Integrated analysis of gene expression signatures associated with colon cancer from three datasets

MicroRNA-34 and gastrointestinal cancers: a player with big functions

It is commonly assumed that gastrointestinal cancer is the most common form of cancer across the globe and is the leading contributor to cancer-related death. The intricate mechanisms underlying the growth of GI cancers have been identified. It is worth mentioning that both non-coding RNAs (ncRNAs) and certain types of RNA, such as circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and microRNAs (miRNAs), can have considerable impact on the development of gastrointestinal (GI) cancers. As a tumour suppressor, in the group of short non-coding regulatory RNAs is miR-34a. miR-34a silences multiple proto-oncogenes at the post-transcriptional stage by targeting them, which inhibits all physiologically relevant cell proliferation pathways. However, it has been discovered that deregulation of miR-34a plays important roles in the growth of tumors and the development of cancer, including invasion, metastasis, and the tumor-associated epithelial-mesenchymal transition (EMT). Further understanding of miR-34a's molecular pathways in cancer is also necessary for the development of precise diagnoses and effective treatments. We outlined the most recent research on miR-34a functions in GI cancers in this review. Additionally, we emphasize the significance of exosomal miR-34 in gastrointestinal cancers.

TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures

Background

In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology.

Methods

This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB. Our methodology can identify small and reliable gene signatures to estimate TMB from custom targeted-sequencing panels. For this purpose, our pipeline has been trained on top of 17 cancer types data obtained from TCGA.

Results

Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES).

Conclusion

We have rigorously analyzed the effectiveness of our methodology on top of several independent datasets. In particular we conducted a comprehensive testing on: (i) 126 samples sourced from the TCGA database; few independent whole-exome sequencing (WES) datasets linked to colon, breast, and liver cancers, all acquired from the EGA and the ICGC Data Portal. This rigorous evaluation clearly highlights the robustness and practicality of our approach, positioning it as a promising avenue for driving substantial progress within the realm of clinical practice.

Identification of a novel macrophage-related prognostic signature in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent and deadliest illnesses all around the world. Growing proofs demonstrate that tumor-associated macrophages (TAMs) are of critical importance in CRC pathogenesis, but their mechanisms remain yet unknown. The current research was designed to recognize underlying biomarkers associated with TAMs in CRC. We screened macrophage-related gene modules through WGCNA, selected hub genes utilizing the LASSO algorithm and COX regression, and established a model. External validation was performed by expression analysis using datasets GSE14333, GSE74602, and GSE87211. After validating the bioinformatics results using real-time quantitative reverse transcription PCR, we identified SPP1, C5AR1, MMP3, TIMP1, ADAM8 as potential biomarkers associated with macrophages in CRC.

P2 purinergic receptors regulate the progression of colorectal cancer

More and more studies have revealed that P2 purinergic receptors play a key role in the progression of colorectal cancer (CRC). P2X and P2Y purinergic receptors can be used as promoters and regulators of CRC and play a dual role in the progression of CRC. CRC microenvironment is rich in ATP and its cleavage products (ADP, AMP, Ado), which act as activators of P2X and P2Y purinergic receptors. The activation of P2X and P2Y purinergic receptors regulates the progression of CRC mainly by regulating the function of immune cells and mediating different signal pathways. In this paper, we focus on the specific mechanisms and functional roles of P2X7, P2Y12, and P2Y2 receptors in the growth and progression of CRC. The antagonistic effects of these selective antagonists of P2X purinergic receptors on the growth, invasion, and metastasis of CRC were further discussed. Moreover, different studies have reported that P2X7 receptor can be used as an effective predictor of patients with CRC. All these indicate that P2 purinergic receptors are a key regulator of CRC. Therefore, antagonizing P2 purinergic receptors may be an innovative treatment for CRC.

