Prognostic significance of PLIN1 expression in human breast cancer

Clinical pathological significance and biological function of PLIN1 in hepatocellular carcinoma: bioinformatics analysis and in vitro experiments

BACKGROUND & AIMS

Perilipin 1 (PLIN1) is an essential lipid droplet surface protein that participates in cell life activities by regulating energy balance and lipid metabolism. PLIN1 has been shown to be closely related to the development of numerous tumor types. The purpose of this work was to elucidate the clinicopathologic significance of PLIN1 in hepatocellular carcinoma (HCC), as well as its impact on the biological functions of HCC cells, and to investigate the underlying mechanisms involved.

METHODS

Public high-throughput RNA microarray and RNA sequencing data were collected to examine PLIN1 levels and clinical significance in patients with HCC. Immunohistochemistry (IHC) and real-time quantitative reverse transcription polymerase chain reaction (RT‒qPCR) were conducted to assess the expression levels and the clinicopathological relevance of PLIN1 in HCC. Then, SK and Huh7 cells were transfected with a lentivirus overexpressing PLIN1. CCK8 assay, wound healing assay, transwell assay, and flow cytometric analysis were conducted to explore the effects of PLIN1 overexpression on HCC cell proliferation, migration, invasion, and cell cycle distribution. Ultimately, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to investigate the underlying mechanisms of PLIN1 in HCC progression based on HCC differentially expressed genes and PLIN1 co-expressed genes.

RESULTS

PLIN1 was markedly downregulated in HCC tissues, which correlated with a noticeably worse prognosis for HCC patients. Additionally, PLIN1 overexpression inhibited the proliferation, migration, and invasion of SK and Huh7 cells in vitro, as well as arresting the HCC cell cycle at the G0/G1 phase. More significantly, energy conversion-related biological processes, lipid metabolism, and cell cycle signalling pathways were the three most enriched molecular mechanisms.

CONCLUSION

The present study revealed that PLIN1 downregulation is associated with poor prognosis in HCC patients and accelerated HCC progression by promoting cellular proliferation, migration, and metastasis, as well as the mechanisms underlying the regulation of lipid metabolism-related pathways in HCC.

Cancer radioresistance is characterized by a differential lipid droplet content along the cell cycle

BACKGROUND

Cancer radiation treatments have seen substantial advancements, yet the biomolecular mechanisms underlying cancer cell radioresistance continue to elude full understanding. The effectiveness of radiation on cancer is hindered by various factors, such as oxygen concentrations within tumors, cells' ability to repair DNA damage and metabolic changes. Moreover, the initial and radiation-induced cell cycle profiles can significantly influence radiotherapy responses as radiation sensitivity fluctuates across different cell cycle stages. Given this evidence and our prior studies establishing a correlation between cancer radiation resistance and an increased number of cytoplasmic Lipid Droplets (LDs), we investigated if LD accumulation was modulated along the cell cycle and if this correlated with differential radioresistance in lung and bladder cell lines.

RESULTS

Our findings identified the S phase as the most radioresistant cell cycle phase being characterized by an increase in LDs. Analysis of the expression of perilipin genes (a family of proteins involved in the LD structure and functions) throughout the cell cycle also uncovered a unique gene cell cycle pattern.

CONCLUSIONS

In summary, although these results require further molecular studies about the mechanisms of radioresistance, the findings presented here are the first evidence that LD accumulation could participate in cancer cells' ability to better survive X-Ray radiation when cells are in the S phase. LDs can represent new players in the radioresistance processes associated with cancer metabolism. This could open new therapeutic avenues in which the use of LD-interfering drugs might enhance cancer sensitivity to radiation.

Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets. Our model achieved significantly better prognosis prediction than the current machine learning and deep learning approaches in various settings. Moreover, an interpretation method was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that were important to distinguish predicted high- and low-risk patients. The significance of the identified features was partially supported by previous studies.

Perilipin1 Expression as a Prognostic Factor in Patients with Squamous Cell Carcinoma of the Lung

Perilipin (PLIN) is a major structural protein located on the surface of lipid droplets. PLIN plays an important role in human metabolism and is associated with metabolic diseases, such as obesity, diabetes, hypertension, and endocrine disorders. The dysregulation of lipid metabolism is one of the most prominent metabolic changes observed in cancers. Therefore, the PLIN protein family has recently attracted attention owing to its role in lipid metabolism and cancer. To date, no studies have addressed the association between the prognosis of lung cancer and PLIN1 expression. For the first time, we found that high PLIN1 expression was significantly correlated with worse disease-free survival (DFS) in lung squamous cell carcinoma (SCC). We examined PLIN1 expression by the immunohistochemical analysis of surgical lung SCC specimens obtained from 94 patients. We analyzed the correlation between PLIN1 expression, clinicopathological data, and patient survival, using a chi-squared test, Kaplan-Meier analysis with log-rank tests, and the multivariate Cox proportional hazards regression test. High PLIN1 expression was significantly correlated with lower DFS in the Kaplan-Meier analysis and the multivariate Cox proportional hazards regression model. High PLIN1 expression was significantly correlated with worse prognosis in lung SCC.

AUTOSURV: INTERPRETABLE DEEP LEARNING FRAMEWORK FOR CANCER SURVIVAL ANALYSIS INCORPORATING CLINICAL AND MULTI-OMICS DATA

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can reveal the underlying disease mechanisms at the molecular level. In this study, we developed a novel deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian cancer patients. Our model achieved significantly better prognosis prediction than the conventional Cox Proportional Hazard model and other competitive deep learning approaches in various settings. Moreover, an interpretation approach was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that made important contributions to distinguishing predicted high- and low-risk patients. The identified associations were partially supported by previous studies.

Challenges in Pharmacological Intervention in Perilipins (PLINs) to Modulate Lipid Droplet Dynamics in Obesity and Cancer

Perilipins (PLINs) are the most abundant proteins in lipid droplets (LD). These LD-associated proteins are responsible for upgrading LD from inert lipid storage structures to fully functional organelles, fundamentally integrated in the lipid metabolism. There are five distinct perilipins (PLIN1-5), each with specific expression patterns and metabolic activation, but all capable of regulating the activity of lipases on LD. This plurality creates a complex orchestrated mechanism that is directly related to the healthy balance between lipogenesis and lipolysis. Given the essential role of PLINs in the modulation of the lipid metabolism, these proteins can become interesting targets for the treatment of lipid-associated diseases. Since reprogrammed lipid metabolism is a recognized cancer hallmark, and obesity is a known risk factor for cancer and other comorbidities, the modulation of PLINs could either improve existing treatments or create new opportunities for the treatment of these diseases. Even though PLINs have not been, so far, directly considered for pharmacological interventions, there are many established drugs that can modulate PLINs activity. Therefore, the aim of this study is to assess the involvement of PLINs in diseases related to lipid metabolism dysregulation and whether PLINs can be viewed as potential therapeutic targets for cancer and obesity.

