A seven-gene signature predicts overall survival of patients with colorectal cancer

The Z isomer of pyridoxilidenerhodanine 5'-phosphate is an efficient inhibitor of human pyridoxine 5'-phosphate oxidase, a crucial enzyme in vitamin B6 salvage pathway and a potential chemotherapeutic target

Pyridoxal 5'-phosphate (PLP), the catalytically active form of vitamin B6, acts as a cofactor in many metabolic processes. In humans, PLP is produced in the reactions catalysed by pyridox(am)ine 5'-phosphate oxidase (PNPO) and pyridoxal kinase (PDXK). Both PNPO and PDXK are involved in cancer progression of many tumours. The silencing of PNPO and PDXK encoding genes determines a strong reduction in tumour size and neoplastic cell invasiveness in models of acute myeloid leukaemia (in the case of PDXK) and ovarian and breast cancer (in the case of PNPO). In the present work, we demonstrate that pyridoxilidenerhodanine 5'-phosphate (PLP-R), a PLP analogue that has been tested by other authors on malignant cell lines reporting a reduction in proliferation, inhibits PNPO in vitro following a mixed competitive and allosteric mechanism. We also show that the unphosphorylated precursor of this inhibitor (PL-R), which has more favourable pharmacokinetic properties according to our predictions, is phosphorylated by PDXK and therefore transformed into PLP-R. On this ground, we propose the prototype of a novel prodrug-drug system as a useful starting point for the development of new, potential, antineoplastic agents.

Integration of microbiome, metabolomics and transcriptome for in-depth understanding of berberine attenuates AOM/DSS-induced colitis-associated colorectal cancer

A type of colorectal cancer (CRC)，Colitis-associated colorectal cancer (CAC)， is closely associated with chronic inflammation and gut microbiota dysbiosis. Berberine (BBR) has a long history in the treatment of intestinal diseases, which has been reported to inhibit colitis and CRC. However, the mechanism of its action is still unclear. Here, this study aimed to explore the potential protective effects of BBR on azoxymethane (AOM)/dextransulfate sodium (DSS)-induced colitis and tumor mice, and to elucidate its potential molecular mechanisms by microbiota, genes and metabolic alterations. The results showed that BBR inhibited the gut inflammation and improved the function of mucosal barrier to ameliorate AOM/DSS-induced colitis. And BBR treatment significantly reduced intestinal tumor development and ki-67 expression of intestinal tissue along with promoted apoptosis. Through microbiota analysis based on the 16 S rRNA gene, we found that BBR treatment improved intestinal microbiota imbalance in AOM/DSS-induced colitis and tumor mice, which were characterized by an increase of beneficial bacteria, for instance Akkermanisa, Lactobacillus, Bacteroides uniformis and Bacteroides acidifaciens. In addition, transcriptome analysis showed that BBR regulated colonic epithelial signaling pathway in CAC mice particularly by tryptophan metabolism and Wnt signaling pathway. Notably, BBR treatment resulted in the enrichment of amino acids metabolism and microbiota-derived SCFA metabolites. In summary, our research findings suggest that the gut microbiota-amino acid metabolism-Wnt signaling pathway axis plays critical role in maintaining intestinal homeostasis, which may provide new insights into the inhibitory effects of BBR on colitis and colon cancer.

Vitamin B6 and diabetes and its role in counteracting advanced glycation end products

Naturally occurring forms of vitamin B6 include six interconvertible water-soluble compounds: pyridoxine (PN), pyridoxal (PL), pyridoxamine (PM), and their respective monophosphorylated derivatives (PNP, PLP, and PMP). PLP is the catalytically active form which works as a cofactor in approximately 200 reactions that regulate the metabolism of glucose, lipids, amino acids, DNA, and neurotransmitters. Most of vitamers can counteract the formation of reactive oxygen species and the advanced glycation end-products (AGEs) which are toxic compounds that accumulate in diabetic patients due to prolonged hyperglycemia. Vitamin B6 levels have been inversely associate with diabetes, while vitamin B6 supplementation reduces diabetes onset and its vascular complications. The mechanisms at the basis of the relation between vitamin B6 and diabetes onset are still not completely clarified. In contrast more evidence indicates that vitamin B6 can protect from diabetes complications through its role as scavenger of AGEs. It has been demonstrated that in diabetes AGEs can destroy the functionality of macromolecules such as protein, lipids, and DNA, thus producing tissue damage that result in vascular diseases. AGEs can be in part also responsible for the increased cancer risk associated with diabetes. In this chapter the relationship between vitamin B6, diabetes and AGEs will be discussed by showing the acquired knowledge and questions that are still open.

Identification of the pyridoxal 5'-phosphate allosteric site in human pyridox(am)ine 5'-phosphate oxidase

Adequate levels of pyridoxal 5'-phosphate (PLP), the catalytically active form of vitamin B6 , and its proper distribution in the body are essential for human health. The PLP recycling pathway plays a crucial role in these processes and its defects cause severe neurological diseases. The enzyme pyridox(am)ine 5'-phosphate oxidase (PNPO), whose catalytic action yields PLP, is one of the key players in this pathway. Mutations in the gene encoding PNPO are responsible for a severe form of neonatal epilepsy. Recently, PNPO has also been described as a potential target for chemotherapeutic agents. Our laboratory has highlighted the crucial role of PNPO in the regulation of PLP levels in the cell, which occurs via a feedback inhibition mechanism of the enzyme, exerted by binding of PLP at an allosteric site. Through docking analyses and site-directed mutagenesis experiments, here we identified the allosteric PLP binding site of human PNPO. This site is located in the same protein region as the allosteric site we previously identified in the Escherichia coli enzyme homologue. However, the identity and arrangement of the amino acid residues involved in PLP binding are completely different and resemble those of the active site of PLP-dependent enzymes. The identification of the PLP allosteric site of human PNPO paves the way for the rational design of enzyme inhibitors as potential anti-cancer compounds.

A Systems Biology Approach to Understand the Racial Disparities in Colorectal Cancer

Racial disparities between Black/African Americans (AA) and White patients in colorectal cancer are an ever-growing area of concern. Black/AA show the highest incidence and have the highest mortality among major U.S. racial groups. There is no definite cause other than possible sociodemographic, socioeconomic, education, nutrition, delivery of healthcare, screening, and cultural factors. A primary limitation in this field is the lack of and small sample size of Black/AA studies. Thus, this study aimed to investigate whether differences in gene expression contribute to this ongoing unanswered racial disparity issue. In this study, we examined transcriptomic data of Black/AA and White patient cohorts using a bioinformatic and systems biology approach. We performed a Kaplan-Meier overall survival analysis between both patient cohorts across critical colorectal cancer signal transduction networks (STN), to determine the differences in significant genes across each cohort. Other bioinformatic analyses performed included PROGENy (pathway responsive genes for activity inference), RNA sequencing differential expression using DESeq2, multivariable-adjusted regression, and other associated Kaplan-Meier analyses. These analyses identified novel prognostic genes independent from each cohort, 176 differentially expressed genes, and specific patient cohort STN survival associations. Despite the overarching limitation, the results revealed several novel differences in gene expression between the colorectal cancer Black/AA and White patient cohorts, which allows one to dive deeper into and understand the behavior on a systems level of what could be driving this racial difference across colorectal cancer. Concretely, this information can guide precision medicine approaches tailored specifically for colorectal cancer racial disparities.

SIGNIFICANCE

The purpose of this work is to investigate the racial disparities in colorectal cancer between Black/AA and White patient cohorts using a systems biology and bioinformatic approach. Our study investigates the underlying biology of each patient cohort. Concretely, the findings of this study include disparity-associated genes and pathways, which provide a tangible starting point to guide precision medicine approaches tailored specifically for colorectal cancer racial disparities.

FKBP5 associated CD8 T cell infiltration is a novel prognostic biomarker in luminal B breast cancer

OBJECTIVE

To investigate the relationship between FKBP prolyl isomerase 5 (FKBP5) gene expression and CD8 T cells in tumour progression and immunology of the luminal B subtype of breast cancer (LBBC) using bioinformatics analyses.

METHODS

The Gene Expression Profiling Interactive Analysis 2, Human Protein Atlas and breast cancer gene-expression miner v4.5 databases were used for data mining and analysing FKBP5, its co-expressed genes and CD8 T cell-related markers. The Tumor IMmune Estimation Resource 2.0 database was used for analysing the correlation and prognosis of FKBP5 and CD8 T cell infiltration level in LBBC.

