Radiotherapy is one of the most common countermeasures for treating a wide range of tumors. However, the radioresistance of cancer cells is still a major limitation for radiotherapy applications. Efforts are continuously ongoing to explore sensitizing targets and develop radiosensitizers for improving the outcomes of radiotherapy. DNA double-strand breaks are the most lethal lesions induced by ionizing radiation and can trigger a series of cellular DNA damage responses (DDRs), including those helping cells recover from radiation injuries, such as the activation of DNA damage sensing and early transduction pathways, cell cycle arrest, and DNA repair. Obviously, these protective DDRs confer tumor radioresistance. Targeting DDR signaling pathways has become an attractive strategy for overcoming tumor radioresistance, and some important advances and breakthroughs have already been achieved in recent years. On the basis of comprehensively reviewing the DDR signal pathways, we provide an update on the novel and promising druggable targets emerging from DDR pathways that can be exploited for radiosensitization. We further discuss recent advances identified from preclinical studies, current clinical trials, and clinical application of chemical inhibitors targeting key DDR proteins, including DNA-PKcs (DNA-dependent protein kinase, catalytic subunit), ATM/ATR (ataxia–telangiectasia mutated and Rad3-related), the MRN (MRE11-RAD50-NBS1) complex, the PARP (poly[ADP-ribose] polymerase) family, MDC1, Wee1, LIG4 (ligase IV), CDK1, BRCA1 (BRCA1 C terminal), CHK1, and HIF-1 (hypoxia-inducible factor-1). Challenges for ionizing radiation-induced signal transduction and targeted therapy are also discussed based on recent achievements in the biological field of radiotherapy.

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DNA damage response signaling pathways and targets for radiotherapy sensitization in cancer.

Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention

Lipid metabolism in cancer: New perspectives and emerging mechanisms.

BackgroundWeighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn).ResultsWe assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices.ConclusionsThe results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.

An Additional K-Means Clustering Step Improves the Biological Features of WGCNA Gene Co-Expression Networks

Tumor cellular metabolism exhibits distinguishing features that collectively enhance biomass synthesis while maintaining redox balance and cellular homeostasis. These attributes reflect the complex interactions between cell-intrinsic factors such as genomic-transcriptomic regulation and cell-extrinsic influences, including growth factor and nutrient availability. Alongside glucose and amino acid metabolism, fatty acid metabolism supports tumorigenesis and disease progression through a range of processes including membrane biosynthesis, energy storage and production, and generation of signaling intermediates. Here, we highlight the complexity of cellular fatty acid metabolism in cancer, the various inputs and outputs of the intracellular free fatty acid pool, and the numerous ways that these pathways influence disease behavior.

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The diversity and breadth of cancer cell fatty acid metabolism

Tumor cells rely on aerobic glycolysis as their main energy resource (Warburg effect). Recent research has highlighted the importance of lipid metabolism in tumor progression, and certain cancers even turn to fatty acids as the main fuel. Related studies have identified alterations of fatty acid metabolism in human bladder cancer (BCa). Our microarray analysis showed that fatty acid metabolism was activated in BCa compared with normal bladder. The free fatty acid (FFA) level was also increased in BCa compared with paracancerous tissues. Inhibition of fatty acid oxidation (FAO) with etomoxir caused lipid accumulation, decreased adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide phosphate (NADPH) levels, suppressed BCa cell growth in vitro and in vivo, and reduced motility of BCa cells via affecting epithelial-mesenchymal transition (EMT)-related proteins. Furthermore, etomoxir induced BCa cell cycle arrest at G0/G1 phase through peroxisome proliferator-activated receptor (PPAR) γ-mediated pathway with alterations in fatty acid metabolism associated gene expression. The cell cycle arrest could be reversed by PPARγ antagonist GW9662. Taken together, our results suggest that inhibition of FAO with etomoxir may provide a novel avenue to investigate new therapeutic approaches to human BCa.

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Fatty acid oxidation inhibitor etomoxir suppresses tumor progression and induces cell cycle arrest via PPARγ-mediated pathway in bladder cancer.