Integrative Analysis of the Predictive Value of Perilipin Family on Clinical Significance, Prognosis and Immunotherapy of Glioma

Gliomas are common tumors of the central nervous system. The PLINs family is widely involved in the regulation of lipid metabolism and has been associated with the development and invasive metastasis of various malignancies. However, the biological role of the PLINs family in gliomas is still unclear. TIMER and UALCAN were used to assess PLINs mRNA expression in gliomas. “Survminer” and “Survival” were used to evaluate the connection between PLINs expression and glioma patients’ survival. cBioPortal was applied to assess PLINs’ genetic alterations in glioblastoma multiforme (GBM) and low-grade glioma (LGG). The correlation of PLINs expression with tumor immune cells was analyzed by TIMER. The expressions of PLIN1, PLIN4, and PLIN5 were decreased in GBM compared to normal tissues. However, PLIN2 and PLIN3 were significantly increased in GBM. Prognostic analysis showed that LGG patients with high PLIN1 expression had better overall survival (OS), and high expression of PLIN2/3/4/5 was associated with unfavorable OS. We further determined that the expression of PLINs members in gliomas was strongly related to tumor immune cells and immune checkpoint-associated genes. PLINS may be potential biomarkers for regulating the tumor microenvironment and predicting the efficacy of immunotherapy. In addition, we determined that PLIN1 may affect glioma patients’ therapeutic sensitivity to temozolomide. Our results demonstrated the biological significance and clinical values of PLINs in gliomas and provide a basis for future in-depth exploration of the specific mechanisms of each member of PLINs in gliomas.

Identification of CFHR4 associated with poor prognosis of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most leading causes of cancer death worldwide. The 5-year survival rate of HCC patients remains low due to the lack of early-stage symptoms. Human complement factor H-related protein 4 (CFHR4) is a critical gene that belongs to the factor H family of plasma glycoproteins, which has not been linked to HCC development. The correlations between CFHR4 and prognosis and tumor-infiltrating lymphocytes in HCC are yet unknown. The present study demonstrated the involvement of CFHR4 in HCC via data mining approaches.

Results

A total of 18 upregulated and 67 down-regulated differentially expressed genes (DEGs) were identified. Importantly, CFHR4, which was screened from DEGs, was shown to express at a lower level in HCC tumor tissue than normal tissues. Western blotting (WB), immunohistochemical (IHC) and quantitative reverse transcription PCR (qRT-PCR) experiments of clinical samples further validated CFHR4 was aberrantly expressed in HCC patients; Data from TCGA showed that CFHR4 was inversely correlated with a cancer family history, histological grade, tumor node metastasis (TNM) stage, and serum AFP level of HCC patients; Univariate and multivariate analyses revealed that low expression of CFHR4 was an independent predictive marker in patients with HCC; Kaplan-Meier analysis showed that the lower expression of CFHR4 was significantly associated with the progression of HCC and poor prognosis rates. Furthermore, TIMER analysis indicated that CFHR4 expression levels had correlations with infiltrating levels of immune cells in HCC.

Conclusion

CFHR4 expression was low in HCC and was significantly related to the poor prognosis of HCC and the level of immune infiltration. CFHR4 played important roles in regulating the initiation and progression of HCC and could be a potential biomarker for the diagnosis and prognosis of HCC.

Methods

The expression of CFHR4 was analyzed by GEO and TCGA-LIHC database and verified by WB and IHC assay. The biological function of CFHR4 was performed by GO and KEGG enrichment analysis, and the genomic alteration of CFHR4 was investigated by cBioPortal database.The correlation between CFHR4 expression and clinical relevance was evaluated through Cox proportional hazards model, and the correlation between CFHR4 expression and tumor immune infiltrates were studied by TIMER database.

Adipose tissue-to-breast cancer crosstalk: Comprehensive insights

The review focuses on mechanistic evidence for the link between obesity and breast cancer. According to the IARC study, there is sufficient evidence that obesity is closely related to a variety of cancers. Among them, breast cancer is particularly disturbed by adipose tissue due to the unique histological structure of the breast. The review introduces the relationship between obesity and breast cancer from two aspects, including factors that promote tumorigenesis or metastasis. We summarize alterations in adipokines and metabolic pathways that contribute to breast cancer development. Breast cancer metastasis is closely related to obesity-induced pro-inflammatory microenvironment, adipose stem cells, and miRNAs. Based on the mechanism by which obesity causes breast cancer, we list possible therapeutic directions, including reducing the risk of breast cancer and inhibiting the progression of breast cancer. We also discussed the risk of autologous breast remodeling and fat transplantation. Finally, the causes of the obesity paradox and the function of enhancing immunity are discussed. Evaluating the balance between obesity-induced inflammation and enhanced immunity warrants further study.

Transcriptomic Analysis of Canine Osteosarcoma from a Precision Medicine Perspective Reveals Limitations of Differential Gene Expression Studies

Despite significant advances in cancer diagnosis and treatment, osteosarcoma (OSA), an aggressive primary bone tumor, has eluded attempts at improving patient survival for many decades. The difficulty in managing OSA lies in its extreme genetic complexity, drug resistance, and heterogeneity, making it improbable that a single-target treatment would be beneficial for the majority of affected individuals. Precision medicine seeks to fill this gap by addressing the intra- and inter-tumoral heterogeneity to improve patient outcome and survival. The characterization of differentially expressed genes (DEGs) unique to the tumor provides insight into the phenotype and can be useful for informing appropriate therapies as well as the development of novel treatments. Traditional DEG analysis combines patient data to derive statistically inferred genes that are dysregulated in the group; however, the results from this approach are not necessarily consistent across individual patients, thus contradicting the basis of precision medicine. Spontaneously occurring OSA in the dog shares remarkably similar clinical, histological, and molecular characteristics to the human disease and therefore serves as an excellent model. In this study, we use transcriptomic sequencing of RNA isolated from primary OSA tumor and patient-matched normal bone from seven dogs prior to chemotherapy to identify DEGs in the group. We then evaluate the universality of these changes in transcript levels across patients to identify DEGs at the individual level. These results can be useful for reframing our perspective of transcriptomic analysis from a precision medicine perspective by identifying variations in DEGs among individuals.

