Identification of molecular biomarkers for pancreatic cancer with mRMR shortest path method

Intermediate conductance calcium-activated potassium channel (KCa3.1) in cancer: Emerging roles and therapeutic potentials

The KCa3.1 channel (also known as the KCNN4, IK1, or SK4 channel) is an intermediate-conductance calcium-activated potassium channel that regulates the membrane potential and maintains calcium homeostasis. Recently, KCa3.1 channels have attracted increasing attention because of their diverse roles in various types of cancers. In cancer cells, KCa3.1 channels regulate key processes, including cell proliferation, cell cycle, migration, invasion, tumor microenvironments, and therapy resistance. In addition, abnormal KCa3.1 expression in cancers is utilized to distinguish between tumor and normal tissues, classify cancer stages, and predict patient survival outcomes. This review comprehensively examines the current understanding of the contribution of KCa3.1 channels to tumor formation, metastasis, and its mechanisms. We evaluated the potential of KCa3.1 as a biomarker for cancer diagnosis and prognosis. Finally, we discuss the advances and challenges of applying KCa3.1 modulators in cancer treatment and propose approaches to overcome these obstacles. In summary, this review highlights the importance of this ion channel as a potent therapeutic target and prognostic biomarker of cancer.

Collagen XVII regulates tumor growth in pancreatic cancer through interaction with the tumor microenvironment

Expression of the gene for collagen XVII (COL17A1) in tumor tissue is positively or negatively associated with patient survival depending on cancer type. High COL17A1 expression is thus a favorable prognostic marker for breast cancer but unfavorable for pancreatic cancer. This study explored the effects of COL17A1 expression on pancreatic tumor growth and their underlying mechanisms. Analysis of published single-cell RNA-sequencing data for human pancreatic cancer tissue revealed that COL17A1 was expressed predominantly in cancer cells rather than surrounding stromal cells. Forced expression of COL17A1 did not substantially affect the proliferation rate of the mouse pancreatic cancer cell lines KPC and AK4.4 in vitro. However, in mouse homograft tumor models in which KPC or AK4.4 cells were injected into syngeneic C57BL/6 or FVB mice, respectively, COL17A1 expression promoted or suppressed tumor growth, respectively, suggesting that the effect of COL17A1 on tumor growth was influenced by the tumor microenvironment. RNA-sequencing analysis of tumor tissue revealed effects of COL17A1 on gene expression profiles (including the expression of genes related to cell proliferation, the immune response, Wnt signaling, and Hippo signaling) that differed between C57BL/6-KPC and FVB-AK4.4 tumors. Our data thus suggest that COL17A1 promotes or suppresses cancer progression in a manner dependent on the interaction of tumor cells with the tumor microenvironment.

SCML2 contributes to tumor cell resistance to DNA damage through regulating p53 and CHK1 stability

SCML2 has been found to be highly expressed in various tumors. However, the extent to which SCML2 is involved in tumorigenesis and cancer therapy is yet to be fully understood. In this study, we aimed to investigate the relationship between SCML2 and DNA damage response (DDR). Firstly, DNA damage stabilizes SCML2 through CHK1-mediated phosphorylation at Ser570. Functionally, this increased stability of SCML2 enhances resistance to DNA damage agents in p53-positive, p53-mutant, and p53-negative cells. Notably, SCML2 promotes chemoresistance through distinct mechanisms in p53-positive and p53-negative cancer cells. SCML2 binds to the TRAF domain of USP7, and Ser441 is a critical residue for their interaction. In p53-positive cancer cells, SCML2 competes with p53 for USP7 binding and destabilizes p53, which prevents DNA damage-induced p53 overactivation and increases chemoresistance. In p53-mutant or p53-negative cancer cells, SCML2 promotes CHK1 and p21 stability by inhibiting their ubiquitination, thereby enhancing the resistance to DNA damage agents. Interestingly, we found that SCML2A primarily stabilizes CHK1, while SCML2B regulates the stability of p21. Therefore, we have identified SCML2 as a novel regulator of chemotherapy resistance and uncovered a positive feedback loop between SCML2 and CHK1 after DNA damage, which serves to promote the chemoresistance to DNA damage agents.

