Geminin a multi task protein involved in cancer pathophysiology and developmental process: A review

An anoikis-related gene signature predicts prognosis, drug sensitivity, and immune microenvironment in cholangiocarcinoma

Background

Cholangiocarcinoma is a malignant invasive biliary tract carcinoma with a poor prognosis. Anoikis-related genes are prognostic features of a variety of cancers. However, the value of prognostication and therapeutic effect of anoikis-related genes in cholangiocarcinoma have not been reported. The aim of this research was developing an ARGs signature associated with cholangiocarcinoma patients.

Methods

We introduced transcriptome data to discover genes that were differentially expressed in cholangiocarcinoma. Subsequently, WGCNA was utilized to screen critical module genes in reference to anoikis. The univariate Cox, Lasso regression and Kaplan-Meier survival were executed to build a prognostic signature. We further performed gene functional enrichment, immune microenvironment and immunotherapy analysis between two risk subgroups. Finally, the pRRophetic algorithm was applied to compare the half inhibitory concentration value of several drugs.

Results

A grand total of 1844 genes with differential expression related to the cholangiocarcinoma patients were identified. Furthermore, we obtained 2678 key module genes related to anoikis. Then, a prognostic signature was developed using the 6 prognostic genes (FXYD2, PCBD1, C1RL, GMNN, LAMA4 and HACL1). Independent prognostic analysis showed that risk score and alcohol could function as separate prognostic variables. We found cetain distinction in the immune microenvironment between the two risk subgroups. Moreover, immunotherapy evaluation showed that the anoikis-related gene signature could be applied as a therapy predictor. Finally, Chemotherapeutic drug sensitivity results showed that the low-risk group responded better to bosutinib, gefitinib, gemcitabine, and paclitaxel, while the high-risk group responded better to axitinib, cisplatin, and imatinib.

Conclusion

The prognostic signature comprised of FXYD2, PCBD1, C1RL, GMNN, LAMA4 and HACL1 based on anoikis-related genes was established, which provided theoretical basis and reference value for the research and treatment of cholangiocarcinoma.

In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene

Single nucleotide polymorphisms (SNPs) are one of the most common determinants and potential biomarkers of human disease pathogenesis. SNPs could alter amino acid residues, leading to the loss of structural and functional integrity of the encoded protein. In humans, members of the minichromosome maintenance (MCM) family play a vital role in cell proliferation and have a significant impact on tumorigenesis. Among the MCM members, the molecular mechanism of how missense SNPs of minichromosome maintenance complex component 6 (MCM6) contribute to DNA replication and tumor pathogenesis is underexplored and needs to be elucidated. Hence, a series of sequence and structure-based computational tools were utilized to determine how mutations affect the corresponding MCM6 protein. From the dbSNP database, among 15,009 SNPs in the MCM6 gene, 642 missense SNPs (4.28%), 291 synonymous SNPs (1.94%), and 12,500 intron SNPs (83.28%) were observed. Out of the 642 missense SNPs, 33 were found to be deleterious during the SIFT analysis. Among these, 11 missense SNPs (I123S, R207C, R222C, L449F, V456M, D463G, H556Y, R602H, R633W, R658C, and P815T) were found as deleterious, probably damaging, affective and disease-associated. Then, I123S, R207C, R222C, V456M, D463G, R602H, R633W, and R658C missense SNPs were found to be highly harmful. Six missense SNPs (I123S, R207C, V456M, D463G, R602H, and R633W) had the potential to destabilize the corresponding protein as predicted by DynaMut2. Interestingly, five high-risk mutations (I123S, V456M, D463G, R602H, and R633W) were distributed in two domains (PF00493 and PF14551). During molecular dynamics simulations analysis, consistent fluctuation in RMSD and RMSF values, high Rg and hydrogen bonds in mutant proteins compared to wild-type revealed that these mutations might alter the protein structure and stability of the corresponding protein. Hence, the results from the analyses guide the exploration of the mechanism by which these missense SNPs of the MCM6 gene alter the structural integrity and functional properties of the protein, which could guide the identification of ways to minimize the harmful effects of these mutations in humans.

