Identification of a nuclear protein, LRRC42, involved in lung carcinogenesis

Psilostachyin C reduces malignant properties of hepatocellular carcinoma cells by blocking CREBBP-mediated transcription of GATAD2B

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality globally. Many herbal medicines and their bioactive compounds have shown anti-tumor properties. This study was conducted to examine the effect of psilostachyin C (PSC), a sesquiterpenoid lactone isolated from Artemisia vulgaris L., in the malignant properties of HCC cells. CCK-8, flow cytometry, wound healing, and Transwell assays revealed that 25 μM PSC treatment significantly suppressed proliferation, cell cycle progression, migration, and invasion of two HCC cell lines (Hep 3B and Huh7) while promoting cell apoptosis. Bioinformatics prediction suggests CREB binding protein (CREBBP) as a promising target of PSC. CREBBP activated transcription of GATA zinc finger domain containing 2B (GATAD2B) by binding to its promoter. CREBBP and GATAD2B were highly expressed in clinical HCC tissues and the acquired HCC cell lines, but their expression was reduced by PSC. Either upregulation of CREBBP or GATAD2B restored the malignant properties of HCC cells blocked by PSC. Collectively, this evidence demonstrates that PSC pocessess anti-tumor functions in HCC cells by blocking CREBBP-mediated transcription of GATAD2B.

Proteomics and Metabolomics Profiling of Platelets and Plasma Mediators of Thrombo-Inflammation in Gestational Hypertension and Preeclampsia

Platelets may be pivotal mediators of the thrombotic and coagulopathic complications of preeclampsia (PE), linking inflammation and thrombosis with endothelial and vascular dysfunction. Both PE and gestational hypertension (GH) fall within the spectrum of hypertensive complications of pregnancy, with GH being a risk factor for preeclampsia. However, it is unclear what biomarkers distinguish PE from GH. Using a discovery size cohort, we aimed to characterize specific plasma and platelet thrombo-inflammatory drivers indicative of PE and differentiate PE from GH. We performed multiplex immunoassays, platelet and plasma quantitative proteomics and metabolomics of PE patients, comparing with non-pregnant (NP), healthy pregnant controls (PC) and GH participants. The expression pattern of plasma proteins and metabolites in PE/GH platelets was distinct from that of NP and PC. Whilst procoagulation in PC may be fibrinogen driven, inter-alpha-trypsin inhibitors ITIH2 and ITIH3 are likely mediators of thrombo-inflammation in GH and PE, and fibronectin and S100A8/9 may be major procoagulant agonists in PE only. Also enriched in PE were CCL1 and CCL27 plasma cytokines, and the platelet leucine-rich repeat-containing protein 27 and 42 (LRRC27/42), whose effects on platelets were explored using STRING analysis. Through protein-protein interactions analysis, we generated a new hypothesis for platelets’ contribution to the thrombo-inflammatory states of preeclampsia.

Considerations for feature selection using gene pairs and applications in large-scale dataset integration, novel oncogene discovery, and interpretable cancer screening

Background

Advancements in transcriptomic profiling have led to the emergence of new challenges regarding data integration and interpretability. Variability between measurement platforms makes it difficult to compare between cohorts, and large numbers of gene features have encouraged the use black box methods that are not easily translated into biologically and clinically meaningful findings. We propose that gene rankings and algorithms that rely on relative expression within gene pairs can address such obstacles.

Methods

We implemented an innovative process to evaluate the performance of five feature selection methods on simulated gene-pair data. Along with TSP, we consider other methods that retain more information in their score calculations, including the magnitude of gene expression change as well as within-class variation. Tree-based rule extraction was also applied to serum microRNA (miRNA) pairs in order to devise a noninvasive screening tool for pancreatic and ovarian cancer.

Results

Gene pair data were simulated using different types of signal and noise. Pairs were filtered using feature selection approaches, including top-scoring pairs (TSP), absolute differences between gene ranks, and Fisher scores. Methods that retain more information, such as the magnitude of expression change and within-class variance, yielded higher classification accuracy using a random forest model. We then demonstrate two powerful applications of gene pairs by first performing large-scale integration of 52 breast cancer datasets consisting of 10,350 patients. Not only did we confirm known oncogenes, but we also propose novel tumorigenic genes, such as BSDC1 and U2AF1, that could distinguish between tumor subtypes. Finally, circulating miRNA pairs were filtered and salient rules were extracted to build simplified tree ensemble learners (STELs) for four types of cancer. These accessible clinical frameworks detected pancreatic and ovarian cancer with 84.8 and 93.6% accuracy, respectively.

Conclusion

Rank-based gene pair classification benefits from careful feature selection methods that preserve maximal information. Gene pairs enable dataset integration for greater statistical power and discovery of robust biomarkers as well as facilitate construction of user-friendly clinical screening tools.

