility, and they highlight the need for careful evaluation in developing anti-aging therapies based on EL-associated alleles.

Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation

By 2030, it is anticipated that there will be 2.2 million new instances of colorectal cancer worldwide, along with 1.1 million yearly deaths. Therefore, it is critical to develop novel biomarkers that could help in CRC early detection. We performed an integrated analysis of four RNA-Seq data sets and TCGA datasets in this study to find novel biomarkers for diagnostic, prediction, and as potential therapeutic for this malignancy, as well as to determine the molecular mechanisms of CRC carcinogenesis. Four RNA-Seq datasets of colorectal cancer were downloaded from the Sequence Read Archive (SRA) database. The metaSeq package was used to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the string platform, and hub genes were identified using the cytoscape software. The gene ontology and KEGG pathway enrichment analysis were performed using enrichR package. Gene diagnostic sensitivity and its association to clinicopathological characteristics were demonstrated by statistical approaches. By using qRT-PCR, GUCA2A and COL3A1 were examined in colon cancer and rectal cancer. We identified 5037 differentially expressed genes, including (4752 upregulated, 285 downregulated) across the studies between CRC and normal tissues. Gene ontology and KEGG pathway analyses showed that the highest proportion of up-regulated DEGs was involved in RNA binding and RNA transport. Integral component of plasma membrane and mineral absorption pathways were identified as containing down-regulated DEGs. Similar expression patterns for GUCA2A and COL3A1 were seen in qRT-PCR and integrated RNA-Seq analysis. Additionally, this study demonstrated that GUCA2A and COL3A1 may play a significant role in the development of CRC.

The Clinical Significance and Role of CXCL1 Chemokine in Gastrointestinal Cancers

One area of cancer research is the interaction between cancer cells and immune cells, in which chemokines play a vital role. Despite this, a comprehensive summary of the involvement of C-X-C motif ligand 1 (CXCL1) chemokine (also known as growth-regulated gene-α (GRO-α), melanoma growth-stimulatory activity (MGSA)) in cancer processes is lacking. To address this gap, this review provides a detailed analysis of CXCL1's role in gastrointestinal cancers, including head and neck cancer, esophageal cancer, gastric cancer, liver cancer (hepatocellular carcinoma (HCC)), cholangiocarcinoma, pancreatic cancer (pancreatic ductal adenocarcinoma), and colorectal cancer (colon cancer and rectal cancer). This paper presents the impact of CXCL1 on various molecular cancer processes, such as cancer cell proliferation, migration, and invasion, lymph node metastasis, angiogenesis, recruitment to the tumor microenvironment, and its effect on immune system cells, such as tumor-associated neutrophils (TAN), regulatory T (T_reg) cells, myeloid-derived suppressor cells (MDSCs), and macrophages. Furthermore, this review discusses the association of CXCL1 with clinical aspects of gastrointestinal cancers, including its correlation with tumor size, cancer grade, tumor-node-metastasis (TNM) stage, and patient prognosis. This paper concludes by exploring CXCL1's potential as a therapeutic target in anticancer therapy.

Pyrazolo[4,3-e]tetrazolo[1,5-b][1,2,4]triazine Sulfonamides as Novel Potential Anticancer Agents: Apoptosis, Oxidative Stress, and Cell Cycle Analysis

The current study continues the evaluation of the anticancer potential of three de novo synthesized pyrazolo[4,3-e]tetrazolo[1,5-b][1,2,4]triazine sulfonamides-MM129, MM130, and MM131-against human cancer cells of HeLa, HCT 116, PC-3, and BxPC-3 lines. The pro-apoptotic activity of the investigated sulfonamides was shown by observations of changes in the mitochondrial transmembrane potential of the tested cells, externalization of phosphatidylserine on the cellular membrane surface, and cell morphology in microscopic imaging. The computational studies have shown that MM129 exhibited the lowest binding energy values when docked against CDK enzymes. In addition, the highest stability was shown for complexes formed between MM129 and CDK5/8 enzymes. All examined compounds induced cell cycle arrest in the G0/G1 phase in the BxPC-3 and PC-3 cells and simultaneously caused the accumulation of cells in the S phase in the HCT 116 cells. In addition, the increase in the subG1 fraction was observed in PC-3 and HeLa cells. The application of a fluorescent H₂DCFDA probe revealed the high pro-oxidative properties of the tested triazine derivatives, especially MM131. In conclusion, the obtained results suggest that MM129, MM130, and MM131 exhibited strong pro-apoptotic properties towards investigated cells, mainly against the HeLa and HCT 116 cell lines, and high pro-oxidative potential as well. Moreover, it is suggested that the anticancer activity of the tested compounds may be associated with their ability to inhibit CDK enzymes activities.