Pancreatic KCa3.1 channels in health and disease

Ion channels play an important role for regulation of the exocrine and the endocrine pancreas. This review focuses on the Ca²⁺-regulated K⁺ channel K_Ca3.1, encoded by the KCNN4 gene, which is present in both parts of the pancreas. In the islets of Langerhans, K_Ca3.1 channels are involved in the regulation of membrane potential oscillations characterizing nutrient-stimulated islet activity. Channel upregulation is induced by gluco- or lipotoxic conditions and might contribute to micro-inflammation and impaired insulin release in type 2 diabetes mellitus as well as to diabetes-associated renal and vascular complications. In the exocrine pancreas K_Ca3.1 channels are expressed in acinar and ductal cells. They are thought to play a role for anion secretion during digestion but their physiological role has not been fully elucidated yet. Pancreatic carcinoma, especially pancreatic ductal adenocarcinoma (PDAC), is associated with drastic overexpression of K_Ca3.1. For pharmacological targeting of K_Ca3.1 channels, we are discussing the possible benefits K_Ca3.1 channel inhibitors might provide in the context of diabetes mellitus and pancreatic cancer, respectively. We are also giving a perspective for the use of a fluorescently labeled derivative of the K_Ca3.1 blocker senicapoc as a tool to monitor channel distribution in pancreatic tissue. In summary, modulating K_Ca3.1 channel activity is a useful strategy for exo-and endocrine pancreatic disease but further studies are needed to evaluate its clinical suitability.

Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer

Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear. Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features. Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore. Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination.

Screening of Serum miRNAs as Diagnostic Biomarkers for Lung Cancer Using the Minimal-Redundancy-Maximal-Relevance Algorithm and Random Forest Classifier Based on a Public Database

BACKGROUND

Lung cancer is one of the deadliest cancers, early diagnosis of which can efficiently enhance patient's survival. We aimed to screening out the serum miRNAs as diagnostic biomarkers for patients with lung cancer.

METHODS

A total of 416 remarkably differentially expressed miRNAs were acquired using the limma package, and next feature ranking was derived by the minimal-redundancy-maximal-relevance method. An incremental feature selection algorithm of a random forest (RF) classifier was utilized to choose the top 5 miRNA combination with the optimum predictive performance. The performance of the RF classifier of top 5 miRNAs was analyzed using the receiver operator characteristic (ROC) curve. Afterward, the classification effect of the 5-miRNA combination was validated through principal component analysis and hierarchical clustering analysis. Analysis of top 5 miRNA expressions between lung cancer patients and normal people was performed based on GSE137140 dataset, and their expression was validated by qPCR. The hierarchical clustering analysis was used to analyze the similarity of 5 miRNAs expression profiles. ROC analysis was undertaken on each miRNA.

RESULTS

We acquired top 5 miRNAs finally, with the Matthews correlation coefficient value as 0.988 and the area under the curve (AUC) value as 0.996. The 5 feature miRNAs were capable of distinguishing most cancer patients and normal people. Furthermore, except for the lowly expressed miR-6875-5p in lung cancer tissue, the other 4 miRNAs all expressed highly in cancer patients. Performance analysis revealed that their AUC values were 0.92, 0.96, 0.94, 0.95, and 0.93, respectively.

CONCLUSION

By and large, the 5 feature miRNAs screened here were anticipated to be effective biomarkers for lung cancer.