Krüppel-like Factor-9 and Krüppel-like Factor-13: Highly Related, Multi-Functional, Transcriptional Repressors and Activators of Oncogenesis

Specificity Proteins/Krüppel-like Factors (SP/KLF family) are a conserved family of transcriptional regulators. These proteins share three highly conserved, contiguous zinc fingers in their carboxy-terminus, requisite for binding to cis elements in DNA. Each SP/KLF protein has unique primary sequence within its amino-terminal and carboxy-terminal regions, and it is these regions which interact with co-activators, co-repressors, and chromatin-modifying proteins to support the transcriptional activation and repression of target genes. Krüppel-like Factor 9 (KLF9) and Krüppel-like Factor 13 (KLF13) are two of the smallest members of the SP/KLF family, are paralogous, emerged early in metazoan evolution, and are highly conserved. Paradoxically, while most similar in primary sequence, KLF9 and KLF13 display many distinct roles in target cells. In this article, we summarize the work that has identified the roles of KLF9 (and to a lesser degree KLF13) in tumor suppression or promotion via unique effects on differentiation, pro- and anti-inflammatory pathways, oxidative stress, and tumor immune cell infiltration. We also highlight the great diversity of miRNAs, lncRNAs, and circular RNAs which provide mechanisms for the ubiquitous tumor-specific suppression of KLF9 mRNA and protein. Elucidation of KLF9 and KLF13 in cancer biology is likely to provide new inroads to the understanding of oncogenesis and its prevention and treatments.

Purine and purinergic receptors in health and disease

Purines and purinergic receptors are widely distributed throughout the human body. Purine molecules within cells play crucial roles in regulating energy metabolism and other cellular processes, while extracellular purines transmit signals through specific purinergic receptors. The ubiquitous purinergic signaling maintains normal neural excitability, digestion and absorption, respiratory movement, and other complex physiological activities, and participates in cell proliferation, differentiation, migration, and death. Pathological dysregulation of purinergic signaling can result in the development of various diseases, including neurodegeneration, inflammatory reactions, and malignant tumors. The dysregulation or dysfunction of purines and purinergic receptors has been demonstrated to be closely associated with tumor progression. Compared with other subtypes of purinergic receptors, the P2X7 receptor (P2X7R) exhibits distinct characteristics (i.e., a low affinity for ATP, dual functionality upon activation, the mediation of ion channels, and nonselective pores formation) and is considered a promising target for antitumor therapy, particularly in patients with poor response to immunotherapy This review summarizes the physiological and pathological significance of purinergic signaling and purinergic receptors, analyzes their complex relationship with tumors, and proposes potential antitumor immunotherapy strategies from tumor P2X7R inhibition, tumor P2X7R overactivation, and host P2X7R activation. This review provides a reference for clinical immunotherapy and mechanism investigation.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer

Background

Most prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients.

Methods

Prognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan-Meier (K-M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model.

Results

A total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues.

Conclusion

A credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.

A Three-Genes Signature Predicting Colorectal Cancer Relapse Reveals LEMD1 Promoting CRC Cells Migration by RhoA/ROCK1 Signaling Pathway

Objective

Colorectal cancer (CRC) patients that experience early relapse consistently exhibit poor survival. However, no effective approach has been developed for the diagnosis and prognosis prediction of postoperative relapsed CRC.

Methods

Multiple datasets from the GEO database and TCGA database were utilized for bioinformatics analysis. WGCNA analyses and RRA analysis were performed to identify key genes. The COX/Lasso regression model was used to construct the recurrence model. Subsequent in vitro experiments further validated the potential role of the hub genes in CRC.

Results

A comprehensive analysis was performed on multiple CRC datasets and a CRC recurrence model was constructed containing LEMD1, SERPINE1, and SIAE. After further validation in two independent databases, we selected LEMD1 for in vitro experiments and found that LEMD1 could regulate CRC cell proliferation, migration, invasion, and promote EMT transition. The Rho-GTPase pulldown experiments further indicated that LEMD1 could affect RhoA activity and regulate cytoskeletal dynamics. Finally, we demonstrated that LEMD1 promoted CRC cell migration through the RhoA/ROCK1 signaling pathway.

Conclusions

In this study, a CRC relapse model consisting of LEMD1, SERPINE1, and SIAE was constructed by comprehensive analysis of multiple CRC datasets. LEMD1 could promote CRC cell migration through the RhoA/ROCK signaling pathway.