Investigating the basis of lineage decisions and developmental trajectories in the dorsal spinal cord through pseudotime analyses

Dorsal interneurons (dIs) in the spinal cord encode the perception of touch, pain, heat, itchiness and proprioception. Previous studies using genetic strategies in animal models have revealed important insights into dI development, but the molecular details of how dIs arise as distinct populations of neurons remain incomplete. We have developed a resource to investigate dI fate specification by combining a single-cell RNA-Seq atlas of mouse embryonic stem cell-derived dIs with pseudotime analyses. To validate this in silico resource as a useful tool, we used it to first identify genes that are candidates for directing the transition states that lead to distinct dI lineage trajectories, and then validated them using in situ hybridization analyses in the developing mouse spinal cord in vivo. We have also identified an endpoint of the dI5 lineage trajectory and found that dIs become more transcriptionally homogeneous during terminal differentiation. This study introduces a valuable tool for further discovery about the timing of gene expression during dI differentiation and demonstrates its utility in clarifying dI lineage relationships.

Investigating the basis of lineage decisions and developmental trajectories in the dorsal spinal cord through pseudotime analyses

Dorsal interneurons (dIs) in the spinal cord encode the perception of touch, pain, heat, itch, and proprioception. While previous studies using genetic strategies in animal models have revealed important insights into dI development, the molecular details by which dIs arise as distinct populations of neurons remain incomplete. We have developed a resource to investigate dI fate specification by combining a single-cell RNA-Seq atlas of mouse ESC-derived dIs with pseudotime analyses. To validate this in silico resource as a useful tool, we used it to first identify novel genes that are candidates for directing the transition states that lead to distinct dI lineage trajectories, and then validated them using in situ hybridization analyses in the developing mouse spinal cord in vivo . We have also identified a novel endpoint of the dI5 lineage trajectory and found that dIs become more transcriptionally homogenous during terminal differentiation. Together, this study introduces a valuable tool for further discovery about the timing of gene expression during dI differentiation and demonstrates its utility clarifying dI lineage relationships.

Summary statement

Pseudotime analyses of embryonic stem cell-derived dorsal spinal interneurons reveals both novel regulators and lineage relationships between different interneuron populations.

Five Critical Gene-Based Biomarkers With Optimal Performance for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world. There is an urgent need to understand the molecular background of HCC to facilitate the identification of biomarkers and discover effective therapeutic targets. Published transcriptomic studies have reported a large number of genes that are individually significant for HCC. However, reliable biomarkers remain to be determined. In this study, built on max-linear competing risk factor models, we developed a machine learning analytical framework to analyze transcriptomic data to identify the most miniature set of differentially expressed genes (DEGs). By analyzing 9 public whole-transcriptome datasets (containing 1184 HCC samples and 672 nontumor controls), we identified 5 critical differentially expressed genes (DEGs) (ie, CCDC107, CXCL12, GIGYF1, GMNN, and IFFO1) between HCC and control samples. The classifiers built on these 5 DEGs reached nearly perfect performance in identification of HCC. The performance of the 5 DEGs was further validated in a US Caucasian cohort that we collected (containing 17 HCC with paired nontumor tissue). The conceptual advance of our work lies in modeling gene-gene interactions and correcting batch effect in the analytic framework. The classifiers built on the 5 DEGs demonstrated clear signature patterns for HCC. The results are interpretable, robust, and reproducible across diverse cohorts/populations with various disease etiologies, indicating the 5 DEGs are intrinsic variables that can describe the overall features of HCC at the genomic level. The analytical framework applied in this study may pave a new way for improving transcriptome profiling analysis of human cancers.

