Risk analysis of colorectal cancer incidence by gene expression analysis

Cellular zinc metabolism and zinc signaling: from biological functions to diseases and therapeutic targets

Zinc metabolism at the cellular level is critical for many biological processes in the body. A key observation is the disruption of cellular homeostasis, often coinciding with disease progression. As an essential factor in maintaining cellular equilibrium, cellular zinc has been increasingly spotlighted in the context of disease development. Extensive research suggests zinc's involvement in promoting malignancy and invasion in cancer cells, despite its low tissue concentration. This has led to a growing body of literature investigating zinc's cellular metabolism, particularly the functions of zinc transporters and storage mechanisms during cancer progression. Zinc transportation is under the control of two major transporter families: SLC30 (ZnT) for the excretion of zinc and SLC39 (ZIP) for the zinc intake. Additionally, the storage of this essential element is predominantly mediated by metallothioneins (MTs). This review consolidates knowledge on the critical functions of cellular zinc signaling and underscores potential molecular pathways linking zinc metabolism to disease progression, with a special focus on cancer. We also compile a summary of clinical trials involving zinc ions. Given the main localization of zinc transporters at the cell membrane, the potential for targeted therapies, including small molecules and monoclonal antibodies, offers promising avenues for future exploration.

Clinical plasma cells-related genes to aid therapy in colon cancer

The tumor immune microenvironment (TIME) of colon cancer (CC) has been associated with extensive immune cell infiltration (IMI). Increasing evidence demonstrated that plasma cells (PC) have an extremely important role in advance of antitumor immunity. Nonetheless, there is a lack of comprehensive analyses of PC infiltration in clinical prognosis and immunotherapy in CC. This study systematically addressed the gene expression model and clinical information of CC patients. Clinical samples were obtained from the TCGA (The Cancer Genome Atlas) databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), GSVA, and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were employed to research the potential mechanism and pathways. Immunophenoscore (IPS) was obtained to evaluate the immunotherapeutic significance of risk score. Half maximal inhibitory concentration (IC50) of chemotherapeutic medicine was predicted by employing the pRRophetic algorithm. A total of 513 CC samples (including 472 tumor samples and 41 normal samples) were collected from the TCGA-GDC database. Significant black modules and 313 candidate genes were considered PC-related genes by accessing WGCNA. Five pivotal genes were established through multiple analyses, which revealed excellent prognostic. The underlying correlation between risk score with tumor mutation burden (TMB) was further explored. In addition, the risk score was obviously correlated with various tumor immune microenvironment (TIME). Also, risk CC samples showed various signaling pathways activity and different pivotal sensitivities to administering chemotherapy. Finally, the biological roles of the CD177 gene were uncovered in CC.

A novel prognostic model based on urea cycle-related gene signature for colorectal cancer

Background

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the world. This study aimed to develop a urea cycle (UC)-related gene signature that provides a theoretical foundation for the prognosis and treatment of patients with CRC.

Methods

Differentially expressed UC-related genes in CRC were confirmed using differential analysis and Venn diagrams. Univariate Cox and least absolute shrinkage and selection operator regression analyses were performed to identify UC-related prognostic genes. A UC-related signature was created and confirmed using distinct datasets. Independent prognostic predictors were authenticated using Cox analysis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts algorithm and Spearman method were applied to probe the linkage between UC-related prognostic genes and tumor immune-infiltrating cells. The Human Protein Atlas database was used to determine the protein expression levels of prognostic genes in CRC and normal tissues. Verification of the expression levels of UC-related prognostic genes in clinical tissue samples was performed using real-time quantitative polymerase chain reaction (qPCR).

Results

A total of 49 DEUCRGs in CRC were mined. Eight prognostic genes (TIMP1, FABP4, MMP3, MMP1, CD177, CA2, S100P, and SPP1) were identified to construct a UC-related gene signature. The signature was then affirmed using an external validation set. The risk score was demonstrated to be a credible independent prognostic predictor using Cox regression analysis. Functional enrichment analysis revealed that focal adhesion, ECM-receptor interaction, IL-17 signaling pathway, and nitrogen metabolism were associated with the UC-related gene signature. Immune infiltration and correlation analyses revealed a significant correlation between UC-related prognostic genes and differential immune cells between the two risk subgroups. Finally, the qPCR results of clinical samples further confirmed the results of the public database.

Conclusion

Taken together, this study authenticated UC-related prognostic genes and developed a gene signature for the prognosis of CRC, which will be of great significance in the identification of prognostic molecular biomarkers, clinical prognosis prediction, and development of treatment strategies for patients with CRC.