Susceptibility Genes and Chromosomal Regions Associated With Non-Syndromic Familial Non-Medullary Thyroid Carcinoma: Some Pathogenetic and Diagnostic Keys

Thyroid cancer is the malignant tumor that is increasing most rapidly in the world, mainly at the expense of sporadic papillary thyroid carcinoma. The somatic alterations involved in the pathogenesis of sporadic follicular cell derived tumors are well recognized, while the predisposing alterations implicated in hereditary follicular tumors are less well known. Since the genetic background of syndromic familial non-medullary carcinoma has been well established, here we review the pathogenesis of non-syndromic familial non-medullary carcinoma emphasizing those aspects that may be useful in clinical and pathological diagnosis. Non-syndromic familial non-medullary carcinoma has a complex and heterogeneous genetic basis involving several genes and loci with a monogenic or polygenic inheritance model. Most cases are papillary thyroid carcinoma (classic and follicular variant), usually accompanied by benign thyroid nodules (follicular thyroid adenoma and/or multinodular goiter). The possible diagnostic and prognostic usefulness of the changes in the expression and/or translocation of various proteins secondary to several mutations reported in this setting requires further confirmation. Given that non-syndromic familial non-medullary carcinoma and sporadic non-medullary thyroid carcinoma share the same morphology and somatic mutations, the same targeted therapies could be used at present, if necessary, until more specific targeted treatments become available.

Identification of potential biomarkers in ovarian carcinoma and an evaluation of their prognostic value

Background

Ovarian cancer is one of the most common malignant tumors in female genital organs, and its incidence rate is high. However, the pathogenesis and prognostic markers of ovarian cancer are unclear. This study sought to screen potential markers of ovarian cancer and to explore their prognostic value.

Methods

The Cancer Genome Atlas, Gene Expression Omnibus, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used in this study. The least absolute shrinkage and selection operator (LASSO), multivariate Cox regression and stepwise regression analysis were chosen to screen genes and construct risk model. Gene Set Enrichment Analysis (GSEA) and an immune-infiltration analysis were performed.

Results

One hundred thirty two co-expressed genes were found. They involved in metabolism, protein phosphorylation, mitochondria, and immune signaling pathways. Twelve genes significantly related to the survival of ovarian cancer were identified. Eight risk genes (i.e., CACNB1, FAM120B, HOXB2, MED19, PTPN2, SMU1, WAC.AS1, and BCL2L11) were further screened and used to construct the risk model. The risk status might be an independent prognostic factor of ovarian cancer, and most of the biological functions of genes expressed in high-risk ovarian cancer were related to synapse, adhesion, and immune-related functions. The clusters of CD4+ T cells and M2 macrophages were high in high-risk status samples.

Conclusions

In ovarian cancer, the abnormal expression of 8 genes, including CACNB1, FAM120B, HOXB2, MED19, PTPN2, SMU1, WAC.AS1, and BCL2L11, is closely related to ovarian cancer progression, and these genes can serve as independent prognosis markers of ovarian cancer.

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.

Methods

We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).

Results

There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.

Conclusions

These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

Identification and clinical validation of gene signatures with grade and survival in head and neck carcinomas

This study aimed to explore gene expression profiles that drive malignancy from low- to high-grade head and neck carcinomas (HNC), as well as to analyze their correlations with survival. Gene expressions and clinical data of HNC were downloaded from the Gene Expression Omnibus (GEO) repository. The significantly differential genes (SDGs) between low- and high-grade HNC were screened. Cox regressions were performed to identify prognostic SDGs of progression-free survival (PFS) and disease-specific survival (DSS). The genes were experimentally validated by RT-PCR in clinical tissue specimens. Thirty-five SDGs were identified in 47 low-grade and 30 high-grade HNC samples. Cox regression analyses showed that CXCL14, SLC44A1, and UBD were significantly associated with DSS, and PPP2R2C and SLC44A1 were associated with PFS. Patients were grouped into high-risk or low-risk groups for prognosis based on gene signatures. High-risk patients had significantly shorter DSS and PFS than low-risk patients (P=0.033 and P=0.010, respectively). Multivariate Cox regression showed HPV (P=0.033), lymph node status (P=0.032), and residual status (P<0.044) were independent risk factors for PFS. ROC curves showed the risk score had better efficacy to predict survival both for DSS and PFS (AUC=0.858 and AUC=0.901, respectively). The results showed CXCL14 and SLC44A1 were significantly overexpressed in the low-grade HNC tissues and the UBD were overexpressed in the high-grade HNC tissues. Our results suggested that SDGs had different expression profiles between the low-grade and high-grade HNC, and these genes may serve as prognostic biomarkers to predict survival.