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.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

Expression of NMU, PPBP and GNG4 in colon cancer and their influences on prognosis

BACKGROUND

This study aims to identify the core genes that influence the prognosis of colon cancer (CC) and analyze their relationships with clinical characteristics.

METHODS

The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified. The top ten core genes were selected by bioinformatics tools and screened through the Oncomine database. The expression of core genes in CC tissues and cells was validated by immunohistochemistry, immunoblotting and quantitative real-time polymerase chain reaction. Spearman correlation was used to analyze the relationship between different parameters. Overall survival was assessed by the Kaplan-Meier method. The area under the curve (AUC) and the receiver operating curve (ROC) were applied to assess the accuracy of genes for predicting prognosis.

RESULTS

There were 1,665 DEGs that were identified from TCGA database. Bioinformatics analysis found that GNGT1, NMU, PPBP, AGT, and GNG4 were differentially expressed in CC tissue. Overexpression of NMU, PPBP, AGT, and GNG4 in CC was associated with shortened survival time (P<0.05). In the validation studies, the high expression levels of NMU, PPBP and GNG4 in CC cells and tissues were confirmed compared to the control groups (P<0.05) and were adverse prognostic biomarkers (P<0.01). The combination prognostic model of the three core genes predicted the 1-, 3-, and 5-year survival of CC with AUCs of 0.868, 0.635 and 0.770, respectively.

CONCLUSIONS

High levels of NMU, PPBP, and GNG4 were associated with poor prognosis in CC. The combination prognostic model of these three genes could be a new option.

Dysregulated Expression of Three Genes in Colorectal Cancer Stratifies Patients into Three Risk Groups

Despite advances in recent years in the study of the molecular profile of sporadic colorectal cancer (sCRC), the specific genetic events that lead to increased aggressiveness or the development of the metastatic process of tumours are not yet clear. In previous studies of the gene expression profile (GEP) using a high-density array (50,000 genes and 6000 miRNAs in a single assay) in sCRC tumours, we identified a 28-gene signature that was found to be associated with an adverse prognostic value for predicting patient survival. Here, we analyse the differential expression of these 28 genes for their possible association with tumour local aggressiveness and metastatic processes in 66 consecutive sCRC patients, followed for >5 years, using the NanoString nCounter platform. The global transcription profile (expression levels of the 28 genes studied simultaneously) allowed us to discriminate between sCRC tumours and nontumoral colonic tissues. Analysis of the biological and functional significance of the dysregulated GEPs observed in our sCRC tumours revealed 31 significantly altered canonical pathways. Among the most commonly altered pathways, we observed the increased expression of genes involved in signalling pathways and cellular processes, such as the PI3K-Akt pathway, the interaction with the extracellular matrix (ECM), and other functions related to cell signalling processes (SRPX2). From a prognostic viewpoint, the altered expression of BST2 and SRPX2 genes were the only independent variables predicting for disease-free survival (DFS). In addition to the pT stage at diagnosis, dysregulated transcripts of ADH1B, BST2, and FER1L4 genes showed a prognostic impact on OS in the multivariate analysis. Based on the altered expression of these three genes, a scoring system was built to stratify patients into low-, intermediate-, and high-risk groups with significantly different 5-year OS rates: 91%, 83%, and 52%, respectively. The prognostic impact was validated in two independent series of sCRC patients from the public GEO database (n = 562 patients). In summary, we show a strong association between the altered expression of three genes and the clinical outcome of sCRC patients, making them potential markers of suitability for adjuvant therapy after complete tumour resection. Additional prospective studies in larger series of patients are required to confirm the clinical utility of the newly identified biomarkers because the number of patients analysed remains small.

