The pan‐cancer mutational landscape of the PPAR pathway reveals universal patterns of dysregulated metabolism and interactions with tumor immunity and hypoxia

Roburic acid inhibits lung cancer metastasis and triggers autophagy as verified by network pharmacology, molecular docking techniques and experiments

Background

Roburic acid (ROB) is a newly discovered tetracyclic triterpene acid extracted from oak galls, which has anti-inflammatory effects, but the mechanism of its anticancer effect is not clear. Our study focuses on exploring the potential mechanism of action of ROB in the treatment of lung cancer using a combination of network pharmacological prediction, molecular docking technique and experimental validation.

Methods

A network pharmacology approach was used to screen the protein targets of ROB and lung cancer, and PPI network analysis and enrichment analysis were performed on the intersecting genes. The tissue and organ distribution of the targets was also evaluated based on the BioGPS database. To ensure the reliability of the network pharmacology prediction results, we proceeded to use molecular docking technique to determine the relationship between drugs and targets. Finally, in vitro experiments with cell lines were performed to further reveal the potential mechanism of ROB for the treatment of lung cancer.

Results

A total of 83 potential targets of ROB in lung cancer were collected and further screened by using Cytoscape software, and 7 targets of PTGS2, CYP19A1, PTGS1, AR, CYP17A1, PTGES and SRD5A1 were obtained as hub genes and 7 hub targets had good binding energy with ROB. GO and KEGG analysis showed that ROB treatment of lung cancer mainly involves Arachidonic acid metabolism, Notch signaling pathway, cancer pathway and PPAR signaling pathway. The results of in vitro experiments indicated that ROB may inhibit the proliferation and metastasis of lung cancer cells and activate the PPARγ signaling pathway, as well as induce cellular autophagy.

Conclusions

The results of this study comprehensively elucidated the potential targets and molecular mechanisms of ROB for the treatment of lung cancer, providing new ideas for further lung cancer therapy.

Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC), ranking as the second-leading cause of global mortality among malignancies, poses a substantial burden on public health worldwide. Anoikis, a type of programmed cell death, serves as a barrier against the dissemination of cancer cells to distant organs, thereby constraining the progression of cancer. Nevertheless, the mechanism of genes related to anoikis in HCC is yet to be elucidated.

Methods

This paper's data (TCGA-HCC) were retrieved from the database of the Cancer Genome Atlas (TCGA). Differential gene expression with prognostic implications for anoikis was identified by performing both the univariate Cox and differential expression analyses. Through unsupervised cluster analysis, we clustered the samples according to these DEGs. By employing the least absolute shrinkage and selection operator Cox regression analysis (CRA), a clinical predictive gene signature was generated from the DEGs. The Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to determine the proportions of immune cell types. The external validation data (GSE76427) were procured from Gene Expression Omnibus (GEO) to verify the performance of the clinical prognosis gene signature. Western blotting and immunohistochemistry (IHC) analysis confirmed the expression of risk genes.

Results

In total, 23 prognostic DEGs were identified. Based on these 23 DEGs, the samples were categorized into four distinct subgroups (clusters 1, 2, 3, and 4). In addition, a clinical predictive gene signature was constructed utilizing ETV4, PBK, and SLC2A1. The gene signature efficiently distinguished individuals into two risk groups, specifically low and high, demonstrating markedly higher survival rates in the former group. Significant correlations were observed between the expression of these risk genes and a variety of immune cells. Moreover, the outcomes from the validation cohort analysis aligned consistently with those obtained from the training cohort analysis. The results of Western blotting and IHC showed that ETV4, PBK, and SLC2A1 were upregulated in HCC samples.

Conclusion

The outcomes of this paper underscore the effectiveness of the clinical prognostic gene signature, established utilizing anoikis-related genes, in accurately stratifying patients. This signature holds promise in advancing the development of personalized therapy for HCC.

Prognostic value of oxidative phosphorylation-related genes in hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is the most prevalent malignancies worldwide. Recently, oxidative phosphorylation (OXPHOS) has received extensive concern as an emerging target in antitumor therapy. However, the OXPHOS-involved underlying genes and clinical utilization in HCC remain worth exploring. The present research aimed to create an OXPHOS-relevant signature in HCC.

PATIENTS AND METHODS

In this study, the prognostic signature genes linked with OXPHOS were identified, and prognostic models were built using least absolute shrinkage and selection operator (LASSO) cox regression analysis. Furthermore, the combination study of immune microenvironment and signature genes looked into the involvement of immune cells in signature-based genes in HCC. Following that, chemotherapeutic drug sensitivity and immunotherapy analysis was implemented to predict clinical efficacy in HCC patients. Finally, clinical samples were collected to measure the expression of OXPHOS-related signature genes.

RESULTS

Following a series of screens, six prognostic signature genes related with OXPHOS were identified: MRPS23, MPV17, MAPK3, IGF2BP2, CDK5, and IDH2, on which a risk model was built. The findings revealed a significant drop in the survival rate of HCC patients as their risk score increased. Meanwhile, independent prognostic study demonstrated that the risk score could accurately identify HCC patients. Immuno-microenvironmental correlation research suggested that the prognostic characteristics could serve as a reference index for both immunotherapy and chemotherapy. Finally, RT-qPCR exhibited a trend in signature gene expression that was consistent with the results.

CONCLUSION

In this study, a total of six prognostic genes associated with OXPHOS were selected and a prognostic model was constructed, providing an essential reference for the study of OXPHOS in HCC.

Co-morbid intersections of cancer and cardiovascular disease and targets for natural drug action: Reprogramming of lipid metabolism

Cancer and cardiovascular diseases are major contributors to global morbidity and mortality, and their seemingly separate pathologies are intricately intertwined. In the context of cancer, the cardiovascular disease encompasses not only the side effects arising from anti-tumor treatments but also the metabolic shifts induced by oncological conditions. A growing body of research indicates that lipid metabolic reprogramming serves as a distinctive hallmark of tumors. Furthermore, anomalies in lipid metabolism play a significant role in the development of cardiovascular disease. This study delves into the cardiac implications of lipid metabolic reprogramming within the cancer context, closely examining abnormalities in lipid metabolism present in tumors, cardiac tissue, and immune cells within the microenvironment. Additionally, we examined risk factors such as obesity and anti-tumor therapy. Despite progress, a gap remains in the availability of drugs targeting lipid metabolism modulation for treating tumors and mitigating cardiac risk, with limited advancement seen in prior studies. Here, we present a review of previous research on natural drugs that exhibit both shared and distinct therapeutic effects on tumors and cardiac health by modulating lipid metabolism. Our aim is to provide insights for potential drug development.

Unraveling the intricate relationship between lipid metabolism and oncogenic signaling pathways

Lipids, the primary constituents of the cell membrane, play essential roles in nearly all cellular functions, such as cell-cell recognition, signaling transduction, and energy provision. Lipid metabolism is necessary for the maintenance of life since it regulates the balance between the processes of synthesis and breakdown. Increasing evidence suggests that cancer cells exhibit abnormal lipid metabolism, significantly affecting their malignant characteristics, including self-renewal, differentiation, invasion, metastasis, and drug sensitivity and resistance. Prominent oncogenic signaling pathways that modulate metabolic gene expression and elevate metabolic enzyme activity include phosphoinositide 3-kinase (PI3K)/AKT, MAPK, NF-kB, Wnt, Notch, and Hippo pathway. Conversely, when metabolic processes are not regulated, they can lead to malfunctions in cellular signal transduction pathways. This, in turn, enables uncontrolled cancer cell growth by providing the necessary energy, building blocks, and redox potentials. Therefore, targeting lipid metabolism-associated oncogenic signaling pathways could be an effective therapeutic approach to decrease cancer incidence and promote survival. This review sheds light on the interactions between lipid reprogramming and signaling pathways in cancer. Exploring lipid metabolism as a target could provide a promising approach for creating anticancer treatments by identifying metabolic inhibitors. Additionally, we have also provided an overview of the drugs targeting lipid metabolism in cancer in this review.

Roles of PPAR activation in cancer therapeutic resistance: Implications for combination therapy and drug development

Therapeutic resistance is a major obstacle to successful treatment or effective containment of cancer. Peroxisome proliferator-activated receptors (PPARs) play an essential role in regulating energy homeostasis and determining cell fate. Despite of the pleiotropic roles of PPARs in cancer, numerous studies have suggested their intricate relationship with therapeutic resistance in cancer. In this review, we provided an overview of the roles of excessively activated PPARs in promoting resistance to modern anti-cancer treatments, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy. The mechanisms through which activated PPARs contribute to therapeutic resistance in most cases include metabolic reprogramming, anti-oxidant defense, anti-apoptosis signaling, proliferation-promoting pathways, and induction of an immunosuppressive tumor microenvironment. In addition, we discussed the mechanisms through which activated PPARs lead to multidrug resistance in cancer, including drug efflux, epithelial-to-mesenchymal transition, and acquisition and maintenance of the cancer stem cell phenotype. Preliminary studies investigating the effect of combination therapies with PPAR antagonists have suggested the potential of these antagonists in reversing resistance and facilitating sustained cancer management. These findings will provide a valuable reference for further research on and clinical translation of PPAR-targeting treatment strategies.

