Somatic synonymous mutations in regulatory elements contribute to the genetic aetiology of melanoma

Identification and Validation of SLC9A2 as A Potential Tumor Suppressor in Colorectal Cancer: Integrating Bioinformatics Analysis with Experimental Confirmation

OBJECTIVE

To uncover the mechanisms underlying the development of colorectal cancer (CRC), we applied bioinformatic analyses to identify key genes and experimentally validated their possible roles in CRC onset and progression.

METHODS

We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis on differentially expressed genes (DEGs), constructed a protein-protein interaction (PPI) network to find the top 10 hub genes, and analyzed their expression in colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ). We also studied the correlation between these genes and immune cell infiltration and prognosis and validated the expression of SLC9A2 in CRC tissues and cell lines using qRT-PCR and Western blotting. Functional experiments were conducted in vitro to investigate the effects of SLC9A2 on tumor growth and metastasis.

RESULTS

We found 130 DEGs, with 45 up-regulated and 85 down-regulated in CRC. GO analysis indicated that these DEGs were primarily enriched in functions related to the regulation of cellular pH, zymogen granules, and transmembrane transporter activity. KEGG pathway analysis revealed that the DEGs played pivotal roles in pancreatic secretion, rheumatoid arthritis, and the IL-17 signaling pathway. We identified 10 hub genes: CXCL1, SLC26A3, CXCL2, MMP7, MMP1, SLC9A2, SLC4A4, CLCA1, CLCA4, and ZG16. GO enrichment analysis showed that these hub genes were predominantly involved in the positive regulation of transcription. Gene expression analysis revealed that CXCL1, CXCL2, MMP1, and MMP7 were highly expressed in CRC, whereas CLCA1, CLCA4, SLC4A4, SLC9A2, SLC26A3, and ZG16 were expressed at lower levels. Survival analysis revealed that 5 key genes were significantly associated with the prognosis of CRC. Both mRNA and protein expression levels of SLC9A2 were markedly reduced in CRC tissues and cell lines. Importantly, SLC9A2 overexpression in SW480 cells led to a notable inhibition of cell proliferation, migration, and invasion. Western blotting analysis revealed that the expression levels of phosphorylated ERK (p-ERK) and phosphorylated JNK (p-JNK) proteins were significantly increased, whereas there were no significant changes in the expression levels of ERK and JNK following SLC9A2 overexpression. Correlation analysis indicated a potential link between SLC9A2 expression and the MAPK signaling pathway.

CONCLUSION

Our study suggests that SLC9A2 acts as a tumor suppressor through the MAPK pathway and could be a potential target for CRC diagnosis and therapy.

What can we learn about acid-base transporters in cancer from studying somatic mutations in their genes?

Acidosis is a chemical signature of the tumour microenvironment that challenges intracellular pH homeostasis. The orchestrated activity of acid-base transporters of the solute-linked carrier (SLC) family is critical for removing the end-products of fermentative metabolism (lactate/H+) and maintaining a favourably alkaline cytoplasm. Given the critical role of pH homeostasis in enabling cellular activities, mutations in relevant SLC genes may impact the oncogenic process, emerging as negatively or positively selected, or as driver or passenger mutations. To address this, we performed a pan-cancer analysis of The Cancer Genome Atlas simple nucleotide variation data for acid/base-transporting SLCs (ABT-SLCs). Somatic mutation patterns of monocarboxylate transporters (MCTs) were consistent with their proposed essentiality in facilitating lactate/H+ efflux. Among all cancers, tumours of uterine corpus endometrial cancer carried more ABT-SLC somatic mutations than expected from median tumour mutation burden. Among these, somatic mutations in SLC4A3 had features consistent with meaningful consequences on cellular fitness. Definitive evidence for ABT-SLCs as 'cancer essential' or 'driver genes' will have to consider microenvironmental context in genomic sequencing because bulk approaches are insensitive to pH heterogeneity within tumours. Moreover, genomic analyses must be validated with phenotypic outcomes (i.e. SLC-carried flux) to appreciate the opportunities for targeting acid-base transport in cancers.

