Dichloroacetate (DCA) and Cancer: An Overview towards Clinical Applications

Significance of pyroptosis-related genes in the diagnosis and classification of diabetic kidney disease

OBJECTIVE

This study aimed to identify the potential biomarkers associated with pyroptosis in diabetic kidney disease (DKD).

METHODS

Three datasets from the Gene Expression Omnibus (GEO) were downloaded and merged into an integrated dataset. Differentially expressed genes (DEGs) were filtered and intersected with pyroptosis-related genes (PRGs). Pyroptosis-related DEGs (PRDEGs) were obtained and analyzed using functional enrichment analysis. Random forest, Least Absolute Shrinkage and Selection Operator, and logistic regression analyses were used to select the features of PRDEGs. These feature genes were used to build a diagnostic prediction model, identify the subtypes of the disease, and analyze their interactions with transcription factors (TFs)/miRNAs/drugs and small molecules. We conducted a comparative analysis of immune cell infiltration at different risk levels of pyroptosis. qRT-PCR was used to validate the expression of the feature genes.

RESULTS

A total of 25 PRDEGs were obtained. These genes were coenriched in biological processes and pathways, such as the regulation of inflammatory responses. Five key genes (CASP1, CITED2, HTRA1, PTGS2, S100A12) were identified and verified using qRT-PCR. The diagnostic model based on key genes has a good diagnostic prediction ability. Five key genes interacted with TFs and miRNAs in 67 and 80 pairs, respectively, and interacted with 113 types of drugs or molecules. Immune infiltration of samples with different pyroptosis risk levels showed significant differences. Thus, CASP1, CITED2, HTRA1, PTGS2 and S100A12 are potential DKD biomarkers.

CONCLUSION

Genes that regulate pyroptosis can be used as predictors of DKD. Early diagnosis of DKD can aid in its effective treatment.

Selective Inhibition of Deamidated Triosephosphate Isomerase by Disulfiram, Curcumin, and Sodium Dichloroacetate: Synergistic Therapeutic Strategies for T-Cell Acute Lymphoblastic Leukemia in Jurkat Cells

T-cell acute lymphoblastic leukemia (T-ALL) is a challenging childhood cancer to treat, with limited therapeutic options and high relapse rates. This study explores deamidated triosephosphate isomerase (dTPI) as a novel therapeutic target. We hypothesized that selectively inhibiting dTPI could reduce T-ALL cell viability without affecting normal T lymphocytes. Computational modeling and recombinant enzyme assays revealed that disulfiram (DS) and curcumin (CU) selectively bind and inhibit dTPI activity without affecting the non-deamidated enzyme. At the cellular level, treatment with DS and CU significantly reduced Jurkat T-ALL cell viability and endogenous TPI enzymatic activity, with no effect on normal T lymphocytes, whereas the combination of sodium dichloroacetate (DCA) with DS or CU showed synergistic effects. Furthermore, we demonstrated that dTPI was present and accumulated only in Jurkat cells, confirming our hypothesis. Finally, flow cytometry confirmed apoptosis in Jurkat cells after treatment with DS and CU or their combination with DCA. These findings strongly suggest that targeting dTPI represents a promising and selective target for T-ALL therapy.

Role and molecular mechanisms of HuangQiSiJunZi decoction for treating triple-negative breast cancer as explored via network pharmacology and bioinformatics analyses

OBJECTIVE

In this study, we evaluated the molecular mechanisms of HuangQiSiJunZi Decoction (HQSJZD) for treating triple-negative breast cancer (TNBC) using network pharmacology and bioinformatics analyses.

METHODS

Effective chemical components together with action targets of HQSJZD were selected based on the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Meanwhile, differentially expressed genes (DEGs) were extracted from TNBC sample data in The Cancer Genome Atlas (TCGA) database. Additionally, we built a protein-protein interaction (PPI) network and acquired hub genes. Gene Expression Omnibus(GEO) datasets were utilized to verify the accuracy of hub gene expression. Additionally, enrichment analyses were conducted on key genes. Furthermore, TNBC severity-related high-risk factors were screened through univariate together with multivariate Cox regressions; next, the logistic regression prediction model was built. Moreover, differential levels of 22 immune cell types in TNBC tissues compared with normal tissues were analyzed. The hub gene levels within pan-cancer and the human body were subsequently visualized and analyzed. Finally, quantitative PCR (RT-qPCR) was used to validate the correlation of the hub genes in TNBC cells.

RESULTS

The study predicted 256 targets of active ingredients and 1791 DEGs in TNBC, and obtained 16 hub genes against TNBC. The prognostic signature based on FOS, MMP9, and PGR was independent in predicting survival. A total of seven types of immune cells, such as CD4 + memory T cells, showed a significant difference in infiltration (p < 0.05), and immune cells were related to the hub genes. The HPA database was adopted for hub gene analyses, and as determined, FOS was highly expressed in most human organs. The results of RT-qPCR validation for the FOS hub gene were consistent with those of bioinformatic analyses.

CONCLUSION

HQSJZD might regulate the interleukin-17 and aging pathways via FOS genes to increase immune cell infiltration in TNBC tissues, and thus, may treat TNBC and improve the prognosis. The FOS genes are likely to be a new marker for TNBC.

Alternative Cancer Therapeutics: Unpatentable Compounds and Their Potential in Oncology

Cancer remains a leading cause of death globally. Cancer patients often seek alternative therapies in addition to, or instead of, conventional treatments like chemotherapy, radiation, and surgery. The progress in medical advancements and early detection provides more treatment options; however, the development of cancer drugs requires a significant amount of time, demands substantial investments, and results in an overall low percent of regulatory approval. The complex relationship between patent protection and pharmaceutical innovation complicates cancer drug development and contributes to high mortality rates. Adjusting patent criteria for alternative cancer therapeutics could stimulate innovation, enhance treatment options, and ultimately improve outcomes for cancer patients. This article explores the potential of alternative cancer therapeutics, chemopreventive agents, natural products, off-patent drugs, generic unpatentable chemicals, and repurposed drugs in cancer treatment, emphasizing the mechanisms and therapeutic potential of these unconventional compounds as combinatorial cancer therapies. The biological pathways, therapeutic effects, and potential to enhance existing therapies are reviewed, demonstrating their cost-effective and accessible options as adjuvant cancer therapies.

