PPARG expression in colorectal cancer and its association with staging and clinical evolution

Regulatory role of PPAR in colorectal cancer

Colorectal cancer (CRC) is one of the most common tumors in the digestive system, and the majority of patients are found to be in advanced stages, which is a burden to human health all over the world. Moreover, in recent years, CRC has been progressively becoming younger, with an increasing incidence mainly among patients <50 years old. Despite the increase in awareness of CRC and the continuous improvement of medical treatment nowadays, the challenge of CRC still needs to be conquered. By now, the pathogenesis of CRC is complex and not fully understood. With the deepening of research, it has been revealed that PPARs, as a transcription factor, are inextricably linked to CRC. This article outlines the mechanisms by which PPARs are involved in CRC development. An in-depth understanding of the pathways related to PPARs may provide new ways of developing effective therapies for CRC with PPARs as potential targets.

Nitro fatty acids: A comprehensive review on analytical methods and levels in health and disease

Nitro fatty acids (NO2-FAs) are biologically active compounds produced from the reaction of unsaturated fatty acids with reactive nitrogen species (RNS). Due to their electrophilic nature, these endogenously produced metabolites can react with nucleophilic targets, producing a spectrum of modulatory and protective effects. Determination of NO2-FAs in biological samples is challenging due to their low nanomolar to picomolar endogenous concentrations, indistinct metabolism, and distribution in many tissues and biofluids. Several attempts have been made to develop precise, standardized, and efficient methodologies for assessing physiological and pathophysiological processes to overcome the difficulties associated with their measurement. This review discusses those approaches utilizing liquid chromatography tandem mass spectrometry (LC‒MS/MS) and gas chromatography tandem mass spectrometry (GC‒MS/MS) for the quantification of NO2-FAs, in addition to a summary of their laboratory synthesis and extraction from biological samples. Clinical associations with different pathological conditions, including hyperlipidaemia, cardiac ischemia and herpes simplex type 2 viral infection (HSV-2), are also discussed.

Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis

OBJECTIVE

The purpose of the study was to investigate the role of exosome and lipid metabolism-related genes (EALMRGs) mRNA levels in the diagnosis and prognosis of Pancreatic Adenocarcinoma (PAAD).

METHODS

The mRNA expression pattern of PAAD and pan-cancers with prognostic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EALMRGs were acquired from GeneCards and MSigDB database after merging and deduplication. Prognostic EALMRGs were screened through univariate COX regression analysis, and a prognostic model was constructed based on these genes by least absolute shrinkage and selection operator (LASSO) regression. The prognostic value of EALMRGs was then validated in pan-cancer data. The time characteristics ROC curve analysis was performed to evaluate the effectiveness of the prognostic genes.

RESULTS

We identified 5 hub genes (ABCB1, CAP1, EGFR, PPARG, SNCA) according to high and low-risk groups of prognoses. The risk formula was verified in three other cohort of pancreatic cancer patients and was explored in pan-cancer data. Additionally, T cell and dendritic cell infiltration was significantly increased in low-risk group. The expression of the 5 hub genes was also identified in single-cell sequencing data of pancreatic cancer with pivotal pathways. Additionally, functional enrichment analysis based on pancreatic cancer data in pancreatic cancer showed that protein serine/threonine kinase activity, focal adhesion, actin binding, cell-substrate junction, organic acid transport, and regulation of transporter activity were significant related to the expression of genes in EALMRGs.

CONCLUSIONS

Our risk formula shows potential prognostic value in multiple cancers and manifest pivotal alterations in immune infiltration and biological pathway in pancreatic cancer.

Differential gene expression of immunity and inflammation genes in colorectal cancer using targeted RNA sequencing

Introduction

Colorectal cancer (CRC) is a heterogeneous disease caused by molecular changes, as driver mutations, gene methylations, etc., and influenced by tumor microenvironment (TME) pervaded with immune cells with both pro- and anti-tumor effects. The studying of interactions between the immune system (IS) and the TME is important for developing effective immunotherapeutic strategies for CRC. In our study, we focused on the analysis of expression profiles of inflammatory and immune-relevant genes to identify aberrant signaling pathways included in carcinogenesis, metastatic potential of tumors, and association of Kirsten rat sarcoma virus (KRAS) gene mutation.