Identification of Down-Regulated ADH1C is Associated With Poor Prognosis in Colorectal Cancer Using Bioinformatics Analysis

Colorectal cancer (CRC) is the second most deadly cancer in the whole world, with the underlying mechanisms largely indistinct. Therefore, we aimed to identify significant pathways and genes involved in the initiation, formation and poor prognosis of CRC using bioinformatics methods. In this study, we compared gene expression profiles of CRC cases with those from normal colorectal tissues from three chip datasets (GSE33113, GSE23878 and GSE41328) to identify 105 differentially expressed genes (DEGs) that were common to the three datasets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that the highest proportion of up-regulated DEGs was involved in extracellular region and cytokine-cytokine receptor interaction pathways. Integral components of membrane and bile secretion pathways were identified as containing down-regulated DEGs. 13 hub DEGs were chosen and their expression were further validated by GEPIA. Only four DEGs (ADH1C, CLCA4, CXCL8 and GUCA2A) were associated with a significantly lower overall survival after the prognosis analysis. Lower ADH1C protein level and higher CXCL8 protein level were verified by immunohistochemical staining and western blot in clinical CRC and normal colorectal tissues. In conclusion, our study indicated that the extracellular tumor microenvironment and bile metabolism pathways play critical roles in the formation and progression of CRC. Furthermore, we confirmed ADH1C being down-regulated in CRC and reported ADH1C as a prognostic predictor for the first time.

Cyclin-Dependent Kinase Inhibitors and Their Therapeutic Potential in Colorectal Cancer Treatment

Cyclin-dependent kinases (CDKs) are key players in cell cycle regulation. So far, more than ten CDKs have been described. Their direct interaction with cyclins allow progression through G1 phase, transitions to S and G2 phase and finally through mitosis (M). While CDK activation is important in cell renewal, its aberrant expression can lead to the development of malignant tumor cells. Dysregulations in CDK pathways are often encountered in various types of cancer, including all gastrointestinal (GI) tract tumors. This prompted the development of CDK inhibitors as novel therapies for cancer. Currently, CDK inhibitors such as CDK4/6 inhibitors are used in pre-clinical studies for cancer treatment. In this review, we will focus on the therapeutic role of various CDK inhibitors in colorectal cancer, with a special focus on the CDK4/6 inhibitors.

Analysis of dynamic molecular networks: the progression from colorectal adenoma to cancer

BACKGROUND

Colorectal cancer (CRC) is one of the deadliest cancers worldwide. It is the fourth most deadly cancer in the world with nearly 900,000 people die every year, the progression of polyps into cancer as one of its most common developmental pathways.

METHODS

This study obtained gene chip data collections from the Gene Expression Omnibus for colorectal adenoma (GSE8671) and colorectal cancer (GSE32323). Differentially expressed genes (DEGs) in normal tissue and different stages of CRC were analyzed for clustering, comparison, and visualization using R software. The Cytoscape plugin DyNetViewer was used to construct a dynamic protein-protein interaction network. Subsequently, through the Database for Annotation, Visualization and Integrated Discovery, the DEGs were functionally annotated and path enriched.

RESULTS

Our study found that the matrix metalloprotein family and chemokines were the key regulatory genes that drove CRC progression. The Wnt signaling pathway, chemokine signaling pathway, and CRC pathway were the pathological pathways for CRC. Maintenance played an important role in this process. In addition, the related nodes and pathways at various stages may be potential mechanisms for promoting dynamic CRC progression.

CONCLUSIONS

Our study provides a better understanding of the dynamic pattern of molecular interaction networks during CRC progression and provides relevant markers for more accurate screening of cancer in polyps.

Clinicopathologic and prognostic implications of Golgi Phosphoprotein 3 in colorectal cancer: A meta-analysis

Background

Golgi Phosphoprotein 3 (GOLPH3) has been implicated in the development of colorectal cancer (CRC). Nevertheless, the clinicopathological and prognostic roles of GOLPH3 in CRC remain undefined. We thus did a meta-analysis to assess GOLPH3 association with the clinicopathological characteristics of patients and evaluate the prognostic significance of GOLPH3 in CRC.