Perilipins at a glance

Lipid droplets (LDs) are ubiquitous organelles that store and supply lipids for energy metabolism, membrane synthesis and production of lipid-derived signaling molecules. While compositional differences in the phospholipid monolayer or neutral lipid core of LDs impact their metabolism and function, the proteome of LDs has emerged as a major influencer in all aspects of LD biology. The perilipins (PLINs) are the most studied and abundant proteins residing on the LD surface. This Cell Science at a Glance and the accompanying poster summarize our current knowledge of the common and unique features of the mammalian PLIN family of proteins, the mechanisms through which they affect cell metabolism and signaling, and their links to disease.

Identification of Crucial lncRNAs for Luminal A Breast Cancer through RNA Sequencing

BACKGROUND

The growing body of evidence indicates aberrant expression of long noncoding RNAs (lncRNAs) in breast cancer. Nevertheless, a few studies have focused on identifying key lncRNAs for patients with luminal A breast cancer. In our study, we tried to find key lncRNAs and mRNAs in luminal A breast cancer.

METHODS

RNA sequencing was performed to identify differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) in luminal A breast cancer. The protein-protein interaction (PPI), DElncRNA-DEmRNA coexpression, DElncRNA-nearby DEmRNA interaction networks, and functional annotation were performed to uncover the function of DEmRNAs. Online databases were used to validate the RNA sequencing result. The diagnostic value of candidate mRNAs was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total number of 1451 DEmRNAs and 272 DElncRNAs were identified. Several hub proteins were identified in the PPI network, including TUBB3, HIST2H3C, MCM2, MYOC, NEK2, LIPE, FN1, FOXJ1, S100A7, and DLK1. In the DElncRNA-DEmRNA coexpression, some hub lncRNAs were identified, including AP001528.2, LINC00968, LINC02202, TRHDE-AS1, LINC01140, AL354707.1, AC097534.1, MIR222HG, and AL662844.4. The mRNA expression result of TFF1, COL10A1, LEP, PLIN1, PGM5-AS1, and TRHDE-AD1 in the GSE98793 was consistent with the RNA sequencing result. The protein expression results of TUBB3, MCM2, MYOC, FN1, S100A7, and TFF1 were consistent with the mRNA expression result COL10A1, LEP, PLIN1, PGM5-AS1, and TRHDE-AD1 were capable of discriminating luminal A breast cancer and normal controls. Four lncRNA-nearby and coexpressed mRNA pairs of HOXC-AS3-HOXC10, AC020907.2-FXYD1, AC026461.1-MT1X, and AC132217.1-IGF2 were identified. AMPK (involved LIPE and LEP) and PPAR (involved PLIN1) were two significantly enriched pathways in luminal A breast cancer.

CONCLUSION

This study could be helpful in unraveling the pathogenesis and providing novel therapeutic strategies for luminal A breast cancer.

OSov: An Interactive Web Server to Evaluate Prognostic Biomarkers for Ovarian Cancer

Simple Summary

The OSov web server incorporates gene expression profiles with clinical risk factors to estimate the ovarian cancers patients’ survival, and provides a tool for multiple analysis, such as forest-plot, uni/multi-variate survival analysis, Kaplan-Meier plot and nomogram construction.

Abstract

Ovarian cancer is one of the most aggressive and highly lethal gynecological cancers. The purpose of our study is to build a free prognostic web server to help researchers discover potential prognostic biomarkers by integrating gene expression profiling data and clinical follow-up information of ovarian cancer. We construct a prognostic web server OSov (Online consensus Survival analysis for Ovarian cancer) based on RNA expression profiles. OSov is a user-friendly web server which could present a Kaplan–Meier plot, forest plot, nomogram and survival summary table of queried genes in each individual cohort to evaluate the prognostic potency of each queried gene. To assess the performance of OSov web server, 163 previously published prognostic biomarkers of ovarian cancer were tested and 72% of them had their prognostic values confirmed in OSov. It is a free and valuable prognostic web server to screen and assess survival-associated biomarkers for ovarian cancer.

A System Pharmacology Model for Decoding the Synergistic Mechanisms of Compound Kushen Injection in Treating Breast Cancer

Breast cancer (BC) is one of the most common malignant tumors among women worldwide and can be treated using various methods; however, side effects of these treatments cannot be ignored. Increasing evidence indicates that compound kushen injection (CKI) can be used to treat BC. However, traditional Chinese medicine (TCM) is characterized by “multi-components” and “multi-targets”, which make it challenging to clarify the potential therapeutic mechanisms of CKI on BC. Herein, we designed a novel system pharmacology strategy using differentially expressed gene analysis, pharmacokinetics synthesis screening, target identification, network analysis, and docking validation to construct the synergy contribution degree (SCD) and therapeutic response index (TRI) model to capture the critical components responding to synergistic mechanisms of CKI in BC. Through our designed mathematical models, we defined 24 components as a high contribution group of synergistic components (HCGSC) from 113 potentially active components of CKI based on ADME parameters. Pathway enrichment analysis of HCGSC targets indicated that Rhizoma Heterosmilacis and Radix Sophorae Flavescentis could synergistically target the PI3K-Akt signaling pathway and the cAMP signaling pathway to treat BC. Additionally, TRI analysis showed that the average affinity of HCGSC and targets involved in the key pathways reached -6.47 kcal/mmol, while in vitro experiments proved that two of the three high TRI-scored components in the HCGSC showed significant inhibitory effects on breast cancer cell proliferation and migration. These results demonstrate the accuracy and reliability of the proposed strategy.

Understanding the Role of Perilipin 5 in Non-Alcoholic Fatty Liver Disease and Its Role in Hepatocellular Carcinoma: A Review of Novel Insights

Consumption of high-calorie foods, such as diets rich in fats, is an important factor leading to the development of steatohepatitis. Several studies have suggested how lipid accumulation creates a lipotoxic microenvironment for cells, leading cells to deregulate their transcriptional and translational activity. This deregulation induces the development of liver diseases such as non-alcoholic fatty liver disease (NAFLD) and subsequently also the appearance of hepatocellular carcinoma (HCC) which is one of the deadliest types of cancers worldwide. Understanding its pathology and studying new biomarkers with better specificity in predicting disease prognosis can help in the personalized treatment of the disease. In this setting, understanding the link between NAFLD and HCC progression, the differentiation of each stage in between as well as the mechanisms underlying this process, are vital for development of new treatments and in exploring new therapeutic targets. Perilipins are a family of five closely related proteins expressed on the surface of lipid droplets (LD) in several tissues acting in several pathways involved in lipid metabolism. Recent studies have shown that Plin5 depletion acts protectively in the pathogenesis of liver injury underpinning the importance of pathways associated with PLIN5. PLIN5 expression is involved in pro-inflammatory cytokine regulation and mitochondrial damage, as well as endoplasmic reticulum (ER) stress, making it critical target of the NAFLD-HCC studies. The aim of this review is to dissect the recent findings and functions of PLIN5 in lipid metabolism, metabolic disorders, and NAFLD as well as the progression of NAFLD to HCC.