Liver cancer-specific mutations in functional domains of ADAR2 lead to the elevation of coding and non-coding RNA editing in multiple tumor-related genes

Mutation is the major cause of phenotypic innovations. Apart from DNA mutations, the alteration on RNA such as the ADAR-mediated A-to-I RNA editing could also shape the phenotype. These two layers of variations have not been systematically combined to study their collective roles in cancers. We collected the high-quality transcriptomes of ten hepatocellular carcinoma (HCC) and the matched control samples. We systematically identified HCC-specific mutations in the exonic regions and profiled the A-to-I RNA editome in each sample. All ten HCC samples had mutations in the CDS of ADAR2 gene (dsRNA-binding domain or catalytic domain). The consequence of these mutations converged to the elevation of ADAR2 efficiency as reflected by the global increase of RNA editing levels in HCC. The up-regulated editing sites (UES) were enriched in the CDS and UTR of oncogenes and tumor suppressor genes (TSG), indicating the possible roles of these target genes in HCC oncogenesis. We present the mutation-ADAR2-UES-oncogene/TSG-HCC axis that explains how mutations at different layers would finally lead to abnormal phenotype. In the light of central dogma, our work provides novel insights into how to fully take advantage of the transcriptome data to decipher the consequence of mutations.

SLC9A2, suppressing by the transcription suppressor ETS1, restrains growth and invasion of osteosarcoma via inhibition of aerobic glycolysis

Recent studies have shown that Solute Carrier Family 9 Member A2 (SLC9A2) could serve as a biomarker for cancer. However, its mechanism of action in osteosarcoma (OS) was still unclear. In this study, the data sets GSE154530 and GSE99671 were downloaded from the Gene Expression Omnibus (GEO) database, and 31 differentially expressed genes (DEGs) related to methylation were screened by bioinformatics analysis tools. Subsequently, SLC9A2 was screened as a candidate gene from DEGs, which was significantly downregulated in OS. CCK-8, transwell, western blotting and Seahorse XFe24 Cell Metabolic Analyzer assays demonstrated that overexpression of SLC9A2 could constrain OS cell proliferation, invasion, and aerobic glycolysis. Dual-luciferase reporter gene assay and chromatin immunoprecipitation (ChIP) assays indicated ETS proto-oncogene 1 (ETS1) was a transcription suppressor of SLC9A2, and overexpression of ETS1 could promote methylation levels in specific regions of the SLC9A2 promoter. ETS1 could promote the proliferation, invasion, and aerobic glycolysis ability of OS cells, as well as tumor growth in vivo by inhibiting the expression of SLC9A2. In addition, SLC9A2, suppressing by ETS1, restrains growth and invasion of OS via inhibition of aerobic glycolysis. Thus, SLC9A2 can function as a key inhibitory factor in the aerobic glycolysis to inhibit proliferation and invasion of OS. This indicated that SLC9A2 has a potential targeted therapeutic effect on OS.

Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer

A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.

Identification and validation of a prognostic-related mutant gene DNAH5 for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide and has a poor prognosis. Thus, there is a need for an effective biomarker to improve and predict the prognosis of HCC.

Methods

RNA sequencing data, simple nucleotide variation data, and clinical data of HCC patients from The Cancer Genome Atlas (TCGA) to identify mutant genes, simple nucleotide variation data, and clinical data of HCC patients from the International Cancer Genome Consortium (ICGC) to validate the prognostic value of mutant genes were the data sources of the present study. To identify the overall survival (OS)-related mutant genes, a Kaplan-Meier (KM) analysis was conducted. We carried out univariate Cox and multivariate Cox regression analyses to identify the independent prognostic factors. We also conducted a correlation analysis of immune cells and mutant genes. To explore the molecular mechanisms of mutant genes, we conducted a gene set enrichment analysis (GSEA). A nomogram was constructed to help predict the prognosis of HCC. In addition, we explored the expression profile of mutant genes in HCC based on a TCGA dataset, an ICGC dataset, and our own HCC tissue samples.

Results

We identified and validated a mutant gene, dynein axonemal heavy chain 5 (DNAH5), which was negatively related to the OS of HCC patients. Univariate Cox and multivariate Cox regression analyses revealed that the mutant gene DNAH5 could act as an independent prognostic factor for HCC. Most pathways of the mutant gene DNAH5 were involved in cancer development and progression based on GSEA analysis. The mutant gene DNAH5 was negatively correlated with monocytes, naive CD4 T cells, activated dendritic cells, and activated mast cells. In addition, the mRNA and protein levels of DNAH5 had a significantly higher level of expression in the tissue samples of patients with HCC. A nomogram consisting of the pathological stage, DNAH5, and tumor mutation burden (TMB) performed well.

Conclusion

The mutant gene DNAH5 had a significantly higher level of expression in the tissue samples of patients with HCC, could act as an independent prognostic factor for HCC, and is a potential new immunotherapy target for HCC.