RESULTS

Upregulated FKBP5 expression was correlated with improved survival in LBBC. Upregulated FKBP5-related CD8 T cell markers were also demonstrated to be significantly correlated with better survival in LBBC and might play a role in the biological activity of FKBP5.

CONCLUSION

These findings suggest that FKBP5 and its associated CD8 T cell infiltration are potential benign prognostic indicators for LBBC.

Exploring the Prognosis-Related Genetic Variation in Gastric Cancer Based on mGWAS

The use of metabolome genome-wide association studies (mGWAS) has been shown to be effective in identifying functional genes in complex diseases. While mGWAS has been applied to biomedical and pharmaceutical studies, its potential in predicting gastric cancer prognosis has yet to be explored. This study aims to address this gap and provide insights into the genetic basis of GC survival, as well as identify vital regulatory pathways in GC cell progression. Genome-wide association analysis of plasma metabolites related to gastric cancer prognosis was performed based on the Generalized Linear Model (GLM). We used a log-rank test, LASSO regression, multivariate Cox regression, GO enrichment analysis, and the Cytoscape software to visualize the complex regulatory network of genes and metabolites and explored in-depth genetic variation in gastric cancer prognosis based on mGWAS. We found 32 genetic variation loci significantly associated with GC survival-related metabolites, corresponding to seven genes, VENTX, PCDH 7, JAKMIP1, MIR202HG, MIR378D1, LINC02472, and LINC02310. Furthermore, this study identified 722 Single nucleotide polymorphism (SNP) sites, suggesting an association with GC prognosis-related metabolites, corresponding to 206 genes. These 206 possible functional genes for gastric cancer prognosis were mainly involved in cellular signaling molecules related to cellular components, which are mainly involved in the growth and development of the body and neurological regulatory functions related to the body. The expression of 23 of these genes was shown to be associated with survival outcome in gastric cancer patients in The Cancer Genome Atlas (TCGA) database. Based on the genome-wide association analysis of prognosis-related metabolites in gastric cancer, we suggest that gastric cancer survival-related genes may influence the proliferation and infiltration of gastric cancer cells, which provides a new idea to resolve the complex regulatory network of gastric cancer prognosis.

Plasma Exosome Gene Signature Differentiates Colon Cancer from Healthy Controls

BACKGROUND

Liquid biopsies have become an integral part of cancer management as minimally invasive options to detect molecular and genetic changes. However, current options show poor sensitivity in peritoneal carcinomatosis (PC). Novel exosome-based liquid biopsies may provide critical information on these challenging tumors. In this initial feasibility analysis, we identified an exosome gene signature of 445 genes (ExoSig445) from colon cancer patients, including those with PC, that is distinct from healthy controls.

METHODS

Plasma exosomes from 42 patients with metastatic and non-metastatic colon cancer and 10 healthy controls were isolated and verified. RNAseq analysis of exosomal RNA was performed and differentially expressed genes (DEGs) were identified by the DESeq2 algorithm. The ability of RNA transcripts to discriminate control and cancer cases was assessed by principal component analysis (PCA) and Bayesian compound covariate predictor classification. An exosomal gene signature was compared with tumor expression profiles of The Cancer Genome Atlas.

RESULTS

Unsupervised PCA using exosomal genes with greatest expression variance showed stark separation between controls and patient samples. Using separate training and test sets, gene classifiers were constructed capable of discriminating control and patient samples with 100% accuracy. Using a stringent statistical threshold, 445 DEGs fully delineated control from cancer samples. Furthermore, 58 of these exosomal DEGs were found to be overexpressed in colon tumors.

CONCLUSIONS

Plasma exosomal RNAs can robustly discriminate colon cancer patients, including patients with PC, from healthy controls. ExoSig445 can potentially be developed as a highly sensitive liquid biopsy test in colon cancer.

A new natural killer cell-specific gene signature predicting recurrence in colorectal cancer patients

The protective role of Natural Killer (NK) cell tumour immunosurveillance has long been recognised in colorectal cancer (CRC). However, as most patients show limited intra-tumoral NK cell infiltration, improving our ability to identify those with high NK cell activity might aid in dissecting the molecular features which underlie NK cell sensitivity. Here, a novel CRC-specific NK cell gene signature that infers NK cell load in primary tissue samples was derived and validated in multiple patient CRC cohorts. In contrast with other NK cell gene signatures that have several overlapping genes across different immune cell types, our NK cell signature has been extensively refined to be specific for CRC-infiltrating NK cells. The specificity of the signature is substantiated in tumour-infiltrating NK cells from primary CRC tumours at the single cell level, and the signature includes genes representative of NK cells of different maturation states, activation status and anatomical origin. Our signature also accurately discriminates murine NK cells, demonstrating the applicability of this geneset when mining datasets generated from preclinical studies. Differential gene expression analysis revealed tumour-intrinsic features associated with NK cell inclusion versus exclusion in CRC patients, with those tumours with predicted high NK activity showing strong evidence of enhanced chemotactic and cytotoxic transcriptional programs. Furthermore, survival modelling indicated that NK signature expression is associated with improved survival outcomes in CRC patients. Thus, scoring CRC samples with this refined NK cell signature might aid in identifying patients with high NK cell activity who could be prime candidates for NK cell directed immunotherapies.

Identification and validation of critical genes with prognostic value in gastric cancer

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality rates. Molecular targeted therapies, including those targeting human epidermal factor receptor 2 (HER2), have proven to be effective in clinical treatment. However, better identification and description of tumor-promoting genes in GC is still necessary for antitumor therapy. Methods: Gene expression and clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Last absolute shrinkage and selection operator (LASSO) Cox regression were applied to build a prognostic model, the Prognosis Score. Functional enrichment and single-sample gene set enrichment analysis (ssGSEA) were used to explore potential mechanisms. Western blotting, RNA interference, cell migration, and wound healing assays were used to detect the expression and function of myosin light chain 9 (MYL9) in GC. Results: A four-gene prognostic model was constructed and GC patients from TCGA and meta-GEO cohorts were stratified into high-prognosis score groups or low-prognosis score groups. GC patients in the high-prognosis score group had significantly poorer overall survival (OS) than those in the low-prognosis score groups. The GC prognostic model was formulated as PrognosisScore = (0.06 × expression of BGN) - (0.008 × expression of ATP4A) + (0.12 × expression of MYL9) - (0.01 × expression of ALDH3A1). The prognosis score was identified as an independent predictor of OS. High expression of MYL9, the highest weighted gene in the prognosis score, was correlated with worse clinical outcomes. Functional analysis revealed that MYL9 is mainly associated with the biological function of epithelial-mesenchymal transition (EMT). Knockdown of MYL9 expression inhibits migration of GC cells in vitro. Conclusion: We found that PrognosisScore is potential reliable prognostic marker and verified that MYL9 promotes the migration and metastasis of GC cells.

Dynamic Expression of Palmitoylation Regulators across Human Organ Development and Cancers Based on Bioinformatics

Protein palmitoylation is a reversible modification process that links palmitate to cysteine residues via a reversible thioester bond. Palmitoylation exerts an important role in human organ development and tumor progression. However, a comprehensive landscape regarding the dynamic expression of palmitoylation regulators in human organ development remains unclear. In this study, we analyzed the dynamic expression of palmitoylation regulators in seven organ development and eight cancer types based on bioinformatics. We found that the expression levels of most palmitoylation regulators were altered after birth. In particular, ZDHHC7/20/21 exhibited converse expression patterns in multiple cancer types. Survival analysis showed that the poor prognosis in patients with kidney renal clear carcinoma (KIRC) is related to low expression of ZDHHC7/20/21, and a high expression of ZDHHC7/20/21 is related to worse survival in patients with liver hepatocellular carcinoma (LIHC). Furthermore, we found that the expression of ZDHHC7 is associated with infiltration levels of some types of immune cells in the tumor microenvironment (TME), and we explored the relationship between ZDHHC7 expression and immune checkpoint (ICP) genes across 33 cancer types. In addition, gene set enrichment analysis (GSEA) results indicated that ZDHHC7 might regulate different genes to mediate the same pathway in different organs. In summary, the comprehensive analysis of palmitoylation regulators reveals their functions in human organ development and cancer, which may provide new insights for developing new tumor markers.