Expression status and prognostic value of the perilipin family of genes in breast cancer

BACKGROUND

The Perilipin (PLIN) family of genes were previously shown to be involved in the formation and degradation of Lipid Droplets (LDs). In addition, they may play important roles in the development and progression of breast cancer. However, the prognostic value of PLIN family members in breast cancer patients remains unclear.

METHODS

Mutations and copy number alterations of PLIN family genes in breast cancer were examined using the cBioportal for Cancer Genomics. In addition, the expression patterns of PLIN family genes were explored using the UCSC Xena online tool. Finally, the Kaplan-Meier Plotter was used to investigate the prognostic value of PLIN family genes in breast cancer.

RESULTS

The findings revealed a low frequency of genetic alterations and amplification was the most frequent change in the PLIN family genes. Additionally, there was an increase in the expression of Perilipin 3 (PLIN3) in breast cancer tissues compared to normal breast tissues. However, expression of the other genes in the PLIN family was significantly lower in breast cancer tissues compared to normal breast tissues. Moreover, there was an increase in the expression levels of Perilipin 1 (PLIN1), PLIN3, Perilipin 4 (PLIN4) and Perilipin 5 (PLIN5) in the luminal A and luminal B subgroups. On the other hand, the expression of Perilipin 2 (PLIN2) was elevated in the human epidermal growth factor receptor 2 (HER2) positive and basal-like subgroups. Furthermore, Kaplan-Meier Plotter analysis demonstrated that high expression of PLIN1 might predict a longer Overall Survival (OS) in patients with breast cancer while overexpression of PLIN2 indicated poor OS of breast cancer patients.

CONCLUSION

The findings from this study indicated that genes in the PLIN family were aberrantly expressed in breast cancer and may serve as novel therapeutic targets as well as prognostic biomarkers for the disease.

Long non-coding RNA ARAP1-AS1 accelerates cell proliferation and migration in breast cancer through miR-2110/HDAC2/PLIN1 axis

Breast cancer (BC) poses a great threaten to women health. Numerous evidences suggest the important role of long non-coding RNAs (lncRNAs) in BC development. In the present study, we intended to investigate the role of ARAP1-AS1 in BC progression. First of all, the GEPIA data suggested that ARAP1-AS1 was highly expressed in breast invasive carcinoma (BRAC) tissues compared with the normal breast tissues. Meanwhile, the expression of ARAP1-AS1 was greatly up-regulated in BC cell lines. ARAP1-AS1 knockdown led to repressed proliferation, strengthened apoptosis and blocked migration of BC cells. Moreover, ARAP1-AS1 could boost HDAC2 expression in BC through sponging miR-2110 via a ceRNA mechanism. Of note, the UCSC predicted that HDAC2 was a potential transcriptional regulator of PLIN1, an identified tumor suppressor in BC progression. Moreover, we explained that the repression of HDAC2 on PLIN1 was owing to its deacetylation on PLIN1 promoter. More importantly, depletion of PLIN1 attenuated the mitigation function of ARAP1-AS1 silence on the malignant phenotypes of BC cells. To sum up, ARAP1-AS1 serves a tumor-promoter in BC development through modulating miR-2110/HDAC2/PLIN1 axis, which may help to develop novel effective targets for BC treatment.

Metabolic Health Together with a Lipid Genetic Risk Score Predicts Survival of Small Cell Lung Cancer Patients

Simple Summary

Despite the progress in surgery and therapies, small cell lung cancer (SCLC) is still one of the most lethal types of cancer. The disease control remains heterogeneous and consequently, the ability to predict patient survival would be of great clinical value. Here, we propose for the first time, a metabolic precision approach for SCLC patients. We found that a healthy metabolic status contributes to increasing SCLC survival. Moreover, we discovered that two lipid metabolism-related genes, racemase and perilipin 1, and a genetic risk score of both genes, predict better SCLC survival. Our results show that a metabolic scenario characterized by metabolic health, lipid gene expression and environmental factors, is crucial for increase SCLC survival.

Abstract

Small cell lung cancer (SCLC) prognosis is the poorest of all types of lung cancer. Its clinical management remains heterogeneous and therefore, the capability to predict survival would be of great clinical value. Metabolic health (MH) status and lipid metabolism are two relevant factors in cancer prevention and prognosis. Nevertheless, their contributions in SCLC outcome have not yet been analyzed. We analyzed MH status and a transcriptomic panel of lipid metabolism genes in SCLC patients, and we developed a predictive genetic risk score (GRS). MH and two lipid metabolism genes, racemase and perilipin 1, are biomarkers of SCLC survival (HR = 1.99 (CI95%: 1.11–3.61) p = 0.02, HR = 0.36 (CI95%: 0.19–0.67), p = 0.03 and HR = 0.21 (CI95%: 0.09–0.47), respectively). Importantly, a lipid GRS of these genes predict better survival (c-index = 0.691). Finally, in a Cox multivariate regression model, MH, lipid GRS and smoking history are the main predictors of SCLC survival (c-index = 0.702). Our results indicate that the control of MH, lipid gene expression and environmental factors associated with lifestyle is crucial for increased SCLC survival. Here, we propose for the first time, a metabolic precision approach for SCLC patients.

Prepubertal exposure to high dose of cadmium induces hypothalamic injury through transcriptome profiling alteration and neuronal degeneration in female rats

Cadmium (Cd) is a toxic metal, which seems to be crucial during the prepubertal period. Cd can destroy the structural integrity of the blood-brain barrier (BBB) and enters into the brain. Although the brain is susceptible to neurotoxicity induced by Cd, the effects of Cd on the brain, particularly hypothalamic transcriptome, are still relatively poorly understood. Therefore, we investigated the molecular effects of Cd exposure on the hypothalamus by profiling the transcriptomic response of the hypothalamus to high dose of Cd (25 mg/kg bw/day cadmium chloride (CdCl₂)) during the prepubertal period in Sprague-Dawley female rats. After sequencing and annotation, differential expression analysis revealed 1656 genes that were differentially expressed that 108 of them were classified into 37 transcription factor (TF) families. According to gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, these differentially expressed genes (DEGs) were involved in different biological processes and neurological disorders including Alzheimer's disease (AD), Huntington's disease (HD), and Parkinson's disease (PD), prolactin signaling pathway, PI3K/Akt signaling, and dopaminergic synapse. Five transcripts were selected for further analyses with Real-time quantitative PCR (RT-qPCR). The RT-qPCR results were mostly consistent with those from the high throughput RNA sequencing (RNA-seq). Cresyl violet staining clearly showed an increased neuronal degeneration in the dorsomedial hypothalamus (DMH) and arcuate (Arc) nuclei of the CdCl₂ group. Overall, this study demonstrates that prepubertal exposure to high doses of Cd induces hypothalamic injury through transcriptome profiling alteration in female rats, which reveals the new mechanisms of pathogenesis of Cd in the hypothalamus.