Actin filament-associated protein 1-antisense RNA1 promotes the development and invasion of tongue squamous cell carcinoma via the AFAP1-AS1/miR-133a-5p/ZIC2 axis

BACKGROUND

The present study aimed to explore the biological role and underlying mechanism of the long non-coding RNA actin filament-associated protein 1-antisense RNA1 (lncRNA AFAP1-AS1) in the progression of tongue squamous cell carcinoma (TSCC).

METHODS

A quantitative reverse transcriptase-PCR (RT-qPCR) was conducted to assess relative levels of the miR-133a-5p, lncRNAs AFAP1-AS1 and zinc finger family member 2 (ZIC2) in TSCC cell lines and specimens, whereas ZIC2 protein levels were measured using western blotting. After modifying the levels of expression of lncRNA AFP1-AS1, miR-133a-5p and ZIC2 using lentivirus or plasmid transfection, we examined AKT/epithelial-mesenchymal transition signaling pathway alterations, in vivo carcinogenesis of TSCC in nude mice and in vitro malignant phenotypes. A dual-luciferase reporter assay was conducted to confirm the targeting relationship between ZIC2 and miR-133a-5p, as well as between miR-133a-5p and lncRNA AFAP1-AS1. Based on The Cancer Genome Atlas (TCGA) database, we additionally validated AFP1-AS1. The potential biological pathway for AFP1-AS1 was investigated using gene set enrichment analysis (GSEA). We also evaluated the clinical diagnostic capacities of AFP1-AS1 and clustered the most potential biomarkers with the Mfuzz expression pattern. Finally, we also made relevant drug predictions for AFP1-AS1.

RESULTS

In TSCC cell lines and specimens, lncRNA AFAP1-AS1 was upregulated. ZIC2 was upregulated in TSCC cells as a result of lncRNA AFAP1-AS1 overexpression, which also promoted TSCC cell migration, invasion, viability, and proliferation. Via the microRNA sponge effect, it was found that lncRNA AFAP1-AS1 could upregulate ZIC2 by competitively inhibiting miR-133a-5p. Interestingly, knockdown of ZIC2 reversed the biological roles of lncRNA AFAP1-AS1 with respect to inducing malignant phenotypes in TSCC cells. In addition, in vivo overexpression of lncRNA AFAP1-AS1 triggered subcutaneous tumor growth in nude mice implanted with TSCC cells and upregulated ZIC2 in the tumors. The TCGA database findings revealed that AFAP1-AS1 was significantly upregulated in TSCC specimens and had good clinical diagnostic value. The results of GSEA showed that peroxisome proliferator-activated receptor signaling pathway was significantly correlated with low expression of AFP1-AS1. Finally, the results of drug prediction indicated that the group with high AFAP1-AS1 expression was more sensitive to docetaxel, AZD4547, AZD7762 and nilotinib.

CONCLUSIONS

The upregulation of lncRNA AFAP1-AS1, which increases TSCC cell viability, migration, proliferation and invasion via the AFAP1-AS1/miR-133a-5p/ZIC2 axis, aids in the progression of TSCC.

Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.

Identification of a bile acid and bile salt metabolism-related lncRNA signature for predicting prognosis and treatment response in hepatocellular carcinoma

Bile acids and salts have been shown to play a role in liver carcinogenesis through DNA damage, inflammation, and tumor proliferation. However, the correlation between bile acid metabolism and hepatocellular carcinoma (HCC) prognosis remains unclear. This study aimed to identify a predictive signature of bile acid and bile salt metabolism-related long non-coding RNAs (lncRNAs) for HCC prognosis and treatment response. The study used HCC RNA-sequencing data and corresponding clinical and prognostic data from The Cancer Genome Atlas. A prognostic model consisting of five bile acid and bile salt metabolism-related lncRNAs was developed and evaluated in a training set, a validation set and an external set. The model demonstrated good performance in predicting HCC prognosis and was shown to be an independent biomarker for prognosis. Additionally, our study revealed a significant association between the signature and immune cell infiltration, as well as its predictive value for therapeutic responses to both immunotherapy and chemotherapy. Furthermore, three LncRNAs (LUCAT1, AL031985.3 and AC015908.3) expression levels in our signature were validated through qRT-PCR in a cohort of 50 pairs of HCC patient tumor samples and corresponding adjacent non-tumor samples, along with 10 samples of normal liver tissue adjacent to benign lesions. These findings suggest that this novel bile acid and bile salt metabolism-related lncRNA signature can independently predict the prognosis of patients with HCC and may be utilized as a potential predictor of response to treatment in this setting.

Targeting PPAR-gamma counteracts tumour adaptation to immune-checkpoint blockade in hepatocellular carcinoma

OBJECTIVE

Therapy-induced tumour microenvironment (TME) remodelling poses a major hurdle for cancer cure. As the majority of patients with hepatocellular carcinoma (HCC) exhibits primary or acquired resistance to antiprogrammed cell death (ligand)-1 (anti-PD-[L]1) therapies, we aimed to investigate the mechanisms underlying tumour adaptation to immune-checkpoint targeting.

DESIGN

Two immunotherapy-resistant HCC models were generated by serial orthotopic implantation of HCC cells through anti-PD-L1-treated syngeneic, immunocompetent mice and interrogated by single-cell RNA sequencing (scRNA-seq), genomic and immune profiling. Key signalling pathway was investigated by lentiviral-mediated knockdown and pharmacological inhibition, and further verified by scRNA-seq analysis of HCC tumour biopsies from a phase II trial of pembrolizumab (NCT03419481).

RESULTS

Anti-PD-L1-resistant tumours grew >10-fold larger than parental tumours in immunocompetent but not immunocompromised mice without overt genetic changes, which were accompanied by intratumoral accumulation of myeloid-derived suppressor cells (MDSC), cytotoxic to exhausted CD8+ T cell conversion and exclusion. Mechanistically, tumour cell-intrinsic upregulation of peroxisome proliferator-activated receptor-gamma (PPARγ) transcriptionally activated vascular endothelial growth factor-A (VEGF-A) production to drive MDSC expansion and CD8+ T cell dysfunction. A selective PPARγ antagonist triggered an immune suppressive-to-stimulatory TME conversion and resensitised tumours to anti-PD-L1 therapy in orthotopic and spontaneous HCC models. Importantly, 40% (6/15) of patients with HCC resistant to pembrolizumab exhibited tumorous PPARγ induction. Moreover, higher baseline PPARγ expression was associated with poorer survival of anti-PD-(L)1-treated patients in multiple cancer types.

CONCLUSION

We uncover an adaptive transcriptional programme by which tumour cells evade immune-checkpoint targeting via PPARγ/VEGF-A-mediated TME immunosuppression, thus providing a strategy for counteracting immunotherapeutic resistance in HCC.

Bioinformatic analysis highlights SNHG6 as a putative prognostic biomarker for kidney renal papillary cell carcinoma

PURPOSE

Kidney renal papillary cell carcinoma (KIRP) is a highly heterogeneous malignancy and current systemic therapeutic strategies are difficult to achieve a satisfactory outcome for advanced disease. Meanwhile, there is a lack of effective biomarkers to predict the prognosis of KIRP.

METHODS

Using TCGA, GTEx, UALCAN, TIMER, TIMER 2.0 and STRING databases, we analyzed the relationship of SNHG6 with KIRP subtypes, tumor-infiltrating immune cells and potential target mRNAs. Based on TCGA data, ROC curves, Kaplan-Meier survival analysis and COX regression analysis were performed to evaluate the diagnostic and prognostic value of SNHG6 in KIRP. Nomogram was used to predict 3- and 5-year disease-specific survival in KIRP patients. In addition, with the help of Genetic ontology and Gene set enrichment analysis, the biological processes and signalling pathways that SNHG6 may be involved in KIRP were initially explored.

RESULTS

In patients with KIRP, SNHG6 was significantly upregulated and associated with a more aggressive subtype (lymph node involvement, pathological stage IV, CIMP phenotype) and poor prognosis. The ROC curve showed good diagnostic efficacy (AUC value: 0.828) and the C-index of the Nomogram for predicting DSS at 3 and 5 years was 0.920 (0.898-0.941). In the immune microenvironment of KIRP, SNHG6 expression levels were negatively correlated with macrophage abundance and positively correlated with cancer-associated fibroblasts. Furthermore, SNHG6 may promote KIRP progression by regulating the expression of molecules such as AURKB, NDC80, UBE2C, NUF2, PTTG1, CENPH, SPC25, CDCA3, CENPM, BIRC5, TROAP, EZH2. Last, GSEA suggests that SNHG6 may be involved in the regulation of the PPAR signalling pathway and the SLIT/ROBO signalling pathway.