Characterization of KIF20A as a prognostic biomarker and therapeutic target for different subtypes of breast cancer

The aim of the present study was to identify novel prognostic biomarkers and therapeutic targets for breast cancer; thus, genes that are frequently overexpressed in several types of breast cancer were screened. Kinesin family member 20A (KIF20A) was identified as a candidate molecule during this process. Immunohistochemical staining performed using tissue microarrays from 257 samples of different breast cancer subtypes revealed that KIF20A was expressed in 195 (75.9%) of these samples, whereas it was seldom expressed in normal breast tissue. KIF20A protein was expressed in all types of breast cancer observed. However, it was more frequently expressed in human epidermal growth factor receptor 2 (HER2)‑positive and triple‑negative breast cancer than in the luminal type. Moreover, KIF20A expression was significantly associated with the poor prognosis of patients with breast cancer. A multivariate analysis indicated that KIF20A expression was an independent prognostic factor for patients with breast cancer. The suppression of endogenous KIF20A expression using small interfering ribonucleic acids or via treatment with paprotrain, a selective inhibitor of KIF20A, significantly inhibited breast cancer cell growth through cell cycle arrest at the G2/M phase and subsequent mitotic cell death. These results suggest that KIF20A is a candidate prognostic biomarker and therapeutic target for different types of breast cancer.

Evolutionary History of GLIS Genes Illuminates Their Roles in Cell Reprograming and Ciliogenesis

The GLIS family transcription factors, GLIS1 and GLIS3, potentiate generation of induced pluripotent stem cells (iPSCs). In contrast, another GLIS family member, GLIS2, suppresses cell reprograming. To understand how these disparate roles arose, we examined evolutionary origins and genomic organization of GLIS genes. Comprehensive phylogenetic analysis shows that GLIS1 and GLIS3 originated during vertebrate whole genome duplication, whereas GLIS2 is a sister group to the GLIS1/3 and GLI families. This result is consistent with their opposing functions in cell reprograming. Glis1 evolved faster than Glis3, losing many protein-interacting motifs. This suggests that Glis1 acquired new functions under weakened evolutionary constraints. In fact, GLIS1 induces induced pluripotent stem cells more strongly. Transcriptomic data from various animal embryos demonstrate that glis1 is maternally expressed in some tetrapods, whereas vertebrate glis3 and invertebrate glis1/3 genes are rarely expressed in oocytes, suggesting that vertebrate (or tetrapod) Glis1 acquired a new expression domain and function as a maternal factor. Furthermore, comparative genomic analysis reveals that glis1/3 is part of a bilaterian-specific gene cluster, together with rfx3, ndc1, hspb11, and lrrc42. Because known functions of these genes are related to cilia formation and function, the last common ancestor of bilaterians may have acquired this cluster by shuffling gene order to establish more sophisticated epithelial tissues involving cilia. This evolutionary study highlights the significance of GLIS1/3 for cell reprograming, development, and diseases in ciliated organs such as lung, kidney, and pancreas.

Adaptively capturing the heterogeneity of expression for cancer biomarker identification

Background

Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells.

Results

In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer.

Conclusions

Expression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use.

Software availability

The source code of aGRP can be downloaded from https://github.com/hqwang126/aGRP.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2437-2) contains supplementary material, which is available to authorized users.

Characterization of KIF11 as a novel prognostic biomarker and therapeutic target for oral cancer

Oral cancer has a high mortality rate, and its incidence is increasing gradually worldwide. As the effectiveness of standard treatments is still limited, the development of new therapeutic strategies is eagerly awaited. Kinesin family member 11 (KIF11) is a motor protein required for establishing a bipolar spindle in cell division. The role of KIF11 in oral cancer is unclear. Therefore, the present study aimed to assess the role of KIF11 in oral cancer and evaluate its role as a prognostic biomarker and therapeutic target for treating oral cancer. Immunohistochemical analysis demonstrated that KIF11 was expressed in 64 of 99 (64.6%) oral cancer tissues but not in healthy oral epithelia. Strong KIF11 expression was significantly associated with poor prognosis among oral cancer patients (P=0.034), and multivariate analysis confirmed its independent prognostic value. In addition, inhibition of KIF11 expression by transfection of siRNAs into oral cancer cells or treatment of cells with a KIF11 inhibitor significantly suppressed cell proliferation, probably through G2/M arrest and subsequent induction of apoptosis. These results suggest that KIF11 could be a potential prognostic biomarker and therapeutic target for oral cancer.

Cell division cycle associated 1 as a novel prognostic biomarker and therapeutic target for oral cancer

Oral cavity carcinoma (OCC) is one of the most common causes of cancer-related death worldwide and has poor clinical outcome after standard therapies. Therefore, new prognostic biomarkers and therapeutic targets for OCC are urgently needed. We selected cell division cycle associated 1 (CDCA1) as a candidate OCC biomarker. Immunohistochemical analysis confirmed that CDCA1 protein was expressed in 67 of 99 OCC tissues (67.7%), but not in healthy oral epithelia. CDCA1 expression was significantly associated with poor prognosis in OCC patients (P=0.0244). Knockdown of CDCA1 by siRNAs significantly increased apoptosis of tumor cells. These data suggest that CDCA1 represents a novel prognostic biomarker and therapeutic target for OCC.