In Vitro Cytotoxic Potential and In Vivo Antitumor Effects of NOS/PDK-Inhibitor T1084

Previously, we showed the antitumor activity of the new NOS/PDK inhibitor T1084 (1-isobutanoyl-2-isopropylisothiourea dichloroacetate). The present study included an assessment of in vitro cytotoxicity against human malignant and normal cells according to the MTT-test and in vivo antitumor effects in solid tumor models in comparison with precursor compounds T1023 (NOS inhibitor; 1-isobutanoyl-2-isopropylisothiourea hydrobromide) and Na-DCA (PDK inhibitor; sodium dichloroacetate), using morphological, histological, and immunohistochemical methods. The effects of T1084 and T1023 on the in vitro survival of normal (MRC-5) and most malignant cells (A375, MFC-7, K562, OAW42, and PC-3) were similar and quantitatively equal. At the same time, melanoma A375 cells showed 2-2.5 times higher sensitivity (IC50: 0.39-0.41 mM) to the cytotoxicity of T1023 and T1084 than other cells. And only HeLa cells showed significantly higher sensitivity to the cytotoxicity of T1084 compared to T1023 (IC50: 0.54 ± 0.03 and 0.81 ± 0.02 mM). Comparative studies of the in vivo antitumor effects of Na-DCA, T1023, and T1084 on CC-5 cervical cancer and B-16 melanoma in mice were conducted with subchronic daily i.p. administration of these agents at an equimolar dose of 0.22 mmol/kg (33.6, 60.0, and 70.7 mg/kg, respectively). Cervical cancer CC-5 fairly quickly evaded the effects of both Na-DCA and T1023. So, from the end of the first week of Na-DCA or T1023 treatment, the tumor growth inhibition (TGI) began to decrease from 40% to an insignificant level by the end of the observation. In contrast, in two independent experiments, CC-5 showed consistently high sensitivity to the action of T1084: a significant antitumor effect with high TGI (43-58%) was registered throughout the observation, without any signs of neoplasia adaptation. The effect of precursor compounds on melanoma B-16 was either minimal (for Na-DCA) or moderate (for T1023) with TGI only 33%, which subsequently decreased by the end of the experiment. In contrast, the effect of T1084 on B-16 was qualitatively more pronounced and steadily increasing; it was accompanied by a 3-fold expansion of necrosis and dystrophy areas, a decrease in proliferation, and increased apoptosis of tumor cells. Morphologically, the T1084 effect was 2-fold superior to the effects of T1023-the TGI index reached 59-62%. This study suggests that the antitumor effects of T1084 develop through the interaction of NOS-dependent and PDK-dependent pathophysiological effects of this NOS/PDK inhibitor. The NOS inhibitory activity of T1084 exerts an anti-angiogenic effect on neoplasia. At the same time, the PDK inhibitory activity of T1084 enhances the cytotoxicity of induced intratumoral hypoxia and suppresses the development of neoplasia adaptation to anti-angiogenic stress. Such properties allow T1084 to overcome tumor resistance and realize a stable synergistic antitumor effect.

Curcumin-Dichloroacetate Hybrid Molecule as an Antitumor Oral Drug against Multidrug-Resistant Advanced Bladder Cancers

Tumor cells produce excessive reactive oxygen species (ROS) but cannot detoxify ROS if they are due to an external agent. An agent that produces toxic levels of ROS, specifically in tumor cells, could be an effective anticancer drug. CMC-2 is a molecular hybrid of the bioactive polyphenol curcumin conjugated to dichloroacetate (DCA) via a glycine bridge. The CMC-2 was tested for its cytotoxic antitumor activities and killed both naïve and multidrug-resistant (MDR) bladder cancer (BCa) cells with equal potency (<1.0 µM); CMC-2 was about 10-15 folds more potent than curcumin or DCA. Growth of human BCa xenograft in mice was reduced by >50% by oral gavage of 50 mg/kg of CMC-2 without recognizable systemic toxicity. Doses that used curcumin or DCA showed minimum antitumor effects. In vitro, the toxicity of CMC-2 in both naïve and MDR cells depended on increased intracellular ROS in tumor cells but not in normal cells at comparable doses. Increased ROS caused the permeabilization of mitochondria and induced apoptosis. Further, adding N-Acetyl cysteine (NAC), a hydroxyl radical scavenger, abolished excessive ROS production and CMC-2's cytotoxicity. The lack of systemic toxicity, equal potency against chemotherapy -naïve and resistant tumors, and oral bioavailability establish the potential of CMC-2 as a potent drug against bladder cancers.

Small molecules targeting mitochondria as an innovative approach to cancer therapy

Cellular death evasion is a defining characteristic of human malignancies and a significant contributor to therapeutic inefficacy. As a result of oncogenic inhibition of cell death mechanisms, established therapeutic regimens seems to be ineffective. Mitochondria serve as the cellular powerhouses, but they also function as repositories of self-destructive weaponry. Changes in the structure and activities of mitochondria have been consistently documented in cancer cells. In recent years, there has been an increasing focus on using mitochondria as a targeted approach for treating cancer. Considerable attention has been devoted to the development of delivery systems that selectively aim to deliver small molecules called "mitocans" to mitochondria, with the ultimate goal of modulating the physiology of cancer cells. This review summarizes the rationale and mechanism of mitochondrial targeting with small molecules in the treatment of cancer, and their impact on the mitochondria. This paper provides a concise overview of the reasoning and mechanism behind directing treatment towards mitochondria in cancer therapy, with a particular focus on targeting using small molecules. This review also examines diverse small molecule types within each category as potential therapeutic agents for cancer.

The Use of Identified Hypoxia-related Genes to Generate Models for Predicting the Prognosis of Cerebral Ischemia‒reperfusion Injury and Developing Treatment Strategies

Cerebral ischemia‒reperfusion injury (CIRI) is a type of secondary brain damage caused by reperfusion after ischemic stroke due to vascular obstruction. In this study, a CIRI diagnostic model was established by identifying hypoxia-related differentially expressed genes (HRDEGs) in patients with CIRI. The ischemia‒reperfusion injury (IRI)-related datasets were downloaded from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), and hypoxia-related genes in the Gene Cards database were identified. After the datasets were combined, hypoxia-related differentially expressed genes (HRDEGs) expressed in CIRI patients were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the HRDEGs were performed using online tools. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed with the combined gene dataset. CIRI diagnostic models based on HRDEGs were constructed via least absolute shrinkage and selection operator (LASSO) regression analysis and a support vector machine (SVM) algorithm. The efficacy of the 9 identified hub genes for CIRI diagnosis was evaluated via mRNA‒microRNA (miRNA) interaction, mRNA-RNA-binding protein (RBP) network interaction, immune cell infiltration, and receiver operating characteristic (ROC) curve analyses. We then performed logistic regression analysis and constructed logistic regression models based on the expression of the 9 HRDEGs. We next established a nomogram and calibrated the prediction data. Finally, the clinical utility of the constructed logistic regression model was evaluated via decision curve analysis (DCA). This study revealed 9 critical genes with high diagnostic value, offering new insights into the diagnosis and selection of therapeutic targets for patients with CIRI. : Not applicable.