Cardioprotective factors against myocardial infarction selected in vivo from an AAV secretome library

Therapies for patients with myocardial infarction and heart failure are urgently needed, in light of the breadth of these conditions and lack of curative treatments. To systematically identify previously unidentified cardioactive biologicals in an unbiased manner in vivo, we developed cardiac FunSel, a method for the systematic, functional selection of effective factors using a library of 1198 barcoded adeno-associated virus (AAV) vectors encoding for the mouse secretome. By pooled vector injection into the heart, this library was screened to functionally select for factors that confer cardioprotection against myocardial infarction. After two rounds of iterative selection in mice, cardiac FunSel identified three proteins [chordin-like 1 (Chrdl1), family with sequence similarity 3 member C (Fam3c), and Fam3b] that preserve cardiomyocyte viability, sustain cardiac function, and prevent pathological remodeling. In particular, Chrdl1 exerted its protective activity by binding and inhibiting extracellular bone morphogenetic protein 4 (BMP4), which resulted in protection against cardiomyocyte death and induction of autophagy in cardiomyocytes after myocardial infarction. Chrdl1 also inhibited fibrosis and maladaptive cardiac remodeling by binding transforming growth factor-β (TGF-β) and preventing cardiac fibroblast differentiation into myofibroblasts. Production of secreted and circulating Chrdl1, Fam3c, and Fam3b from the liver also protected the heart from myocardial infarction, thus supporting the use of the three proteins as recombinant factors. Together, these findings disclose a powerful method for the in vivo, unbiased selection of tissue-protective factors and describe potential cardiac therapeutics.

Identification and Validation of a Novel Prognostic Gene Model for Colorectal Cancer

Aims

Colon cancer (CRC), with high morbidity and mortality, is a common and highly malignant cancer, which always has a bad prognosis. So it is urgent to employ a reasonable manner to assess the prognosis of patients. We developed and validated a gene model for predicting CRC risk.

Methods

The Gene Expression Omnibus (GEO) database was used to extract the gene expression profiles of CRC patients (N = 181) from GEO to identify genes that were differentially expressed between CRC patients and controls and then stable signature genes by firstly using both robust likelihood-based modeling with 1000 iterations and random survival forest variable hunting algorithms. Cluster analysis using the longest distance method was drawn out, and Kaplan–Meier (KM) survival analysis was used to compare the clusters. Meanwhile, the risk score was evaluated in three independent datasets including the GEO and Illumina HiSeq sequencing platforms. The corresponding risk index was calculated, and samples were clustered into high- and low-risk groups according to the median. And survival ROC analysis was used to evaluate the prognostic model. Finally, the Gene Set Enrichment Analysis (GSEA) was performed for further functional enrichment analyses.

Results

A 10-gene model was obtained, including 7 negative impact factors (SLC39A14, AACS, ERP29, LAMP3, TMEM106C, TMED2, and SLC25A3) and 3 positive ones (CNPY2, GRB10, and PBK), which related with several important oncogenic pathways (KRAS signaling, TNF-α signaling pathway, and WNT signaling pathway) and several cancer-related cellular processes (epithelial mesenchymal transition and cellular apoptosis). By using colon cancer datasets from The Cancer Genome Atlas (TCGA), the model was validated in KM survival analysis (P ≤ 0.001) and significant analysis with recurrence time (P = 0.0018).

Conclusions

This study firstly developed a stable and effective 10-gene model by using novel combined methods, and CRC patients might be able to use it as a prognostic marker for predicting their survival and monitoring their long-term treatment.

Prognostic Role of M6A-Associated Immune Genes and Cluster-Related Tumor Microenvironment Analysis: A Multi-Omics Practice in Stomach Adenocarcinoma

N6-methylandrostenedione (m6A) methylation plays a very important role in the development of malignant tumors. The immune system is the key point in the progression of tumors, particularly in terms of tumor treatment and drug resistance. Tumor immunotherapy has now become a hot spot and a new approach for tumor treatment. However, as far as the stomach adenocarcinoma (STAD) is concerned, the in-depth research is still a gap in the m6A-associated immune markers. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases is extremely important for our research, where we obtained gene mutation, gene expression data and relevant clinical information of STAD patients. Firstly, the samples from GEO were used as external validation groups, while the TCGA samples were divided into a training group and an internal validation group randomly. Using the way of Single factor COX-LASSO- and multi-factor Cox to construct the prognostic model. Then, all samples were subjected to cluster analysis to generate high and low expression groups of immune gene. Meanwhile, we also collected the correlation between these types and tumor microenvironment. On this basis, a web version of the dynamic nomogram APP was developed. In addition, we performed microenvironmental correlation, copy number variation and mutation analyses for model genes. The prognostic model for STAD developed here demonstrated a very strong predictive ability. The results of cluster analysis manifested that the immune gene low expression group had lower survival rate and higher degree of immune infiltration. Therefore, the immune gene low expression group was associated with lower survival rates and a higher degree of immune infiltration. Gene set enrichment analysis suggested that the potential mechanism might be related to the activation of immunosuppressive functions and multiple signaling pathways. Correspondingly, the web version of the dynamic nomogram APP produced by the DynNom package has successfully achieved rapid and accurate calculation of patient survival rates. Finally, the multi-omics analysis of model genes further enriched the research content. Interference of RAB19 was confirmed to facilitate migration of STAD cells in vitro, while its overexpression inhibited these features. The prognostic model for STAD constructed in this study is accurate and efficient based on multi-omics analysis and experimental validation. Additionally, the results of the correlation analysis between the tumor microenvironment and m6Ascore are the basics of further exploration of the pathophysiological mechanism in STAD.

DNMBP-AS1 Regulates NHLRC3 Expression by Sponging miR-93-5p/17-5p to Inhibit Colon Cancer Progression

Long non-coding RNAs (LncRNAs) act as competing endogenous RNAs (ceRNAs) in colon cancer (CC) progression, via binding microRNAs (miRNAs) to regulate the expression of corresponding messenger RNAs (mRNAs). This article aims to explore the detailed molecular mechanism of ceRNA in CC. Top mad 5000 lncRNAs and top mad 5000 mRNAs were used to perform weighted gene co-expression network analysis (WGCNA), and key modules were selected. We used 405 lncRNAs in the red module and 145 mRNAs in the purple module to build the original ceRNA network by online databases. The original ceRNA network included 50 target lncRNAs, 41 target miRNAs, and 34 target mRNAs. Fifty target lncRNAs were used to establish a prognostic risk model by univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. LncRNAs in the risk model were used to build the secondary ceRNA network, which contained 9 lncRNAs in the risk model, 35 miRNAs, and 29 mRNAs. Survival analyses of 29 mRNAs in the secondary ceRNA network have shown HOXA10 and NHLRC3 were identified as crucial prognostic factors. Finally, we constructed the last ceRNA network including 5 lncRNAs in the risk model, 8 miRNAs, and 2 mRNAs related to prognosis. Quantitative real-time polymerase chain reaction (qRT-PCR) results revealed that DNMBP-AS1 and FAM87A were down-regulated in CC cells and tissues. Function assays showed that over-expression of DNMBP-AS1 and FAM87A inhibited CC cells proliferation and migration. Mechanism study showed that DNMBP-AS1 served as miR-93-5p/17-5p sponges and relieved the suppression effect of miR-93-5p/17-5p on their target NHLRC3. Our study suggested that DNMBP-AS1 inhibited the progression of colon cancer through the miR-93-5p/17-5p/NHLRC3 axis, which could be potential therapeutic targets for CC.

Prognostic Implication and Oncogenic Role of PNPO in Pan-Cancer

Objective: Pyridoxine 5′-phosphate oxidase (PNPO) is a key enzyme in the metabolism of vitamin B6 and affects the tumorigenesis of ovarian and breast cancers. However, the roles of PNPO in other types of cancer remain unknown.

Methods: The expression of PNPO was interpreted by The Cancer Genome Atlas (TCGA) database and Genotype Tissue-Expression (GTEX) database. Analysis of PNPO genomic alterations and protein expression in human organic tissues was analyzed by the cBioPortal database and human multiple organ tissue arrays. PNPO with drug sensitivity analysis was performed from the CellMiner database. The correlations between PNPO expression and survival outcomes, clinical features, DNA mismatch repair system (MMR), microsatellite instability (MSI), tumor mutation burden (TMB), and immune-associated cell infiltration were analyzed using the TCGA, ESTIMATE algorithm, and TIMER databases. Gene Set Enrichment Analysis (GSEA) was applied to elucidate the biological function of PNPO in pan-cancer.