CONCLUSIONS

Our analysis suggests that a high SNHG6 expression status in KIRP is associated with a poorer prognosis for patients, and also elucidates some potential mechanisms contributing to this poorer outcome. This may provide new insights into the treatment and management of KIRP in the foreseeable future.

Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs

BACKGROUND

Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability.

METHODS

We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs.

RESULTS

Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer's disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015).

CONCLUSIONS

The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.

The prognostic role and metabolic function of GGPS1 in oral squamous cell carcinoma

Background: GGPS1(geranylgeranyl diphosphate synthase 1) is a member of the prenyltransferase family. Abnormal expression of GGPS1 can disrupt the balance between protein farnesylation and geranylgeranylation, thereby affecting a variety of cellular physiologic and pathological processes. However, it is still unknown how this gene could contribute to the prognosis of oral squamous cell carcinoma (OSCC). This study aimed to explore the prognostic role of GGPS1 in OSCC and its relationship with clinical features. Methods: The RNA-seq data and clinical data were obtained from TCGA. The survival analyses, Cox regression analyses, ROC curves, nomograms, calibration curves, and gene function enrichments were established by R software. Results: The results showed that the high expression of GGPS1 in OSCC is related to poor prognosis. At the same time, multivariate Cox regression analyses showed that GGPS1 could be an independent prognostic biomarker, and its gene expression level is closely related to the histological stage of cancer. GGPS1 may promote tumorigenesis because of its metabolic function. Conclusion: This study came to a conclusion that GGPS1, whose high expression has a significantly unfavorable meaning toward the prognosis of OSCC, can act as a novel independent biomarker for OSCC.

DNA methylation-induced ablation of miR-133a accelerates cancer aggressiveness in glioma through upregulating peroxisome proliferator-activated receptor γ

Emerging evidences suggest that miRNAs can be used as theranostic biomarkers for multiple cancers, including glioma. Thus, identification of novel miRNAs for glioma treatment and prognosis becomes necessary and urgent. Here, by analyzing miRNA expression profiles in the glioma and para-cancer tissues by miRNA microarray and verified by RT-PCR, we found that miR-133a was significantly downregulated in the cancerous tissues, and patients with low-expressed miR-133a levels predicted an unfavorable prognosis. The following functional experiments confirmed that overexpression of miR-133a restrained cell proliferation and colony formation abilities, and induced cell cycle arrest to restrain cancer progression in glioma cells. Then, the underlying mechanisms were uncovered, and the peroxisome proliferator-activated receptor γ (PPARγ, PPARG) was verified as the downstream target of miR-133a. Mechanistically, miR-133a negatively regulated PPARG expressions by binding to its 3' untranslated regions (3'UTR). The following rescuing experiments evidenced that miR-133a overexpression-induced anti-cancer effects in glioma cells were abrogated by upregulating PPARγ. Interestingly, we noticed that the promoter region of miR-133a was hypermethylated, and removal of DNA methylation by 5-Azacytidine (AZA) significantly increased the expression levels of miR-133a in glioma cells. Taken together, we concluded that DNA-methylation-induced miR-133a silence contributed to cancer progression in glioma through upregulating PPARγ, and firstly identified the DNA-methylation-regulated miR-133a/PPARG axis as the novel indicators for glioma treatment and prognosis.

Key Genes Are Associated with the Prognosis of Glioma, and Melittin Can Regulate the Expression of These Genes in Glioma U87 Cells

Glioma is the most common primary tumor of the central nervous system. Currently, there is no effective treatment for glioma. Melittin (MT) is the main component of bee venom, which was found to have therapeutic effects on a variety of tumors. In this study, we explored the relationship between key genes regulated by MT and the prognosis of glioma. In cultured glioma U87 and U251 cells, MT inhibited cell proliferation and induces cell apoptosis in a time- and concentration-dependent manner. RNA-seq revealed that MT upregulated 11 genes and downregulated 37 genes. These genes are mainly enriched in cell membrane signaling pathways, such as surface membrane, membrane-enclosed organelles, integral component of membrane, PPAR signaling pathway, and voltage-gated potassium channel. PPI network analysis and literature analysis of 48 genes were performed, and 8 key genes were identified, and these key genes were closely associated with clinical prognosis. Overexpression of PCDH18, PPL, DEPP1, VASN, KCNE4, MYBPH, and C5AR2 genes or low expression of MARCH4 gene in glioma patients was associated with poor survival. qPCR confirmed that MT can regulate the expression of these genes in glioma U87 cells. This study indicated that MT significantly inhibited the growth and regulated the expression of PCDH18, C5AR2, VASN, DEPP1, MYBPH, KCNE4, PPL, and MARCH4 genes in glioma U87 cells in vitro. These genes are closely related to the prognosis of patients with glioma and can be used as independent prognostic factors in patients with glioma. MT is a potential drug for the treatment of glioma.

PPARα at the crossroad of metabolic-immune regulation in cancer

Rewiring metabolism to sustain cell growth, division, and survival is the most prominent feature of cancer cells. In particular, dysregulated lipid metabolism in cancer has received accumulating interest, since lipid molecules serve as cell membrane structure components, secondary signaling messengers, and energy sources. Given the critical role of immune cells in host defense against cancer, recent studies have revealed that immune cells compete for nutrients with cancer cells in the tumor microenvironment and accordingly develop adaptive metabolic strategies for survival at the expense of compromised immune functions. Among these strategies, lipid metabolism reprogramming toward fatty acid oxidation is closely related to the immunosuppressive phenotype of tumor-infiltrated immune cells, including macrophages and dendritic cells. Therefore, it is important to understand the lipid-mediated crosstalk between cancer cells and immune cells in the tumor microenvironment. Peroxisome proliferator-activated receptors (PPARs) consist of a nuclear receptor family for lipid sensing, and one of the family members PPARα is responsible for fatty acid oxidation, energy homeostasis, and regulation of immune cell functions. In this review, we discuss the emerging role of PPARα-associated metabolic-immune regulation in tumor-infiltrated immune cells, and key metabolic events and pathways involved, as well as their influences on antitumor immunity.

Combined transcriptome and proteome analysis of yak PASMCs under hypoxic and normoxic conditions

Background

Yaks are animals that have lived in plateau environments for generations. Yaks can adapt to the hypoxic plateau environment and also pass this adaptability on to the next generation. The lungs are the most important respiratory organs for mammals to adapt to their environment. Pulmonary artery smooth muscle cells play an important role in vascular remodeling under hypoxia, but the genetic mechanism underpinning the yak's ability to adapt to challenging plateau conditions is still unknown.

Methods

A tandem mass tag (TMT) proteomics study together with an RNA-seq transcriptome analysis were carried out on pulmonary artery smooth muscle cells (PASMCs) that had been grown for 72 hours in both normoxic (20% O2) and hypoxic (1% O2) environments. RNA and TP (total protein) were collected from the hypoxic and normoxic groups for RNA-seq transcriptome sequencing and TMT marker protein quantification, and RT-qPCR validation was performed.

Results

A total of 17,711 genes and 6,859 proteins were identified. Further, 5,969 differentially expressed genes (DEGs) and 531 differentially expressed proteins (DEPs) were identified in the comparison group, including 2,924 and 186 upregulated genes and proteins and 3,045 and 345 down-regulated genes and proteins, respectively. The transcriptomic and proteomic analyses revealed that 109 DEGs and DEPs were highly positively correlated, with 77 genes showing the same expression trend. Nine overlapping genes were identified in the HIF-1 signaling pathway, glycolysis / gluconeogenesis, central carbon metabolism in cancer, PPAR signaling pathway, AMPK signaling pathway, and cholesterol metabolism (PGAM1, PGK1, TPI1, HMOX1, IGF1R, OLR1, SCD, FABP4 and LDLR), suggesting that these differentially expressed genes and protein functional classifications are related to the hypoxia-adaptive pathways. Overall, our study offers abundant data for further analysis of the molecular mechanisms in yak PASMCs and their adaptability to different oxygen concentrations.

A novel cuproptosis-related lncRNA signature predicts the prognosis and immune landscape in bladder cancer

Background

Bladder cancer (BLCA) is one of the deadliest diseases, with over 550,000 new cases and 170,000 deaths globally every year. Cuproptosis is a copper-triggered programmed cell death and is associated with the prognosis and immune response of various cancers. Long non-coding RNA (lncRNA) could serve as a prognostic biomarker and is involved in the progression of BLCA.

