Identification of gene expression profiling associated with erlotinib-related skin toxicity in pancreatic adenocarcinoma patients

Identification and validation of a prognostic model based on immune-related genes in ovarian carcinoma

Background

A novel valuable prognostic model has been developed on the basis of immune-related genes (IRGs), which could be used to estimate overall survival (OS) in ovarian cancer (OC) patients in The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset.

Methods

This prognostic model was engineered by employing LASSO regression in training cohort (TCGA dataset). The corresponding growth predictive values of this model for individualized survival was evaluated using survival analysis, receiver operating characteristic curve (ROC curve), and risk curve analysis. Combined with clinical characteristics, a model risk score nomogram for OS was well built. Thereafter, depended on the model risk score, patients were divided into high and low risk subgroups. The survival difference between these subgroups was measured using Kaplan-Meier survival method. In addition, correlations containing pathway enrichment, treatment, immune cell infiltration and the prognostic model were also analyzed. We established the ovarian cancer cell line W038 for this study and identified the performances of GBP1P1 knockdown on a series of activities including cellular proliferation, apoptosis, migration, and invasion of W038 cells in vitro.

Results

We constructed a 25-genes prognostic model (TNFAIP8L3, PI3, TMEM181, GBP1P1 (LOC400759), STX18, KIF26B, MRPS11, CACNA1C, PACSIN3, GMPR, MANF, PYGB, SNRPA1, ST7L, ZBP1, BMPR1B-DT, STAC2, LINC02585, LYPD6, NSG1, ACOT13, FAM120B, LEFTY1, SULT1A2, FZD3). The areas under the curves (AUC) of 1, 2 and 3 years were 0.806, 0.773 and 0.762, in the TCGA cohort, respectively. Besides, the effectiveness of the model was verified using ICGC testing data. Univariate and multivariate Cox regression analysis exposes the risk score as an independent prognosis predictor for OS both in the TCGA and ICGC cohort. In summary, we utilized comprehensive bioinformatics analysis to build an effective prognostic gene model for OC patients. These bioinformatic results suggested that GBP1P1 could act as a novel biomarker for OC. GBP1P1 knockdown substantially inhibited the proliferation, migration, and invasion of W038 cells in vitro, and increased the percentage of apoptotic W038 cells.

Conclusions

The analyses of genetic status of patients with 25-genes model might improve the ability to predict the prognosis of patients with OC and help to select patients suit able to therapies. Immune-related gene GBP1P1 might serve as prognostic biomarker for OC.

Effects of paternal methionine supplementation on sperm DNA methylation and embryo transcriptome in sheep

Environmental effects on gene expression and offspring development can be mediated by epigenetic modifications. It is well established that maternal diet influences DNA methylation patterns and phenotypes in the offspring; however, the epigenetic effects of paternal diet on developing offspring warrants further investigation. Here, we examined how a prepubertal methionine-enriched paternal diet affected sperm DNA methylation and its subsequent effects on embryo gene expression. Three treatment and three control rams were bred to seven ewes, and blastocysts were flushed for RNA extraction. Semen was collected from all rams and submitted for reduced representation bisulfite sequencing analysis. In total, 166 differentially methylated cytosines were identified in the sperm from treatment versus control rams. Nine genes were found to be differentially expressed in embryos produced from treatment versus control rams, and seven differentially methylated cytosines in the sperm were found to be highly correlated with gene expression in the embryos. Our results demonstrate that sperm methylation differences induced by diet may influence fetal programming.

The role of SELENBP1 and its epigenetic regulation in carcinogenic progression

The initiation and progression of cancer is modulated through diverse genetic and epigenetic modifications. The epigenetic machinery regulates gene expression through intertwined DNA methylation, histone modifications, and miRNAs without affecting their genome sequences. SELENBP1 belongs to selenium-binding proteins and functions as a tumor suppressor. Its expression is significantly downregulated and correlates with carcinogenic progression and poor survival in various cancers. The role of SELENBP1 in carcinogenesis has not been fully elucidated, and its epigenetic regulation remains poorly understood. In this review, we summarize recent findings on the function and regulatory mechanisms of SELENBP1 during carcinogenic progression, with an emphasis on epigenetic mechanisms. We also discuss the potential cancer treatment targeting epigenetic modification of SELENBP1, either alone or in combination with selenium-containing compounds or dietary selenium.