Results: The differential analysis showed that the level of PNPO mRNA expression was upregulated in 21 tumor types compared with normal tissues, which was consistent with its protein expression in most cancer types. The abnormal expression of PNPO could predict the survival outcome of patients with esophageal carcinoma (ESCA), kidney renal clear cell carcinoma (KIRC), prostate adenocarcinoma (PRAD), ovarian serous cystadenocarcinoma (OV), and uveal melanoma (UVM). Furthermore, the most frequent mutation type of PNPO genomic was amplified. Moreover, the aberrant PNPO expression was related to MMR, MSI, TMB, and drug sensitivity in various types of cancer. The expression of PNPO was related to the infiltration levels of various immune-associated cells in pan-cancer by ESTIMATE algorithm and TIMER database mining.

Conclusion: Our results suggest that PNPO is a potential molecular biomarker for predicting patient prognosis, drug sensitivity, and immunoreaction in pan-cancer.

PPIP5K2 promotes colorectal carcinoma pathogenesis through facilitating DNA homologous recombination repair

Colorectal carcinoma (CRC) is the second most deadly cancer worldwide. Therapies that take advantage of DNA repair defects have been explored in various tumors but not yet systematically in CRC. Here, we found that Diphosphoinositol Pentakisphosphate Kinase 2 (PPIP5K2), an inositol pyrophosphate kinase, was highly expressed in CRC and associated with a poor prognosis of CRC patients. In vitro and in vivo functional studies demonstrated that PPIP5K2 could promote the proliferation and migration ability of CRC cells independent of its inositol pyrophosphate kinase activity. Mechanically, S1006 dephosphorylation of PPIP5K2 could accelerate its dissociation with 14-3-3 in the cytoplasm, resulting in more nuclear distribution. Moreover, DNA damage treatments such as doxorubicin (DOX) or irradiation (IR) could induce nuclear translocation of PPIP5K2, which subsequently promoted homologous recombination (HR) repair by binding and recruiting RPA70 to the DNA damage site as a novel scaffold protein. Importantly, we verified that S1006 dephosphorylation of PPIP5K2 could significantly enhance the DNA repair ability of CRC cells through a series of DNA repair phenotype assays. In conclusion, PPIP5K2 is critical for enhancing the survival of CRC cells via facilitating DNA HR repair. Our findings revealed an unrecognized biological function and mechanism model of PPIP5K2 dependent on S1006 phosphorylation and provided a potential therapeutic target for CRC patients.

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

Employing bioinformatics analysis to identify hub genes and microRNAs involved in colorectal cancer

The third leading cause of cancer-related deaths in the world, colorectal cancer (CRC) is a global health issue that should be addressed in both diagnostics and therapeutics to improve patient survival rate. Today, microarray data analysis is increasingly being used as a novel and effective method for classification of malignancies and making prognostic assessments. Built upon the concept of microarray data analysis and aimed at the identification of CRC-associated genes, our study has adopted an integrative analysis for the gene expression patterns of four microarray datasets in gene expression omnibus (GEO) and microRNAs (miRNAs) expression profiles. We downloaded four gene expression profiles, i.e., GSE37182, GSE25070, GSE10950, and GSE113513, miRNAs gene expression profiles and differentially expressed genes (DEGs). We used R software, the DAVID database, protein-protein interaction (PPI) networks, the Cytoscape program and receiver operating characteristic (ROC) curve for data analysis. Out of the four gene expression profiles, a total of 43 common DEGs were identified, including 10 hub genes, SLC26A3, CLCA1, GUCA2A, MS4A12, CLCA4, GUCA2B, KRT20, AQP8, MAOA, and ADH1A, and four differentially expressed miRNAs, miR-552, miR-423-5p, miR-502-3p, and miR-490-5p. The highly enriched modes of the signaling pathways among these DEGs were speculated to be involved in various processes including nitrogen metabolism, mineral absorption, pancreatic secretions, and tyrosine metabolism in Kyoto encyclopedia of genes and genomes (KEGG) database. According to our bioinformatics analysis, the DEGs identified in the present study could be considered as significant hallmarks in the molecular mechanisms of CRC development. Our findings may assist scientists with developing novel strategies not only for prediction of CRC, but also for screening and early diagnosis, and treatment of CRC patients.

Identification of ARGLU1 as a potential therapeutic target for gastric cancer based on genome-wide functional screening data

BACKGROUND

Due to the molecular mechanism complexity and heterogeneity of gastric cancer (GC), mechanistically interpretable biomarkers were required for predicting prognosis and discovering therapeutic targets for GC patients.

METHODS

Based on a total of 824 GC-specific fitness genes from the Project Score database, LASSOCox regression was performed in TCGA-STAD cohort to construct a GC Prognostic (GCP) model which was then evaluated on 7 independent GC datasets. Targets prioritization was performed in GC organoids. ARGLU1 was selected to further explore the biological function and molecular mechanism. We evaluated the potential of ARGLU1 serving as a promising therapeutic target for GC using patients derived xenograft (PDX) model.

FINDINGS

The 9-gene GCP model showed a statistically significant prognostic performance for GC patients in 7 validation cohorts. Perturbation of SSX4, DDX24, ARGLU1 and TTF2 inhibited GC organoids tumor growth. The results of tissue microarray indicated lower expression of ARGLU1 was correlated with advanced TNM stage and worse overall survival. Over-expression ARGLU1 significantly inhibited GC cells viability in vitro and in vivo. ARGLU1 could enhance the transcriptional level of mismatch repair genes including MLH3, MSH2, MSH3 and MSH6 by potentiating the recruitment of SP1 and YY1 on their promoters. Moreover, inducing ARGLU1 by LNP-formulated saRNA significantly inhibited tumor growth in PDX model.

INTERPRETATION

Based on genome-wide functional screening data, we constructed a 9-gene GCP model with satisfactory predictive accuracy and mechanistic interpretability. Out of nine prognostic genes, ARGLU1 was verified to be a potential therapeutic target for GC.

FUNDING

National Natural Science Foundation of China.

The clinical relevance of gene expression based prognostic signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers, with more than one million new cases every year. In the last few decades, several advancements in therapeutic and preventative levels have reduced the mortality rate, but new biomarkers are required for improved prognosis. The alterations at the genetic and epigenetic level have been recognized as major players in tumorigenesis. The products of gene expression in the form of mRNA, microRNA, and long-noncoding RNA, have started to emerge as important regulatory molecules, playing an important role in cancer. Gene-expression based prognostic risk scores, which quantify and compare their expression, have emerged as promising biomarkers with enormous clinical value. These composite multi-gene models in which more than one gene is used to predict prognosis have been shown to be significantly effective in identifying patients with multiple clinico-pathological risks like overall mortality, response to chemotherapy, risk of metastasis, etc. The advent of microarray and advanced sequencing technologies have led to the generation of large datasets like TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), which have fueled the search for new biomarkers. Continuous evaluation of these candidate biomarkers in clinical settings is promising to improve the management of CRC. These composite gene signatures provide potential in identifying high-risk patients, which might help clinicians to better manage these patients and design appropriate personalized therapeutic interventions. In this review, we emphasize on composite prognostic scores from diverse resources with clinical utility in CRC.

Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer

Background

Colorectal cancer (CRC) is the second most common cause of cancer death in the United States and the third most common cancer globally. The incidence of CRC tends to be younger, and we urgently need a reliable prognostic assessment strategy.

Methods

Protein expression profile and clinical information of 390 CRC patients/samples were downloaded from the TCPA and TCGA database, respectively. The Kaplan-Meier, Cox regression, and Pearson correlation analysis were applied in this study.

Results

Based on the TCPA and TCGA database, we screened 6 hub proteins and first constructed protein risk signature, all of which were significantly associated with CRC patients' overall survival (OS). The risk score was an independent prognostic factor and significantly related with the size of the tumor in situ (T). 6 hub proteins were differentially expressed in cancer and normal tissues and in different CRC stages, which were validated at the ONCOMINE database. Next, 40 coexpressed proteins of 6 hub proteins were extracted from the TCPA database. In the protein-protein interaction (PPI) network, HER1, HER2, and CTNNB1 were at the center. Function enrichment analysis illustrated that 46 proteins were mainly involved in the EGFR (HER1) tyrosine kinase inhibitor resistance pathway.

Conclusion

Studies indicated that 6 hub proteins might be considered as new targets for CRC therapies, and the protein risk signature can be used to predict the OS of CRC patients.