A nine-gene signature to improve prognosis prediction of colon carcinoma

This study aims to establish a gene model that can robustly and effectively predict the prognosis of colon carcinoma patients via bioinformatics. Data along with clinical information in GSE39582 Series Matrix were firstly downloaded from Gene Expression Omnibus (GEO) database. Next, differentially expressed genes (DEGs) were obtained through "edgeR" analysis. Finally, a risk predication model was established through a series of regression analyses, and then prognostic performance of the model was comprehensively evaluated though Kaplan-Meier and receiver operating characteristic (ROC) analysis. Gene set enrichment analysis (GSEA) was further performed. Totally, 846 DEGs were obtained by analyzing the gene expression data in GSE39582 dataset. A 9-gene signature-based risk predication model was established via regression analyses, and the model-based risk score was formulated as: Riskscore = (-0.1214) * TNFRSF11A + (-0.2617) * TMEM97 + (-0.1041) * LGR5 + 0.0973 * KLK10 + 0.1655 * HOXB8 + 0.227 * FKBP10 + (-0.1312) * CXCL13 + (-0.1316) * CXCL10 + 0.2593 * CD36. Kaplan-Meier curve showed that colon carcinoma patients in the high-risk group had a lower survival rate. GSEA showed that high-risk group and low-risk group displayed significant difference in biological pathways including ECM RECEPTOR INTERACTION. Besides, correlation analysis between the riskscore of the model and clinical features of patients revealed that the model could effectively predict the prognosis of patients in different ages (age>65, age<65) and stages (tumor_stage I/II, tumor_stage III/IV, T3&T4, N0&N1, N2&N3, M0). This study provides a robust model for the prognosis prediction of colon carcinoma, and lays a basis for researching the molecular mechanism underlying the development of colon carcinoma.

Identification of cluster of differentiation molecule-associated microRNAs as potential therapeutic targets for gastrointestinal cancer immunotherapy

BACKGROUND

Cluster of differentiation molecules are markers of immune cells that have been identified as a potential immunotherapeutic target for cancer treatment. MicroRNAs are small non-coding RNAs that act as tumor suppressors or oncogenes whose importance in diagnosis, prognosis, and treatment of gastric and colorectal cancers has been widely reported. However, their association with cluster of differentiation molecules in gastrointestinal cancers has not been well studied. Therefore, our study aimed to analyze the relationship between microRNAs and cluster of differentiation molecules in gastrointestinal cancers, and to identify cluster of differentiation molecule-associated microRNAs as prognostic biomarkers for gastrointestinal cancer patients.

METHODS

Targetscan, Starbase, DIANA microT, and miRDB were used to investigate microRNA profiles that might be correlated with cluster of differentiation molecules in gastrointestinal cancers. Moreover, The Cancer Genome Atlas data analysis was used to investigate the association between cluster of differentiation molecules and microRNA expression in patients with gastric, colon, rectal, pancreatic, and esophageal cancers. The Kaplan-Meier plotter was used to identify the association between overall survival and cluster of differentiation molecule-associated microRNA expression in gastrointestinal cancer patients.

RESULTS

miR-200a, miR-559, and miR-1236 were negatively associated with CD86, CD81, and CD160, respectively, in almost all types of gastrointestinal cancers, which were further verified in the in vitro studies by transfecting microRNA mimics in gastric cancer, colon cancer, pancreatic, and esophageal cell lines.

CONCLUSION

Our study showed that miR-200a, miR-1236, and miR-559 are identified as cluster of differentiation-associated microRNAs in gastrointestinal cancers, providing a novel perspective to identify new therapeutic targets for cancer immunotherapy in gastrointestinal cancer patients.