Methods

An electronic search for relevant articles was conducted in the PubMed, Cochrane Library, Web of Science, Medline, Embase, CNKI, and WanFang databases. Two independent reviewers searched all the literature and finished the data extraction and quality assessment. Odds ratio (OR) or hazard ratio (HR) with 95% confidence interval (CI) were used to assess estimates. Stata software (version12.0) was employed to analyze the data.

Results

A total of 8 published studies were eligible (N = 723 participants). Meta-analysis revealed that GOLPH3 was found to be highly expressed in tumor tissues compared to that of adjacent colorectal tissues (OR, 2.63), and overexpression of GOLPH3 had significant relationship with advanced clinical stage (OR, 3.42). GOLPH3 expression was not correlated with gender (OR, 0.89), age (OR, 0.95), positive lymphatic metastasis (OR, 1.27), tumor size (OR, 1.12), poor differentiation of tumor (OR, 0.56) or T stage (OR, 0.70). Moreover, GOLPH3 overexpression was not associated with worse overall survival (OS) (HR = 1.14, 95% CI: 0.42–1.86, P>0.05) and disease-free survival (DFS) (HR = 0.80, 95% CI:-0.26–1.86, P>0.05).

Conclusions

GOLPH3 overexpression is correlated with tumor stage, which is an adverse clinicopathological characteristic of CRC. But, GOLPH3 can not serve as a useful biomarker in evaluating the progression of CRC.

Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

BACKGROUND

Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a preliminary comparative analysis of pathway enrichment analysis tools. We extended the earlier work by providing more case studies, comparing BiP enrichment performance with other well-known PEA software tools.

METHODS

PEA uses pathway information to discover connections between a list of genes and proteins as well as biological mechanisms, helping researchers to overcome the problem of explaining biological entity lists of interest disconnected from the biological context.

RESULTS

We compared the results of BiP with some existing pathway enrichment analysis tools comprising Centrality-based Pathway Enrichment, pathDIP, and Signaling Pathway Impact Analysis, considering three cancer types (colorectal, endometrial, and thyroid), for a total of six datasets (that is, two datasets per cancer type) obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. We measured the similarities between the overlap of the enrichment results obtained using each couple of cancer datasets related to the same cancer.

CONCLUSION

As a result, BiP identified some well-known pathways related to the investigated cancer type, validated by the available literature. We also used the Jaccard and meet-min indices to evaluate the stability and the similarity between the enrichment results obtained from each couple of cancer datasets. The obtained results show that BiP provides more stable enrichment results than other tools.

Conformational switches and redox properties of the colon cancer-associated human lectin ZG16

Zymogen granule membrane protein 16 (ZG16) is produced in organs that secrete large quantities of enzymes and other proteins into the digestive tract. ZG16 binds microbial pathogens, and lower ZG16 expression levels correlate with colorectal cancer, but the physiological function of the protein is poorly understood. One prominent attribute of ZG16 is its ability to bind glycans, but other aspects of the protein may also contribute to activity. An intriguing feature of ZG16 is a CXXC motif at the carboxy terminus. Here, we describe crystal structures and biochemical studies showing that the CXXC motif is on a flexible tail, where it contributes little to structure or stability but is available to engage in redox reactions. Specifically, we demonstrate that the ZG16 cysteine thiols can be oxidized to a disulfide by quiescin sulfhydryl oxidase 1, which is a sulfhydryl oxidase present together with ZG16 in the Golgi apparatus and in mucus, as well as by protein disulfide isomerase. ZG16 crystal structures also draw attention to a nonproline cis peptide bond that can isomerize within the protein and to the mobility of glycine‐rich loops in the glycan‐binding site. An understanding of the properties of the ZG16 CXXC motif and the discovery of internal conformational switches extend existing knowledge relating to the glycan‐binding activity of the protein.

CLCA4 and MS4A12 as the significant gene biomarkers of primary colorectal cancer

Background: Primary colorectal cancer (PCRC) is a common digestive tract cancer in the elderly. However, the treatment effect of PCRC is still limited, and the long-term survival rate is low. Therefore, further exploring the pathogenesis of PCRC, and searching for specific molecular targets for diagnosis are the development trends of precise medical treatment, which have important clinical significance.