Epigenomic effects of vitamin D in colorectal cancer

Vitamin D regulates a plethora of physiological processes in the human body and has been proposed to exert several anticancer effects. Epigenetics plays an important role in regulating vitamin D actions. In this review, we highlight the recent advances in the understanding of different epigenetic factors such as lncRNAs, miRNAs, methylation and acetylation influenced by vitamin D and its downstream targets in colorectal cancer to find more potential therapeutic targets. We discuss how vitamin D exerts anticancer properties through interactions between the vitamin D receptor and genes (e.g., SLC30A10), the microenvironment, microbiota and other factors in colorectal cancer. Developing therapeutic approaches targeting the vitamin D signaling system will be aided by a better knowledge of the epigenetic impact of vitamin D.

Gene network profiling in muscle-invasive bladder cancer: A systematic review and meta-analysis

BACKGROUND

Determining meta-analysis of transcriptional profiling of muscle-invasive bladder cancer (MIBC) through Gene Expression Omnibus (GEO) datasets has not been investigated. This study aims to define gene expression profiles in MIBC and to identify potential candidate genes and pathways.

OBJECTIVES

To review and evaluate gene expression studies in MIBC through publicly available RNA sequencing (RNA-Seq) and microarray data in order to identify potential prognostic and therapeutic targets for MIBC.

METHODS

A systematic literature search of the Ovid MEDLINE, PubMed, and Wiley Cochrane Central Register of Controlled Trials databases was performed using the terms "gene," "gene expression," and "bladder cancer" January 1, 1990 through March 2021 focused on populations with MIBC.

RESULTS

In the final analysis, GEO datasets were included. Fixed effect model was employed in the meta-analysis. Gene networking connections and gene-set functional analyses of the identified genes as differentially expressed in MIBC were performed using ImaGEO and GeneMANIA software. A heatmap for the upregulated and downregulated genes was generated along with the correlated pathways.

CONCLUSION

A total of 9 genes were reported in this analysis. Six genes were reported as upregulated (ProTα, SPINT1, UBE2E1, RAB25, KPNB1, HDAC1) and 3 genes as downregulated (NUP188, IPO13, NUP124). Genes were found to be involved in "ubiquitin mediated proteolysis," "protein processing in endoplasmic reticulum," "transcriptional misregulation in cancer," and "RNA transport" pathways.

Ontologies and Knowledge Graphs in Oncology Research

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

A Dynamic Transcription Factor Signature Along the Colorectal Adenoma-Carcinoma Sequence in Patients With Co-Occurrent Adenoma and Carcinoma

Background

Colorectal carcinoma (CRC) often arises from benign adenoma after a stepwise accumulation of genetic alterations. Here, we profiled the dynamic landscapes of transcription factors (TFs) in the mucosa-adenoma-carcinoma progression sequence.

Methods

The transcriptome data of co-occurrent adenoma, carcinoma, and normal mucosa samples were obtained from GSE117606. Identification of differentially expressed TFs (DE-TFs) and subsequent function annotation were conducted in R software. Expression patterns of DE-TFs were clustered by Short Time-series Expression Miner software. Thereafter, modular co-expression analysis, Kaplan-Meier survival analysis, mutation profiling, and gene set enrichment analysis were conducted to investigate TF dynamics in colorectal tumorigenesis. Finally, tissue microarrays, including 51 tumors, 32 adenomas, and 53 normal tissues, were employed to examine the expression of significant candidates by immunohistochemistry staining.

Results

Compared to normal tissues, 20 (in adenoma samples) and 29 (in tumor samples) DE-TFs were identified. During the disease course, 28 expression patterns for DE-TFs and four co-expression modules were clustered. Notably, six DE-TFs, DACH1, GTF2IRD1, MEIS2, NR3C2, SOX9, and SPIB, were identified as having a dynamic signature along the colorectal adenoma-carcinoma sequence. The dynamic signature was of significance in GO enrichment, prognosis, and co-expression analysis. Among the 6-TF signature, the roles of GTF2IRD1, SPIB and NR3C2 in CRC progression are unclear. Immunohistochemistry validation showed that GTF2IRD1 enhanced significantly throughout the mucosa-adenoma-carcinoma sequence, while SPIB and NR3C2 kept decreasing in stroma during the disease course.