A Multigene-Panel Study Identifies Single Nucleotide Polymorphisms Associated with Prostate Cancer Risk

The immune system plays a critical role in modulating cancer development and progression. Polymorphisms in key genes involved in immune responses are known to affect susceptibility to cancer. Here, we analyzed 35 genes to evaluate the association between variants of genes involved in immune responses and prostate cancer risk. Thirty-five genes were analyzed in 47 patients with prostate cancer and 43 healthy controls using next-generation sequencing. Allelic and genotype frequencies were calculated in both cohorts, and a generalized linear mixed model was applied to test the relationship between prostate cancer risk and nucleotide substitution. Odds ratios were calculated to describe the association between each single nucleotide polymorphism (SNP) and prostate cancer risk. Significant changes in allelic and genotypic distributions were observed for IL4R, IL12RB1, IL12RB2, IL6, TMPRSS2, and ACE2. Furthermore, a generalized linear mixed model identified statistically significant associations between prostate cancer risk and SNPs in IL12RB2, IL13, IL17A, IL4R, MAPT, and TFNRS1B. Finally, a statistically significant association was observed between IL2RA and TNFRSF1B and Gleason scores, and between SLC11A1, TNFRSF1B and PSA values. We identified SNPs in inflammation and two prostate cancer-associated genes. Our results provide new insights into the immunogenetic landscape of prostate cancer and the impact that SNPs on immune genes may have on affecting the susceptibility to prostate cancer.

Consensus Clustering and Survival-Related Genes of Cuproptosis in Cutaneous Melanoma

As a highly malignant tumor, the morbidity and mortality of cutaneous melanoma (CM) are increasing year by year. A novel type of cell death connected to mitochondrial metabolism is called cuproptosis. Cuproptosis regulates tumor biological behavior. Thus, genes controlling cuproptosis could be a promising candidate bioindicator for cancer therapy. Datasets of CM patients were obtained from the public database that includes clinical information and RNA-seq data. We divided CM patients into three different subgroups by unsupervised clustering method and explored the differences in functional pathways among the three subgroups by GSVA to prove the possible potential mechanism of copper death-related genes in the formation and development of CM. Secondly, we used differential analysis and Cox regression analysis to find the differential genes related to prognosis, constructed the CRG score, found the critical score for dividing high and low CRG score groups, and then analyzed the prognosis and immune infiltration of high and low CRG score groups. The results show a great correlation between OS and CRG scores. Compared with patients with high CRG scores, patients with low CRG scores have a significantly higher survival rate. In a word, copper sagging plays a certain role in the progress of CM.

Pan-cancer identification of the relationship of metabolism-related differentially expressed transcription regulation with non-differentially expressed target genes via a gated recurrent unit network

The transcriptome describes the expression of all genes in a sample. Most studies have investigated the differential patterns or discrimination powers of transcript expression levels. In this study, we hypothesized that the quantitative correlations between the expression levels of transcription factors (TFs) and their regulated target genes (mRNAs) serve as a novel view of healthy status, and a disease sample exhibits a differential landscape (mqTrans) of transcription regulations compared with healthy status. We formulated quantitative transcription regulation relationships of metabolism-related genes as a multi-input multi-output regression model via a gated recurrent unit (GRU) network. The GRU model was trained using healthy blood transcriptomes and the expression levels of mRNAs were predicted by those of the TFs. The mqTrans feature of a gene was defined as the difference between its predicted and actual expression levels. A pan-cancer investigation of the differentially expressed mqTrans features was conducted between the early- and late-stage cancers in 26 cancer types of The Cancer Genome Atlas database. This study focused on the differentially expressed mqTrans features, that did not show differential expression in the actual expression levels. These genes could not be detected by conventional differential analysis. Such dark biomarkers are worthy of further wet-lab investigation. The experimental data also showed that the proposed mqTrans investigation improved the classification between early- and late-stage samples for some cancer types. Thus, the mqTrans features serve as a complementary view to transcriptomes, an OMIC type with mature high-throughput production technologies, and abundant public resources.