Targeting the Hippo/YAP1 signaling pathway in hepatocellular carcinoma: From mechanisms to therapeutic drugs (Review)

The Hippo signaling pathway plays a pivotal role in regulating cell growth and organ size. Its regulatory effects on hepatocellular carcinoma (HCC) encompass diverse aspects, including cell proliferation, invasion and metastasis, tumor drug resistance, metabolic reprogramming, immunomodulatory effects and autophagy. Yes‑associated protein 1 (YAP1), a potent transcriptional coactivator and a major downstream target tightly controlled by the Hippo pathway, is influenced by various molecules and pathways. The expression of YAP1 in different cell types within the liver tumor microenvironment exerts varying effects on tumor outcomes, warranting careful consideration. Therefore, research on YAP1‑targeted therapies merits attention. This review discusses the composition and regulation mechanism of the Hippo/YAP1 signaling pathway and its relationship with HCC, offering insights for future research and cancer prevention strategies.

Development of a diagnostic model based on glycolysis-related genes and immune infiltration in intervertebral disc degeneration

Background

The glycolytic pathway and immune response play pivotal roles in the intervertebral disc degeneration (IDD) progression. This study aimed to develop a glycolysis-related diagnostic model and analyze its relationship with the immune response to IDD.

Methods

GSE70362, GSE23130, and GSE15227 datasets were collected and merged from the Gene Expression Omnibus, and differential expression analysis was performed. Glycolysis-related differentially expressed genes (GLRDEGs) were identified, and a machine learning-based diagnostic model was constructed and validated, followed by Gene Set Enrichment Analysis (GSEA). Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed, and mRNA-miRNA and mRNA-transcription factor (TF) interaction networks were constructed. Immune infiltration was analyzed using single-sample GSEA (ssGSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm between high- and low-risk groups.

Results

In the combined dataset, samples from 31 patients with IDD and 55 normal controls were analyzed, revealing differential expression of 16 GLRDEGs between the two groups. Using advanced machine learning techniques (LASSO, support vector machine, and random forest algorithms), we identified eight common GLRDEGs (PXK, EIF3D, WSB1, ZNF185, IGFBP3, CKAP4, RPL15, and, SSR1) and developed a diagnostic model, which demonstrated high accuracy in distinguishing IDD from control samples (area under the curve, 0.935). We identified 42 mRNA-miRNA and 33 mRNA-TF interaction pairs. Using the RiskScore from the diagnostic model, the combined dataset was stratified into high- and low-risk groups. SsGSEA revealed significant differences in the infiltration abundances of the four immune cell types between the groups. The CIBERSORT algorithm revealed the strongest correlation between resting natural killer (NK) cells and ZNF185 in the low-risk group and between CD8+ T cells and SSR1 in the high-risk group.

Conclusions

Our study reveals a potential interplay between glycolysis-associated genes and immune infiltration in IDD pathogenesis. These findings contribute to our understanding of IDD and may guide development of novel diagnostic markers and therapeutic interventions.

The antitumor effect of diisopropylamine dichloroacetate on non-small cell lung cancer and its influence on the tumor immune microenvironment

Objective

To evaluate the antitumor effects of diisopropylamine dichloroacetate (DADA) alone or in combination with chemotherapy/radiotherapy/immunotherapy in NSCLC and explore the underlying mechanisms involved.

Methods

MTT, UV spectrophotometry, flow cytometry, fluorescence microscopy, and clonogenic survival assays were used. In LLC mouse models, the antitumor effects of radiotherapy, DADA, and the anti-PD-1 antibody alone or in combination were evaluated, and the T cell numbers were evaluated in different groups.

Results

DADA significantly inhibited lactate production and promoted apoptosis in NSCLC in vitro. Compared with pemetrexed or DADA alone, the combination of DADA with pemetrexed significantly inhibited proliferation and promoted apoptosis (p<0.05). This may be related to the decrease in the mitochondrial membrane potential in the combined group. Moreover, compared with radiotherapy alone, the combination of DADA with radiotherapy induced remarkable DNA damage. In vivo, the combination of radiotherapy, an anti-PD-1 antibody and DADA resulted in superior tumor inhibition than the combination of radiotherapy and anti-PD-1 antibody did (p < 0.05). The underlying mechanism may be partially related to the increased number of CD3+ T cells in the triplet combination group (p < 0.05).

Discussion

Our results showed that DADA has strong antitumor effects on NSCLC, either alone or in combination with chemotherapy or radiotherapy. Interestingly, the combination of radiotherapy, anti-PD-1 antibody and DADA had a more pronounced tumor-suppressing effect, which may be related to DADA-induced T-cell generation by reducing local lactic acid production in the tumor microenvironment. This study lays the foundation for further exploration of DADA in lung cancer, especially in the era of immunotherapy, on the basis of its possible immunomodulatory effects.

Immune infiltration analysis based on pyroptosis-related gene in metabolic dysfunction-associated fatty liver disease

Introduction

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a prevalent chronic disease that can involve pyroptosis. The primary objective of this study was to conduct a thorough and comprehensive analysis the pyroptosis-related genes in MAFLD.

Methods

We identified pyroptosis-related differentially expressed genes (PRDEGs) in both healthy individuals and MAFLD patients. Using various bioinformatic approaches, we conducted an immune infiltration analysis from multiple perspectives.

Results

A total of 20 pyroptosis-related LASSO genes were obtained, and 10 hub genes were used to do immune infiltration analysis. The hub genes were utilized in the construction of interaction networks between mRNA-miRNA and mRNA-TF. Immune characteristics analysis revealed multiple immune cell types significantly related to PRDEG expression, particularly genes HSP90AA1, TSLP, CDK9, and BRD4.

Conclusion

Pyroptosis-related immune infiltration might be a mechanism of MAFLD progression and offers a research direction for potential treatment techniques.

Exploring the mystery of tumor metabolism: Warburg effect and mitochondrial metabolism fighting side by side

The metabolic reconfiguration of tumor cells constitutes a pivotal aspect of tumor proliferation and advancement. This study delves into two primary facets of tumor metabolism: the Warburg effect and mitochondrial metabolism, elucidating their contributions to tumor dominance. The Warburg effect facilitates efficient energy acquisition by tumor cells through aerobic glycolysis and lactic acid fermentation, offering metabolic advantages conducive to growth and proliferation. Simultaneously, mitochondrial metabolism, serving as the linchpin of sustained tumor vitality, orchestrates the tricarboxylic acid cycle and electron transport chain, furnishing a steadfast and dependable wellspring of biosynthesis for tumor cells. Regarding targeted therapy, this discourse examines extant strategies targeting tumor glycolysis and mitochondrial metabolism, underscoring their potential efficacy in modulating tumor metabolism while envisaging future research trajectories and treatment paradigms in the realm of tumor metabolism. By means of a thorough exploration of tumor metabolism, this study aspires to furnish crucial insights into the regulation of tumor metabolic processes, thereby furnishing valuable guidance for the development of novel therapeutic modalities. This comprehensive deliberation is poised to catalyze advancements in tumor metabolism research and offer novel perspectives and pathways for the formulation of cancer treatment strategies in the times ahead.