Alterations of Lipid Metabolism in Cancer: Implications in Prognosis and Treatment

Cancer remains the second leading cause of mortality worldwide. In the course of this multistage and multifactorial disease, a set of alterations takes place, with genetic and environmental factors modulating tumorigenesis and disease progression. Metabolic alterations of tumors are well-recognized and are considered as one of the hallmarks of cancer. Cancer cells adapt their metabolic competences in order to efficiently supply their novel demands of energy to sustain cell proliferation and metastasis. At present, there is a growing interest in understanding the metabolic switch that occurs during tumorigenesis. Together with the Warburg effect and the increased glutaminolysis, lipid metabolism has emerged as essential for tumor development and progression. Indeed, several investigations have demonstrated the consequences of lipid metabolism alterations in cell migration, invasion, and angiogenesis, three basic steps occurring during metastasis. In addition, obesity and associated metabolic alterations have been shown to augment the risk of cancer and to worsen its prognosis. Consequently, an extensive collection of tumorigenic steps has been shown to be modulated by lipid metabolism, not only affecting the growth of primary tumors, but also mediating progression and metastasis. Besides, key enzymes involved in lipid-metabolic pathways have been associated with cancer survival and have been proposed as prognosis biomarkers of cancer. In this review, we will analyze the impact of obesity and related tumor microenviroment alterations as modifiable risk factors in cancer, focusing on the lipid alterations co-occurring during tumorigenesis. The value of precision technologies and its application to target lipid metabolism in cancer will also be discussed. The degree to which lipid alterations, together with current therapies and intake of specific dietary components, affect risk of cancer is now under investigation, and innovative therapeutic or preventive applications must be explored.

TSPAN1 promotes autophagy flux and mediates cooperation between WNT-CTNNB1 signaling and autophagy via the MIR454-FAM83A-TSPAN1 axis in pancreatic cancer

Pancreatic cancer is one of the most aggressive tumors associated with a poor clinical prognosis, weakly effective therapeutic options. Therefore, there is a strong impetus to discover new therapeutic targets in pancreatic cancer. In the present study, we first demonstrated that TSPAN1 is upregulated in pancreatic cancer and that TSPAN1 depletion decreases pancreatic cancer cell proliferation in vitro and in vivo. TSPAN1 expression was correlated with poor overall survival of pancreatic cancer patients. Moreover, we demonstrated that TSPAN1 is a novel positive regulator of macroautophagy/autophagy characterized by decreased LC3-II and SQSTM1/p62 expressions, inhibited puncta formation of GFP-LC3 and autophagic vacuoles. We also demonstrated that tspan1 mutation impaired autophagy in the zebrafish model. Furthermore, we showed that TSPAN1 promoted autophagy maturation via direct binding to LC3 by two conserved LIR motifs. Mutations in the LIR motifs of TSPAN1 resulted in a loss of the ability to induce autophagy and promote pancreatic cancer proliferation. Second, we discovered two conservative TCF/LEF binding elements present in the promoter region of the TSPAN1 gene, which was further verified through luciferase activity and ChIP assays. Furthermore, TSPAN1 was upregulated by FAM83A through the canonical WNT-CTNNB1 signaling pathway. We further demonstrated that both TSPAN1 and FAM83A are both direct targets of MIR454 (microRNA 454). Additionally, we revealed the role of MIR454-FAM83A-TSPAN1 in the proliferation of pancreatic cancer cells in vitro and in vivo. Our findings suggest that components of the MIR454-FAM83A-TSPAN1 axis may be valuable prognosis markers or therapeutic targets for pancreatic cancer.Abbreviations: AMPK: adenosine 5'-monophosphate (AMP)-activated protein kinase; APC: APC regulator of WNT signaling pathway; ATG: autophagy related; AXIN2: axin 2; BECN1: beclin 1; CCND1: cyclin D1; CSNK1A1/CK1α: casein kinase 1 alpha 1; CTNNB1/β-catenin: catenin beta 1; DAPI: 4'6-diamino-2-phenylindole; EBSS: Earle's balanced salt solution; EdU: 5-ethynyl-20-deoxyuridine; FAM83A: family with sequence similarity 83 member A; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GFP: green fluorescent protein; GSEA: gene set enrichment analysis; GSK3B: glycogen synthase kinase 3 beta; IHC: immunohistochemical; LAMP1: lysosomal associated membrane protein 1; LIR: LC3-interacting region; MAP1LC3/LC3, microtubule associated protein 1 light chain 3; MIR454: microRNA 454; miRNA: microRNA; MKI67: antigen identified by monoclonal antibody Ki 67; MTOR: mechanistic target of rapamycin kinase; MTT: 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide; MYC: MYC proto-oncogene, bHLH transcription factor; OS: overall survival; PDAC: pancreatic ductal adenocarcinoma; RAB7A: RAB7A, member RAS oncogene family; shRNA: short hairpin RNA; SQSTM1: sequestosome 1; TBE: TCF/LEF binding element; TCGA: The Cancer Genome Atlas; TCF/LEF: transcription factor/lymphoid enhancer binding factor; TCF4: transcription factor 4; TSPAN1: tetraspanin 1; TUNEL: terminal deoxynucleotidyl transferase mediated dUTP nick end labeling; UTR: untranslated region; WT: wild type.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

OBJECTIVE

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

METHODS

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

RESULTS

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

CONCLUSIONS

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

Objective

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

Methods

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

Results

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

Conclusions

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

LncRNA PVT1 promotes gemcitabine resistance of pancreatic cancer via activating Wnt/β-catenin and autophagy pathway through modulating the miR-619-5p/Pygo2 and miR-619-5p/ATG14 axes

BACKGROUND

Pancreatic cancer is one of the most lethal malignancies and has an extremely poor diagnosis and prognosis. The development of resistance to gemcitabine is still a major challenge. The long noncoding RNA PVT1 was reported to be involved in carcinogenesis and chemoresistance; however, the mechanism by which PVT1 regulates the sensitivity of pancreatic cancer to gemcitabine remains poorly understood.