Comprehensive Analysis of the Sorafenib-Associated Druggable Targets on Differential Gene Expression and ceRNA Network in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant pathological type of liver cancer. Several therapeutic treatments, including sorafenib and regorafenib, have only modestly improved survival in patients with HCC. The aim of this study was to investigate the expression profiles and the regulation of competitive endogenous RNAs (ceRNAs) of the sorafenib-related target genes in HCC. Based on clinical information and expression profiles of HCC clinical samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, shared differentially expressed genes (DEGs) were analyzed and identified. Sorafenib-associated DEGs (SADs) were obtained by intersecting the DEGs with the sorafenib target genes from SuperTarget database. The expression patterns of SADs were verified in the Oncomine database. The biological functions of the SADs were annotated by gene set enrichment analysis (GSEA). In addition, a ceRNA network associated with SADs was constructed. Long non-coding RNAs (lncRNAs) in network that were significantly associated with overall survival were identified as prognosis of patients by Cox regression analysis. Finally, the expression levels of prognostic genes in HCC tissues and cell lines were verified using qRT-PCR. Gene expression differential analysis yielded a total of 146 common DEGs were obtained, including 21 upregulated and 125 downregulated DEGs. Among them, ten SADs were detected to be differentially expressed between tumor and normal tissues, including AXL, CYP2C19, CYP2C8, CYP2C9, CYP3A4, FGFR2, GMNN, PDGFRA, and TTK. GSEA analysis grouped them into three categories by function. The first category (CYP2C19, CYP2C8, CYP2C9 and CYP3A4) and second category (GMNN, TTK and EGER2) had the opposite roles in the enriched terms and pathways, while the third class (AXL and PDGFRA) has enrichment terms and pathways that intersect with those of the first and second categories. A ceRNA network associated with SADs was also constructed including 49 lncRNAs, 14 miRNAs, and 8 mRNAs. Three of these lncRNAs, SNHG7, GAS5 and HCP5, were found upregulated in HCC tissues and to be independent predictors in HCC patients. Significant correlations were found in expression between the prognostic lncRNAs and SADs. Ten SADs were systematically identified using expression data from HCC and normal tissues from TCGA and GEO datasets. GSEA analysis provided us with insight into the function of SADs. In the future, we will continue to explore the mechanisms of coordinated regulation of SADs-related prognostic lncRNAs and SADs at the ceRNA axis level and their potential functions in the development of HCC.

High expression of GMNN predicts malignant progression and poor prognosis in ACC

BACKGROUND

Adrenocortical carcinoma (ACC) is a rare endocrine neoplasm, which is characterized by poor prognosis and high recurrence rate. Novel and reliable prognostic and metastatic biomarkers are lacking for ACC patients. This study aims at screening potential prognostic biomarkers and therapeutic targets of ACC through bioinformatic methods and immunohistochemical (IHC) analysis.

METHODS

In the present study, by using the Gene Expression Omnibus (GEO) database we identified differentially expressed genes (DEGs) in ACC and validated these DEGs in The Cancer Genome Atlas (TCGA) ACC cohort. A DEGs-based signature was additionally constructed and we assessed its prognosis and prescient worth for ACC by survival analysis and nomogram. Immunohistochemistry (IHC) was used to verify the relationship between hub gene-GMNN expressions and clinicopathologic outcomes in ACC patients.

RESULTS

A total of 24 DEGs correlated with the prognosis of ACC were screened from the TCGA and GEO databases. Five DEGs were subsequently selected in a signature which was closely related to the survival rates of ACC patients and GMNN was identified as the core gene in this signature. Univariate and multivariate Cox regression showed that the GMNN was an independent prognostic factor for ACC patients (P < 0.05). Meanwhile, GMNN was closely related to the OS and PFI of ACC patients treated with mitotane (P < 0.001). IHC confirmed that GMNN protein was overexpressed in ACC tissues compared with normal adrenal tissues and significantly correlated with stage (P = 0.011), metastasis (P = 0.028) and Ki-67 index (P = 0.014).

CONCLUSIONS

GMNN is a novel tumor marker for predicting the malignant progression, metastasis and prognosis of ACC, and may be a potential therapeutic target for ACC.