Telomere-related gene risk model for prognosis prediction in colorectal cancer

Background

Colorectal cancer (CRC) is the third-most prevalent cancer globally. The biological significance of telomeres in CRC carcinogenesis and progression is underscored by accumulating data. Nevertheless, not much is known about how telomere-related genes (TRGs) affect CRC prognosis. Therefore, the aim of this study was to investigate the role of TRGs in CRC prognosis.

Methods

We retrospectively obtained the expression profiles and clinical data of CRC patients from public databases. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, we created a telomere-related risk model to predict survival outcomes, identifying ten telomere-related differentially expressed genes (TRDEGs). Based on TRDEGs, we stratified patients from The Cancer Genome Atlas (TCGA) into low- and high-risk subsets. Subsequently, we conducted comprehensive analyses, including survival assessment, immune cell infiltration, drug sensitivity, and prediction of molecular interactions using Kaplan-Meier curves, ESTIMATE, CIBERSORT, OncoPredict, and other approaches.

Results

The model showed exceptional predictive accuracy for survival. Significant differences in survival were observed between the two groups of participants grouped according to the model (P<0.001), and this difference was further confirmed in the external validation set (GSE39582) (P=0.004). Additionally, compared to the low-risk group, the high-risk group exhibited significantly advanced tumor node metastasis (TNM) stages, lower proportions of activated CD4+ T cells, effector memory CD4+ T cells, and memory B cells, but increased ratios of M2 macrophages and regulatory T cells (Tregs), elevated tumor immune dysfunction and exclusion (TIDE) scores, and diminished sensitivity to dabrafenib, lapatinib, camptothecin, docetaxel, and telomerase inhibitor IX, reflecting the signature's capacity to distinguish clinical pathological characteristics, immune environment, and drug efficacy. Finally, we validated the expression of the ten TRDEGs (ACACB, TPX2, SRPX, PPARGC1A, CD36, MMP3, NAT2, MMP10, HIGD1A, and MMP1) through quantitative real-time polymerase chain reaction (qRT-PCR) and found that compared to normal cells, the expression levels of ACACB, HIGD1A, NAT2, PPARGC1A, and TPX2 in CRC cells were elevated, whereas those of CD36, SRPX, MMP1, MMP3, and MMP10 were reduced.

Conclusions

Overall, we constructed a telomere-related biomarker capable of predicting prognosis and treatment response in CRC individuals, offering potential guidance for drug therapy selection and prognosis prediction.

Integrative analyses of genes associated with oxidative stress and cellular senescence in triple-negative breast cancer

Background

Oxidative stress and cellular senescence (OSCS) have great impacts on the occurrence and progression of triple-negative breast cancer (TNBC). This study was intended to construct a prognostic model based on oxidative stress and cellular senescence related difference expression genes (OSCSRDEGs) for TNBC.

Methods

The Cancer Genome Atlas (TCGA) databases and two Gene Expression Omnibus (GEO) databases were used to identify OSCSRDEGs. The relationship between OSCSRDEGs and immune infiltration was examined using single-sample gene-set enrichment analysis (ssGSEA), ESTIMATE, and the CIBERSORT algorithm. Least absolute shrinkage and selection operator (LASSO) regression analyses, Cox regression and Kaplan-Meier analysis were employed to construct a prognostic model. Receiver operating characteristic (ROC) curves, nomograms, and decision curve analysis (DCA) were used to evaluate the prognostic efficacy. Gene Set Enrichment Analysis (GSEA) Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to explore the potential functions and mechanism.

Results

A comprehensive analysis identified a total of 27 OSCSRDEGs, out of which 15 genes selected for development of a prognostic model. A high degree of statistical significance was observed for the riskscores derived from this model to accurately predict TNBC Overall survival. The decision curve analysis (DCA) and ROC curve analysis further confirmed the superior accuracy of the OSCSRDEGs prognostic model in predicting efficacy. Notably, the nomogram analysis highlighted that DMD exhibited the highest utility within the model. In comparison between high and low OSCScore groups, the infiltration abundance of immune cells was statistically different in the TCGA-TNBC dataset.

Conclusion

These studies have effectively identified four essential OSCSRDEGs (CFI, DMD, NDRG2, and NRP1) and meticulously developed an OSCS-associated prognostic model for individuals diagnosed with TNBC. These discoveries have the potential to significantly contribute to the comprehension of the involvement of OSCS in TNBC.

Identification of novel endoplasmic reticulum-related genes and their association with immune cell infiltration in major depressive disorder

BACKGROUND

Several lines of evidence point to an interaction between genetic predisposition and environmental factors in the onset of major depressive disorder (MDD). This study is aimed to investigate the pathogenesis of MDD by identifying key biomarkers, associated immune infiltration using bioinformatic analysis and human postmortem sample.

METHODS

The Gene Expression Omnibus (GEO) database of GSE98793 was adopted to identify hub genes linked to endoplasmic reticulum (ER) stress-related genes (ERGs) in MDD. Another GEO database of GSE76826 was employed to validate the novel target associated with ERGs and immune infiltration in MDD. Moreover, human postmortem sample from MDD patients was utilized to confirm the differential expression analysis of hub genes.

RESULTS

We discovered 12 ER stress-related differentially expressed genes (ERDEGs). A LASSO Cox regression analysis helped construct a diagnostic model for these ERDEGs, incorporating immune infiltration analysis revealed that three hub genes (ERLIN1, SEC61B, and USP13) show the significant and consistent expression differences between the two groups. Western blot analysis of postmortem brain samples indicated notably higher expression levels of ERLIN1 and SEC61B in the MDD group, with USP13 also tending to increase compared to control group.

LIMITATIONS

The utilization of the MDD gene chip in this analysis was sourced from the GEO database, which possesses a restricted number of pertinent gene chip samples.

CONCLUSIONS

These findings indicate that ERDEGs especially including ERLIN1, SEC61B, and USP13 associated the infiltration of immune cells may be potential diagnostic indicators for MDD.

The prognostic and clinical value of genes associate with immunity and amino acid Metabolism in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the commonest subtype of primary lung cancer. A comprehensive analysis of the association of immunity with amino acid metabolism in LUAD is critical for understanding the disease.

Methods

The present study examined LUAD and noncancerous cases from the TCGA database. Differentially expressed genes (DEGs) between LUAD and noncancerous tissues were detected by analyzing processed expression profiles. We cross-referenced the up-regulated DEGs with Immune and Amino Acid Metabolism-related genes (I&AAMGs), resulting in Immune and Amino Acid Metabolism related differentially expressed genes (IAAAMRDEGs). The STRING database was employed to analyze PPI on IAAAMRDEGs, obtaining excavated hub genes, whose biological processes, molecular functions and cellular components were examined with GO/KEGG. Potential mechanisms related to LUAD were investigated by GSEA and GSVA. A prognostic model was built by LASSO-COX analysis, taking into consideration risk scores and prognostic factors to determine biomarkers affecting LUAD occurrence and prognosis.