Methods

The gene expression profile of cuproptosis-related lncRNAs was analyzed by using data from The Cancer Genome Atlas. Cox regression analysis and least absolute shrinkage and selection operator analysis were performed to construct a cuproptosis-related lncRNA prognostic signature. The predictive performance of this signature was verified by ROC curves and a nomogram. We also explored the difference in immune-related activity, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE), and drug sensitivity between the high- and low-risk groups.

Results

We successfully constructed a cuproptosis-related lncRNA prognostic signature for BLCA including eight lncRNAs (RNF139-AS1, LINC00996, NR2F2-AS1, AL590428.1, SEC24B-AS1, AC006566.1, UBE2Q1-AS1, and AL021978.1). Multivariate Cox analysis suggested that age, clinical stage, and risk score were the independent risk factors for predicting prognosis of BLCA. Further analysis revealed that this signature not only had higher diagnostic efficiency compared to other clinical features but also had a good performance in predicting the 1-year, 3-year, and 5-year overall survival rate in BLCA. Notably, BLCA patients with a low risk score seemed to be associated with an inflamed tumor immune microenvironment and had a higher TMB level than those with a high risk score. In addition, patients with a high risk score had a higher TIDE score and a higher half maximal inhibitory concentration value of many therapeutic drugs than those with a low risk score.

Conclusion

We identified a novel cuproptosis-related lncRNA signature that could predict the prognosis and immune landscape of BLCA.

Targeting lipid metabolism reprogramming of immunocytes in response to the tumor microenvironment stressor: A potential approach for tumor therapy

The tumor microenvironment (TME) has become a major research focus in recent years. The TME differs from the normal extracellular environment in parameters such as nutrient supply, pH value, oxygen content, and metabolite abundance. Such changes may promote the initiation, growth, invasion, and metastasis of tumor cells, in addition to causing the malfunction of tumor-infiltrating immunocytes. As the neoplasm develops and nutrients become scarce, tumor cells transform their metabolic patterns by reprogramming glucose, lipid, and amino acid metabolism in response to various environmental stressors. Research on carcinoma metabolism reprogramming suggests that like tumor cells, immunocytes also switch their metabolic pathways, named “immunometabolism”, a phenomenon that has drawn increasing attention in the academic community. In this review, we focus on the recent progress in the study of lipid metabolism reprogramming in immunocytes within the TME and highlight the potential target molecules, pathways, and genes implicated. In addition, we discuss hypoxia, one of the vital altered components of the TME that partially contribute to the initiation of abnormal lipid metabolism in immune cells. Finally, we present the current immunotherapies that orchestrate a potent antitumor immune response by mediating the lipid metabolism of immunocytes, highlight the lipid metabolism reprogramming capacity of various immunocytes in the TME, and propose promising new strategies for use in cancer therapy.

m6A-induced lncDBET promotes the malignant progression of bladder cancer through FABP5-mediated lipid metabolism

The limited effect of adjuvant therapy for advanced bladder cancer (BCa) leads to a poor prognosis. Increasing evidence has shown that RNA N6-methyladenosine (m6A) modification plays important functional roles in tumorigenesis. Nevertheless, the role and mechanism of m6A-modified noncoding RNAs (ncRNAs) in BCa remain largely unknown. Methods: RT-PCR, western blotting and ONCOMINE dataset were used to determine the dominant m6A-related enzyme in BCa. M6A-lncRNA epitranscriptomic microarray was used to screen candidate targets of METTL14. RT-PCR, MeRIP and TCGA dataset were carried out to confirm the downstream target of METTL14. CHIRP/MS was conducted to identify the candidate proteins binding to lncDBET. RT-PCR, western blotting, RIP and KEGG analysis were used to confirm the target of lncDBET. The levels of METTL14, lncDBET and FABP5 were tested in vitro and in vivo. CCK-8, EdU, transwell and flow cytometry assays were performed to determine the oncogenic function of METTL14, lncDBET and FABP5, and their regulatory networks. Results: We identified that the m6A level of total RNA was elevated and that METTL14 was the dominant m6A-related enzyme in BCa. m6A modification mediated by METTL14 promoted the malignant progression of BCa by promoting the expression of lncDBET. Upregulated lncDBET activated the PPAR signalling pathway to promote the lipid metabolism of cancer cells through direct interaction with FABP5, thus promoting the malignant progression of BCa in vitro and in vivo. Conclusions: Our study establishes METTL14/lncDBET/FABP5 as a critical oncogenic axis in BCa.

Comprehensive metabolomics study on the pathogenesis of anaplastic astrocytoma via UPLC-Q/TOF-MS

Anaplastic astrocytoma (AA) is a malignant carcinoma whose pathogenesis remains to be fully elucidated. System biology techniques have been widely used to clarify the mechanism of diseases from a systematic perspective. The present study aimed to explore the pathogenesis and novel potential biomarkers for the diagnosis of AA according to metabolic differences. Patients with AA (n = 12) and healthy controls (n = 15) were recruited. Serum was assayed with untargeted ultraperformance liquid chromatography-quadrupole/time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) metabolomic techniques. The data were further evaluated using multivariate analysis and bioinformatic methods based on the KEGG database to determine the distinct metabolites and perturbed pathways. Principal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) identified the significance of the distinct metabolic pattern between patients with AA and healthy controls (P < .001) in both ESI modes. Permutation testing confirmed the validity of the OPLS-DA model (permutation = 200, Q2 < 0.5). In total, 24 differentiated metabolites and 5 metabolic pathways, including sphingolipid, glycerophospholipid, caffeine, linoleic acid, and porphyrin metabolism, were identified based on the OPLS-DA model. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide were recognized as potential biomarkers with excellent sensitivity and specificity (area under the curve > 98%). These findings indicate that the perturbed metabolic pattern related to immune regulation and cellular signal transduction is associated with the pathogenesis of AA. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide could be used as biomarkers of AA in future clinical practice. This study provides a therapeutic basis for further studies on the mechanism and precise clinical diagnosis of AA.

Development and Validation of a Novel PPAR Signaling Pathway-Related Predictive Model to Predict Prognosis in Breast Cancer

This study is aimed at exploring the potential mechanism of the PPAR signaling pathway in breast cancer (BRCA) and constructing a novel prognostic-related risk model. We used various bioinformatics methods and databases to complete our exploration in this research. Based on TCGA database, we use multiple extension packages based on the R language for data conversion, processing, and statistics. We use LASSO regression analysis to establish a prognostic-related risk model in BRCA. And we combined the data of multiple online websites, including GEPIA, ImmuCellAI, TIMER, GDSC, and the Human Protein Atlas database to conduct a more in-depth exploration of the risk model. Based on the mRNA data in TCGA database, we conducted a preliminary screening of genes related to the PPAR signaling pathway through univariate Cox analysis, then used LASSO regression analysis to conduct a second screening, and successfully established a risk model consisting of ten genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide breast cancer patients into high- and low-risk groups with significant prognostic differences (P = 1.92e - 05) based on this risk model. Combined with the clinical data in TCGA database, there is a correlation between the risk model and the patient's N, T, gender, and fustat. The results of multivariate Cox regression show that the risk score of this risk model can be used as an independent risk factor for BRCA patients. In particular, we draw a nomogram that can predict the 5-, 7-, and 10-year survival rates of BRCA patients. Subsequently, we conducted a series of pancancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to investigate drug sensitivity. Finally, to gain insight into the predictive value and protein expression of these risk model genes in breast cancer, we used GEO and HPA databases for validation. This study provides valuable clues for future research on the PPAR signaling pathway in BRCA.

Development and Validation of a Novel PPAR Signaling Pathway-Related Predictive Model to Predict Prognosis in Breast Cancer

This study is aimed at exploring the potential mechanism of the PPAR signaling pathway in breast cancer (BRCA) and constructing a novel prognostic-related risk model. We used various bioinformatics methods and databases to complete our exploration in this research. Based on TCGA database, we use multiple extension packages based on the R language for data conversion, processing, and statistics. We use LASSO regression analysis to establish a prognostic-related risk model in BRCA. And we combined the data of multiple online websites, including GEPIA, ImmuCellAI, TIMER, GDSC, and the Human Protein Atlas database to conduct a more in-depth exploration of the risk model. Based on the mRNA data in TCGA database, we conducted a preliminary screening of genes related to the PPAR signaling pathway through univariate Cox analysis, then used LASSO regression analysis to conduct a second screening, and successfully established a risk model consisting of ten genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide breast cancer patients into high- and low-risk groups with significant prognostic differences (P = 1.92e - 05) based on this risk model. Combined with the clinical data in TCGA database, there is a correlation between the risk model and the patient's N, T, gender, and fustat. The results of multivariate Cox regression show that the risk score of this risk model can be used as an independent risk factor for BRCA patients. In particular, we draw a nomogram that can predict the 5-, 7-, and 10-year survival rates of BRCA patients. Subsequently, we conducted a series of pancancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to investigate drug sensitivity. Finally, to gain insight into the predictive value and protein expression of these risk model genes in breast cancer, we used GEO and HPA databases for validation. This study provides valuable clues for future research on the PPAR signaling pathway in BRCA.