Methods: The public data were downloaded from Gene Expression Omnibus (GEO) database. Verification for repeatability of intra-group data was performed by Pearson’s correlation test and principal component analysis. Differentially expressed genes (DEGs) between normal and PCRC were identified, and the protein–protein interaction (PPI) network was constructed. Significant module and hub genes were found in the PPI network. A total of 192 PCRC patients were recruited between 2010 and 2019 from the Fourth Hospital of Hebei Medical University. RT-PCR was used to measure the relative expression of CLCA4 and MS4A12. Furthermore, the study explored the effect of expression of CLCA4 and MS4A12 for overall survival.

Results: A total of 53 DEGs were identified between PCRC and normal colorectal tissues. Ten hub genes concerned to PCRC were screened, namely CLCA4, GUCA2A, GCG, SST, MS4A12, PLP1, CHGA, PYY, VIP, and GUCA2B. The PCRC patients with low expression of CLCA4 and MS4A12 has a worse overall survival than high expression of CLCA4 and MS4A12 (P<0.05).

Conclusion: The research of DEGs in PCRC (53 DEGs, 10 hub genes, especially CLCA4 and MS4A12) and related signaling pathways is conducive to the differential analysis of the molecular mechanism of PCRC.

Integration of gene expression data identifies key genes and pathways in colorectal cancer

Colorectal cancer (CRC) is one of the most common malignant tumor and prevalent cause of cancer-related death worldwide. In this study, we analyzed the gene expression profiles of patients with CRC with the aim of better understanding the molecular mechanism and key genes in CRC. Four gene expression profiles including, GSE9348, GSE41328, GSE41657, and GSE113513 were downloaded from GEO database. The data were processed using R programming language, in which 319 common differentially expressed genes including 94 up-regulated and 225 down-regulated were identified. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were conducted to find the most significant enriched pathways in CRC. Based on the GO and KEGG pathway analysis, the most important dysregulated pathways were regulation of cell proliferation, biocarbonate transport, Wnt, and IL-17 signaling pathways, and nitrogen metabolism. The protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software and hub genes including MYC, CXCL1, CD44, MMP1, and CXCL12 were identified as the most critical hub genes. The present study enhances our understanding of the molecular mechanisms of the CRC, which might potentially be applied in the treatment strategies of CRC as molecular targets and diagnostic biomarkers.

A Novel Prognostic Score Based on ZG16 for Predicting CRC Survival

Background

Colorectal cancer (CRC) is one of the lethal malignant tumors worldwide. However, the underlying mechanism of CRC and its biomarkers remain unclear. The aim of this study was to identify the key genes associated with CRC and to further explore their prognostic significance.

Methods

Four expression profile datasets (GSE41657, GSE74602, GSE113513, and GSE40967) downloaded from Gene Expression Omnibus (GEO) and one RNAseq dataset of CRC from The Cancer Genome Atlas (TCGA) database were included in our study. The Cox model was utilized for univariate or multivariate survival analysis. GEPIA and HAP database were adopted for verification of DEGs (ZG16). The decision curve analysis (DCA) and time-dependent ROC were chosen for evaluating the prognostic effectiveness of biomarkers.

Results

In total, 88 differentially expressed genes (DEGs) were identified, and the GO and KEGG enrichment analyses of DEGs were processed. After, the protein–protein interaction (PPI) network was constructed and 15 hub genes including ZG16 were identified. The differential expression of ZG16 between tumor and normal colorectal tissues were further verified in GEPIA and HAP database. Subsequent survival indicated that expression of ZG16 is negatively correlated with overall survival of OS and is an independent prognostic factor for CRC patients. Furthermore, the construction of a prognostic score containing ZG16, TNM stage and age exhibited superior effectiveness for predicting long-term survival of CRC patients. Additionally, our results were verified using the GSE40967 dataset, which indicated an improved performance of combined risk score based on ZG16 for predicting OS of CRC patients.

Conclusion

ZG16 is a potential parameter for predicting prognosis in CRC. Furthermore, a combination of ZG16, TNM stage, and age allows improved prognosis of CRC.