Six-gene-based prognostic model predicts overall survival in patients with uveal melanoma

BACKGROUND

Uveal melanoma (UM) is the most common primary intraocular tumor in adults, which has a high mortality rate and worse prognosis. Therefore, early potential molecular detection and prognostic evaluation seem more important for early diagnosis and treatment.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas-Uveal melanomas database. Survival genes were identified by univariate analysis and were regarded to be associated with the overall survival of UM patients. Then, pathway enrichment analysis of these survival genes was performed. Robust likelihood-based survival model and multivariate survival analysis were conducted to identify more reliable genes and the prognostic signature for UM survival prediction. Two internal datasets and another two UM datasets from Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.

RESULTS

Firstly, 2,010 survival genes were screened by univariate survival analysis. GO and KEGG analysis revealed that these genes were mainly involved in pathways such as mRNA processing, RNA splicing, spliceosome and ubiquitin mediated proteolysis. Secondly, a six-gene signature was identified by Robust likelihood-based survival model approach. The gene expression of the six genes can successfully divide UM samples into high- and low-risk groups and have strong survival prediction ability. What's more, the expression of six genes was compared in 80 healthy adipose tissue samples obtained from GTEx (Genotype-Tissue Expression) database and further validated in internal datasets and GEO datasets, which also can predict UM patient survival.

CONCLUSIONS

The six genes (SH2D3A, TMEM201, LZTS1, CREG1, NIPA1 and HIST1H4E) model might play a vital role in prognosis of UM, which should be helpful for further insight into the treatment of uveal melanoma.

Analysis of DNA methylation-driven genes for predicting the prognosis of patients with colorectal cancer

Aberrant promoter methylation and ensuing abnormal gene expression are important epigenetic mechanisms that contribute to colorectal oncogenesis. Yet, the prognostic significance of such methylation-driven genes in colorectal cancer (CRC) remains obscure. Herein, a total of 181 genes were identified as the methylation-driven molecular features of CRC by integrated analysis of the expression profiles and the matched DNA methylation data from The Cancer Genome Atlas (TCGA) database. Among them, a five-gene signature (POU4F1, NOVA1, MAGEA1, SLCO4C1, and IZUMO2) was developed as a risk assessment model for predicting the clinical outcomes in CRC. The Kaplan-Meier analysis and Harrell's C index demonstrated that the risk assessment model significantly distinguished the patients in high or low-risk groups (p-value < 0.0001 log-rank test, HR: 2.034, 95% CI: 1.419-2.916, C index: 0.655). The sensitivity and specificity were validated by the receiver operating characteristic (ROC) analysis. Furthermore, different pharmaceutical treatment responses were observed between the high-risk and low-risk groups. Indeed, the methylation-driven gene signature could act as an independent prognostic evaluation biomarker for assessing the OS of CRC patients and guiding the pharmaceutical treatment. Compared with known biomarkers, the methylation-driven gene signature could reveal cross-omics molecular features for improving clinical stratification and prognosis.

Multiple paragangliomas: a case report

Background

Carotid and vagal paragangliomas (CPGLs and VPGLs) are rare neoplasms that arise from the paraganglia located at the bifurcation of carotid arteries and vagal trunk, respectively. Both tumors can occur jointly as multiple paragangliomas accounting for approximately 10 to 20% of all head and neck paragangliomas. However, molecular and genetic mechanisms underlying the pathogenesis of multiple paragangliomas remain elusive.

Case presentation

We report a case of multiple paragangliomas in a patient, manifesting as bilateral CPGL and unilateral VPGL. Tumors were revealed via computed tomography and ultrasound study and were resected in two subsequent surgeries. Both CPGLs and VPGL were subjected to immunostaining for succinate dehydrogenase (SDH) subunits and exome analysis. A likely pathogenic germline variant in the SDHD gene was indicated, while likely pathogenic somatic variants differed among the tumors.

Conclusions

The identified germline variant in the SDHD gene seems to be a driver in the development of multiple paragangliomas. However, different spectra of somatic variants identified in each tumor indicate individual molecular mechanisms underlying their pathogenesis.

Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer

The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from The Cancer Genome Atlas. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation (4-DysReg) signature, which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.

Pyridoxine 5'-phosphate oxidase is correlated with human breast invasive ductal carcinoma development

Pyridoxine 5′-phosphate oxidase (PNPO) is a converting enzyme for an active form of vitamin B6. This study aims to evaluate the biological function and the regulatory mechanism of PNPO in human breast invasive ductal carcinoma (IDC). We unveiled for the first time that PNPO was upregulated in patients with IDC and was correlated with the overall survival of patients with metastasis at the later stages. Suppression of PNPO inhibited breast cancer cell proliferation, migration, invasion and colony formation, arrested cell cycle at the G2/M phase and induced cell apoptosis. PNPO was positively correlated with lncRNA MALAT1 which was negatively correlated with miR-216b-5p. PNPO was down-regulated and up-regulated by miR-216b-5p mimics and inhibitors, respectively, in breast cancer cells. A microRNA response element was found in both PNPO and MALAT1 transcripts for miR-216b-5p and the dual-luciferase reporter assay confirmed the binding of these transcripts. Knockdown of MALAT1 resulted in an increase of miR-216b-5p and a decrease of PNPO mRNA, indicating a regulatory mechanism of competing endogenous RNAs. Taken together, these results reveal the biological function and a regulatory mechanism of PNPO, in which the MALAT1/miR-216b-5p/PNPO axis may be important in IDC development. Targeting this axis may have therapeutic potential for breast cancer.

Analysis of immune-related signatures of colorectal cancer identifying two different immune phenotypes: Evidence for immune checkpoint inhibitor therapy

Immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of numerous types of cancer, including colorectal cancer (CRC). Patients with CRC and deficient mismatch repair or high microsatellite instability could benefit from ICI treatment, although the response rate of most patients is low. Therefore, the immune subtyping of patients with CRC is required in order to determine the subtypes suitable for ICI treatment. The present study used a cohort of patients with CRC from The Cancer Genome Atlas (TCGA) to perform molecular subtyping, with results validated in three CRC cohorts from the Gene Expression Omnibus. Non-negative matrix factorization was used to achieve consensus molecular subtyping. The tumor immune dysfunction and exclusion algorithm was used to predict potential ICI therapy responses and gene set enrichment analysis was performed to define different pathways associated with the immune response. Two distinct subtypes of CRC were finally identified in TCGA cohorts, which were characterized as significantly different prognostic subtypes (low-risk and high-risk subtypes). Higher expression of programmed death-ligand 1, higher proportion of tumor-infiltrating lymphocytes and tumor mutation burden were significantly enriched in the low-risk subtype. Further pathway analysis revealed that the low-risk subtype was associated with immune response activation and signaling pathways involved in ‘antigen processing and presentation’. Three independent CRC cohorts were used to validate the above findings. In summary, two clinical CRC subtypes were identified, which are characterized by significantly different survival outcomes and immune infiltration patterns. The findings of the present study suggest that ICI treatment may be more effective in the low-risk CRC subtype.

Dynamic gene regulatory network reconstruction and analysis based on clinical transcriptomic data of colorectal cancer

Inferring dynamic regulatory networks that rewire at different stages is a reasonable way to understand the mechanisms underlying cancer development. In this study, we reconstruct the stage-specific gene regulatory networks (GRNs) for colorectal cancer to understand dynamic changes of gene regulations along different disease stages. We combined multiple sets of clinical transcriptomic data of colorectal cancer patients and employed a supervised approach to select initial gene set for network construction. We then developed a dynamical system-based optimization method to infer dynamic GRNs by incorporating mutual information-based network sparsification and a dynamic cascade technique into an ordinary differential equations model. Dynamic GRNs at four different stages of colorectal cancer were reconstructed and analyzed. Several important genes were revealed based on the rewiring of the reconstructed GRNs. Our study demonstrated that reconstructing dynamic GRNs based on clinical transcriptomic profiling allows us to detect the dynamic trend of gene regulation as well as reveal critical genes for cancer development which may be important candidates of master regulators for further experimental test.