Establishment of prognostic risk model and drug sensitivity based on prognostic related genes of esophageal cancer

At present, the treatment of esophageal cancer (EC) is mainly surgical and drug treatment. However, due to drug resistance, these therapies can not effectively improve the prognosis of patients with the EC. Therefore, a multigene prognostic risk scoring system was constructed by bioinformatics analysis method to provide a theoretical basis for the prognosis and treatment decision of EC. The gene expression profiles and clinical data of esophageal cancer patients were gathered from the Cancer Genome Atlas TCGA database, and the differentially expressed genes (DEGs) were screened by R software. Genes with prognostic value were screened by Kaplan Meier analysis, followed by functional enrichment analysis. A cox regression model was used to construct the prognostic risk score model of DEGs. ROC curve and survival curve were utilized to evaluate the performance of the model. Univariate and multivariate Cox regression analysis was used to evaluate whether the model has an independent prognostic value. Network tool mirdip was used to find miRNAs that may regulate risk genes, and Cytoscape software was used to construct gene miRNA regulatory network. GSCA platform is used to analyze the relationship between gene expression and drug sensitivity. 41 DEGs related to prognosis were pre-liminarily screened by survival analysis. A prognostic risk scoring model composed of 8 DEGs (APOA2, COX6A2, CLCNKB, BHLHA15, HIST1H1E, FABP3, UBE2C and ERO1B) was built by Cox regression analysis. In this model, the prognosis of the high-risk score group was poor (P < 0.001). The ROC curve showed that (AUC = 0.862) the model had a good performance in predicting prognosis. In Cox regression analysis, the comprehensive risk score can be employed as an independent prognostic factor of the EC. HIST1H1E, UBE2C and ERO1B interacted with differentially expressed miRNAs. High expression of HIST1H1E was resistant to trametinib, selumetinib, RDEA119, docetaxel and 17-AAG, High expression of UBE2C was resistant to masitinib, and Low expression of ERO1B made the EC more sensitive to FK866. We constructed an EC risk score model composed of 8 DEGs and gene resistance analysis, which can provide reference for prognosis prediction, diagnosis and treatment of the EC patients.

The Expression of PPAR Pathway-Related Genes Can Better Predict the Prognosis of Patients with Colon Adenocarcinoma

The postoperative survival time and quality of life of patients with colon adenocarcinoma (COAD) varies widely. In order to make accurate decisions after surgery, clinicians need to distinguish patients with different prognostic trends. However, we still lack effective methods to predict the prognosis of COAD patients. Accumulated evidences indicated that the inhibition of peroxisome proliferator-activated receptors (PPARs) and a portion of their target genes were associated with the development of COAD. Our study found that the expression of several PPAR pathway-related genes were linked to the prognosis of COAD patients. Therefore, we developed a scoring system (named PPAR-Riskscore) that can predict patients' outcomes. PPAR-Riskscore was constructed by univariate Cox regression based on the expression of 4 genes (NR1D1, ILK, TNFRSF1A, and REN) in tumor tissues. Compared to typical TNM grading systems, PPAR-Riskscore has better predictive accuracy and sensitivity. The reliability of the system was tested on six external validation datasets. Furthermore, PPAR-Riskscore was able to evaluate the immune cell infiltration and chemotherapy sensitivity of each tumor sample. We also combined PPAR-Riskscore and clinical features to create a nomogram with greater clinical utility. The nomogram can help clinicians make precise treatment decisions regarding the possible long-term survival of patients after surgery.

Development and verification of a hypoxia- and immune-associated prognosis signature for esophageal squamous cell carcinoma

BACKGROUND

Esophageal cancer is one of the most common gastrointestinal malignancies worldwide, with high morbidity and mortality in China. The clinical importance of the interaction between hypoxia and immune status in the tumor microenvironment has been established in esophageal squamous cell carcinoma (ESCC). This study aims to develop a new hypoxia- and immune-based gene signature to predict the survival of ESCC patients.

METHODS

The RNA-sequencing and clinical data of 173 cases of ESCC and 271 normal tissues were obtained from The Cancer Genome Atlas (TCGA) data portal and the Genotype-Tissue Expression (GTEx) database. Hypoxia-related genes (HRGs) and immune-related genes (IRGs) were retrieved from publicly shared data. Differentially expressed gene (DEG) analyses were carried out by the DESeq2 method using the edgeR package in R. Based on the intersection of the DEGs and HRGs/IRGs, differentially expressed HRGs (DEHRGs) and differentially expressed IRGs (DEIRGs) were obtained. DEHRGs and DEIRGs associated with prognosis were evaluated using univariate Cox proportional hazards analysis. A prognostic risk score model was constructed according to the genes acquired through Cox regression. Univariate analysis and Cox proportional hazards analysis were used to determine the independent prognostic factors related to prognosis. A nomogram was developed to predict the 1-, 2-, and 3-year overall survival (OS) probability.

RESULTS

A total of 73 intersecting genes were obtained as DEHRGs and a total of 548 intersecting genes were obtained as DEIRGs. The risk score was established using 8 genes (FABP7, TLR1, SYTL1, APLN, OSM, EGFR, IL17RD, MYH9) acquired from univariate Cox analysis. Based on this 8-gene-based risk score, a risk prognosis classifier was constructed to classify the samples into high- and low-risk groups according to the median risk score. The nomogram model was constructed to predict the OS of ESCC patients.

CONCLUSIONS

The hypoxia- and immune-based gene signature might serve as a prognostic classifier for clinical decision-making regarding individualized management, follow-up plans, and treatment strategies for ESCC patients.

Advantages and drawbacks of dexamethasone in glioblastoma multiforme

The most widespread, malignant, and deadliest type of glial tumor is glioblastoma multiforme (GBM). Despite radiation, chemotherapy, and radical surgery, the median survival of afflicted individuals is about 12 months. Unfortunately, existing therapeutic interventions are abysmal. Dexamethasone (Dex), a synthetic glucocorticoid, has been used for many years to treat brain edema and inflammation caused by GBM. Several investigations have recently shown that Dex also exerts antitumoral effects against GBM. On the other hand, more recent disputed findings have questioned the long-held dogma of Dex treatment for GBM. Unfortunately, steroids are associated with various undesirable side effects, including severe immunosuppression and metabolic changes like hyperglycemia, which may impair the survival of GBM patients. Current ideas and concerns about Dex's effects on GBM cerebral edema, cell proliferation, migration, and its clinical outcomes were investigated in this study.

How cancer cells remodel lipid metabolism: strategies targeting transcription factors

Reprogramming of lipid metabolism has received increasing recognition as a hallmark of cancer cells because lipid dysregulation and the alteration of related enzyme profiles are closely correlated with oncogenic signals and malignant phenotypes, such as metastasis and therapeutic resistance. In this review, we describe recent findings that support the importance of lipids, as well as the transcription factors involved in cancer lipid metabolism. With recent advances in transcription factor analysis, including computer-modeling techniques, transcription factors are emerging as central players in cancer biology. Considering the limited number and the crucial role of transcription factors associated with lipid rewiring in cancers, transcription factor targeting is a promising potential strategy for cancer therapy.

Analysis of the Expression and Prognostic Potential of a Novel Metabolic Regulator ANGPTL8/Betatrophin in Human Cancers

The angiopoietin-like protein (ANGPTL) family members, except for the novel atypical member ANGPTL8/betatrophin, have been reported to participate in angiogenesis, inflammation and cancer. ANGPTL8/betatrophin is a metabolic regulator that is involved in lipid metabolism and glucose homeostasis. However, little is known about the expression and prognostic value of ANGPTL8/betatrophin in human cancers. In this study, we first conducted detailed analyses of ANGPTL8/betatrophin expression in cancer/normal samples via the Human Protein Atlas (HPA), Gene Expression Profiling Interactive Analysis (GEPIA), DriverDBv3, ENCORI and UALCAN databases. ANGPTL8/betatrophin showed high tissue specificity (enriched in the liver) and cell-type specificity (enriched in HepG2 and MCF7 cell lines). More than one databases demonstrated that the gene expression of ANGPTL8/betatrophin was significantly lower in cholangiocarcinoma (CHOL), breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), uterine corpus endometrial carcinoma (UCEC), and significantly higher in kidney renal clear cell carcinoma (KIRC) compared with that in normal samples. However, the protein expression of ANGPTL8/betatrophin displayed opposite results in clear cell renal cell carcinoma (ccRCC)/KIRC. Based on the expression profiles, the prognostic value was evaluated with the GEPIA, DriverDBv3, Kaplan Meier plotter and ENCORI databases. Two or more databases demonstrated that ANGPTL8/betatrophin significantly affected the survival of KIRC, uterine corpus endometrial carcinoma (UCEC), pheochromocytoma and paraganglioma (PCPG) and sarcoma (SARC); patients with PCPG and SARC may benifit from high ANGPTL8/betatrophin expression while high ANGPTL8/betatrophin expression was associated with poor prognosis in KIRC and UCEC. Functional analyses with the GeneMANIA, Metascape and STRING databases suggested that ANGPTL8/betatrophin was mainly involved in lipid homeostasis, especially triglyceride and cholesterol metabolism; glucose homeostasis, especially insulin resistance; AMPK signaling pathway; PI3K/Akt signaling pathway; PPAR signaling pathway; mTOR signaling pathway; HIF-1 signaling pathway; autophagy; regulation of inflammatory response. ANGPTL8/betatrophin may be a promising prognostic biomarker and therapeutic target, thus providing evidence to support further exploration of its role in defined human cancers.