Conclusions

Our study provided a dynamic 6-TF signature throughout the course of colorectal mucosa-adenoma-carcinoma. These findings deepened the understanding of colorectal cancer pathogenesis.

Centenarian Exomes as a Tool for Evaluating the Clinical Relevance of Germline Tumor Suppressor Mutations

Objectives:

The aim of the present study was to evaluate the clinical relevance of mutations in tumor suppressor genes using whole-exome sequencing data from centenarians and young healthy individuals.

Methods:

Two pools, one of centenarians and one of young individuals, were constructed and whole-exome sequencing was performed. We examined the whole-exome sequencing data of Bulgarian individuals for carriership of tumor suppressor gene variants.

Results:

Of all variants annotated in both pools, 5080 (0.06%) are variants in tumor suppressor genes but only 46 show significant difference in allele frequencies between the two studied groups. Four variants (0.004%) are pathogenic/risk factors according to single nucleotide polymorphism database: rs1566734 in PTPRJ, rs861539 in XRCC3, rs203462 in AKAP10, and rs486907 in RNASEL.

Discussion:

Based on their high minor allele frequencies and presence in the centenarian group, we could reclassify them from pathogenic/risk factors to benign. Our study shows that centenarian exomes can be used for re-evaluating the clinically uncertain variants.

Overexpression of gene DEP domain containing 1 and its clinical prognostic significance in colorectal cancer

Background

Colorectal cancer (CRC) is one of the most commonly seen malignancies worldwide, yet its regulatory mechanisms still need to be further illuminated. Abundant evidence revealed that aberrant expression of cancer‐related genes contributes to CRC progression. DEP domain containing 1 (DEPDC1) has been found to play a crucial role in the carcinogenesis and development of malignancies. Nevertheless, limited studies have been concerned with the role of DEPDC1 in CRC. This study aimed to investigate the relationship between DEPDC1 expression and CRC clinicopathological parameters.

Methods

Solid CRC tissues and adjacent noncancerous tissues (ANCTs) (n = 150) were chosen randomly to detect the mRNA expression levels of DEPDC1 by real‐time quantitative reverse transcription‐polymerase chain reaction (RT‐qPCR). Formalin‐fixed, paraffin‐embedded (FFPE) blocks of CRC tissues and ANCTs (n = 150) were acquired to examine DEPDC1 protein expression levels by immunohistochemistry (IHC).

Results

DEPDC1 was significantly overexpressed in CRC tissues than that in ANCTs (P < .05). High protein expression of DEPDC1 was associated with poorer TNM stage and recurrence (P < .001 and P = .003, respectively). Kaplan‐Meier survival analysis showed significantly shorter overall survival (OS) and disease‐free survival (DFS) in DEPDC1 protein high‐expression group compared with low‐expression group (P < .05). Univariate analysis demonstrated that DEPDC1 protein expression was correlated with DFS (P = .005) and OS (P = .006). Multivariate analysis revealed that the combination of DEPDC1 protein expression and TNM stage has statistical significance in CRC prognosis prediction (P = .024 and P = .009, respectively).

Conclusions

DEPDC1 may act as a potential biomarker for CRC detection as well as a prognostic predictor concerning the survival of CRC patients.

Comprehensive analysis of the expression of SLC30A family genes and prognosis in human gastric cancer

The solute carrier 30 (SLC30) family genes play a fundamental role in various cancers. However, the diverse expression patterns, prognostic value, and potential mechanism of SLC30A family genes in gastric cancer (GC) remain unknown. Herein, we analyzed the expression and survival data of SLC30A family genes in GC patients using multiple bioinformatic approaches. Expression data of SLC30A family genes for GC patients were extracted from the Cancer Genome Atlas (TCGA) and genetic alteration frequency assessed by using cBioportal database. And validated the expression of SLC30A family genes in GC tissues and corresponding normal tissues. The prognostic value of SLC30A family genes in gastric cancer patients were explored using Kaplan–Meier plotter database. Functional enrichment analysis performed using DAVID database and clusterProfiler package. And ssGSEA algorithm was performed to explore the relationship between the SLC30A family genes and the infiltration of immune cells. We found that the median expression levels of SLC30A1-3, 5–7, and 9 were significantly upregulated in gastric cancer tissues compared to non-cancerous tissues, while SLC30A4 was downregulated. Meanwhile, SLC30A1-7, and 9 were significantly correlated with advanced tumor stage and nodal metastasis status, SLC30A5-7, and 9–10 were significantly related to the Helicobacter pylori infection status of GC patients. High expression of five genes (SLC30A1, 5–7, and 9) was significantly correlated with better overall survival (OS), first progression survival (FPS), and post progression survival (PPS). Conversely, upregulated SLC30A2-4, 8, and 10 expression was markedly associated with poor OS, FP and PPS. And SLC30A family genes were closely associated with the infiltration of immune cells. The present study implied that SLC30A5 and 7 may be potential biomarkers for predicting prognosis in GC patients, SLC30A2 and 3 play an oncogenic role in GC patients and could provide a new strategy for GC patients treatment.