The multifaceted role of vitamin B6 in cancer: Drosophila as a model system to investigate DNA damage

A perturbed uptake of micronutrients, such as minerals and vitamins, impacts on different human diseases, including cancer and neurological disorders. Several data converge towards a crucial role played by many micronutrients in genome integrity maintenance and in the establishment of a correct DNA methylation pattern. Failure in the proper accomplishment of these processes accelerates senescence and increases the risk of developing cancer, by promoting the formation of chromosome aberrations and deregulating the expression of oncogenes. Here, the main recent evidence regarding the impact of some B vitamins on DNA damage and cancer is summarized, providing an integrated and updated analysis, mainly centred on vitamin B₆. In many cases, it is difficult to finely predict the optimal vitamin rate that is able to protect against DNA damage, as this can be influenced by a given individual's genotype. For this purpose, a precious resort is represented by model organisms which allow limitations imposed by more complex systems to be overcome. In this review, we show that Drosophila can be a useful model to deeply understand mechanisms underlying the relationship between vitamin B₆ and genome integrity.

A Hallmark-Based Six-Gene Expression Signature to Assess Colorectal Cancer and Its Recurrence Risk

Purpose: As part of the effort to establish a general profile for solid tumors, the aim of this study was to develop a real-time polymerase chain reaction (RT-PCR)-based assay to assess colorectal cancer (CRC) and its recurrence risk utilizing the limited amounts of tissues available from biopsies through colonoscopy. Materials and Methods: Six candidate genes, reflecting the hallmarks of cancer cells, were identified by analyzing the gene expression profiles of primary invasive tumors in the public database. The expression of these genes in CRC and noncancerous colon tissues was quantified by RT-quantitative PCR. Classifiers were then generated to distinguish the tumors from the normal colon tissues, and to assess the risk of CRC recurrence based on the disease-free survival time, overall survival time, and metastatic status of the patients. Results: The expression profile of a five-gene panel was utilized to build a model that is capable of distinguishing CRC cancer tissues from noncancerous colorectal tissues (p < 0.0001). A classifier based on the expression signature of four genes, three of which were included in the five-gene panel, was then developed for assessing the tumor recurrence risk. This classifier could correctly identify those with a poor likelihood of survival (high risk of recurrence) >80% of time. There was a significant difference in disease-free survival time between patients in the low recurrence group and those in the high-risk group. Conclusion: The expression signatures of the six genes that reflect the genetic hallmarks of cancer cells could serve as a biomarker for identifying CRC and assessing the risk of recurrence with high sensitivity and specificity.

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion: Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.

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.

Whole exome sequencing study of a Chinese concurrent cancer family

Cancer is one of the leading causes of mortality in China, and poses a threat to public health due to its increasing incidence and mortality rates. Concurrent cancer is defined as one or more organs in the same individual having ≥2 primary malignancies occurring simultaneously or successively; however, concurrent cases are rare and poorly studied. The present study recruited a Chinese family presenting multiple cases of concurrent cancer and performed whole exome sequencing in one unaffected and two affected individuals to identify the causative mutations. DNA was extracted from peripheral blood and tumor tissue samples. Following an exome capture and quality test, the qualified library was sequenced as 100 bp paired-end reads on an Ion Torrent platform. Clean data were obtained by filtering out the low-quality reads. Subsequently, bioinformatics analyses were performed using the clean data. After mapping and annotating in 1000 Genomes Project database, the existing SNP database and the Cancer Gene Census (CGC) database, it was revealed that the NADH:ubiquinone oxidoreductase core subunit S7 gene was a candidate gene with somatic mutations, and a subset of 16 genes were candidate genes with germline mutations. The findings of the present study may improve the understanding of the molecular pathogenesis of concurrent cancer.

Identification and Clinical Validation of a Novel 4 Gene-Signature with Prognostic Utility in Colorectal Cancer

Colorectal cancer (CRC) is a high burden disease with several genes involved in tumor progression. The aim of the present study was to identify, generate and clinically validate a novel gene signature to improve prediction of overall survival (OS) to effectively manage colorectal cancer. We explored The Cancer Genome Atlas (TCGA), COAD and READ datasets (597 samples) from The Protein Atlas (TPA) database to extract a total of 595 candidate genes. In parallel, we identified 29 genes with perturbations in > 6 cancers which are also affected in CRC. These genes were entered in cBioportal to generate a 17 gene panel with highest perturbations. For clinical validation, this gene panel was tested on the FFPE tissues of colorectal cancer patients (88 patients) using Nanostring analysis. Using multivariate analysis, a high prognostic score (composite 4 gene signature-DPP7/2, YWHAB, MCM4 and FBXO46) was found to be a significant predictor of poor prognosis in CRC patients (HR: 3.42, 95% CI: 1.71-7.94, p < 0.001 *) along with stage (HR: 4.56, 95% CI: 1.35-19.15, p = 0.01 *). The Kaplan-Meier analysis also segregated patients on the basis of prognostic score (log-rank test, p = 0.001 *). The external validation using GEO dataset (GSE38832, 122 patients) corroborated the prognostic score (HR: 2.7, 95% CI: 1.99-3.73, p < 0.001 *). Additionally, higher score was able to differentiate stage II and III patients (130 patients) on the basis of OS (HR: 2.5, 95% CI: 1.78-3.63, p < 0.001 *). Overall, our results identify a novel 4 gene prognostic signature that has clinical utility in colorectal cancer.

Prognostic 4-lncRNA-based risk model predicts survival time of patients with head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a common malignant disease with high mortality rates. Recently, long non-coding RNAs (lncRNAs) have been demonstrated to participate in a number of important biological functions and could serve as prognostic biomarkers in the field of oncology. Therefore, the present study aimed to identify an lncRNA-based model that was associated with prognosis. RNA-sequencing data was downloaded from The Cancer Genome Atlas and R software was used to analyze the data. Univariate analyses, robust likelihood analyses and multivariate analyses were performed to screen out key lncRNA candidates associated with prognosis and construct a risk model. A Kaplan-Meier plot was constructed for survival analysis. LncBase and Starbase were used to identify the miRNA and protein targets. Gene set enrichment analysis was used for functional analysis. As a result, a 4-lncRNA (ALMS1-IT1, RP11-359J14.2, CTB-178M22.2 and RP11-347C18.5) based risk model was identified and patients in the high-risk group were revealed to have a lower survival rate than patients in the low-risk group. A nomogram that could predict the survival of patients was plotted. A total of 79 target miRNAs and 61 target proteins were identified. The gene set enrichment analysis results revealed that nutrient metabolism pathways were enriched in the high-risk group and immune regulation pathways were enriched in the low-risk group. In summary, a 4-lncRNA based risk model was identified that was associated with prognosis, which may serve as a prognosis prediction biomarker for HNSCC.

Pyridoxine/pyridoxamine 5'-phosphate oxidase (Sgll/PNPO) is important for DNA integrity and glucose homeostasis maintenance in Drosophila

Pyridoxine/pyridoxamine 5'-phosphate oxidase (PNPO) and pyridoxal kinase (PDXK) cooperate to produce pyridoxal 5'-phosphate (PLP), the active form of vitamin B6. PDXK phosphorylates pyridoxine, pyridoxamine, and pyridoxal by producing PNP, PMP, and PLP, whereas PNPO oxidizes PNP, PMP, into PLP. We previously demonstrated that PDXK depletion in Drosophila and human cells impacts on glucose metabolism and DNA integrity. Here we characterized sgll, the Drosophila ortholog of PNPO gene, showing that its silencing by RNA interference elicits chromosome aberrations (CABs) in brains and induces diabetic hallmarks such as hyperglycemia and small body size. We showed that in sgll^RNAi neuroblasts CABs are largely produced by the genotoxic effect of the advanced glycation end products triggered by high glucose. As in sgll^RNAi cells, part of PLP is still produced by PDXK activity, these data suggest that PLP dosage need to be tightly regulated to guarantee glucose homeostasis and DNA integrity.

Integrative analysis from multi-centre studies identifies a function-derived personalized multi-gene signature of outcome in colorectal cancer

Colorectal cancer (CRC) is highly heterogeneous leading to variable prognosis and treatment responses. Therefore, it is necessary to explore novel personalized and reproducible prognostic signatures to aid clinical decision‐making. The present study combined large‐scale gene expression profiles and clinical data of 1828 patients with CRC from multi‐centre studies and identified a personalized gene prognostic signature consisting of 46 unique genes (called function‐derived personalized gene signature [FunPGS]) from an integrated statistics and function‐derived perspective. In the meta‐training and multiple independent validation cohorts, the FunPGS effectively discriminated patients with CRC with significantly different prognosis at the individual level and remained as an independent factor upon adjusting for clinical covariates in multivariate analysis. Furthermore, the FunPGS demonstrated superior performance for risk stratification with respect to other recently reported signatures and clinical factors. The complementary value of the molecular signature and clinical factors was further explored, and it was observed that the composite signature called IMCPS greatly improved the predictive performance of survival estimation relative to molecular signatures or clinical factors alone. With further prospective validation in clinical trials, the FunPGS may become a promising and powerful personalized prognostic tool for stratifying patients with CRC in order to achieve an optimal systemic therapy.