METHODS

The viability of pancreatic cancer cells was assessed by MTT assay in vitro and xenograft tumor formation assay in vivo. The expression levels of PVT1 and miR-619-5p were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Western blotting analysis and qRT-PCR were performed to assess the protein and mRNA levels of Pygo2 and ATG14, respectively. Autophagy was explored via autophagic flux detection under confocal microscopy and autophagic vacuole investigation under transmission electron microscopy (TEM). The functional role and mechanism of PVT1 were further investigated by gain- and loss-of-function assays in vitro.

RESULTS

In the present study, we demonstrated that PVT1 was up-regulated in gemcitabine-resistant pancreatic cancer cell lines. Gain- and loss-of-function assays revealed that PVT1 impaired sensitivity to gemcitabine in vitro and in vivo. We further found that PVT1 up-regulated the expression of both Pygo2 and ATG14 and thus regulated Wnt/β-catenin signaling and autophagic activity to overcome gemcitabine resistance through sponging miR-619-5p. Moreover, we discovered three TCF/LEF binding elements (TBEs) in the promoter region of PVT1, and activation of Wnt/β-catenin signaling mediated by the up-regulation of Pygo2 increased PVT1 expression by direct binding to the TBE region. Furthermore, PVT1 was discovered to interact with ATG14, thus promoting assembly of the autophagy specific complex I (PtdIns3K-C1) and ATG14-dependent class III PtdIns3K activity.

CONCLUSIONS

These data indicate that PVT1 plays a critical role in the sensitivity of pancreatic cancer to gemcitabine and highlight its potential as a valuable target for pancreatic cancer therapy.

Systems biology comprehensive analysis on breast cancer for identification of key gene modules and genes associated with TNM-based clinical stages

Breast cancer (BC), as one of the leading causes of death among women, comprises several subtypes with controversial and poor prognosis. Considering the TNM (tumor, lymph node, metastasis) based classification for staging of breast cancer, it is essential to diagnose the disease at early stages. The present study aims to take advantage of the systems biology approach on genome wide gene expression profiling datasets to identify the potential biomarkers involved at stage I, stage II, stage III, and stage IV as well as in the integrated group. Three HER2-negative breast cancer microarray datasets were retrieved from the GEO database, including normal, stage I, stage II, stage III, and stage IV samples. Additionally, one dataset was also extracted to test the developed predictive models trained on the three datasets. The analysis of gene expression profiles to identify differentially expressed genes (DEGs) was performed after preprocessing and normalization of data. Then, statistically significant prioritized DEGs were used to construct protein-protein interaction networks for the stages for module analysis and biomarker identification. Furthermore, the prioritized DEGs were used to determine the involved GO enrichment and KEGG signaling pathways at various stages of the breast cancer. The recurrence survival rate analysis of the identified gene biomarkers was conducted based on Kaplan-Meier methodology. Furthermore, the identified genes were validated not only by using several classification models but also through screening the experimental literature reports on the target genes. Fourteen (21 genes), nine (17 genes), eight (10 genes), four (7 genes), and six (8 genes) gene modules (total of 53 unique genes out of 63 genes with involving those with the same connectivity degree) were identified for stage I, stage II, stage III, stage IV, and the integrated group. Moreover, SMC4, FN1, FOS, JUN, and KIF11 and RACGAP1 genes with the highest connectivity degrees were in module 1 for abovementioned stages, respectively. The biological processes, cellular components, and molecular functions were demonstrated for outcomes of GO analysis and KEGG pathway assessment. Additionally, the Kaplan-Meier analysis revealed that 33 genes were found to be significant while considering the recurrence-free survival rate as an alternative to overall survival rate. Furthermore, the machine learning calcification models show good performance on the determined biomarkers. Moreover, the literature reports have confirmed all of the identified gene biomarkers for breast cancer. According to the literature evidence, the identified hub genes are highly correlated with HER2-negative breast cancer. The 53-mRNA signature might be a potential gene set for TNM based stages as well as possible therapeutics with potentially good performance in predicting and managing recurrence-free survival rates at stages I, II, III, and IV as well as in the integrated group. Moreover, the identified genes for the TNM-based stages can also be used as mRNA profile signatures to determine the current stage of the breast cancer.

Towards the early detection of ductal carcinoma (a common type of breast cancer) using biomarkers linked to the PPAR(γ) signaling pathway

Breast cancer is a leading cause of morbidity and mortality among women comprising about 12% females worldwide. The underlying alteration in the gene expression, molecular mechanism and metabolic pathways responsible for incidence and progression of breast tumorigenesis are yet not completely understood. In the present study, potential biomarker genes involved in the early progression for early diagnosis of breast cancer has been detailed. Regulation and Gene profiling of Ductal Carcinoma In-situ (DCIS), Invasive Ductal Carcinoma (IDC) and healthy samples have been analyzed to follow their expression pattern employing normalization, statistical calculation, DEGs annotation and Protein-Protein Interaction (PPI) network. We have performed a comparative study on differentially expressed genes among Healthy vs DCIS, Healthy vsIDC and DCIS vs IDC. We found MCM102 and SLC12A8as consistently over-expressed and LEP, SORBS1, SFRP1, PLIN1, FABP4, RBP4, CD300LG, ID4, CRYAB, ECRG4, G0S2, FMO2, ADAMTS5, CAV1, CAV2, ABCA8, MAMDC2, IGFBP6, CLDN11, TGFBR3as under-expressed genes in all the 3 conditions categorized for pre-invasive and invasive ductal breast carcinoma. These genes were further studied for the active pathways where PPAR(γ) signaling pathway was found to be significantly involved. The gene expression profile database can be a potential tool in the early diagnosis of breast cancer.

Integral membrane protein 2A inhibits cell growth in human breast cancer via enhancing autophagy induction

Background

Breast cancer is a life-threatening disease in females and the leading cause of mortality among the female population, presenting huge challenges for prognosis and treatment. ITM2A is a member of the BRICHOS superfamily, which are thought to have a chaperone function. ITM2A has been identified to related to ovarian cancer progress recently. However, the biological role of ITM2A in breast cancer remains largely unclear.

Methods

Quantitative real-time polymerase chain reaction (qRT-PCR), western blotting assay and immunohistochemistry staining were used to analyzed the expression level of ITM2A. The patient overall survival versus ITM2A expression level was evaluated by Kaplan-Meier analysis. MTT assay, EdU incorporation assay and colony formation assay were used to evaluated the role of ITM2A on breast cancer cell proliferation. Autophagy was explored through autophagic flux detection using a confocal microscope and autophagic vacuoles investigation under a transmission electron microscopy (TEM). In vitro kinase assay was used to investigated the phosphorylation modification of ITM2A by HUNK.