Methods

A total of 91 patients were enrolled in the study. Using NGS, differential gene expression analysis of 11 tumor samples and 11 matching non-tumor controls was carried out by applying a targeted RNA panel for inflammation and immunity genes containing 475 target genes. The obtained data were evaluated by the CLC Genomics Workbench and R library. The significantly differentially expressed genes (DEGs) were analyzed in Reactome GSA software, and some selected DEGs were used for real-time PCR validation.

Results

After prioritization, the most significant differences in gene expression were shown by the genes TNFRSF4, IRF7, IL6R, NR3CI, EIF2AK2, MIF, CCL5, TNFSF10, CCL20, CXCL11, RIPK2, and BLNK. Validation analyses on 91 samples showed a correlation between RNA-seq data and qPCR for TNFSF10, RIPK2, and BLNK gene expression. The top differently regulated signaling pathways between the studied groups (cancer vs. control, metastatic vs. primary CRC and KRAS positive and negative CRC) belong to immune system, signal transduction, disease, gene expression, DNA repair, and programmed cell death.

Conclusion

Analyzed data suggest the changes at more levels of CRC carcinogenesis, including surface receptors of epithelial or immune cells, its signal transduction pathways, programmed cell death modifications, alterations in DNA repair machinery, and cell cycle control leading to uncontrolled proliferation. This study indicates only basic molecular pathways that enabled the formation of metastatic cancer stem cells and may contribute to clarifying the function of the IS in the TME of CRC. A precise identification of signaling pathways responsible for CRC may help in the selection of personalized pharmacological treatment.

Evaluation of Expression Levels of NFATc2 and PPARG Genes Two Effector Elements of WNT Pathway in Non-Small Cell Lung Carcinoma

Background

There is an emergency need in discovering an efficient profile of molecular biomarkers for early diagnosis of Non-small cell lung cancer (NSCLC). Transcription factors as important groups of regulators that are able to adjust the cell cycles have attracted the attention of most researchers recently. NFATc2 and PPARG are two important factors that have been selected for this project to assess their potential for being a biomarker for NSCLC.

Materials and Methods

Here in this study, 50 NSCLC patients were included. During bronchoscopy, which was their routine diagnostic approach, we collected tumoral and marginal normal tissues. After the extraction of the total RNA from the tissues, cDNA was synthesized, and the transcriptional level of NFATc2 and PPARG was examined by quantitative real-time PCR. Subsequently, the data were analyzed by proper statistical analyses.

Results

The mRNA expression of NFATc2 and PPARG were down-regulated in biopsy tissues of NSCLC patients compared with their pair marginal tissues (Pvalues were 0.0011 and <0.0001 respectively). Moreover, both of them had significant AUC (area under the curve) in the ROC curve analysis (0.65 for NFATc2 and 0.81 for PPARG, Pvalue <0.05).

Conclusion

It appears that mRNA expression of NFATc2 and PPARG possesses the potential to be regarded as a diagnostic or prognostic biomarker for NSCLC.

Integrated transcriptome and network analysis identifies EZH2/CCNB1/PPARG as prognostic factors in breast cancer

Breast cancer (BC) has high morbidity, with significant relapse and mortality rates in women worldwide. Therefore, further exploration of its pathogenesis is of great significance. This study selected therapy genes and possible biomarkers to predict BC using bioinformatic methods. To this end, the study examined 21 healthy breasts along with 457 BC tissues in two Gene Expression Omnibus (GEO) datasets and then identified differentially expressed genes (DEGs). Survival-associated DEGs were screened using the Kaplan-Meier curve. Based on Gene Ontology (GO) annotation, survival-associated DEGs were mostly associated with cell division and cellular response to hormone stimulus. The enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathway was mostly correlated with cell cycle and tyrosine metabolism. Using overlapped survival-associated DEGs, a survival-associated PPI network was constructed. PPI analysis revealed three hub genes (EZH2, CCNB1, and PPARG) by their degree of connection. These hub genes were confirmed using The Cancer Genome Atlas (TCGA)-BRCA dataset and BC tissue samples. Through Gene Set Enrichment Analysis (GSEA), the molecular mechanism of the potential therapy and prognostic genes were evaluated. Thus, hub genes were shown to be associated with KEGG_CELL_CYCLE and VANTVEER_BREAST_CANCER_POOR_PROGNOSIS gene sets. Finally, based on integrated bioinformatics analysis, this study identified three hub genes as possible prognostic biomarkers and therapeutic targets for BC. The results obtained further understanding of the underground molecular mechanisms related to BC occurrence and prognostic outcomes.