Results

Totally 377 genes were detected at the intersection of upregulated DEGs and I&AAMGs. Analysis of PPI on these 377 IAAAMRDEGs yielded 17 hub genes. A LASSO regression analysis was utilized to assess the prognostic values of the 17 hub genes. Validation using the combined dataset confirmed 4 genes, e.g., polo-like kinase (PLK1), Ribonucleotide Reductase Subunit M2 (RRM2), Thyroid Hormone Receptor Interactor 13 (TRIP13), and Hyaluronan-Mediated Motility Receptor (HHMR). The model's accuracy was further assessed by ROC curve analysis and the COX model. In addition, immunohistochemical staining obtained from the HPA database, revealed enhanced PLK1 expression in LUAD samples.

Conclusion

LUAD pathogenesis is highly associated with immunity and amino acid metabolism. The PLK1, RRM2, TRIP13, and HMMR genes have prognostic values for LUAD. PLK1 upregulation in LUAD might be involved in tumorigenesis by modulating the cell cycle and represents a potential prognostic factor in clinic.

Association between gut microbiota and hepatocellular carcinoma and biliary tract cancer: A mendelian randomization study

BACKGROUND

An increasing number of studies have begun to discuss the relationship between gut microbiota and diseases, yet there is currently a lack of corresponding articles describing the association between gut microbiota and hepatocellular carcinoma (HCC) and biliary tract cancer (BTC). This study aims to explore the relationship between them using Mendelian randomization (MR) analysis method.

AIM

To assess the relationship between gut microbiota and HCC and BTC.

METHODS

We obtained Genome-wide association study (GWAS) data for the gut microbiome from the intestinal microbiota genomic library (MiBioGen, https://mibiogen.gcc.rug.nl/). Additionally, we accessed data pertaining to HCC and BTC from the IEU open GWAS platform (https://gwas.mrcieu.ac.uk/). Our analysis employed fundamental instrumental variable analysis methods, including inverse-variance weighted, MR and Egger. To ensure the dependability of the results, we subjected the results to tests for multiple biases and heterogeneity.

RESULTS

During our investigation, we discovered 11 gut microbiota linked to an increased risk to BTC and HCC. The former included the genus Eubacterium hallii group (P = 0.017), Candidatus Soleaferrea (P = 0.034), Flavonifractor (P = 0.021), Lachnospiraceae FCS020 (P = 0.034), the order Victivallales (P = 0.018), and the class Lentisphaeria (P = 0.0.18). The latter included the genus Desulfovibrio (P = 0.042), Oscillibacter (P = 0.023), the family Coriobacteriaceae (P = 0.048), the order Coriobacteriales (P = 0.048), and the class Coriobacteriia (P = 0.048). Furthermore, in BTC, we observed 2 protective gut microbiota namely the genus Dorea (P = 0.041) and Lachnospiraceae ND3007 group (P = 0.045). All results showed no evidence of multiplicity or heterogeneity.

CONCLUSION

This study explores a causal link between gut microbiota and HCC and BTC. These insights may enhance the mechanistic knowledge of microbiota-related HCC and BTC pathways, potentially informing therapeutic strategies.

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer

Background

Thyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet.

Methods

We identified PANoptosis-related differentially expressed genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas.

Results

A LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P < 0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95 % CI: 1.170-3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue.

Conclusion

A potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

Dichloroacetate for Cancer Treatment: Some Facts and Many Doubts

Rarely has a chemical elicited as much controversy as dichloroacetate (DCA). DCA was initially considered a dangerous toxic industrial waste product, then a potential treatment for lactic acidosis. However, the main controversies started in 2008 when DCA was found to have anti-cancer effects on experimental animals. These publications showed contradictory results in vivo and in vitro such that a thorough consideration of this compound's in cancer is merited. Despite 50 years of experimentation, DCA's future in therapeutics is uncertain. Without adequate clinical trials and health authorities' approval, DCA has been introduced in off-label cancer treatments in alternative medicine clinics in Canada, Germany, and other European countries. The lack of well-planned clinical trials and its use by people without medical training has discouraged consideration by the scientific community. There are few thorough clinical studies of DCA, and many publications are individual case reports. Case reports of DCA's benefits against cancer have been increasing recently. Furthermore, it has been shown that DCA synergizes with conventional treatments and other repurposable drugs. Beyond the classic DCA target, pyruvate dehydrogenase kinase, new target molecules have also been recently discovered. These findings have renewed interest in DCA. This paper explores whether existing evidence justifies further research on DCA for cancer treatment and it explores the role DCA may play in it.

A novel mitochondrial metabolism-related gene signature for predicting the prognosis of oesophageal squamous cell carcinoma

Oesophageal squamous cell carcinoma (ESCC) is one of the most lethal cancers worldwide. Due to the important role of mitochondrial metabolism in cancer progression, a clinical prognostic model based on mitochondrial metabolism and clinical features was constructed in this study to predict the prognosis of ESCC. Firstly, the mitochondrial metabolism scores (MMs) were calculated based on 152 mitochondrial metabolism-related genes (MMRGs) by single sample gene set enrichment analysis (ssGSEA). Subsequently, univariate Cox regression and LASSO algorithm were used to identify prognosis-associated MMRG and risk-stratify patients. Functional enrichment, interaction network and immune-related analyses were performed to explore the features differences in patients at different risks. Finally, a prognostic nomogram incorporating clinical factors was constructed to assess the prognosis of ESCC. Our results found there were differences in clinical features between the MMs-high group and the MMs-low group in the TCGA-ESCC dataset (P<0.05). Afterwards, we identified 6 MMRGs (COX10, ACADVL, IDH3B, AKR1A1, LIAS, and NDUFB8) signature that could accurately distinguish high-risk and low-risk ESCC patients. A predictive nomogram that combined the 6 MMRGs with sex and N stage to predict the prognosis of ESCC was constructed, and the areas under the receiver operating characteristic (ROC) curve at 1, 2 and 3 years were 0.948, 0.927 and 0.848, respectively. Finally, we found that COX10, one of 6 MMRGs, could inhibit the malignant progression of ESCC in vitro. In summary, we constructed a clinical prognosis model based on 6 MMRGs and clinical features which can accurately predict the prognosis of ESCC patients.

Gene signatures of endoplasmic reticulum stress and mitophagy for prognostic risk prediction in lung adenocarcinoma

Genes associated with endoplasmic reticulum stress (ERS) and mitophagy can be conducive to predicting solid tumour prognosis. The authors aimed to develop a prognosis prediction model for these genes in lung adenocarcinoma (LUAD). Relevant gene expression and clinical information were collected from public databases including Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A total of 265 differentially expressed genes was finally selected (71 up-regulated and 194 downregulated) in the LUAD dataset. Among these, 15 candidate ERS and mitophagy genes (ATG12, CSNK2A1, MAP1LC3A, MAP1LC3B, MFN2, PGAM5, PINK1, RPS27A, SQSTM1, SRC, UBA52, UBB, UBC, ULK1, and VDAC1) might be critical to LUAD based on the expression analysis after crossing with the ERS and mitochondrial autophagy genes. The prediction model demonstrated the ability to effectively predict the 5-, 3-, and 1-year prognoses of LUAD patients in both GEO and TCGA databases. Moreover, high VDAC1 expression was associated with poor overall survival in LUAD (p < 0.001), suggesting it might be a critical gene for LUAD prognosis prediction. Overall, the prognosis model based on ERS and mitophagy genes in LUAD can be useful for evaluating the prognosis of patients with LUAD, and VDAC1 may serve as a promising biomarker for LUAD prognosis.