Results

Our data showed that the expression of integral membrane protein 2A (ITM2A) was significantly down-regulated in human breast cancer tissues and cell lines. Kaplan-Meier analysis indicated that patients presenting with reduced ITM2A expression exhibited poor overall survival, and expression significantly correlated with age, progesterone receptor status, TNM classification and tumor stage. ITM2A overexpression significantly inhibited the proliferation of breast cancer cells. By studying several autophagic markers and events in human breast cancer SKBR-3 cells, we further demonstrated that ITM2A is a novel positive regulator of autophagy through an mTOR-dependent manner. Moreover, we found that ITM2A was phosphorylated at T35 by HUNK, a serine/threonine kinase significantly correlated with human breast cancer overall survival and HER2-induced mammary tumorigenesis.

Conclusion

Our study provided evidence that ITM2A functions as a novel prognostic marker and represents a potential therapeutic target.

Electronic supplementary material

The online version of this article (10.1186/s12964-019-0422-7) contains supplementary material, which is available to authorized users.

Study of Gene Expression Profiles of Breast Cancers in Indian Women

Breast cancer is the most common cancer among women globally. In India, the incidence of breast cancer has increased significantly during the last two decades with a higher proportion of the disease at a young age compared to the west. To understand the molecular processes underlying breast cancer in Indian women, we analysed gene expression profiles of 29 tumours and 9 controls using microarray. In the present study, we obtained 2413 differentially expressed genes, consisting of overexpressed genes such as COL10A1, COL11A1, MMP1, MMP13, MMP11, GJB2, and CST1 and underexpressed genes such as PLIN1, FABP4, LIPE, AQP7, LEP, ADH1A, ADH1B, and CIDEC. The deregulated pathways include cell cycle, focal adhesion and metastasis, DNA replication, PPAR signaling, and lipid metabolism. Using PAM50 classifier, we demonstrated the existence of molecular subtypes in Indian women. In addition, qPCR validation of expression of metalloproteinase genes, MMP1, MMP3, MMP11, MMP13, MMP14, ADAMTS1, and ADAMTS5 showed concordance with that of the microarray data; wherein we found a significant association of ADAMTS5 down-regulation with older age (≥55 years) of patients. Together, this study reports gene expression profiles of breast tumours from the Indian subcontinent, throwing light on the pathways and genes associated with the breast tumourigenesis in Indian women.

PLIN3 is up-regulated and correlates with poor prognosis in clear cell renal cell carcinoma

BACKGROUND

PLIN3, one of the members of the perilipin family, has been reported to be involved in the formation and accumulation of lipid droplets. However, the expression levels and diagnostic and prognostic value of PLIN3 in renal cell carcinoma (RCC) remain unclear.

METHODS

Bioinformatic analysis was used to assess the levels of PLIN3 and the correlation between PLIN3 levels and clinicopathological parameters in renal cancer. The expression levels of PLIN3 were determined in human RCC tissues and cell lines by western blot, immunofluorescence and immunohistochemistry assays. Receiver operating characteristic curves and Kaplan-Meier curves were used to analyze the diagnostic and prognostic significance of PLIN3 in RCC.

RESULTS

The expression level of PLIN3 was elevated in RCC tissues and cell lines, which was consistent with the analysis of the TCGA and Oncomine cancer database. The receiver operating characteristic curve indicated that high PLIN3 expression can distinguish cancer tissues from normal kidney tissues (area under the curve = 0.7270, P<0.0001). Kaplan-Meier curves revealed that elevated PLIN3 predicted poor disease-free survival and overall survival.

CONCLUSIONS

PLIN3 is highly expressed in kidney cancer, and high expression of PLIN3 can serve as a useful diagnostic and prognostic biomarker. PLIN3 functional inhibition can be used as a new clinical treatment option.

Silencing of perilipin by short hairpin RNA inhibits proliferation and induces apoptosis in liposarcoma cells

Previous studies have identified that perilipin-1 (PLIN1) is a highly specific marker for liposarcoma. However, its functions have yet to be fully elucidated. The aim of the present study was to investigate the potential role of PLIN1 in the proliferation, migration and apoptosis of liposarcoma cells. Short hairpin RNA was designed to inhibit PLIN1 levels. Cell proliferation was monitored by Cell Counting Kit‑8 assay and cell migration determined by wound healing assay. Flow cytometry was performed to assess the cell cycle distributions and apoptosis in liposarcoma cells. The results demonstrated that the expression of PLIN1 was significantly upregulated in liposarcoma tumor tissues compared with normal adipose tissues. Silencing of PLIN1 by short hairpin RNA significantly inhibited proliferation and migration and induced G1 phase cell cycle arrest and apoptosis in liposarcoma cell lines. It was identified that PLIN1 serves a crucial role in the pathogenesis and progression of liposarcoma and may be a potential therapeutic target for its clinical management.

Overexpression of PLIN2 is a prognostic marker and attenuates tumor progression in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common renal malignancy in adults, the incidence of which continues to increase. The lipid droplet protein perilipin 2 (PLIN2), which was originally considered an RNA transcript, is markedly expressed during adipocyte differentiation. In addition, it has been observed to be elevated in numerous types of cancer, including ccRCC; however, its essential function remains unclear in ccRCC. The present study examined the expression of PLIN2 in ccRCC, and aimed to determine the association between PLIN2 expression and patient survival. The present study mined the transcriptional, clinicopathological and survival data of PLIN2 in patients with ccRCC through The Cancer Genome Atlas. The expression levels of PLIN2 were also detected in human ccRCC tissues and cell lines by western blotting and immunohistochemistry, and its biological role was identified by functional analysis. The results demonstrated that PLIN2 was predominantly elevated in RCC tissues and cells. In addition, the expression levels of PLIN2 were significantly associated with various clinicopathological factors, and high PLIN2 expression was associated with a good prognosis. The results of a multivariate analysis demonstrated that high PLIN2 expression was an independent prognostic indicator of overall survival (hazard ratio, 0.586; P=0.001). Furthermore, PLIN2 knockdown promoted proliferation of ccRCC cells, and enhanced cell invasion and migration. These results suggested that PLIN2 may be considered a novel prognostic factor in ccRCC and a specific diagnostic indicator for patients with ccRCC. Furthermore, it could be a potential novel target for the clinical treatment of ccRCC.