Our study's goal was to screen novel biomarkers that could accurately predict the progression and prognosis of bladder cancer (BC). Firstly, we used the Gene Expression Omnibus (GEO) dataset GSE37815 to screen differentially expressed genes (DEGs). Secondly, we used the DEGs to construct a co-expression network by weighted gene co-expression network analysis (WGCNA) in GSE71576. We then screened the brown module, which was significantly correlated with the histologic grade (r = 0.85, p = 1e-12) of BC. We conducted functional annotation on all genes of the brown module and found that the genes of the brown module were mainly significantly enriched in "cell cycle" correlation pathways. Next, we screened out two real hub genes (ANLN, HMMR) by combining WGCNA, protein-protein interaction (PPI) network and survival analysis. Finally, we combined the GEO datasets (GSE13507, GSE37815, GSE31684, GSE71576). Oncomine, Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA) dataset to confirm the predict value of the real hub genes for BC progression and prognosis. A gene-set enrichment analysis (GSEA) revealed that the real hub genes were mainly enriched in "bladder cancer" and "cell cycle" pathways. A survival analysis showed that they were of great significance in predicting the prognosis of BC. In summary, our study screened and confirmed that two biomarkers could accurately predict the progression and prognosis of BC, which is of great significance for both stratification therapy and the mechanism study of BC.

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Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis.

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein-protein interaction (PPI) network analysis. After verification of TCGA's ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis.

Peroxisome proliferator-activated receptor gamma (PPARγ) is a member of the nuclear receptor family of ligand-activated transcription factors and plays an important role in regulating cell proliferation, inflammation and lipid and glucose homeostasis. Our results revealed that PPARγ was up-regulated in human bladder cancer (BCa) tissues both at transcriptional and translational levels. Moreover, down-regulation of PPARγ mRNA or inhibition of PPARγ function (using GW9662, antagonist of PPARγ) could significantly suppress the proliferation of BCa cells. Furthermore, the cell cycle arrested in G0/G1 phase was also induced by the down-regulated PPARγ possibly through AKT-mediated up-regulation of p21/p27, whereas no significant transformation of apoptosis was observed. In addition, knockdown or inhibition of PPARγ might reduce the invasion and migration of BCa cells by affecting epithelial-mesenchymal transition-related proteins through AKT/GSK3β signalling pathway. Additionally, in vivo studies showed that BCa cell proliferation was significantly suppressed by GW9662. In conclusion, our results indicated that PPARγ might be crucial for BCa tumorigenesis by interfering with the motility and viability of BCa cells.

PPARγ inhibition regulates the cell cycle, proliferation and motility of bladder cancer cells

Background There is no good prognostic model that could predict the prognosis of bladder cancer (BCa) and the benefit of immunotherapy. Methods Through the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a 13-mRNA immune signature from the TCGA cohort (n = 406). We validated its prognostic value and predictive value for the benefit of immunotherapy with four independent validation cohort (GSE13507 [n = 256], GSE31684 [n = 93], GSE32894 [n = 308], and IMvigor210 cohort [n = 298]). Results Our results indicating that high-risk group with higher inhibitory immune cell infiltration (regulatory T cells [Tregs] and macrophage, etc), higher expression of immune checkpoints, and more T cell suppressive pathways (transforming growth factor β [TGF-β], epithelial-mesenchymal transition [EMT], etc) were activated. Besides, the immune signature showed a good predictive value for the benefit of immunotherapy in a cohort of urothelial carcinoma patients treated with PD-L1. Conclusions The immune signature constructed is convenient to classify the immunotherapeutic susceptibility of patients with BCa, so as to achieve precision immunotherapy for BCa.

Yejinpeng Wang

Papers

Fatty acid oxidation inhibitor etomoxir suppresses tumor progression and induces cell cycle arrest via PPARγ-mediated pathway in bladder cancer.

Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis.

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis.

PPARγ inhibition regulates the cell cycle, proliferation and motility of bladder cancer cells

Immune-related signature predicts the prognosis and immunotherapy benefit in bladder cancer.