A long noncoding RNAs signature to improve survival prediction in endometrioid endometrial cancer

PURPOSE

The dysregulation of long noncoding RNAs (lncRNAs) has been reported to be correlated to carcinogenesis and cancer progression. Endometrial cancer (EC), arising from the endometrium or the inner lining of the uterus, is one of the most common gynecological malignancies. We aim to explore the prognostic value of the lncRNAs in patients with endometrioid endometrial cancer (EEC) and to identify the potential lncRNA signature for predicting survival of patients with EEC.

METHODS

We performed a genome-wide analysis of the lncRNA expression profiling in The Cancer Genome Atlas (TCGA)-Uterine Corpus Endometrial Carcinoma database (408 EEC) to identify the prognosis related lncRNAs for the EEC. The patients with EEC were randomly divided into a training set (204 for endometrioid) and a testing set (204 for endometrioid). The lncRNA signature was identified in the training set, and then independently validated in the testing set and the complete set (training set plus testing set).

RESULTS

We developed a nine-lncRNA signature-based risk score in the patients with EEC. The risk score based on the novel lncRNAs signature was able to separate patients of training set into high-and low-risk groups with significantly different overall survival and progression-free survival. These can also be successfully confirmed in the testing set and complete set.

CONCLUSION

A nine-lncRNA expression signature was identified and validated which can predict EEC patient's survival. These findings may have important implications in the understanding of the potential therapeutic methods for patients with EEC.

PTPRB promotes metastasis of colorectal carcinoma via inducing epithelial-mesenchymal transition

Dysregulation of protein tyrosine phosphatase, receptor type B (PTPRB) correlates with the development of a variety of tumors. Here we show that PTPRB promotes metastasis of colorectal cancer (CRC) cells via inducing epithelial-mesenchymal transition (EMT). We find that PTPRB is expressed at significantly higher levels in CRC tissues compared to adjacent nontumor tissues and in CRC cell lines with high invasion. PTPRB knockdown decreased the number of invasive CRC cells in an in vitro wound healing model, and also reduced tumor metastasis in vivo. Conversely, PTPRB overexpression promoted CRC cell invasion in vitro and metastasis in vivo. PTPRB overexpression decreased vimentin expression and promoted E-cadherin expression, consistent with promotion of EMT, while PTPRB knockdown had the opposite effect. Hypoxic conditions induced EMT and promoted invasion in CRC cells, but these effects were eliminated by PTPRB knockdown. EMT blockade via TWIST1 knockdown inhibited the migration and invasiveness of CRC cells, and even increased PTPRB expression could not reverse this effect. Altogether, these data support the conclusion that PTPRB promotes invasion and metastasis of CRC cells via inducing EMT, and that PTPRB would be a novel therapeutic target for the treatment of CRC.

Harnessing 13C-labeled myo-inositol to interrogate inositol phosphate messengers by NMR

The analysis of inositol poly- and pyrophosphates, an important group of eukaryotic messengers, is enabled by applying ¹³C-labeled inositol.

Inositol poly- and pyrophosphates (InsPs and PP-InsPs) are an important group of metabolites and mediate a wide range of processes in eukaryotic cells. To elucidate the functions of these molecules, robust techniques for the characterization of inositol phosphate metabolism are required, both at the biochemical and the cellular level. Here, a new tool-set is reported, which employs uniformly ¹³C-labeled compounds ([¹³C₆]myo-inositol, [¹³C₆]InsP₅, [¹³C₆]InsP₆, and [¹³C₆]5PP-InsP₅), in combination with commonly accessible NMR technology. This approach permitted the detection and quantification of InsPs and PP-InsPs within complex mixtures and at physiological concentrations. Specifically, the enzymatic activity of IP6K1 could be monitored in vitro in real time. Metabolic labeling of mammalian cells with [¹³C₆]myo-inositol enabled the analysis of cellular pools of InsPs and PP-InsPs, and uncovered high concentrations of 5PP-InsP₅ in HCT116 cells, especially in response to genetic and pharmacological perturbation. The reported method greatly facilitates the analysis of this otherwise spectroscopically silent group of molecules, and holds great promise to comprehensively analyze inositol-based signaling molecules under normal and pathological conditions.

A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients

BACKGROUND

Glioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice.

MATERIAL AND METHODS

Using the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS and differentially expressed between GBM tissues and the adjacent normal tissues. A robust likelihood-based survival modeling approach was applied to select the best genes for modeling. After the prognostic nomogram was generated, an independent dataset on different platform was used to evaluate its effectiveness.

RESULTS

We identified 168 differentially expressed genes associated with the OS. Five of these genes were selected to generate a gene prognostic nomogram. The external validation demonstrated that 5-gene prognostic nomogram has the capability of predicting the OS of GBM patients.

CONCLUSION

We developed a novel and convenient prognostic tool based on five genes that exhibited clinical value in predicting the survival probability for newly diagnosed GBM patients, and all of these five genes could represent potential target genes for the treatment of GBM. The development of this model will provide a good reference for cancer researchers.

Potential Applications of DNA, RNA and Protein Biomarkers in Diagnosis, Therapy and Prognosis for Colorectal Cancer: A Study from Databases to AI-Assisted Verification

In order to find out the most valuable biomarkers and pathways for diagnosis, therapy and prognosis in colorectal cancer (CRC) we have collected the published CRC biomarkers and established a CRC biomarker database (CBD: http://sysbio.suda.edu.cn/CBD/index.html). In this study, we analysed the single and multiple DNA, RNA and protein biomarkers as well as their positions in cancer related pathways and protein-protein interaction (PPI) networks to describe their potential applications in diagnosis, therapy and prognosis. CRC biomarkers were collected from the CBD. The RNA and protein biomarkers were matched to their corresponding DNAs by the miRDB database and the PubMed Gene database, respectively. The PPI networks were used to investigate the relationships between protein biomarkers and further detect the multiple biomarkers. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were used to analyse biological functions of the biomarkers. AI classification techniques were utilized to further verify the significances of the multiple biomarkers in diagnosis and prognosis for CRC. We showed that a large number of the DNA, RNA and protein biomarkers were associated with the diagnosis, therapy and prognosis in various degrees in the CRC biomarker networks. The CRC biomarkers were closely related to the CRC initiation and progression. Moreover, the biomarkers played critical roles in cellular proliferation, apoptosis and angiogenesis and they were involved in Ras, p53 and PI3K pathways. There were overlaps among the DNA, RNA and protein biomarkers. AI classification verifications showed that the combined multiple protein biomarkers played important roles to accurate early diagnosis and predict outcome for CRC. There were several single and multiple CRC protein biomarkers which were associated with diagnosis, therapy and prognosis in CRC. Further, AI-assisted analysis revealed that multiple biomarkers had potential applications for diagnosis and prognosis in CRC.

Survival marker genes of colorectal cancer derived from consistent transcriptomic profiling

Background

Identification of biomarkers associated with the prognosis of different cancer subtypes is critical to achieve better therapeutic assistance. In colorectal cancer (CRC) the discovery of stable and consistent survival markers remains a challenge due to the high heterogeneity of this class of tumors. In this work, we identified a new set of gene markers for CRC associated to prognosis and risk using a large unified cohort of patients with transcriptomic profiles and survival information.

Results

We built an integrated dataset with 1273 human colorectal samples, which provides a homogeneous robust framework to analyse genome-wide expression and survival data. Using this dataset we identified two sets of genes that are candidate prognostic markers for CRC in stages III and IV, showing either up-regulation correlated with poor prognosis or up-regulation correlated with good prognosis. The top 10 up-regulated genes found as survival markers of poor prognosis (i.e. low survival) were: DCBLD2, PTPN14, LAMP5, TM4SF1, NPR3, LEMD1, LCA5, CSGALNACT2, SLC2A3 and GADD45B. The stability and robustness of the gene survival markers was assessed by cross-validation, and the best-ranked genes were also validated with two external independent cohorts: one of microarrays with 482 samples; another of RNA-seq with 269 samples. Up-regulation of the top genes was also proved in a comparison with normal colorectal tissue samples. Finally, the set of top 100 genes that showed overexpression correlated with low survival was used to build a CRC risk predictor applying a multivariate Cox proportional hazards regression analysis. This risk predictor yielded an optimal separation of the individual patients of the cohort according to their survival, with a p-value of 8.25e-14 and Hazard Ratio 2.14 (95% CI: 1.75–2.61).