Fatty acid metabolism in breast cancer subtypes

Dysregulation of fatty acid metabolism is recognized as a component of malignant transformation in many different cancers, including breast; yet the potential for targeting this pathway for prevention and/or treatment of cancer remains unrealized. Evidence indicates that proteins involved in both synthesis and oxidation of fatty acids play a pivotal role in the proliferation, migration and invasion of breast cancer cells. The following essay summarizes data implicating specific fatty acid metabolic enzymes in the genesis and progression of breast cancer, and further categorizes the relevance of specific metabolic pathways to individual intrinsic molecular subtypes of breast cancer. Based on mRNA expression data, the less aggressive luminal subtypes appear to rely on a balance between de novo fatty acid synthesis and oxidation as sources for both biomass and energy requirements, while basal-like, receptor negative subtypes overexpress genes involved in the utilization of exogenous fatty acids. With these differences in mind, treatments may need to be tailored to individual subtypes.

Bioinformatic analysis reveals the key pathways and genes in early-onset breast cancer

Early-onset breast cancer is the most prevalent cancer in the female. To identify the differentially expressed genes and the key signaling pathways in early-onset breast cancer, we have carried out the bioinformatic analysis of an RNA array dataset in the GEO database, GSE109169, which was acquired from early-onset breast cancer patient. A total of 118 differentially expressed genes in early-onset breast cancer were significantly changed compared with that in adjacent normal tissues. Most of these genes are classified into three categories: signaling molecule, enzyme modulator, and hydrolase. Gene ontology terms reveal that most of these genes are involved in cellular and metabolic processes, biological regulation, binding and catalytic activities, and receptor regulation. Protein-protein interaction network was constructed and has two highly enriched modules: one with up-regulated genes and the other with down-regulated genes. The singling pathways are mainly enriched in the cellular immune system, lipid metabolism and other types of metabolic pathways. Finally, we have plotted the Kaplan-Meier curves of two up-regulated and two down-regulated genes for the overall survival prediction in breast cancer. These results greatly expand the current view of early-onset breast cancer and shed light on the discovery of drug candidates and the improvement for the prognosis.

Pygopus2 inhibits the efficacy of paclitaxel-induced apoptosis and induces multidrug resistance in human glioma cells

Anti-microtubule drugs, such as paclitaxel (PTX), are extensively used for the treatment of numerous cancers. However, growing evidence has shown that PTX resistance, either intrinsic or acquired, frequently occurs in patients and results in the failure of treatment, contributing to the high cancer mortality rate. Therefore, it is necessary to identify the genes or pathways involved in anti-microtubule drug resistance for future successful treatment of cancers. Pygopus2 (Pygo2), which contains a Zn-coordinated plant homeodomain (PHD) finger domain, is critical for β-catenin-dependent transcriptional switches in normal and malignant tissues and is over-expressed in various cancers, including human brain glioma. In this study, we report that over-expression of Pygo2 inhibited the efficacy of PTX and contributed to cell multidrug resistance in two different ways. First, over-expression of Pygo2 inhibited the PTX-induced phosphorylation of B-cell lymphoma 2 (Bcl-2), suppressing the proteolytic cleavage of procaspase-8/9 and further inhibiting the activation of caspase-3, which also inhibits the activation of the JNK/SAPK pathway, ultimately inhibiting cell apoptosis. Second, over-expression of Pygo2 facilitated the expression of P-glycoprotein, which acts as a drug efflux pump, by promoting the transcription of Multi-drug resistance 1 (MDR1) at the MDR1 promoter loci, resulting in acceleration of the efflux of PTX.

An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping

BACKGROUND

Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer).

RESULTS

We obtained from public data repositories a collection of breast cancer gene expression datasets with over 7000 patients. An integrated meta-analysis approach to gene expression connectivity mapping was developed, which involved unified processing and normalization of raw gene expression data, systematic removal of batch effects, and multiple runs of balanced sampling for differential expression analysis. Differentially expressed genes stringently selected were used to construct multiple non-joint gene signatures representing the same biological state. Remarkably these non-joint gene signatures retrieved from connectivity mapping separate lists of candidate drugs with significant overlaps, providing high confidence in their predicted effects on breast cancers. Of particular note, among the top 26 compounds identified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer drugs.

CONCLUSIONS

A few candidate drugs with potential to enhance breast cancer or increase the risk of the disease were also identified; further investigation on a large population is required to firmly establish their effects on breast cancer risks. This work thus provides a novel approach and an applicable example for identifying medications with potential to alter cancer risks through gene expression connectivity mapping.

Serial-omics of P53-/-, Brca1-/- Mouse Breast Tumor and Normal Mammary Gland

This study demonstrates a liquid-liquid extraction for the sequential tandem mass spectrometry (LC-MS/MS) analysis of non-polar lipids, polar metabolites, proteins and phosphorylation sites from a single piece of tissue. Extraction of 10 mg BRCA-/-, p53-/- breast tumor tissue or normal mammary gland tissue with methyl-tert-butyl ether (MTBE) results in three phases: an upper non-polar phase containing 1,382 lipids, a lower polar phase with 805 metabolites and a precipitated protein pellet with 4,792 proteins with 1,072 phosphorylation sites. Comparative analysis revealed an activated AKT-mTOR pathway in tumors. Tumors also showed a reduction of phosphorylation sites involved in transcription and RNA splicing and decreased abundance of enzymes in lipid synthesis. Analysis of polar metabolites revealed a reduction in glycolysis, pentose phosphate pathway, polyamines and nucleotides, but an increase in TCA and urea cycle intermediates. Analysis of lipids revealed a shift from high triglycerides in mammary gland to high phospholipid levels in tumors. The data were integrated into a model showing breast tumors exhibit features on the proteomic, lipidomic and metabolomic level that are distinct from normal breast tissue. Our integrative technique lends itself to samples such as tumor biopsies, dried blood spots and fluids including urine and CSF to develop biomarkers of disease.

Identification of glycerol-3-phosphate dehydrogenase 1 as a tumour suppressor in human breast cancer

In the present study, we found the mRNA expression level of glycerol-3-phosphate dehydrogenase (GPD1) was significantly downregulated in human breast cancer patients. Patients with reduced GPD1 expression exhibited poorer overall metastatic relapse-free survival (p = 0.0013). Further Cox proportional hazard model analysis revealed that the reduced expression of GPD1 is an independent predictor of overall survival in oestrogen receptor-positive (p = 0.0027, HR = 0.91, 95% CI = 0.85–0.97, N = 3,917) and nodal-negative (p = 0.0013, HR = 0.87, 95% CI = 0.80–0.95, N = 2,456) breast cancer patients. We also demonstrated that GPD1 was a direct target of miR-370, which was significantly upregulated in human breast cancer. We further showed that exogenous expression of GPD1 in human MCF-7 and MDA-MB-231 breast cancer cells significantly inhibited cell proliferation, migration, and invasion. Our results, therefore, suggest a novel tumour suppressor function for GPD1 and contribute to the understanding of cancer metabolism.