Application of ensemble clustering and survival tree analysis for identifying prognostic clinicogenomic features in patients with colorectal cancer from the 100,000 Genomes Project

OBJECTIVE

The objective of this study was to employ ensemble clustering and tree-based risk model approaches to identify interactions between clinicogenomic features for colorectal cancer using the 100,000 Genomes Project.

RESULTS

Among the 2211 patients with colorectal cancer (mean age of diagnosis: 67.7; 59.7% male), 16.3%, 36.3%, 39.0% and 8.4% had stage 1, 2, 3 and 4 cancers, respectively. Almost every patient had surgery (99.7%), 47.4% had chemotherapy, 7.6% had radiotherapy and 1.4% had immunotherapy. On average, tumour mutational burden (TMB) was 18 mutations/Mb and 34.4%, 31.3% and 25.7% of patients had structural or copy number mutations in KRAS, BRAF and NRAS, respectively. In the fully adjusted Cox model, patients with advanced cancer [stage 3 hazard ratio (HR)  =  3.2; p  <  0.001; stage 4 HR  =  10.2; p  <  0.001] and those who had immunotherapy (HR  =  1.8; p  <  0.04) or radiotherapy (HR  =  1.5; p  <  0.02) treatment had a higher risk of dying. The ensemble clustering approach generated four distinct clusters where patients in cluster 2 had the best survival outcomes (1-year: 98.7%; 2-year: 96.7%; 3-year: 93.0%) while patients in cluster 3 (1-year: 87.9; 2-year: 70.0%; 3-year: 53.1%) had the worst outcomes. Kaplan-Meier analysis and log rank test revealed that the clusters were separated into distinct prognostic groups (p  <  0.0001). Survival tree or recursive partitioning analyses were performed to further explore risk groups within each cluster. Among patients in cluster 2, for example, interactions between cancer stage, grade, radiotherapy, TMB, BRAF mutation status were identified. Patients with stage 4 cancer and TMB  ≥  1.6 mutations/Mb had 4 times higher risk of dying relative to the baseline hazard in that cluster.

Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis

INTRODUCTION/OBJECTIVES

Rheumatoid arthritis (RA) and osteoarthritis (OA) are two common joint diseases with similar clinical manifestations. Our study aimed to identify differential gene biomarkers in the synovial tissue between RA and OA using bioinformatics analysis and validation.

METHOD

GSE36700, GSE1919, GSE12021, GSE55235, GSE55584, and GSE55457 datasets were downloaded from the Gene Expression Omnibus database. A total of 57 RA samples and 46 OA samples were included. The differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also performed. Protein-protein interaction (PPI) network of DEGs and the hub genes were constructed and visualized via Search Tool for the Retrieval of Interacting Genes/Proteins, Cytoscape, and R. Selected hub genes were validated via reverse transcription-polymerase chain reaction.

RESULTS

A total of 41 DEGs were identified. GO functional enrichment analysis showed that DEGs were enriched in immune response, signal transduction, regulation of immune response for biological process, in plasma membrane and extracellular region for cell component, and antigen binding and serine-type endopeptidase activity for molecular function. KEGG pathway analysis showed that DEGs were enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. PPI network analysis established 70 nodes and 120 edges and 15 hub genes were identified. The expression of CXCL13, CXCL10, and ADIPOQ was statistically different between RA and OA synovial tissue.

CONCLUSION

Differential expression of CXCL13, CXCL10, and ADIPOQ between RA and OA synovial tissue may provide new insights for understanding the RA development and difference between RA and OA. Key Points • Bioinformatics analysis was used to identify the differentially expressed genes in the synovial tissue between rheumatoid arthritis and osteoarthritis. • CXCL13, CXCL10, and ADIPOQ might provide new insight for understanding the differences between RA and OA.

Traditional Chinese Medicine Xihuang Wan Inhibited Lewis Lung Carcinoma in a Syngeneic Model, Equivalent to Cytotoxic Chemotherapy, by Altering Multiple Signaling Pathways

BACKGROUND/AIM

Xihuang Wan (XHW), a traditional Chinese medicine (TCM), has been used in China for a variety of cancers including lung cancer. The present study evaluated the efficacy of XHW on a Lewis lung mouse model and explored the potential mechanism via transcriptomics.

MATERIALS AND METHODS

The mice were randomized into 6 groups: 1) untreated control (n=10); 2) low-dose XHW; 3) medium-dose XHW; 4) high-dose XHW; 5) cisplatin; and 6) untreated blank (n=4). Lewis lung carcinoma (LLC) cells were injected subcutaneously except for the 4 mice in the blank group. The body weight and tumor length and width were measured every 3 days. RNA-sequencing was performed on tumors in the high-dose XHW group and the control group.

RESULTS

XHW inhibited the growth of LLC in a syngeneic mouse model, without toxicity, with equivalent efficacy to cisplatin. RNA-sequencing demonstrated that many signaling pathways were involved in XHW-mediated inhibition of LLC, including tumor necrosis factor, estrogen, cyclic guanosine 3', 5'-monophosphate-protein kinase G, apelin and the peroxisome proliferator-activated receptor signaling pathways.

CONCLUSION

XHW inhibited LLC carcinoma through different pathways and shows clinical promise for patients who cannot tolerate platinum-based drugs.

Crohn's disease-associated ATG16L1 T300A genotype is associated with improved survival in gastric cancer

BACKGROUND

A non-synonymous single nucleotide polymorphism of the ATG16L1 gene, T300A, is a major Crohn's disease (CD) susceptibility allele, and is known to be associated with increased apoptosis induction in the small intestinal crypt base in CD subjects and mouse models. We hypothesized that ATG16L1 T300A genotype also correlates with increased tumor apoptosis and therefore could lead to superior clinical outcome in cancer subjects.

METHODS

T300A genotyping by Taqman assay was performed for gastric carcinoma subjects who underwent resection from two academic medical centers. Transcriptomic analysis was performed by RNA-seq on formalin-fixed paraffin-embedded cancerous tissue. Tumor apoptosis and autophagy were determined by cleaved caspase-3 and p62 immunohistochemistry, respectively. The subjects' genotypes were correlated with demographics, various histopathologic features, transcriptome, and clinical outcome.

FINDINGS

Of the 220 genotyped subjects, 163 (74%) subjects carried the T300A allele(s), including 55 (25%) homozygous and 108 (49%) heterozygous subjects. The T300A/T300A subjects had superior overall survival than the other groups. Their tumors were associated with increased CD-like lymphoid aggregates and increased tumor apoptosis without concurrent increase in tumor mitosis or defective autophagy. Transcriptomic analysis showed upregulation of WNT/β-catenin signaling and downregulation of PPAR, EGFR, and inflammatory chemokine pathways in tumors of T300A/T300A subjects.

INTERPRETATION

Gastric carcinoma of subjects with the T300A/T300A genotype is associated with repressed EGFR and PPAR pathways, increased tumor apoptosis, and improved overall survival. Genotyping gastric cancer subjects may provide additional insight for clinical stratification.

Identification of hub genes associated with obesity-induced hepatocellular carcinoma risk based on integrated bioinformatics analysis

Obesity, which has become one of the biggest public health problems of the twenty-first century, accompanies many chronic conditions, including cancer. On the other hand, liver cancer, which is known to be associated with obesity, is considered another serious threat to public health. However, the underlying drivers of the development of obesity-associated hepatocellular carcinoma (HCC) remain blurry. The current study attempted to identify the key genes and pathways in the obesity-induced development of HCC using integrated bioinformatics analyses. Obesity and HCC-associated gene expression datasets were downloaded from Gene Expression Omnibus (GEO) and analyzed to identify overlapping differentially expressed genes (DEGs) and hub genes. The prognostic potentials, survival analysis, and expression levels of hub genes were further assessed. Moreover, the correlation between hub genes and the immune cells infiltration was analyzed. The findings of this research revealed that both mRNA and protein expression levels of the four hub genes (IGF1, ACADL, CYP2C9, and G6PD) involved in many important metabolic pathways are remarkably altered in both obese individuals and patients with HCC. The results demonstrated that these dysregulated genes in both obesity and HCC may serve as considerable targets for the prevention and treatment of HCC development in obese individuals.