An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer

One of the deadliest diseases which affects the large intestine is colon cancer. Older adults are typically affected by colon cancer though it can happen at any age. It generally starts as small benign growth of cells that forms on the inside of the colon, and later, it develops into cancer. Due to the propagation of somatic alterations that affects the gene expression, colon cancer is caused. A standardized format for assessing the expression levels of thousands of genes is provided by the DNA microarray technology. The tumors of various anatomical regions can be distinguished by the patterns of gene expression in microarray technology. As the microarray data is too huge to process due to the curse of dimensionality problem, an amalgamated approach of utilizing bilevel feature selection techniques is proposed in this paper. In the first level, the genes or the features are dimensionally reduced with the help of Multivariate Minimum Redundancy–Maximum Relevance (MRMR) technique. Then, in the second level, six optimization techniques are utilized in this work for selecting the best genes or features before proceeding to classification process. The optimization techniques considered in this work are Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), League Championship Optimization (LCO), Beetle Antennae Search Optimization (BASO), Crow Search Optimization (CSO), and Fruit Fly Optimization (FFO). Finally, it is classified with five suitable classifiers, and the best results show when IWO is utilized with MRMR, and then classified with Quadratic Discriminant Analysis (QDA), a classification accuracy of 99.16% is obtained.

Identification of microRNA-451a as a Novel Circulating Biomarker for Colorectal Cancer Diagnosis

Background

Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Successful treatment of CRC relies on accurate early diagnosis, which is currently a challenge due to its complexity and personalized pathologies. Thus, novel molecular biomarkers are needed for early CRC detection.

Methods

Gene and microRNA microarray profiling of CRC tissues and miRNA-seq data were analyzed. Candidate microRNA biomarkers were predicted using both CRC-specific network and miRNA-BD tool. Validation analyses were carried out to interrogate the identified candidate CRC biomarkers.

Results

We identified miR-451a as a potential early CRC biomarker circulating in patient's serum. The dysregulation of miR-451a was revealed both in primary tumors and in patients' sera. Downstream analysis validated the tumor suppressor role of miR-451a and high sensitivity of miR-451a in CRC patients, further confirming its potential role as CRC circulation biomarker.

Conclusion

The miR-451a is a potential circulating biomarker for early CRC diagnosis.

Enrichment of Up-regulated and Down-regulated Gene Clusters Using Gene Ontology, miRNAs and lncRNAs in Colorectal Cancer

AIM AND OBJECTIVE

It is interesting to find the gene signatures of cancer stages based on the omics data. The aim of study was to evaluate and to enrich the array data using gene ontology and ncRNA databases in colorectal cancer.

METHODS

The human colorectal cancer data were obtained from the GEO databank. The downregulated and up-regulated genes were identified after scoring, weighing and merging of the gene data. The clusters with high-score edges were determined from gene networks. The miRNAs related to the gene clusters were identified and enriched. Furthermore, the long non-coding RNA (lncRNA) networks were predicted with a central core for miRNAs.

RESULTS

Based on cluster enrichment, genes related to peptide receptor activity (1.26E-08), LBD domain binding (3.71E-07), rRNA processing (2.61E-34), chemokine (4.58E-19), peptide receptor (1.16E-19) and ECM organization (3.82E-16) were found. Furthermore, the clusters related to the non-coding RNAs, including hsa-miR-27b-5p, hsa-miR-155-5p, hsa-miR-125b-5p, hsa-miR-21-5p, hsa-miR-30e-5p, hsa-miR-588, hsa-miR-29-3p, LINC01234, LINC01029, LINC00917, LINC00668 and CASC11 were found.

CONCLUSION

The comprehensive bioinformatics analyses provided the gene networks related to some non-coding RNAs that might help in understanding the molecular mechanisms in CRC.