Predicting Prognostic Effects of Acupuncture for Depression Using the Electroencephalogram

Depression is considered to be a major public health problem with significant implications for individuals and society. Patients with depression can be with complementary therapies such as acupuncture. Predicting the prognostic effects of acupuncture has a big significance in helping physicians make early interventions for patients with depression and avoid malignant events. In this work, a novel framework of predicting prognostic effects of acupuncture for depression based on electroencephalogram (EEG) recordings is presented. Specifically, EEG, as a widely used measurement to evaluate the therapeutic effects of acupuncture, is utilized for predicting prognostic effects of acupuncture. Max-relevance and min-redundancy (mRMR), with merits of removing redundant information among selected features and remaining high relevance between selected features and response variable, is employed to select important lead-rhythm features extracted from EEG recordings. Then, according to the subject Hamilton Depression Rating Scale (HAMD) scores before and after acupuncture for eight weeks, the reduction rate of HAMD score is calculated as a measure of the prognostic effects of acupuncture. Finally, five widely used machine learning methods are utilized for building the predicting models of prognostic effects of acupuncture for depression. Experimental results show that nonlinear machine learning methods have better performance than linear ones on predicting prognostic effects of acupuncture using EEG recordings. Especially, the support vector machine with Gaussian kernel (SVM-RBF) can achieve the best and most stable performance using the mRMR with both evaluating criteria of FCD and FCQ for feature selection. Both mRMR-FCD and mRMR-FCQ obtain the same best performance, where the accuracy and F 1 score are 84.61% and 86.67%, respectively. Moreover, lead-rhythm features selected by mRMR-FCD and mRMR-FCQ are analyzed. The top seven selected lead-rhythm features have much higher mRMR evaluating scores, which guarantee the good predicting performance for machine learning methods to some degree. The presented framework in this work is effective in predicting the prognostic effects of acupuncture for depression. It can be integrated into an intelligent medical system and provide information on the prognostic effects of acupuncture for physicians. Informed prognostic effects of acupuncture for depression in advance and taking interventions can greatly reduce the risk of malignant events for patients with mental disorders.

Establishment of an antibody specific for AMIGO2 improves immunohistochemical evaluation of liver metastases and clinical outcomes in patients with colorectal cancer

Instruction

The human amphoterin-induced gene and open reading frame (AMIGO) was identified as a novel cell adhesion molecule of type I transmembrane protein. AMIGO2 is one of three members of the AMIGO family (AMIGO1, 2, and 3), and the similarity between them is approximately 40% at the amino acid level. We have previously shown that AMIGO2 functions as a driver of liver metastasis. Immunohistochemical analysis of AMIGO2 expression in colorectal cancer (CRC) using a commercially available anti-AMIGO2 mouse monoclonal antibody clone sc-373699 (sc mAb) correlated with liver metastasis and poor prognosis. However, the sc mAb was found to be cross-reactive with all three molecules in the AMIGO family.

Methods

We generated a rat monoclonal antibody clone rTNK1A0012 (rTNK mAb) for human AMIGO2. The rTNK mAb was used to re-evaluate the association between AMIGO2 expression and liver metastases/clinical outcomes using the same CRC tissue samples previously reported with sc mAb.

Results

Western blot analysis revealed that a rTNK mAb was identified as being specific for AMIGO2 protein and did not cross-react with AMIGO1 and AMIGO3. The rTNK mAb and sc mAb showed higher AMIGO2 expression, which correlates with a high frequency of liver metastases (65.3% and 47.5%, respectively), while multivariate analysis showed that AMIGO2 expression was an independent prognostic factor for liver metastases (p = 7.930E-10 and p = 1.707E-5). The Kaplan-Meier analyses showed that the rTNK mAb (p = 0.004), but not sc mAb (p = 0.107), predicted worse overall survival in patients with high AMIGO2 expression. The relationship between AMIGO2 expression and poor disease-specific survival showed a higher level of significance for rTNK mAb (p = 0.00004) compared to sc mAb (p = 0.001).