A Targeted Next-Generation Sequencing Panel to Genotype Gliomas

Gliomas account for the majority of primary brain tumors. Glioblastoma is the most common and malignant type. Based on their extreme molecular heterogeneity, molecular markers can be used to classify gliomas and stratify patients into diagnostic, prognostic, and therapeutic clusters. In this work, we developed and validated a targeted next-generation sequencing (NGS) approach to analyze variants or chromosomal aberrations correlated with tumorigenesis and response to treatment in gliomas. Our targeted NGS analysis covered 13 glioma-related genes (ACVR1, ATRX, BRAF, CDKN2A, EGFR, H3F3A, HIST1H3B, HIST1H3C, IDH1, IDH2, P53, PDGFRA, PTEN), a 125 bp region of the TERT promoter, and 54 single nucleotide polymorphisms (SNPs) along chromosomes 1 and 19 for reliable assessment of their copy number alterations (CNAs). Our targeted NGS approach provided a portrait of gliomas’ molecular heterogeneity with high accuracy, specificity, and sensitivity in a single workflow, enabling the detection of variants associated with unfavorable outcomes, disease progression, and drug resistance. These preliminary results support its use in routine diagnostic neuropathology.

A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Background

Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.

Methods

Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.

Results

A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.

Conclusion

Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01975-z.

Synonymous mutations that regulate translation speed might play a non-negligible role in liver cancer development

Background

Synonymous mutations do not change the protein sequences. Automatically, they have been regarded as neutral events and are ignored in the mutation-based cancer studies. However, synonymous mutations will change the codon optimality, resulting in altered translational velocity.

Methods

We fully utilized the transcriptome and translatome of liver cancer and normal tissue from ten patients. We profiled the mutation spectrum and examined the effect of synonymous mutations on translational velocity.

Results

Synonymous mutations that increase the codon optimality significantly enhanced the translational velocity, and were enriched in oncogenes. Meanwhile, synonymous mutations decreasing codon optimality slowed down translation, and were enriched in tumor suppressor genes. These synonymous mutations significantly contributed to the translational changes in tumor samples compared to normal samples.

Conclusions

Synonymous mutations might play a role in liver cancer development by altering codon optimality and translational velocity. Synonymous mutations should no longer be ignored in the genome-wide studies.

Synonymous variants that disrupt messenger RNA structure are significantly constrained in the human population

Background

The role of synonymous single-nucleotide variants in human health and disease is poorly understood, yet evidence suggests that this class of “silent” genetic variation plays multiple regulatory roles in both transcription and translation. One mechanism by which synonymous codons direct and modulate the translational process is through alteration of the elaborate structure formed by single-stranded mRNA molecules. While tools to computationally predict the effect of non-synonymous variants on protein structure are plentiful, analogous tools to systematically assess how synonymous variants might disrupt mRNA structure are lacking.

Results

We developed novel software using a parallel processing framework for large-scale generation of secondary RNA structures and folding statistics for the transcriptome of any species. Focusing our analysis on the human transcriptome, we calculated 5 billion RNA-folding statistics for 469 million single-nucleotide variants in 45,800 transcripts. By considering the impact of all possible synonymous variants globally, we discover that synonymous variants predicted to disrupt mRNA structure have significantly lower rates of incidence in the human population.

Conclusions

These findings support the hypothesis that synonymous variants may play a role in genetic disorders due to their effects on mRNA structure. To evaluate the potential pathogenic impact of synonymous variants, we provide RNA stability, edge distance, and diversity metrics for every nucleotide in the human transcriptome and introduce a “Structural Predictivity Index” (SPI) to quantify structural constraint operating on any synonymous variant. Because no single RNA-folding metric can capture the diversity of mechanisms by which a variant could alter secondary mRNA structure, we generated a SUmmarized RNA Folding (SURF) metric to provide a single measurement to predict the impact of secondary structure altering variants in human genetic studies.

DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis

Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. Here, an effective and no parameter algorithm, named DriverSubNet, is presented for detecting driver genes by effectively mining the mutation and gene expression information based on subnetwork enrichment analysis. Compared with the existing classic methods, DriverSubNet can rank driver genes and filter out passenger genes more efficiently in terms of precision, recall, and F1 score, as indicated by the analysis of four cancer datasets. The method recovered about 50% more known cancer driver genes in the top 100 detected genes than those found in other algorithms. Intriguingly, DriverSubNet was able to find these unknown cancer driver genes which could act as potential therapeutic targets and useful prognostic biomarkers for cancer patients. Therefore, DriverSubNet may act as a useful tool for the identification of driver genes by subnetwork enrichment analysis.