Functional Assessment of Four Novel Immune-Related Biomarkers in the Pathogenesis of Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma whose pathogenesis is not well understood. We aimed at identifying novel immune-related biomarkers that could be valuable in the diagnosis and prognosis of ccRCC.

Methods

The Robust Rank Aggregation (RRA) method was used to integrate differently expressed genes (DEGs) of 7 Gene Expression Omnibus (GEO) datasets and obtain robust DEGs. Weighted gene co-expression network analyses (WGCNA) were performed to identify hub genes associated with clinical traits in The Cancer Genome Atlas (TCGA) database. Comprehensive bioinformatic analyses were used to explore the role of hub genes in ccRCC.

Results

Four hub genes IFI16, LMNB1, RHBDF2 and TACC3 were screened by the RRA method and WGCNA. These genes were found to be up-regulated in ccRCC, an upregulation that could be due to their associations with late TNM stages and tumor grades. The Receiver Operating Characteristic (ROC) curve and Kaplan-Meier survival analysis showed that the four hub genes had great diagnostic and prognostic values for ccRCC, while Gene Set Enrichment Analysis (GSEA) showed that they were involved in immune signaling pathways. They were also found to be closely associated with multiple tumor-infiltrating lymphocytes and critical immune checkpoint expressions. The results of Quantitative Real-time PCR (qRT-PCR) and immunohistochemical staining (IHC) analysis were consistent with bioinformatics analysis results.

Conclusion

The four hub genes were shown to have great diagnostic and prognostic values and played key roles in the tumor microenvironment of ccRCC.

Identification of a Potential PPAR-Related Multigene Signature Predicting Prognosis of Patients with Hepatocellular Carcinoma

Peroxisome proliferator-activated receptors (PPARs) and part of their target genes have been reported to be related to the progression of hepatocellular carcinoma (HCC). The prognosis of HCC is not optimistic, and more accurate prognostic markers are needed. This study focused on discovering potential prognostic markers from the PPAR-related gene set. The mRNA data and clinical information of HCC were collected from TCGA and GEO platforms. Univariate Cox and lasso Cox regression analyses were used to screen prognostic genes of HCC. Three genes (MMP1, HMGCS2, and SLC27A5) involved in the PPAR signaling pathway were selected as the prognostic signature of HCC. A formula was established based on the expression values and multivariate Cox regression coefficients of selected genes, that was, risk score = 0.1488∗expression value of MMP1 + (−0.0393)∗expression value of HMGCS2 + (−0.0479)∗expression value of SLC27A5. The prognostic ability of the three-gene signature was assessed in the TCGA HCC dataset and verified in three GEO sets (GSE14520, GSE36376, and GSE76427). The results showed that the risk score based on our signature was a risk factor with a HR (hazard ratio) of 2.72 (95%CI (Confidence Interval) = 1.87 ~ 3.95, p < 0.001) for HCC survival. The signature could significantly (p < 0.0001) distinguish high-risk and low-risk patients with poor prognosis for HCC. In addition, we further explored the independence and applicability of the signature with other clinical indicators through multivariate Cox analysis (p < 0.001) and nomogram analysis (C‐index = 0.709). The above results indicate that the combination of MMP1, HMGCS2, and SLC27A5 selected from the PPAR signaling pathway could effectively, independently, and applicatively predict the prognosis of HCC. Our research provided new insights to the prognosis of HCC.

Identification and validation of potential mRNA- microRNA- long-noncoding RNA (mRNA-miRNA-lncRNA) prognostic signature for cervical cancer

Cervical cancer is one of the most common causes of cancer deaths in women due to poor prognosis and high mortality rates. A novel mRNA-miRNA-lncRNA signature linked to prognosis of cervical cancer is needed to help clinicians judge the prognosis of individual patients more accurately. On the basis of GEO datasets, a total of 161 upregulated and 242 downregulated DE-mRNAs were identified firstly. Among them, eight potential biomarkers were found to have prognostic values with cervical cancer and miRNAs-lncRNAs related to these biomarkers were then analyzed to create mRNA-miRNA-lncRNA networks in cervical cancer. Moreover, in vitro experiments such as qRT-PCR, western blot and Edu assays were also performed to validate these promising targets. On the basis of these findings, a total of eight mRNA-miRNA-lncRNA subnetworks were finally established as a novel mRNA-miRNA-lncRNA signature and independent prognostic indicator of clinically relevant parameters by ROC analysis, univariate and multivariate Cox regression. Since some work of validation was done, it is believed that this mRNA-miRNA-lncRNA prognostic signature may be applied as a potential clinical judgment to estimate the prognosis of cervical cancer.

Large-Scale Proteomic Analysis of Follicular Lymphoma Reveals Extensive Remodeling of Cell Adhesion Pathway and Identifies Hub Proteins Related to the Lymphomagenesis

Simple Summary

Follicular lymphoma represents the major subtype of indolent B-cell non-Hodgkin lymphomas, ranging from about 20 to 30% of all B-NHLs cases in western countries. Yet, the global proteome profile of follicular lymphoma remains largely undocumented; thus, we aimed to employ for the first time a comprehensive proteomic analysis to outline its molecular landscape. A total of 15 lymphoma fine-needle aspiration biopsy samples and 14 controls were evaluated by label-free quantitative proteomics. Among the 7673 proteins identified in our dataset, 1186 proteins were differentially expressed between lymphoma and control samples. Importantly, dysregulated proteins were enriched in biological processes such as B-cell receptor signaling pathway, cellular adhesion molecules pathway, or membrane trafficking. Additionally, we identified several novel hub proteins related to lymphomagenesis. To summarize, we have determined the molecular characteristics of follicular lymphoma and discovered proteins which may hold potential for biomarkers or therapeutic targets.

Abstract

Follicular lymphoma (FL) represents the major subtype of indolent B-cell non-Hodgkin lymphomas (B-NHLs) and results from the malignant transformation of mature B-cells in lymphoid organs. Although gene expression and genomic studies have identified multiple disease driving gene aberrations, only a few proteomic studies focused on the protein level. The present work aimed to examine the proteomic profiles of follicular lymphoma vs. normal B-cells obtained by fine-needle aspiration biopsy (FNAB) to gain deep insight into the most perturbed pathway of FL. The cells of interest were purified by magnetic-activated cell sorting (MACS). High-throughput proteomic profiling was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and allowed to identify of 6724 proteins in at least 75% of each group of samples. The ‘Total Protein Approach’ (TPA) was applied to the absolute quantification of proteins in this study. We identified 1186 differentially abundant proteins (DAPs) between FL and control samples, causing an extensive remodeling of several molecular pathways, including the B-cell receptor signaling pathway, cellular adhesion molecules, and PPAR pathway. Additionally, the construction of protein–protein interactions networks (PPINs) and identification of hub proteins allowed us to indicate the key player proteins for FL pathology. Finally, ICAM1, CD9, and CD79B protein expression was validated in an independent cohort by flow cytometry (FCM), and the results were consistent with the mass spectrometry (MS) data.

Metabolic reprogramming of T regulatory cells in the hypoxic tumor microenvironment

Metabolic dysregulation in the hypoxic tumor microenvironment (TME) is considered as a hallmark of solid tumors, leading to changes in biosynthetic pathways favoring onset, survival and proliferation of malignant cells. Within the TME, hypoxic milieu favors metabolic reprogramming of tumor cells, which subsequently affects biological properties of tumor-infiltrating immune cells. T regulatory cells (Tregs), including both circulating and tissue-resident cells, are particularly susceptible to hypoxic metabolic signaling that can reprogram their biological and physicochemical properties. Furthermore, metabolic reprogramming modifies Tregs to utilize alternative substrates and undergo a plethora of metabolic events to meet their energy demands. Major impact of this metabolic reprogramming can result in differentiation, survival, excessive secretion of immunosuppressive cytokines and proliferation of Tregs within the TME, which in turn dampen anti-tumor immune responses. Studies on fine-tuning of Treg metabolism are challenging due to heterogenicity of tissue-resident Tregs and their dynamic functions. In this review, we highlight tumor intrinsic and extrinsic factors, which can influence Treg metabolism in the hypoxic TME. Moreover, we focus on metabolic reprogramming of Tregs that could unveil potential regulatory networks favoring tumorigenesis/progression, and provide novel insights, including inhibitors against acetyl-coA carboxylase 1 and transforming growth factor beta into targeting Treg metabolism for therapeutic benefits.