Conclusions

These results indicate that the developed rTNK1A0012 mAb is an antibody that specifically recognizes AMIGO2 by immunohistochemistry and can be a more reliable and applicable method for the diagnostic detection of liver metastases and worse prognosis in patients with high AMIGO2-expressing CRC.

Ion Channel Signature in Healthy Pancreas and Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the fourth most common cause of cancer-related deaths in United States and Europe. It is predicted that PDAC will become the second leading cause of cancer-related deaths during the next decades. The development of PDAC is not well understood, however, studies have shown that dysregulated exocrine pancreatic fluid secretion can contribute to pathologies of exocrine pancreas, including PDAC. The major roles of healthy exocrine pancreatic tissue are secretion of enzymes and bicarbonate rich fluid, where ion channels participate to fine-tune these biological processes. It is well known that ion channels located in the plasma membrane regulate multiple cellular functions and are involved in the communication between extracellular events and intracellular signaling pathways and can function as signal transducers themselves. Hereby, they contribute to maintain resting membrane potential, electrical signaling in excitable cells, and ion homeostasis. Despite their contribution to basic cellular processes, ion channels are also involved in the malignant transformation from a normal to a malignant phenotype. Aberrant expression and activity of ion channels have an impact on essentially all hallmarks of cancer defined as; uncontrolled proliferation, evasion of apoptosis, sustained angiogenesis and promotion of invasion and migration. Research indicates that certain ion channels are involved in the aberrant tumor growth and metastatic processes of PDAC. The purpose of this review is to summarize the important expression, localization, and function of ion channels in normal exocrine pancreatic tissue and how they are involved in PDAC progression and development. As ion channels are suggested to be potential targets of treatment they are furthermore suggested to be biomarkers of different cancers. Therefore, we describe the importance of ion channels in PDAC as markers of diagnosis and clinical factors.

Whole Transcriptomic Analysis of Apigenin on TNFα Immuno-activated MDA-MB-231 Breast Cancer Cells

BACKGROUND

Triple-negative breast cancer is categorized by a lack of hormone receptors, inefficacy of anti-estrogen or aromatase inhibitor chemotherapies and greater mortality rates in African American populations. Advanced-stage breast tumors have a high concentration of tumor necrosis factor-α (TNFα) throughout the tumor/stroma milieu, prompting sustained release of diverse chemokines (i.e. C-C motif chemokine ligand 2 (CCL2)/CCL5). These potent chemokines can subsequently direct mass infiltration of leukocyte sub-populations to lodge within the tumor, triggering a loss of tumor immune surveillance and subsequent rapid tumor growth. Previously, we demonstrated that in the MDA-MB-231 TNBC cell line, TNFα evoked a rise in immune signaling proteins: CCL2, granulocyte macrophage colony-stimulating factor, interleukin (IL)1α, IL6 and inhibitor of nuclear factor kappa-B kinase subunit epsilon (IKBKε) all of which were attenuated by apigenin, a dietary flavonoid found in chamomile and parsley.

MATERIALS AND METHODS

The present work elucidates changes evoked by TNFα in the presence or absence of apigenin by examining the entire transcriptome for mRNA and long intergenic non-coding RNA with Affymetrix Hugene-2.1_ST human microarrays. Differential gene-expression analysis was conducted on 48,226 genes.