Impact of posttranslational modifications in pancreatic carcinogenesis and treatments

Pancreatic cancer (PC) is a highly aggressive cancer, with a 9% 5-year survival rate and a high risk of recurrence. In part, this is because PC is composed of heterogeneous subgroups with different biological and functional characteristics and personalized anticancer treatments are required. Posttranslational modifications (PTMs) play an important role in modifying protein functions/roles and are required for the maintenance of cell viability and biological processes; thus, their dysregulation can lead to disease. Different types of PTMs increase the functional diversity of the proteome, which subsequently influences most aspects of normal cell biology or pathogenesis. This review primarily focuses on ubiquitination, SUMOylation, and NEDDylation, as well as the current understanding of their roles and molecular mechanisms in pancreatic carcinogenesis. Additionally, we briefly summarize studies and clinical trials on PC treatments to advance our knowledge of drugs available to target the ubiquitination, SUMOylation, and NEDDylation PTM types. Further investigation of PTMs could be a critical field of study in relation to PC, as they have been implicated in the initiation and progression of many other types of cancer.

FAM46C inhibits cell proliferation and cell cycle progression and promotes apoptosis through PTEN/AKT signaling pathway and is associated with chemosensitivity in prostate cancer

Family with sequence similarity 46 member C (FAM46C) is a non-canonical poly(A) polymerase that is associated with tumorigenesis. However, its role in prostate cancer development is not fully understood. Herein, we determined expression pattern of FAM46C in prostate cancer and further identified its effect on the tumorigenesis and chemosensitivity. FAM46C expression was decreased in prostate cancer tissues and cell lines compared with corresponding controls. FAM46C expression was significantly associated with the Gleason score, tumor size and overall survival. FAM46C knockdown in 22RV1 and DU145 cells significantly inhibited apoptosis and promoted cell proliferation and cell cycle progression as well as activation of AKT. FAM46C overexpression had an inverse effect in DU145 cells and inhibited tumor growth in vivo. FAM46C inhibited cell proliferation and cell cycle progression and induced apoptosis via the PTEN/AKT signaling pathway. FAM46C promoted PTEN expression through inhibiting PTEN ubiquitination. The prostate cancer cells and patient-derived xenograft (PDX) mice with high-FAM46C-expressing demonstrated an enhanced chemosensitivity to docetaxel. These findings suggest that FAM46C control cell proliferation, cell cycle and apoptosis through PTEN/AKT signaling pathway and is associated with chemosensitivity of prostate cancer. Modulation of their levels may offer a new approach for improving anti-tumor efficacy for chemotherapeutic agents in prostate cancer.

Down-regulation of miR-10b represses cell vitality in osteosarcoma and is inversely associated with prognosis via interacting with FAM46C: Running title: MiR-10b/FAM46C axis modulates OS progression

The purpose of this exploration was to detect the biological effects of miR-10b/FAM46C pair on osteosarcoma (OS) development. By accessing to the Gene Expression Omnibus (GEO) database, we achieved expressional profiles of miR-10b and FAM46C. Kaplan-Meier method was applied to determine the overall survival rates of OS patients. MiR-10b mimic/inhibitor were utilized to alter miR-10b expression. Overexpression of FAM46C was induced by pcDNA3.1-FAM46C. QRT-PCR and western blot were conducted to assess the expression levels. Cell counting kit-8 (CCK-8) and transwell assays were employed to evaluate the proliferative, invasive and migratory properties of OS cells. Pearson correlation analysis was performed to confirm the association between miR-10b and FAM46C. Dual-luciferase reporter assay was conducted to determine the target of miR-10b. The overall survival of OS patients was inversely correlated with miR-10b expression. MiR-10b was increased in OS compared with normal controls. Depletion of miR-10b attenuated the proliferation, invasion and migration of MG-63 cells. FAM46C was considered as a target gene of miR-10b and inversely related with miR-10b. Overexpression of FAM46C could inhibit cell growth, invasion and migration in OS; furthermore, it also can enforced the miR-10b inhibitor-induced effects on cell behaviors of OS cells. Down-regulation of miR-10b played a suppressive effect on the cell activity in OS cells, which provides a novel insight into the advance of OS therapeutic therapies.