Crosstalk between DNA methylation and gene expression in colorectal cancer, a potential plasma biomarker for tracing this tumor

Colorectal cancer (CRC), the second leading cause of cancer mortality, constitutes a significant global health burden. An accurate, noninvasive detection method for CRC as complement to colonoscopy could improve the effectiveness of treatment. In the present study, SureSelectXT Methyl-Seq was performed on cancerous and normal colon tissues and CLDN1, INHBA and SLC30A10 were found as candidate methylated genes. MethyLight assay was run on formalin-fixed paraffin-embedded (FFPE) and fresh case and control tissues to validate the methylation of the selected gene. The methylation was significantly different (p-values < 2.2e-16) with a sensitivity of 87.17%; at a specificity cut-off of 100% in FFPE tissues. Methylation studies on fresh tissues, indicated a sensitivity of 82.14% and a specificity cut-off of 92% (p-values = 1.163e-07). The biomarker performance was robust since, normal tissues indicated a significant 22.1-fold over-expression of the selected gene as compared to the corresponding CRC tissues (p-value < 2.2e-16) in the FFPE expression assay. In our plasma pilot study, evaluation of the tissue methylation marker in the circulating cell-free DNA, demonstrated that 9 out of 22 CRC samples and 20 out of 20 normal samples were identified correctly. In summary, there is a clinical feasibility that the offered methylated gene could serve as a candidate biomarker for CRC diagnostic purpose, although further exploration of our candidate gene is warranted.

Integrative Gene Expression Profiling Analysis to Investigate Potential Prognostic Biomarkers for Colorectal Cancer

BACKGROUND Despite noteworthy advancements in the multidisciplinary treatment of colorectal cancer (CRC) and deeper understanding in the molecular mechanisms of CRC, many of CRC patients with histologically identical tumors present different treatment response and prognosis. Thus, more evidence on novel predictive and prognostic biomarkers for CRC remains urgently needed. This study aims to identify potential prognostic biomarkers for CRC with integrative gene expression profiling analysis. MATERIAL AND METHODS Differential expression analysis of paired CRC and adjacent normal tissue samples in 6 microarray datasets was independently performed, and the 6 datasets were integrated by the robust rank aggregation method to detect consistent differentially expressed genes (DEGs). Aberrant expression patterns of these genes were further validated in RNA sequencing data. Then, gene set enrichment analysis (GSEA) was performed to investigate significantly dysregulated biological functions in CRC. Finally, univariate, LASSO and multivariate Cox regression models were built to identify key prognostic genes in CRC patients. RESULTS A total of 990 DEGs (495 downregulated and 495 upregulated genes) were acquired after integratedly analyzing the 6 microarray datasets, and 4131 DEGs (2050 downregulated and 2081 upregulated genes) were obtained from the RNA sequencing dataset. Subsequently, these DEGs were intersected and 885 consistent DEGs were finally identified, including 458 downregulated and 427 upregulated genes. Two risky prognostic genes (TIMP1 and LZTS3) and 5 protective prognostic genes (AXIN2, CXCL1, ITLN1, CPT2 and CLDN23) were identified, which were significantly associated with the prognosis of CRC. CONCLUSIONS The 7 genes that we identified would provide more evidence for further applying novel diagnostic and prognostic biomarkers in clinical practice to facilitate personalized treatment of CRC.

Identification of Potential Biomarkers with Diagnostic Value in Pituitary Adenomas Using Prediction Analysis for Microarrays Method

Pituitary adenomas are the most common intrasellar tumors. Patients should be identified at an early stage so that effective treatment can be implemented. The study aims at detecting the potential biomarkers with diagnostic value of pituitary adenomas. Using a total of seven gene expression profiles (GEPs) of the datasets from the Gene Expression Omnibus (GEO) database, we first screened 1980 significant differentially expressed genes (DEGs). Then, we employed the prediction analysis for microarray (PAM) algorithm to identify 340 significant DEGs able to differ pituitary tumor from normal samples, which include 208 upregulated DEGs and 132 downregulated DEGs. DAVID database was used to carry out the enrichment analysis on Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathways. We found that upregulated candidates were enriched in protein folding and metabolic pathways. Downregulated DEGs saw a significant enrichment in insulin receptor signaling pathway and hedgehog signaling pathway. Based on the protein-protein interaction (PPI) network as well as module analysis, we determined ten hub genes including PHLPP, ENO2, ACTR1A, EHHADH, EHMT2, FOXO1, DLD, CCT2, CSNK1D, and CETN2 that could be potential biomarkers with diagnostic value in pituitary adenomas. In conclusion, the study contributes to reliable and potential molecular biomarkers with diagnostic value. Moreover, these potential biomarkers may be used for prognosis and new therapeutic targets for the pituitary adenomas.