Assessing equivalent and inverse change in genes between diverse experiments

Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.

Single-cell transcriptome analysis defines heterogeneity of the murine pancreatic ductal tree

To study disease development, an inventory of an organ's cell types and understanding of physiologic function is paramount. Here, we performed single-cell RNA-sequencing to examine heterogeneity of murine pancreatic duct cells, pancreatobiliary cells, and intrapancreatic bile duct cells. We describe an epithelial-mesenchymal transitory axis in our three pancreatic duct subpopulations and identify osteopontin as a regulator of this fate decision as well as human duct cell dedifferentiation. Our results further identify functional heterogeneity within pancreatic duct subpopulations by elucidating a role for geminin in accumulation of DNA damage in the setting of chronic pancreatitis. Our findings implicate diverse functional roles for subpopulations of pancreatic duct cells in maintenance of duct cell identity and disease progression and establish a comprehensive road map of murine pancreatic duct cell, pancreatobiliary cell, and intrapancreatic bile duct cell homeostasis.

Overview of New Treatments with Immunotherapy for Breast Cancer and a Proposal of a Combination Therapy

According to data from the U.S. National Cancer Institute, cancer is one of the leading causes of death worldwide with approximately 14 million new cases and 8.2 million cancer-related deaths in 2018. More than 60% of the new annual cases in the world occur in Africa, Asia, Central America, and South America, with 70% of cancer deaths in these regions. Breast cancer is the most common cancer in women, with 266,120 new cases in American women and an estimated 40,920 deaths for 2018. Approximately one in six women diagnosed with breast cancer will die in the coming years. Recently, novel therapeutic strategies have been implemented in the fight against breast cancer, including molecules able to block signaling pathways, an inhibitor of poly [ADP-ribose] polymerase (PARP), growth receptor blocker antibodies, or those that reactivate the immune system by inhibiting the activities of inhibitory receptors like cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programmed death protein 1 (PD-1). However, novel targets include reactivating the Th1 immune response, changing tumor microenvironment, and co-activation of other components of the immune response such as natural killer cells and CD8+ T cells among others. In this article, we review advances in the treatment of breast cancer focused essentially on immunomodulatory drugs in targeted cancer therapy. Based on this knowledge, we formulate a proposal for the implementation of combined therapy using an extracorporeal immune response reactivation model and cytokines plus modulating antibodies for co-activation of the Th1- and natural killer cell (NK)-dependent immune response, either in situ or through autologous cell therapy. The implementation of "combination immunotherapy" is new hope in breast cancer treatment. Therefore, we consider the coordinated activation of each cell of the immune response that would probably produce better outcomes. Although more research is required, the results recently achieved by combination therapy suggest that for most, if not all, cancer patients, this tailored therapy may become a realistic approach in the near future.

Induction of apoptosis in breast cancer cells by naphthylisoquinoline alkaloids

Breast cancer is one of the most common types of cancer in the world and a major cause of mortality. Present therapeutic strategies against breast cancer have severe drawbacks such as allergies, damage to healthy tissues, reoccurrence of cancer, and emergence of drug resistance. Naphthylisoquinoline alkaloids are a group of structurally unique natural products produced by tropical lianas belonging to the plant families Dioncophyllaceae and Ancistrocladaceae indigenous to Asia and Africa. These secondary metabolites have been reported to show anti-infective activity, but they also act against leukemic and pancreatic cancer cells. In the present study we have tested the potential of eleven mono- and dimeric naphthylisoquinoline compounds against two breast cancer cell lines, MCF-7 and MDA-MB-231. Three out of the compounds (agents 1, 4, and 11) showed significant activities against both tested cancer cell lines. Further mechanistic investigations revealed that all of the three substances induce apoptotic cell death via its intrinsic pathway by causing deformation of the nuclear membrane, disruption of the mitochondrial membrane potential (MMP), and elevated reactive oxygen species (ROS) production in both cell lines. Flow cytometric analysis using Annexin V - FITC/PI double staining showed an increased number of apoptotic cells in both, the early and the late phases.