RESULTS

TNFα caused up-regulation of 75 genes and down-regulation of 10. Of these, apigenin effectively down-regulated 35 of the 75 genes which were up-regulated by TNFα. These findings confirm our previous work, specifically for the TNFα-evoked spike in IL1A vs. untreated controls [+21-fold change (FC), p<0.0001] being attenuated by apigenin in the presence of TNFa (-15 FC vs. TNFα, p<0.0001). Similar trends were seen for apigenin-mediated down-regulation of TNFα-up-regulated transcripts: IKBKE (TNFα: 4.55 FC vs. control, p<0.001; and TNFα plus apigenin: -4.92 FC, p<0.001), CCL2 (2.19 FC, p<0.002; and -2.12 FC, p<0.003), IL6 (3.25 FC, p<0.020; and -2.85 FC, p<0.043) and CSF2 (TNFα +6.04 FC, p<0.001; and -2.36 FC, p<0.007). In addition, these data further establish more than a 65% reduction by apigenin for the following transcripts which were also up-regulated by TNFα: cathepsin S (CTSS), complement C3 (C3), laminin subunit gamma 2 (LAMC2), (TLR2), toll-like receptor 2 G protein-coupled receptor class C group 5 member B (GPRC5B), contactin-associated protein 1 (CNTNAP1), claudin 1 (CLDN1), nuclear factor of activated T-cells 2 (NFATC2), C-X-C motif chemokine ligand 10 (CXCL10), CXCL11, interleukin 1 receptor-associated kinase 3 (IRAK3), nuclear receptor subfamily 3 group C member 2 (NR3C2), interleukin 32 (IL32), IL24, slit guidance ligand 2 (SLIT2), transmembrane protein 132A (TMEM132A), TMEM171, signal transducing adaptor family member 2 (STAP2), mixed lineage kinase domain-like pseudokinase (MLKL), kinase insert domain receptor (KDR), BMP-binding endothelial regulator (BMPER), and kelch-like family member 36 (KLHL36).

CONCLUSION

There is a possible therapeutic role for apigenin in down-regulating diverse genes associated with tumorigenic leukocyte sub-population infiltration by triple-negative breast cancer. The data have been deposited into the Gene Expression Omnibus for public analysis at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120550.

Protein tyrosine phosphatase receptor type R (PTPRR) antagonizes the Wnt signaling pathway in ovarian cancer by dephosphorylating and inactivating β-catenin

Despite a lack of mutations, accumulating evidence supports an important role for the Wnt/β-catenin pathway in ovarian tumorigenesis. However, the molecular mechanism that contributes to the aberrant activation of the Wnt signaling cascade in ovarian cancer has not been fully elucidated. Here, we found that protein tyrosine phosphatase receptor type R (PTPRR) suppressed the activation of the Wnt/β-catenin pathway in ovarian cancer. We performed an shRNA-based biochemical screen, which identified PTPRR as being responsible for tyrosine dephosphorylation of β-catenin on Tyr-142, a key site controlling the transcriptional activity of β-catenin. Of note, PTPRR was down-regulated in ovarian cancers, and ectopic PTPRR re-expression delayed ovarian cancer cell growth both in vitro and in vivo Using a proximity-based tagging system and RNA-Seq analysis, we identified a signaling nexus that includes PTPRR, α-catenin, β-catenin, E-cadherin, and AT-rich interaction domain 3C (ARID3C) in ovarian cancer. Immunohistochemistry staining of human samples further suggested that PTPRR expression is inversely correlated with disease prognosis. Collectively, our findings indicate that PTPRR functions as a tumor suppressor in ovarian cancer by dephosphorylating and inactivating β-catenin. These results suggest that PTPRR expression might have utility as a prognostic marker for predicting overall survival.

Key genes associated with pancreatic cancer and their association with outcomes: A bioinformatics analysis