Integrating proteomics and transcriptomics for the identification of potential targets in early colorectal cancer

Colorectal cancer (CRC) is one of the most common malignancies worldwide. At present, CRC can often be treated upon diagnosis at stage I or II, or when dysplasia is detected; however, 60-70% of cases are not diagnosed until they have developed into late stages of the disease or until the malignancy is identified. Diagnosis of CRC at an early stage remains a challenge due to the absence of early-stage-specific biomarkers. To identify potential targets of early stage CRC, label-free proteomics analysis was applied to paired tumor-benign tissue samples from patients with stage II CRC (n=21). A total of 2,968 proteins were identified; corresponding RNA-Sequencing data were retrieved from The Cancer Genome Atlas-colon adenocarcinoma. Numerous bioinformatics methods, including differential expression analysis, weighted correlation network analysis, Gene Ontology and protein-protein interaction analyses, were applied to the proteomics and transcriptomics data. A total of 111 key proteins, which appeared as both differentially expressed proteins and mRNAs in the hub module, were identified as key candidates. Among these, three potential targets [protein-arginine deiminase type-2 (PADI2), Fc fragment of IgG binding protein (FCGBP) and phosphoserine aminotransferase 1] were identified from the pathological data. Furthermore, the survival analysis indicated that PADI2 and FCGBP were associated with the prognosis of CRC. The findings of the present study suggested potential targets for the identification of early stage CRC, and may improve understanding of the mechanism underlying the occurrence of CRC.

CLDN8 promotes colorectal cancer cell proliferation, migration, and invasion by activating MAPK/ERK signaling

Background

Claudin 8 (CLDN8), an integral membrane protein that constitutes tight junctions in cell membranes, was recently implicated in tumor progression. However, its roles in colorectal cancer (CRC) progression and metastasis remain unknown.

Methods

In this study, we examined the effect of CLDN8 on the progression of CRC, including cell proliferation, migration, and invasion, and determines its underlying molecular mechanism using in vitro CRC cell lines and in vivo mouse xenograft models.

Results

We found that CLDN8 expression in human CRC tissues was significantly higher than that in adjacent normal tissues. The knockdown of CLDN8 markedly suppressed the proliferation, migration, and invasion of SW480 and HT-29 CRC cells, whereas the overexpression of CLDN8 notably promoted tumor progression in SW480 and HT-29 CRC cells. Mechanistic studies revealed that CLDN8 upregulated p-ERK (p-PKB/AKT) and MMP9 in CRC cells. Notably, the MAPK/ERK inhibitor PD98095 dramatically attenuated the effects of CLDN8 on p-ERK and MMP9. Moreover, PD98095 remarkably blocked the tumor-promoting activity of CLDN8. The knockdown of CLDN8 also inhibited the in vivo tumor growth in a nude mouse xenograft model. Collectively, CLDN8 promoted CRC cell proliferation, migration, and invasion, at least in part, by activating the MAPK/ERK signaling pathway.

Conclusion

These findings suggest that CLDN8 exhibits an oncogenic effect in human CRC progression.

OMICfpp: a fuzzy approach for paired RNA-Seq counts

BACKGROUND

RNA sequencing is a widely used technology for differential expression analysis. However, the RNA-Seq do not provide accurate absolute measurements and the results can be different for each pipeline used. The major problem in statistical analysis of RNA-Seq and in the omics data in general, is the small sample size with respect to the large number of variables. In addition, experimental design must be taken into account and few tools consider it.

RESULTS

We propose OMICfpp, a method for the statistical analysis of RNA-Seq paired design data. First, we obtain a p-value for each case-control pair using a binomial test. These p-values are aggregated using an ordered weighted average (OWA) with a given orness previously chosen. The aggregated p-value from the original data is compared with the aggregated p-value obtained using the same method applied to random pairs. These new pairs are generated using between-pairs and complete randomization distributions. This randomization p-value is used as a raw p-value to test the differential expression of each gene. The OMICfpp method is evaluated using public data sets of 68 sample pairs from patients with colorectal cancer. We validate our results through bibliographic search of the reported genes and using simulated data set. Furthermore, we compared our results with those obtained by the methods edgeR and DESeq2 for paired samples. Finally, we propose new target genes to validate these as gene expression signatures in colorectal cancer. OMICfpp is available at http://www.uv.es/ayala/software/OMICfpp_0.2.tar.gz .