DOCK4 Is a Platinum-Chemosensitive and Prognostic-Related Biomarker in Ovarian Cancer

Ovarian carcinoma (OV) is a lethal gynecological malignancy. Most OV patients develop resistance to platinum-based chemotherapy and recurrence. Peroxisome proliferator-activated receptors (PPARs) are the ligand activating transcription factor of the nuclear receptor superfamily. PPARs as important transcriptional regulators regulate important physiological processes such as lipid metabolism, inflammation, and wound healing. Several reports point out that PPARs can also have an effect on the sensitivity of tumor cells to platinum-based chemotherapy drugs. However, the role of PPAR-target related genes (PPAR-TRGs) in chemotherapeutic resistance of OV remains unclear. The present study is aimed at optimizing candidate genes by integrating platinum-chemotherapy expression data and PPAR family genes with their targets. The gene expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. A total of 4 genes (AP2A2, DOCK4, HSDL2, and PDK4) were the candidate differentially expressed genes (DEGs) of PPAR-TRGs with platinum chemosensitivity. After conducting numerous survival analyses using different cohorts, we found that only the upexpression of DOCK4 has important significance with the poor prognosis of OV patients. Meanwhile, DOCK4 is detected in plasma and enriched in neutrophil and monocyte cells of the blood. We further found that there were significant correlations between DOCK4 expression and the levels of CD4+ T cell infiltration, dendritic cell infiltration, and neutrophil infiltration in OV. In addition, we verified the expression level of DOCK4 in OV cell lines treated with platinum drugs and found that DOCK4 is potentially responsive to platinum drugs. In conclusion, DOCK4 is potentially associated with immune cell infiltration and represents a valuable prognostic biomarker in ovarian cancer patients.

An integrative pan-cancer investigation reveals common genetic and transcriptional alterations of AMPK pathway genes as important predictors of clinical outcomes across major cancer types

BACKGROUND

The AMP-activated protein kinase (AMPK) is an evolutionarily conserved regulator of cellular energy homeostasis. As a nexus for transducing metabolic signals, AMPK cooperates with other energy-sensing pathways to modulate cellular responses to metabolic stressors. With metabolic reprogramming being a hallmark of cancer, the utility of agents targeting AMPK has received continued scrutiny and results have demonstrated conflicting effects of AMPK activation in tumorigenesis. Harnessing multi-omics datasets from human tumors, we seek to evaluate the seemingly pleiotropic, tissue-specific dependencies of AMPK signaling dysregulation.

METHODS

We interrogated copy number variation and differential transcript expression of 92 AMPK pathway genes across 21 diverse cancers involving over 18,000 patients. Cox proportional hazards regression and receiver operating characteristic analyses were used to evaluate the prognostic significance of AMPK dysregulation on patient outcomes.

RESULTS

A total of 24 and seven AMPK pathway genes were identified as having loss- or gain-of-function features. These genes exhibited tissue-type dependencies, where survival outcomes in glioma patients were most influenced by AMPK inactivation. Cox regression and log-rank tests revealed that the 24-AMPK-gene set could successfully stratify patients into high- and low-risk groups in glioma, sarcoma, breast and stomach cancers. The 24-AMPK-gene set could not only discriminate tumor from non-tumor samples, as confirmed by multidimensional scaling analyses, but is also independent of tumor, node and metastasis staging. AMPK inactivation is accompanied by the activation of multiple oncogenic pathways associated with cell adhesion, calcium signaling and extracellular matrix organization. Anomalous AMPK signaling converged on similar groups of transcriptional targets where a common set of transcription factors were identified to regulate these targets. We also demonstrated crosstalk between pro-catabolic AMPK signaling and two pro-anabolic pathways, mammalian target of rapamycin and peroxisome proliferator-activated receptors, where they act synergistically to influence tumor progression significantly.

CONCLUSION

Genetic and transcriptional aberrations in AMPK signaling have tissue-dependent pro- or anti-tumor impacts. Pan-cancer investigations on molecular changes of this pathway could uncover novel therapeutic targets and support risk stratification of patients in prospective trials.

The hypoxic tumour microenvironment: A safe haven for immunosuppressive cells and a therapeutic barrier to overcome

Dating back to the seminal work of Paul Ehrlich, the idea of harnessing our immune system to eliminate cancerous cells is now over a century old. In the presence of a functional immune system that so efficiently guards the host against developing neoplasms, tumour cells must evolve sophisticated strategies to escape immune destruction in order to give rise to clinically detectable cancers. A new way of treating cancer would thus be to target the immune system itself rather than the tumour, and extensive studies in randomised trials have cemented the possibility of using immunotherapy for treating advanced-stage cancers. Immunotherapy, however, is only tolerated in a minority of patients and in many cases, patients suffer from adverse immune-related reactions when the immune system goes into overdrive. A primary barrier thwarting the development of effective immunotherapy seems to coalesce into the peculiarities of the tumour microenvironment for which hypoxia is a key feature. Here, we review emerging themes on how hypoxia contributes to immune suppression and obstructs anti-tumour effector cell functions. We discuss the challenges and opportunities relating to the potential for dually targeting hypoxia and the immune system to promote durable and favourable responses in cancer patients.

An immunoevasive strategy through clinically-relevant pan-cancer genomic and transcriptomic alterations of JAK-STAT signaling components

BACKGROUND

Since its discovery almost three decades ago, the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway has paved the road for understanding inflammatory and immunity processes related to a wide range of human pathologies including cancer. Several studies have demonstrated the importance of JAK-STAT pathway components in regulating tumor initiation and metastatic progression, yet, the extent of how genetic alterations influence patient outcome is far from being understood.

METHODS

Focusing on 133 genes involved in JAK-STAT signaling, we investigated genomic, transcriptomic and clinical profiles of over 18,000 patients representing 21 diverse cancer types. We identified a core set of 28 putative gain- or loss-of-function JAK-STAT genes that correlated with survival outcomes using Cox proportional hazards regression and Kaplan-Meier analyses. Differential expression analyses between high- and low-expressing patient groups were performed to evaluate the consequences of JAK-STAT misexpression.

RESULTS

We found that copy number alterations underpinning transcriptional dysregulation of JAK-STAT pathway genes differ within and between cancer types. Integrated analyses uniting genomic and transcriptomic datasets revealed a core set of JAK-STAT pathway genes that correlated with survival outcomes in brain, renal, lung and endometrial cancers. High JAK-STAT scores were associated with increased mortality rates in brain and renal cancers, but not in lung and endometrial cancers where hyperactive JAK-STAT signaling is a positive prognostic factor. Patients with aberrant JAK-STAT signaling demonstrated pan-cancer molecular features associated with misexpression of genes in other oncogenic pathways (Wnt, MAPK, TGF-β, PPAR and VEGF). Brain and renal tumors with hyperactive JAK-STAT signaling had increased regulatory T cell gene (Treg) expression. A combined model uniting JAK-STAT and Tregs allowed further delineation of risk groups where patients with high JAK-STAT and Treg scores consistently performed the worst.

CONCLUSION

Providing a pan-cancer perspective of clinically-relevant JAK-STAT alterations, this study could serve as a framework for future research investigating anti-tumor immunity using combination therapy involving JAK-STAT and immune checkpoint inhibitors.

Aberrations in Notch-Hedgehog signalling reveal cancer stem cells harbouring conserved oncogenic properties associated with hypoxia and immunoevasion

Background

Cancer stem cells (CSCs) have innate abilities to resist even the harshest of therapies. To eradicate CSCs, parallels can be drawn from signalling modules that orchestrate pluripotency. Notch-Hedgehog hyperactivation are seen in CSCs, yet, not much is known about their conserved roles in tumour progression across cancers.

Methods

Employing a comparative approach involving 21 cancers, we uncovered clinically-relevant, pan-cancer drivers of Notch and Hedgehog. GISTIC datasets were used to evaluate copy number alterations. Receiver operating characteristic and Cox regression were employed for survival analyses.

Results

We identified a Notch-Hedgehog signature of 13 genes exhibiting high frequencies of somatic amplifications leading to transcript overexpression. The signature successfully predicted patients at risk of death in five cancers (n = 2278): glioma (P < 0.0001), clear cell renal cell (P = 0.0022), papillary renal cell (P = 0.00099), liver (P = 0.014) and stomach (P = 0.011). The signature was independent of other clinicopathological parameters and offered an additional resolution to stratify similarly-staged tumours. High-risk patients exhibited features of stemness and had more hypoxic tumours, suggesting that hypoxia may influence CSC behaviour. Notch-Hedgehog⁺ CSCs had an immune privileged phenotype associated with increased regulatory T cell function.

Conclusion

This study will set the stage for exploring adjuvant therapy targeting the Notch-Hedgehog axis to help optimise therapeutic regimes leading to successful CSC elimination.