Cell kinetic markers in cutaneous squamous and basal cell carcinoma of the head and neck

Introduction

Proliferation markers play a significant role in the biologic behavior of tumors. Geminin is a known inhibitor of the cell cycle and DNA replication and has not been previously reported in cutaneous basal and squamous cell carcinomas of the head and neck.

Objectives

We aimed to investigate proliferation markers ki67, MCM2, and geminin in head and neck cutaneous basal and squamous cell carcinomas.

Methods

Forty cases of each tumor were immuostained with ki67, MCM2, and geminin followed by assessment of labeling indices (LIs). MCM2/ki67- and geminin/ki67-ratios were also determined; t-test was used for statistical analysis (p < 0.05).

Results

There was no significant difference in ki67 (p = 0.06) and MCM2 (p = 0.46) between cutaneous basal and squamous cell carcinomas; however, geminin LI was significantly higher in squamous cell carcinomas compared to cutaneous basal cell carcinomas (p < 0.001). Only geminin/ki67 showed a significant difference between the two tumors with the ratio showing significantly higher numbers in squamous cell carcinomas (p = 0.015).

Conclusions

Geminin could be regarded as an effective factor in the pathogenesis of head and neck cutaneous cutaneous basal cell carcinomas and squamous cell carcinomas and may be one of the responsible elements in the difference between the biologic behavior of these tumors.

Giants and monsters: Unexpected characters in the story of cancer recurrence

Polyploid giant cancer cells (PGCC) constitute a dangerous subpopulation of cancer cells and are a driving force in cancer recurrence. These unique cells arise from diploid tumor cells in response to stress encountered in the tumor microenvironment or during cancer therapy. PGCC are greatly dedifferentiated, acquire pluripotency, and are able to replicate through a form of asymmetric division called neosis, which results in new populations that are themselves able to differentiate into new cell types or to re-establish tumors. Progeny tend to be more genetically unstable than the founding population due to the dysregulation required to transition through a PGCC state. Therefore, cancers that escape stressors through this mechanism tend to re-emerge with a more aggressive phenotype that is therapy resistant. This review focuses on the clinical significance of PGCC, the need for standardized nomenclature and molecular markers, as well as possible avenues to develop therapies aimed at PGCC and the process of neosis. The biology underlying the development of PGCC including cell cycle checkpoint dysregulation, stress responses, dedifferentiation, stemness and epithelial-mesenchymal transition is discussed.

Expression of geminin, p16, and Ki67 in cervical intraepithelial neoplasm and normal tissues

Geminin is a protein involved in cell cycle progression. We aimed to evaluate the diagnostic value of geminin expression in cervical intraepithelial neoplasm (CIN).The expression of geminin, p16, and Ki67 was examined in 95 samples, including CIN1 (n = 45), CIN2/3 (n = 40), and normal cervical tissues (n = 10) by immunohistochemistry. The correlation between geminin or p16 expression and human papillomavirus (HPV) status was also evaluated.Geminin expression was negative in all normal tissues and expressed in 13.3% of CIN1 and 90.0% of CIN2/3. P16 expression was demonstrated in 24.4% of CIN1 and 87.5% of CIN2/3. The corresponding Ki67 expression was 35.6% and 95.0%. The specificity of geminin for differentiating between CIN1 and CIN2/3 was 86.7%, while for p16 and Ki67 the corresponding values were 75.6% and 64.4%. The sensitivity of geminin, p16, and Ki67 was 90.0%, 87.5%, and 95.0%, respectively. The positive predictive value (PPV) and accuracy of geminin were higher than p16 and Ki67. In addition, geminin expression showed a weak correlation with HPV status, but there was no association between p16 expression and HPV status.These results suggested that geminin had a high degree of sensitivity and specificity in determining CIN2/3. In addition to p16 and Ki67, geminin might be used as a new biomarker to distinguish between CIN1 and CIN2/3.