Structural Perspectives of the CYP3A Family and Their Small Molecule Modulators in Drug Metabolism

Cytochrome P450 enzymes function to catalyze a wide range of reactions, many of which are critically important for drug response. Members of the human cytochrome P450 3A (CYP3A) family are particularly important in drug clearance, and they collectively metabolize more than half of all currently prescribed medications. The ability of these enzymes to bind a large and structurally diverse set of compounds increases the chances of their modulating or facilitating drug metabolism in unfavorable ways. Emerging evidence suggests that individual enzymes in the CYP3A family play discrete and important roles in catalysis and disease progression. Here we review the similarities and differences among CYP3A enzymes with regard to substrate recognition, metabolism, modulation by small molecules, and biological consequence, highlighting some of those with clinical significance. We also present structural perspectives to further characterize the basis of these comparisons.

Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasets

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor with rapid progression and poor prognosis. In the present study, 11 high‑quality microarray datasets, comprising 334 tumor samples and 151 non‑tumor samples from the Gene Expression Omnibus, were screened, and integrative meta‑analysis of expression data was used to identify gene signatures that differentiate between PDAC and normal pancreatic tissues. Following the identification of differentially expressed genes (DEGs), two‑way hierarchical clustering analysis was performed for all DEGs using the gplots package in R software. Hub genes were then determined through protein‑protein interaction network analysis using NetworkAnalyst. In addition, functional annotation and pathway enrichment analyses of all DEGs were conducted in the Database for Annotation, Visualization, and Integrated Discovery. The expression levels and Kaplan‑Meier analysis of the top 10 upregulated and downregulated genes were verified in The Cancer Genome Atlas. A total of 1,587 DEGs, including 1,004 upregulated and 583 downregulated genes, were obtained by comparing PDAC with normal tissues. Of these, hematological and neurological expressed 1, integrin subunit α2 (ITGA2) and S100 calcium‑binding protein A6 (S100A6) were the top upregulated genes, and kinesin family member 1A, Dymeclin and β‑secretase 1 were the top downregulated genes. Reverse transcription‑quantitative PCR was performed to examine the expression levels of S100A6, KRT19 and GNG7, and the results suggested that S100A6 was significantly upregulated in PDAC compared with normal pancreatic tissues. ITGA2 overexpression was significantly associated with shorter overall survival times, whereas family with sequence similarity 46 member C overexpression was strongly associated with longer overall survival times. In addition, network‑based meta‑analysis confirmed growth factor receptor‑bound protein 2 and histone deacetylase 5 as pivotal hub genes in PDAC compared with normal tissue. In conclusion, the results of the present meta‑analysis identified PDAC‑related gene signatures, providing new perspectives and potential targets for PDAC diagnosis and treatment.

Construction and Validation of a 9-Gene Signature for Predicting Prognosis in Stage III Clear Cell Renal Cell Carcinoma

Purpose: Aim of this study was to develop a multi-gene signature to help better predict prognosis for stage III renal cell carcinoma (RCC) patients.

Methods: Fourteen pairs of stage III tumor and normal tissues mRNA expression data from GSE53757 and 16 pairs mRNA expression data from TCGA clear cell RCC database were used to analyze differentially expressed genes between tumor and normal tissues. Common different expressed genes in both datasets were used for further modeling. Lasso Cox regression analysis was performed to select and build prognostic multi-gene signature in TCGA stage III kidney cancer patients (N = 122). Then, the multi-gene signature was validated in stage III renal cancer cases in Fudan University Shanghai Cancer Center (N = 77). C-index and time-dependent ROC were used to test the efficiency of this signature in predicting overall survival.