MMP7 as a potential biomarker of colon cancer and its prognostic value by bioinformatics analysis

ABSTRACT

Colon cancer is one of the most common cancers in the world. To identify the candidate genes in the carcinogenesis and progression of colon cancer, the microarray datasets GSE10950, GSE44861 and GSE74602 were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and functional enrichment analyses were performed. A total of 176 DEGs were identified, consisting of 55 genes upregulated and 121 genes downregulated in colon cancer tissues compared to non-cancerous tissues. The DEGs were mainly enriched in mineral absorption, nitrogen metabolism and complement and coagulation cascades. By using STRING database analysis, we constructed a coexpression network composed of 140 nodes and 280 edges for the DEGs with a combined score >0.4 and a significant interaction relation. Thirteen hub genes were identified, and poor OS of patients was only associated with high expression of Matrix Metallopeptidase 7 (MMP7), which may be involved in the carcinogenesis, invasion or recurrence of colon cancer. In conclusion, we propose that the DEGs and hub genes identified in the present study may be regarded as diagnostic biomarkers for colon cancer. Moreover, the overexpression of MMP7 may correlate with poor prognosis.

P2X receptors in cancer growth and progression

It is increasingly appreciated that ion channels have a crucial role in tumors, either as promoters of cancer cell growth, or modulators of immune cell functions, or both. Among ion channels, P2X receptors have a special status because they are gated by ATP, a common and abundant component of the tumor microenvironment. Furthermore, one P2X receptor, i.e. P2X7, may also function as a conduit for ATP release, thus fuelling the increased extracellular ATP level in the tumor interstitium. These findings show that P2X receptors and extracellular ATP are indissoluble partners and key regulators of tumor growth, and suggest the exploitation of the extracellular ATP-P2X partnership to develop innovative therapeutic approaches to cancer.

Bioinformatic Identification of Hub Genes and Analysis of Prognostic Values in Colorectal Cancer

The purpose of this study is to discover novel hub genes which are helpful for diagnosis, prognosis, and targeted therapy in colorectal cancer (CRC) by using bioinformatics analysis. GSE74602, GSE110225, and GSE113513 were extracted from the gene expression omnibus (GEO). Differentially expressed genes (DEGs) in expression profiles were identified by GEO2R. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses of the DEGs were carried out in the Database for Annotation, Visualization, and Integrated Discovery (DAVID). String database and cytoscape were used for building protein-protein interaction (PPI) network and module analysis. The UALCAN was used for in-depth analysis of data of CRC patients from The Cancer Genome Atlas (TCGA) to identify expression levels and overall survival rates of hub genes. The DEGs included 107 up-regulation genes and 232 down-regulation genes. Twenty-nine (29) hub genes and two significant modules were screened from PPI network. The expression levels of hub genes in TCGA were verified. Survival analysis curve indicated high expression of CCNA2, CCNB1, DLGAP5, were related to high survival rates, and low expression of TIMP1 were associated with high survival rates. These results suggest that DEGs may be the hub genes of CRC, and CCNA2, CCNB1, DLGAP5, TIMP1 may be the potential prognostic markers of CRC.