An exosomes-related lncRNA prognostic model correlates with the immune microenvironment and therapy response in lung adenocarcinoma

Recent research highlights the significance of exosomes and long noncoding RNAs (lncRNAs) in cancer progression and drug resistance, but their role in lung adenocarcinoma (LUAD) is not fully understood. We analyzed 121 exosome-related (ER) mRNAs from the ExoBCD database, along with mRNA and lncRNA expression profiles of TCGA-LUAD using "DESeq2", "survival," "ConsensusClusterPlus," "GSVA," "estimate," "glmnet," "clusterProfiler," "rms," and "pRRophetic" R packages. This comprehensive approach included univariate cox regression, unsupervised consensus clustering, GSEA, functional enrichment analysis, and prognostic model construction. Our study identified 134 differentially expressed ER-lncRNAs, with 19 linked to LUAD prognosis. These ER-lncRNAs delineated two patient subtypes, one with poorer outcomes. Additionally, 286 differentially expressed genes were related to these ER-lncRNAs, 261 of which also correlated with LUAD prognosis. We constructed an ER-lncRNA-related prognostic model and calculated an ER-lncRNA-related risk score (ERS), revealing that a higher ERS correlates with poor overall survival in both the Meta cohort and two validation cohorts. The ERS potentially serves as an independent prognostic factor, and the prognostic model demonstrates superior predictive power. Notably, significant differences in the immune landscape were observed between the high- and low-ERS groups. Drug sensitivity analysis indicated varying responses to common chemotherapy drugs based on ERS stratification, with the high-ERS group showing greater sensitivity, except to rapamycin and erlotinib. Experimental validation confirmed that thymidine kinase 1 enhances lung cancer invasion, metastasis, and cell cycle progression. Our study pioneers an ER-lncRNA-related prognostic model for LUAD, proposing that ERS-based risk stratification could inform personalized treatment strategies to improve patient outcomes.

A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma

Chemoresistance is a key obstacle in the long-term survival of patients with locally and advanced lung adenocarcinoma (LUAD). This study used bioinformatic analysis to reveal the chemoresistance of gene-neutrophil extracellular traps (NETs) associated with LUAD. RNA sequencing data and LUAD expression patterns were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The GeneCards database was used to identify NETosis-related genes (NRGs). To identify hub genes with significant and consistent expression, differential analysis was performed using the TCGA-LUAD and GEO datasets. LUAD subtypes were determined based on these hub genes, followed by prognostic analysis. Immunological scoring and infiltration analysis were conducted using NETosis scores (N-scores) derived from the TCGA-LUAD dataset. A clinical prognostic model was established and analyzed, and its clinical applications explored. Twenty-two hub genes were identified, and consensus clustering was used to identify two subgroups based on their expression levels. The Kaplan-Meier (KM) curves demonstrated statistically significant differences in prognosis between the two LUAD subtypes. Based on the median score, patients were further divided into high and low N-score groups, and KM curves showed that the N-scores were more precise at predicting the prognosis of patients with LUAD for overall survival (OS). Immunological infiltration analysis revealed significant differences in the abundances of 10 immune cell infiltrates between the high and low N-score groups. Risk scores indicated significant differences in prognosis between the two extreme score groups. The risk scores for the prognostic model also indicated significant differences between the two groups. The results provide new insights into NETosis-related differentially expressed genes (NRDEGs) associated with chemotherapy resistance in patients with LUAD. The established prognostic model is promising and could help with clinical applications to evaluate patient survival and therapeutic efficiency.

Pharmacological Effects of Botanical Drugs on Myocardial Metabolism in Chronic Heart Failure

Although there have been significant advances in the treatment of heart failure in recent years, chronic heart failure remains a leading cause of cardiovascular disease-related death. Many studies have found that targeted cardiac metabolic remodeling has good potential for the treatment of heart failure. However, most of the drugs that increase cardiac energy are still in the theoretical or testing stage. Some research has found that botanical drugs not only increase myocardial energy metabolism through multiple targets but also have the potential to restore the balance of myocardial substrate metabolism. In this review, we summarized the mechanisms by which botanical drugs (the active ingredients/formulas/Chinese patent medicines) improve substrate utilization and promote myocardial energy metabolism by activating AMP-activated protein kinase (AMPK), peroxisome proliferator-activated receptors (PPARs) and other related targets. At the same time, some potential protective effects of botanical drugs on myocardium, such as alleviating oxidative stress and dysbiosis signaling, caused by metabolic disorders, were briefly discussed.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Identification and functional analysis of lactic acid metabolism-related differentially expressed genes in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality rate that seriously threatens human health. We aimed to investigate the expression, prognostic value, and immune cell infiltration of lactic acid metabolism-related genes (LAMRGs) in HCC using bioinformatics. Methods: The HCC database (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) was downloaded from the Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) between normal and tumor groups were identified. The LAMRGs were obtained from literature and GeneCards and MSigDB databases. Lactic acid metabolism-related differentially expressed genes (LAMRDEGs) in HCC were screened from the DEGs and LAMRGs. Functional enrichment analyses of the screened LAMRDEGs were further conducted using Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA). The genes were used in multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model. Then, a protein-protein interaction network was constructed using STRING and CTD databases. Furthermore, the CIBERSORTx online database was used to assess the relationship between immune cell infiltration and hub genes. Results: Twenty-eight lactic acid metabolism-related differentially expressed genes (LAMRDEGs) were identified. The GO and KEGG analyses showed that the LAMRDEGs were related to the prognosis of HCC. The GSEA indicated that the LAMRDEGs were significantly enriched in tumor related pathways. In the multivariate Cox regression analysis, 14 key genes (E2F1, SERPINE1, GYS2, SPP1, PCK1, CCNB1, CYP2C9, IGFBP3, KDM8, RCAN1, ALPL, FBP1, NQO1, and LCAT) were found to be independent prognostic factors of HCC. Finally, the LASSO and Cox regression analyses showed that six key genes (SERPINE1, SPP1, CCNB1, CYP2C9, NQO1, and LCAT) were associated with HCC prognosis. Moreover, the correlation analyses revealed that the expression of the six key genes were associated with immune infiltrates of HCC. Conclusion: The LAMRDEGs can predict the prognosis and may be associated with immune cells infiltration in patients with HCC. These genes might be the promising biomarkers for the prognosis and treatment of HCC.