Results: In total, 1,370 common different expressed genes were found between tumor and normal tissues in both datasets. After Lasso Cox modeling, nine mRNAs were finally identified to build a classifier. Using this classifier, we could classify stage III clear cell RCC patients into high-risk group and low-risk group. Prognosis was significantly different between these groups in discovery TCGA cohort, validation FUSCC cohort and entire set (All P < 0.001). Multivariate cox regression in entire set (N = 199) revealed that risk group classified by 9-gene signature, age of diagnosis, pN stage and ISUP grade were independent prognostic factor of overall survival in stage III kidney cancer patients.

Conclusion: We developed a robust multi-gene classifier that can effectively classify stage III RCC patients into groups with low and high risk of poor prognosis. This signature may help select high-risk patients who require more aggressive adjuvant target therapy or immune therapy.

Update review of skin adverse events during treatment of lung cancer and colorectal carcinoma with epidermal growth receptor factor inhibitors

The past decades have witnessed a rapid increase in the use of molecularly targeted therapies. One class of agents includes the epidermal growth factor receptor inhibitors (EGFRIs), which afford patients longer progression-free survival (PFS) times, especially among non-small cell lung cancer (NSCLC) and metastatic colorectal carcinoma (mCRC). Certain adverse effects, particularly skin toxicity, are mainly manifested as rash, xerosis, pruritus, nails changes, hair changes and mucositis. Previous studies reported the adverse events occurred based on the cutaneous inflammation reaction. Treatment recommended glucocorticoids and antibiotics. It is suggested that skin toxicity is an important issue because it usually affects patients' quality of life (QoL) and still causes dose reduction or discontinuation of targeted therapies. For these reasons, more and more oncologists and dermatologists recognize the importance of recognition and management of skin toxicities with the expansion in availability of EGFRIs. In this review, we conducted a systematic review of recent data to examine the types and frequencies of dermatologic toxicities associated with anti-epidermal growth factor receptor (EGFR) therapies in NSCLC and mCRC. In addition, we would like to explore the management and treatment options currently used by clinicians based on the possible mechanism.

A competing endogenous RNA network identifies novel mRNA, miRNA and lncRNA markers for the prognosis of diabetic pancreatic cancer

Pancreatic cancer (PaC) is highly associated with diabetes mellitus (DM). However, the mechanisms are insufficient. The study aimed to uncover the underlying regulatory mechanism on diabetic PaC and find novel biomarkers for the disease prognosis. Two RNA-sequencing (RNA-seq) datasets, GSE74629 and GSE15932, as well as relevant data in TCGA were utilized. After pretreatment, differentially expressed genes (DEGs) or miRNAs (DEMs) or lncRNAs (DELs) between diabetic PaC and non-diabetic PaC patients were identified, and further examined for their correlations with clinical information. Prognostic RNAs were selected using KM curve. Optimal gene set for classification of different samples were recognized by support vector machine. Protein-protein interaction (PPI) network was constructed for DEGs based on protein databases. Interactions among three kinds of RNAs were revealed in the 'lncRNA-miRNA-mRNA' competing endogenous RNA (ceRNA) network. A group of 32 feature genes were identified that could classify diabetic PaC from non-diabetic PaC, such as CCDC33, CTLA4 and MAP4K1. This classifier had a high accuracy on the prediction. Seven lncRNAs were tied up with prognosis of diabetic PaC, especially UCA1. In addition, crucial DEMs were selected, such as hsa-miR-214 (predicted targets: MAP4K1 and CCDC33) and hsa-miR-429 (predicted targets: CTLA4). Notably, interactions of 'HOTAIR-hsa-miR-214-CCDC33' and 'CECR7-hsa-miR-429-CTLA4' were highlighted in the ceRNA network. Several biomarkers were identified for diagnosis of diabetic PaC, such as HOTAIR, CECR7, UCA1, hsa-miR-214, hsa-miR-429, CCDC33 and CTLA4. 'HOTAIR-hsa-miR-214-CCDC33' and 'CECR7-hsa-miR-429-CTLA4' regulations might be two important mechanisms for the disease progression.