Calcium Permeable Channels in Cancer Hallmarks

Cancer, the second cause of death worldwide, is characterized by several common criteria, known as the “cancer hallmarks” such as unrestrained cell proliferation, cell death resistance, angiogenesis, invasion and metastasis. Calcium permeable channels are proteins present in external and internal biological membranes, diffusing Ca²⁺ ions down their electrochemical gradient. Numerous physiological functions are mediated by calcium channels, ranging from intracellular calcium homeostasis to sensory transduction. Consequently, calcium channels play important roles in human physiology and it is not a surprise the increasing number of evidences connecting calcium channels disorders with tumor cells growth, survival and migration. Multiple studies suggest that calcium signals are augmented in various cancer cell types, contributing to cancer hallmarks. This review focuses in the role of calcium permeable channels signaling in cancer with special attention to the mechanisms behind the remodeling of the calcium signals. Transient Receptor Potential (TRP) channels and Store Operated Channels (SOC) are the main extracellular Ca²⁺ source in the plasma membrane of non-excitable cells, while inositol trisphosphate receptors (IP₃R) are the main channels releasing Ca²⁺ from the endoplasmic reticulum (ER). Alterations in the function and/or expression of these calcium channels, as wells as, the calcium buffering by mitochondria affect intracellular calcium homeostasis and signaling, contributing to the transformation of normal cells into their tumor counterparts. Several compounds reported to counteract several cancer hallmarks also modulate the activity and/or the expression of these channels including non-steroidal anti-inflammatory drugs (NSAIDs) like sulindac and aspirin, and inhibitors of polyamine biosynthesis, like difluoromethylornithine (DFMO). The possible role of the calcium permeable channels targeted by these compounds in cancer and their action mechanism will be discussed also in the review.

Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model

In recent years, accumulating evidences have shown that microRNA (miRNA) plays an important role in the exploration and treatment of diseases, so detection of the associations between miRNA and disease has been drawn more and more attentions. However, traditional experimental methods have the limitations of high cost and time- consuming, a computational method can help us more systematically and effectively predict the potential miRNA-disease associations. In this work, we proposed a novel network embedding-based heterogeneous information integration method to predict miRNA-disease associations. More specifically, a heterogeneous information network is constructed by combining the known associations among lncRNA, drug, protein, disease, and miRNA. After that, the network embedding method Learning Graph Representations with Global Structural Information (GraRep) is employed to learn embeddings of nodes in heterogeneous information network. In this way, the embedding representations of miRNA and disease are integrated with the attribute information of miRNA and disease (e.g. miRNA sequence information and disease semantic similarity) to represent miRNA-disease association pairs. Finally, the Random Forest (RF) classifier is used for predicting potential miRNA-disease associations. Under the 5-fold cross validation, our method obtained 85.11% prediction accuracy with 80.41% sensitivity at the AUC of 91.25%. In addition, in case studies of three major Human diseases, 45 (Colon Neoplasms), 42 (Breast Neoplasms) and 44 (Esophageal Neoplasms) of top-50 predicted miRNAs are respectively verified by other miRNA-disease association databases. In conclusion, the experimental results suggest that our method can be a powerful and useful tool for predicting potential miRNA-disease associations.

Germline Mutations in Familial Papillary Thyroid Cancer

Thyroid cancer, predominantly of papillary histology (PTC), is a common cancer mostly diagnosed sporadically. Hereditary PTC is encountered in ~ 5% of cases and may present at an earlier age, with greater risks of metastasis and recurrence, compared with sporadic cases. The molecular basis of hereditary PTC is unknown in most cases. In this study, the genetic basis of hereditary PTC in three Brazilian families was investigated. Whole exome sequencing (WES) was carried out for probands in each family, and validated, pathogenic/likely pathogenic sequence variants (P/LPSVs) were genotyped in additional family members to establish their putative pathogenic role. Overall, seven P/LPSVs in seven novel genes were detected: p.D283N*ANXA3, p.Y157S*NTN4, p.G172W*SERPINA1, p.G188S*FKBP10, p.R937C*PLEKHG5, p.L32Q*P2RX5, and p.Q76*SAPCD1. These results indicate that these novel genes are seemingly associated with hereditary PTC, but extension and validation in other PTC families are required.

Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts

BACKGROUND

Several reports have identified different patterns of Parkinson's disease progression in individuals carrying missense variants in GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinson's disease, however, has not been well studied.

OBJECTIVES

To test the association between genetic variants and the clinical features of Parkinson's disease on a genomewide scale.

METHODS

We accumulated individual data from 12 longitudinal cohorts in a total of 4093 patients with 22,307 observations for a median of 3.81 years. Genomewide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently identified disease-risk variants, were also investigated post hoc for candidate associations with these phenotypes.

RESULTS

Two variants were genomewide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (hazard ratio 2.04 [1.58-2.62]; P value = 3.46E-8). Rs61863020(G>A), an intergenic variant and expression quantitative trait loci for α-2A adrenergic receptor, was associated with a lower prevalence of insomnia at baseline (odds ratio 0.63 [0.52-0.75]; P value = 4.74E-8). In the targeted analysis, we found 9 associations between known Parkinson's risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K; rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and an APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients.