The targeted inhibition of prostate cancer by iron-based nanoparticles based on bioinformatics

Prostate cancer is an epithelial malignant tumor of the prostate, and it is one of the malignant tumors with a high incidence of urogenital system in men. The local treatment of prostate cancer is mainly radical resection and radical radiotherapy, but they are not applicable to advanced prostate cancer. Systemic therapy mainly includes targeted therapy and immunotherapy which could cause many complications, and will affect the prognosis and quality of life of patients. It is urgent to find new treatments for prostate cancer. Bioinformatics offers hope for us to find reliable therapeutic targets. Bioinformatics can use the tumor informations in database and analyze them to screen out the best differentially expressed genes. Using the selected differentially expressed genes as targets, a gene interference plasmid was designed, and the constructed plasmid was used for targeted gene therapy. There are some problems about gene therapy that need to be solved, such as how to transfer genes to target cells is also an important challenge. Due to their large molecular weight and hydrophilic nature, they cannot enter cells through passive diffusion mechanisms. Here we synthesized a DNA carrier used surface modified iron based nanoparticles, and used it to load plasmid including ShRNA which can inhibit the expression of oncogene SLC4A4 selected by bioinformatics’ method. After that we use this iron based nanoparticles/plasmid DNA nanocomposite to treat prostate cancer cells in vitro and in vivo. The target gene SLC4A4 we had selected using bioinformatics had a strong effect on the proliferation of prostate cells; Our nanocomposite could inhibit the expression of SLC4A4 effectively, it had strong inhibitory effects on prostate cancer cells both in vivo and in vitro, and can be used as a potential method for prostate cancer treatment.

Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning

Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signature-based diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic 'fingerprints' rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nano- technologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies.

Important players in carcinogenesis as potential targets in cancer therapy: an update

The development of cancer is a problem that has accompanied mankind for years. The growing number of cases, emerging drug resistance, and the need to reduce the serious side effects of pharmacotherapy are forcing scientists to better understand the complex mechanisms responsible for the initiation, promotion, and progression of the disease. This paper discusses the modulation of the particular stages of carcinogenesis by selected physiological factors, including: acetylcholine (ACh), peroxisome proliferator-activated receptors (PPAR), fatty acid-binding proteins (FABPs), Bruton's tyrosine kinase (Btk), aquaporins (AQPs), insulin-like growth factor-2 (IGF-2), and exosomes. Understanding their role may contribute to the development of more effective and safer therapies based on new binding sites.

Gene Expression Network Analysis of Precursor Lesions in Familial Pancreatic Cancer

Purpose: High-grade pancreatic intraepithelial neoplasia (PanIN) are aggressive premalignant lesions, associated with risk of progression to pancreatic ductal adenocarcinoma (PDAC). A depiction of co-dysregulated gene activity in high-grade familial pancreatic cancer (FPC)-related PanIN lesions may characterize the molecular events during the progression from familial PanIN to PDAC. Materials and Methods: We performed weighted gene coexpression network analysis (WGCNA) to identify clusters of coexpressed genes associated with FPC-related PanIN lesions in 13 samples with PanIN-2/3 from FPC predisposed individuals, 6 samples with PDAC from sporadic pancreatic cancer (SPC) patients, and 4 samples of normal donor pancreatic tissue. Results: WGCNA identified seven differentially expressed gene (DEG) modules and two commonly expressed gene (CEG) modules with significant enrichment for Gene Ontology (GO) terms in FPC and SPC, including three upregulated (p < 5e-05) and four downregulated (p < 6e-04) gene modules in FPC compared to SPC. Among the DEG modules, the upregulated modules include 14 significant genes (p < 1e-06): ALOX12-AS1, BCL2L11, EHD4, C4B, BTN3A3, NDUFA11, RBM4B, MYOC, ZBTB47, TTTY15, NAPRT, LOC102606465, LOC100505711, and PTK2. The downregulated modules include 170 genes (p < 1e-06), among them 13 highly significant genes (p < 1e-10): COL10A1, SAMD9, PLPP4, COMP, POSTN, IGHV4-31, THBS2, MMP9, FNDC1, HOPX, TMEM200A, INHBA, and SULF1. The DEG modules are enriched for GO terms related to mitochondrial structure and adenosine triphosphate metabolic processes, extracellular structure and binding properties, humoral and complement mediated immune response, ligand-gated ion channel activity, and transmembrane receptor activity. Among the CEG modules, IL22RA1, DPEP1, and BCAT1 were found as highly connective hub genes associated with both FPC and SPC. Conclusion: FPC-related PanIN lesions exhibit a common molecular basis with SPC as shown by gene network activities and commonly expressed high-connectivity hub genes. The differential molecular pathology of FPC and SPC involves multiple coexpressed gene clusters enriched for GO terms including extracellular activities and mitochondrion function.