CONCLUSIONS

Our study shows that OMICfpp is an accurate method for differential expression analysis in RNA-Seq data with paired design. In addition, we propose the use of randomized p-values pattern graphic as a powerful and robust method to select the target genes for experimental validation.

Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis

Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it is of vital significance to build proper models that can avoid the above problems and predict the CRC more accurately. In this paper, we use an ensemble model to classify samples into healthy and CRC groups and improve prediction performance. The proposed model is composed of three functional modules. The first module mainly performs the function of removing redundant genes. The main feature genes are selected using minimum redundancy maximum relevance (mRMR) method to reduce the dimensionality of features thereby increasing the prediction results. The second module aims to solve the problem caused by imbalanced data using hybrid sampling algorithm RUSBoost. The third module focuses on the classification algorithm optimization. We use mixed kernel function (MKF) based support vector machine (SVM) model to classify an unknown sample into healthy individuals and CRC patients, and then, the Whale Optimization Algorithm (WOA) is applied to find most optimal parameters of the proposed MKF-SVM. The final results show that the proposed model achieves higher G-means than other comparable models. The conclusion comes to show that RUSBoost wrapping WOA + MKF-SVM model can be applied to improve the predictive performance of colorectal cancer based on the imbalanced data.

Meta-analysis of association between Arg326Gln (rs1503185) and Gln276Pro (rs1566734) polymorphisms of PTPRJ gene and cancer risk

Protein tyrosine phosphatase receptor type J (PTPRJ, DEP1) is a tumour suppressor gene that negatively regulates such processes as angiogenesis, cell proliferation and migration and is one of the genes important for tumour development. Similar to other phosphatase genes, PTPRJ is also described as an oncogene. Among various genetic changes characteristic for this gene, single nucleotide polymorphisms (SNPs) constituting benign genetic variants that can modulate its function have been described. We focused on Gln276Pro and Arg326Gln missense polymorphisms and performed a meta-analysis using data from 2930 and 852 patients for Gln276Pro and Arg326Gln respectively in different cancers. A meta-analysis was performed based on five articles accessed via the PubMed and Research Gate databases. Our meta-analysis revealed that for Arg326Gln, the presence of the Arg (C) allele was associated with lower risk of some cancers, the strongest association was observed for colorectal cancer patients, and there was no association between Gln276Pro (G>T) polymorphism and cancer risk. The polymorphisms Arg326Gln and Gln276Pro of the PTPRJ gene are not associated with an increased risk of cancer except for the Arg326Gln polymorphism in colorectal cancer. Large-scale studies should be performed to verify the impact of this SNP on individual susceptibility to colorectal cancer for given individuals.

Neuroevolution as a tool for microarray gene expression pattern identification in cancer research

Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides aid in this challenge by simultaneously classifying microarray data and selecting the subset of more relevant genes. The main algorithm, FS-NEAT, was adapted by the addition of new structural operators designed for this high dimensional data. In addition, a rigorous filtering and preprocessing protocol was employed to select quality microarray datasets for the proposed method, selecting 13 datasets from three different cancer types. The results show that Neuroevolution was able to successfully classify microarray samples when compared with other methods in the literature, while also finding subsets of genes that can be generalized for other algorithms and carry relevant biological information. This approach detected 177 genes, and 82 were validated as already being associated to their respective cancer types and 44 were associated to other types of cancer, becoming potential targets to be explored as cancer biomarkers. Five long non-coding RNAs were also detected, from which four don't have described functions yet. The expression patterns found are intrinsically related to extracellular matrix, exosomes and cell proliferation. The results obtained in this work could aid in unraveling the molecular mechanisms underlying the tumoral process and describe new potential targets to be explored in future works.

CD177+ neutrophils suppress epithelial cell tumourigenesis in colitis-associated cancer and predict good prognosis in colorectal cancer