Pancreatic cancer is a highly malignant neoplastic disease of the digestive system. In the present study, the dataset GSE62165 was downloaded from the Gene Expression Omnibus (GEO) database. GSE62165 contained the data of 118 pancreatic ductal adenocarcinoma samples (38 early‑stage tumors, 62 lymph node metastases and 18 advanced tumors) and 13 control samples. Differences in the expression levels of genes between normal tissues and early‑stage tumors were investigated. A total of 240 differentially expressed genes (DEGs) were identified using R software 3.5 (137 upregulated genes and 103 downregulated genes). Then, the differentially expressed genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. The following 18 core genes were identified using Cytoscape, based on the protein‑interaction network of DEGs determined using the online tool STRING: EGF, ALB, COL17A1, FN1, TIMP1, PLAU, PLA2G1B, IGFBP3, PLAUR, VCAN, COL1A1, PNLIP, CTRL, PRSS3, COMP, CPB1, ITGA2 and CEL. The pathways of the core genes were primarily associated with pancreatic secretion, protein digestion and absorption, and focal adhesion. Finally, survival analyses of core genes in pancreatic cancer were conducted using the UALCAN online database. It was revealed that PLAU and COL17A1 were significantly associated with poor prognosis (P<0.05). The expression levels of genes in primary pancreatic cancer tissues were then compared; only one gene, COL17A1, was identified to be significantly differentially expressed. Finally, another dataset from GEO, GSE28735, was analyzed to verify the upregulated expression of COL17A1. Taken together, the results of the present study have indicated that the expression of COL17A1 gene may be associated with the occurrence and development of pancreatic cancer.

Inhibition of Karyopherin beta 1 suppresses prostate cancer growth

Prostate cancer (PCa) initiation and progression requires activation of numerous oncogenic signaling pathways. Nuclear-cytoplasmic transport of oncogenic factors is mediated by Karyopherin proteins during cell transformation. However, the role of nuclear transporter proteins in PCa progression has not been well defined. Here, we report that the KPNB1, a key member of Karyopherin beta subunits, is highly expressed in advanced prostate cancers. Further study showed that targeting KPNB1 suppressed the proliferation of prostate cancer cells. The knockdown of KPNB1 reduced nuclear translocation of c-Myc, the expression of downstream cell cycle modulators, and phosphorylation of regulator of chromatin condensation 1 (RCC1), a key protein for spindle assembly during mitosis. Meanwhile, CHIP assay demonstrated the binding of c-Myc to KPNB1 promoter region, which indicated a positive feedback regulation of KPNB1 expression mediated by the c-Myc. In addition, NF-κB subunit p50 translocation to nuclei was blocked by KPNB1 inhibition, which led to an increase in apoptosis and a decrease in tumor sphere formation of PCa cells. Furthermore, subcutaneous xenograft tumor models with a stable knockdown of KPNB1 in C42B PCa cells validated that the inhibition of KPNB1 could suppress the growth of prostate tumor in vivo. Moreover, the intravenously administration of importazole, a specific inhibitor for KPNB1, effectively reduced PCa tumor size and weight in mice inoculated with PC3 PCa cells. In summary, our data established the functional link between KPNB1 and PCa prone c-Myc, NF-kB, and cell cycle modulators. More importantly, inhibition of KPNB1 could be a new therapeutic target for PCa treatment.

A similarity-based method for prediction of drug side effects with heterogeneous information

Drugs can produce intended therapeutic effects to treat different diseases. However, they may also cause side effects at the same time. For an approved drug, it is best to detect all side effects it can produce. Otherwise, it may bring great risks for pharmaceuticals companies as well as be harmful to human body. It is urgent to design quick and reliable identification methods to detect the side effects for a given drug. In this study, a binary classification model was proposed to predict drug side effects. Different from most previous methods, our model termed the pair of drug and side effect as a sample and convert the original problem to a binary classification problem. Based on the similarity idea, each pair was represented by five features, each of which was derived from a type of drug property. The strong machine learning algorithm, random forest, was adopted as the prediction engine. The ten-fold cross-validation on five datasets with different negative samples indicated that the proposed model yielded a good performance of Matthews correlation coefficient around 0.550 and AUC around 0.8492. In addition, we also analyzed the contribution of each drug property for construction of the model. The results indicated that drug similarity in fingerprint was most related to the prediction of drug side effects and all drug properties gave less or more contributions.