Progression Risk Assessment of Post-surgical Papillary Thyroid Carcinoma Based on Circular RNA-Associated Competing Endogenous RNA Mechanisms

Background: Accurate risk assessment of post-surgical progression in papillary thyroid carcinoma (PTC) patients is critical. Exploring key differentially expressed mRNAs (DE-mRNAs) regulated by differentially expressed circular RNAs (circRNAs) via the ceRNA mechanism could help establish a novel assessment tool. Methods: ceRNA network was established based on differentially expressed RNAs and correlation analysis. DE-mRNAs within the ceRNA network associated with progression-free interval (PFI) of PTC were identified to construct a prognostic ceRNA regulatory subnetwork. least absolute shrinkage and selection operator (LASSO)-Cox regression was applied to identify hub DE-mRNAs and establish a novel DE-mRNA signature in predicting PFI of PTC. Results: Six hub DE-mRNAs, namely, CLCNKB, FXBO27, FXYD6, RIMS2, SPC24, and CDKN2A, were identified to be most significantly related to the PFI of PTC, and a prognostic DE-mRNA signature was proposed. A nomogram incorporating the DE-mRNA signature and clinical parameters was established to improve the progression risk assessment in post-surgical PTC, which was superior to the American Thyroid Association risk stratification system and distant Metastasis, patient Age, Completeness of resection, local Invasion, and tumor Size (MACIS) score American Joint Committee on Cancer staging system. Conclusions: Based on the circRNA-associated ceRNA RNA mechanism, a DE-mRNA signature and prognostic nomogram was established, which may improve the progression risk assessment in post-surgical PTC.

Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data

Background

Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner.

Results

This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations.

Conclusions

Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.

A miRNA-clinicopathological nomogram for the prediction of central lymph node metastasis in papillary thyroid carcinoma-analysis from TCGA database

It is of significance to evaluate central lymph node status in patients with papillary thyroid carcinoma (PTC), because it can decrease postoperative complications resulting from unnecessary prophylactic central lymph node dissection (CLND). Due to the low sensitivity and specificity of neck ultrasonography in the evaluation of central lymph node metastasis (CLNM), it is urgently required to find alternative biomarkers to predict CLNM in PTC patients, which is the main purpose of this study.RNA-sequencing datasets and clinical data of 506 patients with thyroid carcinoma from the Cancer Genome Atlas (TCGA) database were downloaded and analyzed to identify differentially expressed miRNAs (DEMs), which can independently predict CLNM in PTC. A nomogram predictive of CLNM was developed based on clinical characteristics and the identified miRNAs. Receiver operating characteristics curves were drawn to evaluate the predictive performance of the nomogram. Bioinformatics analyses, including target genes identification, functional enrichment analysis, and protein-protein interaction network, were performed to explore the potential roles of the identified DEMs related to CLNM in PTC.A total of 316 PTC patients were included to identify DEMs. Two hundred thirty-seven (75%) PTC patients were randomly selected from the 316 patients as a training set, while the remaining 79 (25%) patients were regarded as a testing set for validation. Two DEMs, miRNA-146b-3p (HR: 1.327, 95% CI = 1.135-1.551, P = .000) and miRNA-363-3p (HR: 0.714, 95% CI = 0.528-0.966, P = .029), were significantly associated with CLNM. A risk score based on these 2 DEMs and calculating from multivariate logistic regression analysis, was significantly lower in N0 group over N1a group in both training (N0 vs N1a: 2.04 ± 1.01 vs 2.73 ± 0.61, P = .000) and testing (N0 vs N1a: 2.20 ± 0.93 vs 2.79 ± 0.68, P = .003) sets. The nomogram including risk score, age, and extrathyroidal extension (ETE) was constructed in the training set and was then validated in the testing set, which showed better prediction value than the other three predictors (risk score, age, and ETE) in terms of CLNM identification. Bioinformatics analyses revealed that 5 hub genes, SLC6A1, SYT1, COL19A1, RIMS2, and COL1A2, might involve in pathways including extracellular matrix organization, ion transmembrane transporter activity, axon guidance, and ABC transporters.On the basis of this study, the nomogram including risk score, age, and ETE showed good prediction of CLNM in PTC, which has a potential to facilitate individualized decision for surgical plans.

The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): computational methods and applications in medical genomics

In this editorial, we briefly summarized the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9-11, 2019 at Columbus, Ohio, USA. We further introduced the 19 research articles included in this supplement issue, covering four major areas, namely computational method development, genomics analysis, network-based analysis and biomarker prediction. The selected papers perform cutting edge computational research applied to a broad range of human diseases such as cancer, neural degenerative and chronic inflammatory disease. They also proposed solutions for fundamental medical genomics problems range from basic data processing and quality control to functional interpretation, biomarker and drug prediction, and database releasing.