Neutrophils are found to be infiltrated in tumour tissues of patients with colitis-associated cancer (CAC) and colorectal cancer (CRC), and CD177 is mainly expressed in neutrophils. In our study, expression of CD177 in tumour tissues from patients with CAC or CRC was analysed byquantitative real-time polymerase chain reaction, flow cytometry and immunohistochemistry. We recruited 378 patients with CRC, determined CD177 expression in tumours and examined its correlation with clinicopathological features. Moreover, CAC model was induced in wild-type and CD177-/- mice by azoxymethane/dextran sodium sulphate. CD177+ neutrophils were significantly increased in colon tumour tissues from patients with CRC or CAC compared with controls. Expression of CD177 mRNA and percentages of CD177+ neutrophils were also markedly increased in tumour tissues from CRC patients compared with controls. Patients with high density of CD177+ neutrophils had better overall survival and disease-free survival compared with controls. Multivariate analyses revealed that the density of CD177+ neutrophils was an independent factor in predicting overall survival and disease-free survival. Consistently, CD177 depletion aggravated azoxymethane/dextran sodium sulphate-induced CAC in mice. Expression of Ki67 and proliferating cell nuclear antigen was increased in tumour tissues from CD177-/- mice compared with wild-type counterparts. Moreover, CD177-/- neutrophils failed to migrate in response to fMLP[AU: Please expand fMLP, DN, TNM and HIF-1α.] stimulation compared with wild-type controls. Our data indicate that CD177+ neutrophils suppress epithelial cell tumourigenesis and act as an independent factor in predicting the prognosis in patients with CRC. CD177+ neutrophils may serve as a novel therapeutic target in the treatment and predict the prognosis of CAC and CRC.

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput 'omics' researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Human colorectal cancer initiation is bidirectional, and cell growth, metabolic genes and transporter genes are early drivers of tumorigenesis

The role of stem cells in the development of solid tumors remains controversial. In colorectal cancers (CRC), this is complicated by the conflicting "top-down" or "bottom-up" hypotheses of cancer initiation. We profiled the expressions of genes from the top (T) and bottom (B) crypt fractions of normal-appearing human colonic mucosa (M) at least 20 cm away from the tumor as a baseline and compared this to the genes of matched mucosa adjacent to tumors (M_T) in twenty-three sporadic CRC patients. In thirteen patients, the genetic distance (M-M_T) between the B fractions is smaller than the distance between the T fractions, indicating that the expressions diverge further in the top fractions (B < T). In the remaining patients, the reverse effect is observed (B > T). Assuming that a greater genetic divergence in the top or bottom fractions indicates that position as the initiation site, it is thus equally likely that human CRC initiates from 'top-down' via de-differentiated colonocytes or 'bottom-up' via dysregulated intestinal stem cells. Dysregulated genes that persist until tumor stage are not limited to tumor suppressors or oncogenes but include metabolic and transporter genes such as CA7, PHLPP2, and AQP8.

Genome-Wide Analysis Identified a Number of Dysregulated Long Noncoding RNA (lncRNA) in Human Pancreatic Ductal Adenocarcinoma

BACKGROUND

Long noncoding RNAs have been shown to play crucial roles in cancer biology, while the long noncoding RNA landscapes of pancreatic ductal adenocarcinoma have not been completely characterized. We aimed to determine whether long noncoding RNA could serve as early diagnostic biomarkers for pancreatic ductal adenocarcinoma.

METHOD

We conducted a genome-wide microarray analysis on pancreatic ductal adenocarcinoma and their adjacent noncancerous tissues from 8 Chinese patients.

RESULTS

A total of 3352 significantly differentially expressed long noncoding RNAs were detected. Of total, 1249 long noncoding RNAs were upregulated and 2103 were downregulated (fold change ≥2, P < 0.05, FDR <0.05). These differentially expressed long noncoding RNAs were not evenly distributed among chromosomes in human genome. Hierarchical clustering of these differentially expressed long noncoding RNAs revealed large variabilities in long noncoding RNA expression among individual patient, indicating that certain long noncoding RNAs could play a unique role or be used as a biomarker for specific subtype of pancreatic ductal adenocarcinoma. Gene Ontology enrichment and pathway analysis identified several remarkably dysregulated pathways in pancreatic ductal adenocarcinoma tissue, such as interferon-γ-mediated signaling pathway, mitotic cell cycle and proliferation, extracellular matrix receptor interaction, focal adhesion, and regulation of actin cytoskeleton. The co-expression network analysis detected 393 potential interactions between 80 differentially expressed long noncoding RNAs and 105 messenger RNAs. We experimentally verified 7 most markedly dysregulated long noncoding RNAs from the network.

CONCLUSION

Our study provided a genome-wide survey of dysregulated long noncoding RNAs and long noncoding RNA/messenger RNA co-regulation networks in pancreatic ductal adenocarcinoma tissue. These dysregulated long noncoding RNA/messenger RNA networks could be used as biomarkers to provide early diagnosis of pancreatic ductal adenocarcinoma or its subtype, predict prognosis, and evaluate treatment efficacy.