Buffalo Milk Whey Activates Necroptosis and Apoptosis in a Xenograft Model of Colorectal Cancer

Recent pharmacological research on milk whey, a byproduct of the dairy industry, has identified several therapeutic properties that could be exploited in modern medicine. In the present study, we investigated the anticancer effects of whey from Mediterranean buffalo (Bubalus bubalis) milk. The antitumour effect of delactosed milk whey (DMW) was evaluated using the HCT116 xenograft mouse model of colorectal cancer (CRC). There were no discernible differences in tumour growth between treated and untreated groups. Nevertheless, haematoxylin and eosin staining of the xenograft tissues showed clearer signs of different cell death in DMW-treated mice compared to vehicle-treated mice. Detailed biochemical and molecular biological analyses revealed that DMW was able to downregulate the protein expression levels of c-myc, phospho-Histone H3 (ser 10) and p-ERK. Moreover, DMW also activated RIPK1, RIPK3, and MLKL axis in tumour tissues from xenograft mice, thus, suggesting a necroptotic effect. The necroptotic pathway was accompanied by activation of the apoptotic pathway as revealed by increased expression of both cleaved caspase-3 and PARP-1. At the molecular level, DMW-induced cell death was also associated with (i) upregulation of SIRT3, SIRT6, and PPAR-γ and (ii) downregulation of LDHA and PPAR-α. Overall, our results unveil the potential of whey as a source of biomolecules of food origin in the clinical setting of novel strategies for the treatment of CRC.

Tensor decomposition based on the potential low-rank and [Formula: see text]-shrinkage generalized threshold algorithm for analyzing cancer multiomics data

Tensor Robust Principal Component Analysis (TRPCA) has achieved promising results in the analysis of genomics data. However, the TRPCA model under the existing tensor singular value decomposition ([Formula: see text]-SVD) framework insufficiently extracts the potential low-rank structure of the data, resulting in suboptimal restored components. Simultaneously, the tensor nuclear norm (TNN) defined based on [Formula: see text]-SVD uses the same standard to handle various singular values. TNN ignores the difference of singular values, leading to the failure of the main information that needs to be well preserved. To preserve the heterogeneous structure in the low-rank information, we propose a novel TNN and extend it to the TRPCA model. Potential low-rank space may contain important information. We learn the low-rank structural information from the core tensor. The singular value space contains the association information between genes and cancers. The [Formula: see text]-shrinkage generalized threshold function is utilized to preserve the low-rank properties of larger singular values. The optimization problem is solved by the alternating direction method of the multiplier (ADMM) algorithm. Clustering and feature selection experiments are performed on the TCGA data set. The experimental results show that the proposed model is more promising than other state-of-the-art tensor decomposition methods.

Integrated Analysis of the Expression Characteristics, Prognostic Value, and Immune Characteristics of PPARG in Breast Cancer

Background: Breast cancer (BRCA) is the most frequent malignancy. Identification of potential biomarkers could help to better understand and combat the disease at early stages. Methods: We selected the overlapping genes of differential expressed genes and genes in BRCA-highly correlated modules by Weighted Gene Co-Expression Network Analysis (WGCNA) in TCGA and GEO data and performed KEGG and GO enrichment. PPARG was achieved from Protein-Protein Interaction (PPI) network analysis and prognostic analysis. TIMER, UALCAN, GEO, TCGA, and western blot analysis were used to validate the expression of PPARG in BRCA. PPARG was further analyzed by DNA methylation, immune parameters, and tumor mutation burden. Results: Among 381 overlapping genes, the lipid metabolic process was identified as highly enriched pathways in BRCA by TCGA and GEO data. When the prognostic analysis of 10 core genes by PPI network was performed, results revealed that high expression of PPARG was significantly correlated to a better prognosis. PPARG was lesser expression in BRCA according to TIMER, UALCAN, GEO, TCGA, and western blot in both mRNA level and protein level. PPARG had several high DNA methylation level sites and the methylation level is negatively correlated to expression. PPARG is also correlated to TNM stages, tumor microenvironment, and tumor burden. Conclusions: Findings of our study identified the PPARG as a potential biomarker by confirming its low expression in BRCA and its correlation to prognosis. Moreover, its correlation to DNA methylation and tumor microenvironment may guide new therapeutic strategies for BRCA patients.