Unveiling the mitophagy puzzle in non-alcoholic fatty liver disease (NAFLD): Six hub genes for early diagnosis and immune modulatory roles

Background

Non-alcoholic fatty liver disease (NAFLD) stands as a predominant chronic liver ailment globally, yet its pathogenesis remains elusive. This study aims to identify Hub mitophagy-related genes (MRGs), and explore the underlying pathological mechanisms through which these hub genes regulate NAFLD.

Methods

A total of 3 datasets were acquired from the GEO database and integrated to identify differentially expressed genes (DEGs) in NAFLD and perform Gene Set Enrichment Analysis (GSEA). By intersecting DEGs with MRGs, mitophagy-related differentially expressed genes (MRDEGs) were obtained. Then, hub MRGs with diagnostic biomarker capability for NAFLD were screened and a diagnostic prediction model was constructed and assessed using Nomogram, Decision Curve Analysis (DCA), and ROC curves. Functional enrichment analysis was conducted on the identified hub genes to explore their biological significance. Additionally, regulatory networks were constructed using databases. NAFLD was stratified into high and low-risk groups based on the Riskscore from the diagnostic prediction model. Furthermore, single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were employed to analyze immune cell infiltration patterns and the relationship between Hub MRGs and immune cells.

Results

The integrated dataset comprised 122 NAFLD samples and 31 control samples. After screening, 18 MRDEGs were identified. Subsequently, six hub MRGs (NR4A1, PPP2R2A, P4HA1, TUBB6, DUSP1, NAMPT) with diagnostic potential were selected through WGCNA, logistic regression, SVM, RF, and LASSO models, all significantly downregulated in NAFLD samples compared to the control group. A diagnostic prediction model based on these six genes demonstrated robust predictive performance. Functional enrichment analysis of the six hub genes revealed involvement in processes such as protein phosphorylation or dephosphorylation. Correlation analysis demonstrated a significant association between hub MRGs and infiltrating immune cells.

Conclusion

We identified six hub MRGs in NAFLD and constructed a diagnostic prediction model based on these six genes, applicable for early NAFLD diagnosis. These genes may participate in regulating NAFLD progression through the modulation of mitophagy and immune activation. Our findings may contribute to subsequent clinical and basic research on NAFLD.

Targeting mitochondrial bioenergetics by combination treatment with imatinib and dichloroacetate in human erythroleukemic K‑562 and colorectal HCT‑116 cancer cells

Tumor malignant cells are characterized by dysregulation of mitochondrial bioenergetics due to the 'Warburg effect'. In the present study, this metabolic imbalance was explored as a potential target for novel cancer chemotherapy. Imatinib (IM) downregulates the expression levels of SCΟ2 and FRATAXIN (FXN) genes involved in the heme‑dependent cytochrome c oxidase biosynthesis and assembly pathway in human erythroleukemic IM‑sensitive K‑562 chronic myeloid leukemia cells (K‑562). In the present study, it was investigated whether the treatment of cancer cells with IM (an inhibitor of oxidative phosphorylation) separately, or together with dichloroacetate (DCA) (an inhibitor of glycolysis), can inhibit cell proliferation or cause death. Human K‑562 and IM‑chemoresistant K‑562 chronic myeloid leukemia cells (K‑562R), as well as human colorectal carcinoma cells HCT‑116 (+/+p53) and (‑/‑p53, with double TP53 knock-in disruptions), were employed. Treatments of these cells with either IM (1 or 2 µM) and/or DCA (4 mΜ) were also assessed for the levels of several process biomarkers including SCO2, FXN, lactate dehydrogenase A, glyceraldehyde‑3‑phosphate dehydrogenase, pyruvate kinase M2, hypoxia inducing factor‑1a, heme oxygenase‑1, NF‑κB, stem cell factor and vascular endothelial growth factor via western blot analysis. Computational network biology models were also applied to reveal the connections between the ten proteins examined. Combination treatment of IM with DCA caused extensive cell death (>75%) in K‑562 and considerable (>45%) in HCT‑116 (+/+p53) cultures, but less in K‑562R and HCT‑116 (‑/‑p53), with the latter deficient in full length p53 protein. Such treatment, markedly reduced reactive oxygen species levels, as measured by flow‑cytometry, in K‑562 cells and affected the oxidative phosphorylation and glycolytic biomarkers in all lines examined. These findings indicated, that targeting of cancer mitochondrial bioenergetics with such a combination treatment was very effective, although chemoresistance to IM in leukemia and the absence of a full length p53 in colorectal cells affected its impact.

Management of ulcerative colitis by dichloroacetate: Impact on NFATC1/NLRP3/IL1B signaling based on bioinformatics analysis combined with in vivo experimental verification

The inflammatory response in ulcerative colitis (UC) could be relieved by the conventional immunomodulatory agents; 5-aminosalicylic acid, corticosteroids, or azathioprine. However, the low remission rates and the intolerance to these agents necessitate investigation of gene expression signature in UC that could influence the therapeutic efficacy of drugs, as well as the interference with persistence genes by novel therapeutic option. Three microarray datasets (GSE66407, GSE38713 and GSE14580) from the NCBI-GEO database were utilized. Differentially expressed genes between samples of patients with UC and healthy ones were analyzed using R software. In addition, in vivo study using oxazolone-induced UC in BALB/c mice was carried out to investigate the proposed therapeutic efficacy of dichloroacetate (DCA). The bioinformatics analysis revealed the persistence of NLRP3, NFATC1, and IL1B in UC despite treatment with common therapeutic agents. DCA administration to oxazolone-treated mice showed remarkable interference with those persistence genes. Western blotting analysis for NLRP3, NFATC1, nuclear/total NF-κB, and cleaved caspase-1 revealed the ability of DCA to reduce the expression levels of these proteins in oxazolone-treated mice. Additionally, the inflammatory cytokines IL-1β and IL-13 were reduced in colonic tissue by DCA treatment. The therapeutic efficacy of DCA was further confirmed by the apparent reduction in histopathological scoring, disease activity index, and the normalization of colon length. Therefore, DCA could be suggested as a novel and promising therapeutic option in UC based on its ability to interfere with the persistence of NFATC1/NLRP3/IL1B signaling. That merits further safety/toxicological pre-clinical assessment and update of bioavailability/metabolism data prior to clinical investigation.