Inferring Novel Tumor Suppressor Genes with a Protein-Protein Interaction Network and Network Diffusion Algorithms

Extensive studies on tumor suppressor genes (TSGs) are helpful to understand the pathogenesis of cancer and design effective treatments. However, identifying TSGs using traditional experiments is quite difficult and time consuming. Developing computational methods to identify possible TSGs is an alternative way. In this study, we proposed two computational methods that integrated two network diffusion algorithms, including Laplacian heat diffusion (LHD) and random walk with restart (RWR), to search possible genes in the whole network. These two computational methods were LHD-based and RWR-based methods. To increase the reliability of the putative genes, three strict screening tests followed to filter genes obtained by these two algorithms. After comparing the putative genes obtained by the two methods, we designated twelve genes (e.g., MAP3K10, RND1, and OTX2) as common genes, 29 genes (e.g., RFC2 and GUCY2F) as genes that were identified only by the LHD-based method, and 128 genes (e.g., SNAI2 and FGF4) as genes that were inferred only by the RWR-based method. Some obtained genes can be confirmed as novel TSGs according to recent publications, suggesting the utility of our two proposed methods. In addition, the reported genes in this study were quite different from those reported in a previous one.

Depletion of nuclear import protein karyopherin alpha 7 (KPNA7) induces mitotic defects and deformation of nuclei in cancer cells

Background

Nucleocytoplasmic transport is a tightly regulated process carried out by specific transport machinery, the defects of which may lead to a number of diseases including cancer. Karyopherin alpha 7 (KPNA7), the newest member of the karyopherin alpha nuclear importer family, is expressed at a high level during embryogenesis, reduced to very low or absent levels in most adult tissues but re-expressed in cancer cells.

Methods

We used siRNA-based knock-down of KPNA7 in cancer cell lines, followed by functional assays (proliferation and cell cycle) and immunofluorescent stainings to determine the role of KPNA7 in regulation of cancer cell growth, proper mitosis and nuclear morphology.

Results

In the present study, we show that the silencing of KPNA7 results in a dramatic reduction in pancreatic and breast cancer cell growth, irrespective of the endogenous KPNA7 expression level. This growth inhibition is accompanied by a decrease in the fraction of S-phase cells as well as aberrant number of centrosomes and severe distortion of the mitotic spindles. In addition, KPNA7 depletion leads to reorganization of lamin A/C and B1, the main nuclear lamina proteins, and drastic alterations in nuclear morphology with lobulated and elongated nuclei.

Conclusions

Taken together, our data provide new important evidence on the contribution of KPNA7 to the regulation of cancer cell growth and the maintenance of nuclear envelope environment, and thus deepens our understanding on the impact of nuclear transfer proteins in cancer pathogenesis.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4261-5) contains supplementary material, which is available to authorized users.

Inferring anatomical therapeutic chemical (ATC) class of drugs using shortest path and random walk with restart algorithms

The anatomical therapeutic chemical (ATC) classification system is a widely accepted drug classification scheme. This system comprises five levels and includes several classes in each level. Drugs are classified into classes according to their therapeutic effects and characteristics. The first level includes 14 main classes. In this study, we proposed two network-based models to infer novel potential chemicals deemed to belong in the first level of ATC classification. To build these models, two large chemical networks were constructed using the chemical-chemical interaction information retrieved from the Search Tool for Interactions of Chemicals (STITCH). Two classic network algorithms, shortest path (SP) and random walk with restart (RWR) algorithms, were executed on the corresponding network to mine novel chemicals for each ATC class using the validated drugs in a class as seed nodes. Then, the obtained chemicals yielded by these two algorithms were further evaluated by a permutation test and an association test. The former can exclude chemicals produced by the structure of the network, i.e., false positive discoveries. By contrast, the latter identifies the most important chemicals that have strong associations with the ATC class. Comparisons indicated that the two models can provide quite dissimilar results, suggesting that the results yielded by one model can be essential supplements for those obtained by the other model. In addition, several representative inferred chemicals were analyzed to confirm the reliability of the results generated by the two models. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.