Conclusions

The results presented in this work provide a solid rationale for the prognostic utility of a new set of genes in CRC, demonstrating their potential to predict colorectal tumor progression and evolution towards poor survival stages. Our study does not provide a fixed gene signature for prognosis and risk prediction, but instead proposes a robust set of genes ranked according to their predictive power that can be selected for additional tests with other CRC clinical cohorts.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5193-9) contains supplementary material, which is available to authorized users.

A patient-oriented clinical decision support system for CRC risk assessment and preventative care

Background

Colorectal Cancer (CRC) is the third leading cause of cancer death among men and women in the United States. Research has shown that the risk of CRC associates with genetic and lifestyle factors. It is possible to prevent or minimize certain CRC risks by adopting a healthy lifestyle. Existing Clinical Decision Support Systems (CDSS) mainly targeted physicians as the CDSS users. As a result, the availability of patient-oriented CDSS is limited. Our project is to develop patient-oriented CDSS for active CRC management.

Methods

We implemented an online patient-oriented CRC CDSS for the public to learn about CRC, assess CRC risk levels, understand personalized CRC risk factors, and seek professional advices for people with CRC concerns. The system is implemented based on the Django Model-View-Controller (MVC) framework with an extensible background MySQL database. A CRC absolute risk prediction model is applied to calculate the personalized CRC risk score with a user-friendly web survey. An interactive dashboard using advanced data visualization technics will display and interpret the risk scores and factors. Based on the risk assessment, a structured decision tree algorithm will provide the recommendations on customized CRC screening methods. The CDSS also provides a search function for preferred providers and hospitals based on geographical information and patient preferences.

Results

A prototype of the patient-oriented CRC CDSS has been developed. It provides an open assessment of potential CRC risks via an online survey. The CRC risk predictive model has been implemented. The prediction outcomes of risk levels and factors are presented to the users through a personalized interactive visualization interface, to guide the public on how to reduce the CRC risks by changing their living styles (such as smoking and drinking) and diet characteristics (such as consumptions of red meat and milk). The CDSS will also provide customized recommendations on screening methods based on the corresponding risk factors. For users seeking professional clinicians, the CDSS also provides a convenient tool for searching nearby hospitals and available doctors based on the location preferences and providers characteristics (such as gender, language, and specialty).

Conclusions

This CRC CDSS prototype provides a patient-friendly interface for CRC risk assessment and gives a personalized interpretation on important CRC risk factors. It is a useful tool to educate the public on CRC, to provide guidance on minimizing CRC risks, and to promote early CRC screening that reduces the CRC occurrences.

Electronic supplementary material

The online version of this article (10.1186/s12911-018-0691-x) contains supplementary material, which is available to authorized users.

Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers.

Materials and methods

One hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between HCC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses, and protein–protein interaction network were used. Cox proportional hazards regression analysis was used to identify significant prognostic DEGs, with which a gene expression signature prognostic prediction model was identified in The Cancer Genome Atlas (TCGA) project discovery cohort. The robustness of this panel was assessed in the GSE14520 cohort. We verified details of the gene expression level of the key molecules through TCGA, GEO, and qPCR and used immunohistochemistry for substantiation in HCC tissues. The methylation states of these genes were also explored.

Results

Ninety-eight genes, consisting of 13 upregulated and 85 downregulated genes, were screened out in three datasets. KEGG and Gene ontology analysis for the DEGs revealed important biological features of each subtype. Protein–protein interaction network analysis was constructed, consisting of 64 nodes and 115 edges. A subset of four genes (SPINK1, TXNRD1, LCAT, and PZP) that formed a prognostic gene expression signature was established from TCGA and validated in GSE14520. Next, the expression details of the four genes were validated with TCGA, GEO, and clinical samples. The expression panels of the four genes were closely related to methylation states.

Conclusion

This study identified a novel four-gene signature biomarker for predicting the prognosis of HCC. The biomarkers may also reveal molecular mechanisms underlying development of the disease and provide new insights into interventional strategies.

Identification of a six-gene signature with prognostic value for patients with endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P-value <0.01 in the training set. Next, using robust likelihood-based survival modeling, we developed a six-gene signature (CTSW, PCSK4, LRRC8D, TNFRSF18, IHH, and CDKN2A) with a prognostic function in UCEC. A prognostic risk score system was developed by multivariate Cox proportional hazard regression based on this six-gene signature. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer overall survival (OS) outcomes than those in the low-risk group (log-rank test P-value <0.0001). This signature was further validated in the testing dataset and the entire TCGA dataset. In conclusion, we conducted an integrated study to develop a six-gene signature for the prognostic prediction of patients with UCEC. Our findings may provide novel biomarkers for prognosis and have significant implications in the understanding of therapeutic targets for UCEC.

Nomogram Integrating Genomics with Clinicopathologic Features Improves Prognosis Prediction for Colorectal Cancer

The current tumor staging system is insufficient for predicting the outcomes for patients with colorectal cancer because of its phenotypic and genomic heterogeneity. Integrating gene expression signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system. Twenty-seven signatures that used gene expression data to predict colorectal cancer prognosis were identified and re-analyzed using bioinformatic methods. Next, clinically annotated colorectal cancer samples (n = 1710) with the corresponding expression profiles, that predicted a patient's probability of cancer recurrence, were pooled to evaluate their prognostic values and establish a clinicopathologic-genomic nomogram. Only 2 of the 27 signatures evaluated showed a significant association with prognosis and provided a reasonable prediction accuracy in the pooled cohort (HR, 2.46; 95% CI, 1.183-5.132, P < 0.001; AUC, 60.83; HR, 2.33; 95% CI, 1.218-4.453, P < 0.001; AUC, 71.34). By integrating the above signatures with prognostic clinicopathologic features, a clinicopathologic-genomic nomogram was cautiously constructed. The nomogram successfully stratified colorectal cancer patients into three risk groups with remarkably different DFS rates and further stratified stage II and III patients into distinct risk subgroups. Importantly, among patients receiving chemotherapy, the nomogram determined that those in the intermediate- (HR, 0.98; 95% CI, 0.255-0.679, P < 0.001) and high-risk (HR, 0.67; 95% CI, 0.469-0.957, P = 0.028) groups had favorable responses.Implications: These findings offer evidence that genomic data provide independent and complementary prognostic information, and incorporation of this information refines the prognosis of colorectal cancer. Mol Cancer Res; 16(9); 1373-84. ©2018 AACR.

Pyridoxine 5'-phosphate oxidase is a novel therapeutic target and regulated by the TGF-β signalling pathway in epithelial ovarian cancer

Pyridoxine 5'-phosphate oxidase (PNPO) is an enzyme that converts pyridoxine 5'-phosphate into pyridoxal 5'-phosphate (PLP), an active form of vitamin B6 implicated in several types of cancer. However, the role of PNPO and its regulatory mechanism in epithelial ovarian cancer (EOC) are unknown. In the present study, PNPO expression in human ovarian tumour tissue and its association with the clinicopathological features of patients with EOC were examined. Further, the biological function of PNPO in EOC cells and in xenograft was evaluated. We demonstrated for the first time that PNPO was overexpressed in human EOC. Knockdown of PNPO induced EOC cell apoptosis, arrested cell cycle at G2/M phase, decreased cell proliferation, migration and invasion. Xenografts of PNPO-shRNA-expressing cells into the nude mouse attenuated tumour growth. PNPO at mRNA and protein levels in EOC cells was decreased after transforming growth factor-β1 (TGF-β1) treatment. The inhibitory effect of TGF-β1 on PNPO expression was abolished in the presence of SB-431542, a TGF-β type I receptor kinase inhibitor. Moreover, we found that TGF-β1-mediated PNPO expression was at least in part through the upregulation of miR-143-3p. These data indicate a mechanism underlying PNPO regulation by the TGF-β signalling pathway. Furthermore, PLP administration reduced PNPO expression and decreased EOC cell proliferation, suggesting a feedback loop between PLP and PNPO. Thus, our findings reveal that PNPO can serve as a novel tissue biomarker of EOC and may be a potential target for therapeutic intervention.