CONCLUSIONS

We identified novel genetic factors associated with heterogeneity of Parkinson's disease. The results can be used for validation or hypothesis tests regarding Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

Emerging roles for multifunctional ion channel auxiliary subunits in cancer

Several superfamilies of plasma membrane channels which regulate transmembrane ion flux have also been shown to regulate a multitude of cellular processes, including proliferation and migration. Ion channels are typically multimeric complexes consisting of conducting subunits and auxiliary, non-conducting subunits. Auxiliary subunits modulate the function of conducting subunits and have putative non-conducting roles, further expanding the repertoire of cellular processes governed by ion channel complexes to processes such as transcellular adhesion and gene transcription. Given this expansive influence of ion channels on cellular behaviour it is perhaps no surprise that aberrant ion channel expression is a common occurrence in cancer. This review will focus on the conducting and non-conducting roles of the auxiliary subunits of various Ca2+, K+, Na+ and Cl- channels and the burgeoning evidence linking such auxiliary subunits to cancer. Several subunits are upregulated (e.g. Cavβ, Cavγ) and downregulated (e.g. Kvβ) in cancer, while other subunits have been functionally implicated as oncogenes (e.g. Navβ1, Cavα2δ1) and tumour suppressor genes (e.g. CLCA2, KCNE2, BKγ1) based on in vivo studies. The strengthening link between ion channel auxiliary subunits and cancer has exposed these subunits as potential biomarkers and therapeutic targets. However further mechanistic understanding is required into how these subunits contribute to tumour progression before their therapeutic potential can be fully realised.

A six-microRNA risk score model predicts prognosis in esophageal squamous cell carcinoma

Esophageal cancer ranks the eighth most common cancer and the sixth most common cause of cancer death worldwide. MicroRNAs (miRNAs) are small noncoding RNAs that regulate a wide variety of cancer-related cellular processes. In the current study, a series of previously published gene expression microarray data from Gene Expression Ominus and The Cancer Genome Atlas were downloaded and further divided into training, internal, and external validation sets. Least absolute shrinkage and selectionator operator Cox regression model along with 10-fold cross-validation was performed to select the miRNAs associated with the prognosis of esophageal squamous cell carcinoma (ESCC) and constructed a six-miRNA signature. Then the prediction accuracy of this signature was assessed in validation and test set using Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curves and dynamic area under the ROC curve. According to the result, the prediction accuracy of miRNA signature was much better than that of tumor-node-metastasis (TNM) stage in all the three sets. Stratified analysis also demonstrated that the predict ability of this signature was independent of TNM stage. Finally, function experiments including apoptosis and colony formation assay were performed to further reveal the regulatory role of miRNAs in ESCC. Our study demonstrated the promising potential application of this novel six-miRNA signature as an independent biomarker for survival prediction of ESCC patients.

MicroRNA-183 inhibits A375 human melanoma cell migration and invasion by targeting Ezrin and MMP-9

To assess the influence of microRNA-183 (miR-183) on the migration and invasion of A375 human melanoma cells, an A375 cell line with stable miR-183 overexpression or knockdown was constructed using lentiviral transfection. The change of miR-183 expression in these cells and in non-transfected controls was verified using reverse transcription-quantitative polymerase chain reaction. The impact of miR-183 on experimental A375 cell migration and invasion was assessed using a scratch and Transwell assay. The expression of Ezrin and matrix metalloprotease-9 (MMP-9), which are two mediator proteins that serve roles in tumor cell migration and invasion, were analyzed in each cell group via western blotting. The results of the present study indicated that miR-183 overexpression significantly inhibits A375 cell migration and invasion, which may be facilitated by miR-183 knockdown. Furthermore, Ezrin and MMP-9 protein levels were negatively associated with miR-183 expression, indicating that miR-183 may function as a tumor suppressor by inhibiting the expression of these two proteins. Additionally, miR-183 downregulation may be associated with the progression of melanoma.