The Neuropeptide System and Colorectal Cancer Liver Metastases: Mechanisms and Management

Colorectal cancer (CRC), classified as the third most prevalent cancer worldwide, remains to be a clinical and research challenge. It is estimated that ~50% of CRC patients die from distant metastases, with treatment of this complication still posing significant difficulties. While liver metastasis (LM) cascade is known in the literature, its mechanisms are still unclear and remain studied in different research models. A connection is suggested between nervous system dysfunctions and a range of Neurotransmitters (Nts) (including Neuropeptides, NPs), Neurotrophins (Ntt) and their receptors (Rs) in CRC liver metastasis development. Studies on the role of NP/NP-Rs in the progression and metastasis of CRC, show the complexity of brain–tumor interactions, caused by their different forms of release to the extracellular environment (endocrine, autocrine, paracrine and neurocrine). Many stages of LM are connected to the activity of pro-inflammatory, e.g., Corticotropin-releasing Hormone Receptor 1 (CRHR1), Neuropeptide Y (NPY) and Neurotensin (NT), anti-inflammatory, e.g., Calcitonin Gene-related Peptide (CGRP), CRHR2 and Vasoactive Intestinal Polypeptide (VIP) or dual role neuropeptides, e.g., Substance P (SP). The regulation of the local immunological profile (e.g., CRH/CRHRs), dysfunctions of enteroprotective role of NPs on epithelial cells (e.g., NT/NT-R), as well as structural-functional changes in enteric nervous system innervation of the tumor are also important. More research is needed to understand the exact mechanisms of communication between the neurons and tumor cells. The knowledge on the mechanisms regulating tumor growth and different stages of metastasis, as well as effects of the action of a numerous group of Nts/NPs/Ntt as growth factors, have implications for future therapeutic strategies. To obtain the best treatment outcomes, it is important to use signaling pathways common for many NPs, as well to develop a range of broad-spectrum antagonists. This review aims to summarize the current knowledge on the importance of neuroactive molecules in the promotion of the invasion-metastasis cascade in CRC, as well as the improvements of clinical management of CRC liver metastasis.

Prognostic value of small nucleolar RNAs (snoRNAs) for colon adenocarcinoma based on RNA sequencing data

Although the molecular studies of single gastrointestinal tumors have been widely reported by media, it is not clear about the function of small nucleolar RNA (snoRNA) in the progression, development and prognostic significance in colon adenocarcinoma, and its certain molecular mechanisms and functions remain to be studied. This study aims to dig out the gene expression data profile of colon adenocarcinoma and construct the prognostic molecular pathology prediction-evaluation, ultimately revealing the clinical prognostic value of snoRNA in colon adenocarcinoma. 932 differentially expressed snoRNAs of the colon adenocarcinoma were obtained by edgeR R package. Only 4 prognostically-significant snoRNAs (SNORD14E, SNORD67, SNORD12C, and SNORD17) (P < 0.05) were discovered after univariate COX regression mode analysis. Moreover, through multivariate COX regression mode analysis, 2 prognostically-significant snoRNAs (SNORD14E and SNORD67) (P < 0.05) were obtained. Using the above 473 COAD samples, a prognostic model of risk score was constructed. The inflection point of the prognostic risk score acted as a boundary to divide the patients into high-risk and low-risk groups. The K-M survival curve of the prognostic model of risk score revealed that high risk group has a lower survival rate (P < 0.05). The research has successfully provided valuable prognostic factors and prognostic models for patients with malignant colon tumor.

Identification of key genes for predicting colorectal cancer prognosis by integrated bioinformatics analysis

Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.