Targeting the pyruvate dehydrogenase complex/pyruvate dehydrogenase kinase (PDC/PDK) axis to discover potent PDK inhibitors through structure-based virtual screening and pharmacological evaluation

Proliferating cancer cells are characterized by the Warburg effect, a metabolic alteration in which ATP is generated from cytoplasmic glycolysis instead of oxidative phosphorylation. The pyruvate dehydrogenase complex/pyruvate dehydrogenase kinase (PDC/PDK) axis plays a crucial role in this effect and has been identified as a potential target for anticancer drug development. Herein, we present the discovery and pharmacological evaluation of potent PDK inhibitors targeting the PDK/PDC axis. We successfully identified 6 compounds from a small molecule library through a structure-based virtual screening campaign and evaluated their enzymatic inhibitory potencies for PDK1-4. Our results indicated that compound 1 exhibited submicromolar inhibitory activities against PDK1-3 (IC50 = 109.3, 135.8, and 458.7 nM, respectively), but is insensitive to PDK4 (IC50 = 8.67 μM). Furthermore, compound 1 inhibited the proliferation of A549 cells with an EC50 value of 10.7 μM. In addition, compound 1 induced cell apoptosis, arrested the cell cycle at the S phase, and reduced cell invasion and migration, while showing low in vivo toxicity at a high dose. Based on these observations, it can be concluded that compound 1 is a promising anti-PDK1-3 lead that merits further investigation.

Expression of pyroptosis-associated genes and construction of prognostic model for thyroid cancer

Background

Thyroid cancer (THCA) is a common endocrine malignancy. This study aimed to explore the expression of pyroptosis-related genes in THCA and establish a prognosis prediction model.

Methods

Differentially expressed pyroptosis-related genes (DEPRGs) were identified in The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB). Subsequently, these genes were subjected to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A least absolute shrinkage and selection operator (LASSO) regression model was employed to establish a DEPRG signature, and its reliability was validated through survival analysis and receiver operating characteristic (ROC) curves. Patients in TCGA-THCA cohort were stratified into two risk groups. The biological functions of the genes between the two risk groups were assessed through gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Finally, expression of DEPRGs was validated using datasets from the Gene Expression Omnibus (GEO) and the Human Protein Atlas (HPA) databases.

Results

Six DEPRGs were identified in TCGA dataset. Through LASSO Cox regression analysis, we determined IL6, TP63, NOD1, and BAX to be significant. Kaplan-Meier survival analysis demonstrated that patients with THCA expressing high levels of NOD1 and classified as low-risk individuals exhibited prolonged survival. The multifactorial ROC curves yielded area under the curve (AUC) scores exceeding 0.7 for risk score, age, and T-stage, affirming their significance as independent prognostic factors as determined by multivariate analysis. Additionally, we observed elevated expression levels of BAX and NOD1 in THCA using data derived from the HPA database and the GEO dataset.

Conclusions

We established a novel DEPRG signature for predicting the prognosis of THCA, potentially offering a promising therapeutic marker for THCA treatment.

Neuroprotective Effects and Therapeutic Potential of Dichloroacetate: Targeting Metabolic Disorders in Nervous System Diseases

Dichloroacetate (DCA) is an investigational drug used to treat lactic acidosis and malignant tumours. It works by inhibiting pyruvate dehydrogenase kinase and increasing the rate of glucose oxidation. Some studies have documented the neuroprotective benefits of DCA. By reviewing these studies, this paper shows that DCA has multiple pharmacological activities, including regulating metabolism, ameliorating oxidative stress, attenuating neuroinflammation, inhibiting apoptosis, decreasing autophagy, protecting the blood‒brain barrier, improving the function of endothelial progenitor cells, improving mitochondrial dynamics, and decreasing amyloid β-protein. In addition, DCA inhibits the enzyme that metabolizes it, which leads to peripheral neurotoxicity due to drug accumulation that may be solved by individualized drug delivery and nanovesicle delivery. In summary, in this review, we analyse the mechanisms of neuroprotection by DCA in different diseases and discuss the causes of and solutions to its adverse effects.

Metabolic mechanisms in physiological and pathological cardiac hypertrophy: new paradigms and challenges

Cardiac metabolism is vital for heart function. Given that cardiac contraction requires a continuous supply of ATP in large quantities, the role of fuel metabolism in the heart has been mostly considered from the perspective of energy production. However, the consequence of metabolic remodelling in the failing heart is not limited to a compromised energy supply. The rewired metabolic network generates metabolites that can directly regulate signalling cascades, protein function, gene transcription and epigenetic modifications, thereby affecting the overall stress response of the heart. In addition, metabolic changes in both cardiomyocytes and non-cardiomyocytes contribute to the development of cardiac pathologies. In this Review, we first summarize how energy metabolism is altered in cardiac hypertrophy and heart failure of different aetiologies, followed by a discussion of emerging concepts in cardiac metabolic remodelling, that is, the non-energy-generating function of metabolism. We highlight challenges and open questions in these areas and finish with a brief perspective on how mechanistic research can be translated into therapies for heart failure.

Prognostic and immune correlation analysis of mitochondrial autophagy and aging-related genes in lung adenocarcinoma

PURPOSE

Mitophagy and aging (MiAg) are very important pathophysiological mechanisms contributing to tumorigenesis. MiAg-related genes have prognostic value in lung adenocarcinoma (LUAD). However, prognostic, and immune correlation studies of MiAg-related genes in LUAD are lacking.

METHODS

MiAg differentially expressed genes (DEGs) in LUAD were obtained from public sequencing datasets. A prognostic model including MiAg DEGs was constructed according to patients divided into low- and high-risk groups. Gene Ontology, gene set enrichment analysis, gene set variation analysis, CIBERSORT immune infiltration analysis, and clinical characteristic correlation analyses were performed for functional annotation and correlation of MiAgs with prognosis in patients with LUAD.

RESULTS

Seven MiAg DEGs of LUAD were identified: CAV1, DSG2, DSP, MYH11, NME1, PAICS, PLOD2, and the expression levels of these genes were significantly correlated (P < 0.05). The RiskScore of the MiAg DEG prognostic model demonstrated high predictive ability of overall survival of patients diagnosed with LUAD. Patients with high and low MiAg phenotypic scores exhibited significant differences in the infiltration levels of eight types of immune cells (P < 0.05). The multi-factor DEG regression model showed higher efficacy in predicting 5-year survival than 3- and 1-year survival of patients with LUAD.

CONCLUSIONS

Seven MiAg-related genes were identified to be significantly associated with the prognosis of patients diagnosed with LUAD. Moreover, the identified MiAg DEGs might affect the immunotherapy strategy of patients with LUAD.

The coenzyme A precursor pantethine enhances antitumor immunity in sarcoma

The tumor microenvironment is a dynamic network of stromal, cancer, and immune cells that interact and compete for resources. We have previously identified the Vanin1 pathway as a tumor suppressor of sarcoma development via vitamin B5 and coenzyme A regeneration. Using an aggressive sarcoma cell line that lacks Vnn1 expression, we showed that the administration of pantethine, a vitamin B5 precursor, attenuates tumor growth in immunocompetent but not nude mice. Pantethine boosts antitumor immunity, including the polarization of myeloid and dendritic cells towards enhanced IFNγ-driven antigen presentation pathways and improved the development of hypermetabolic effector CD8+ T cells endowed with potential antitumor activity. At later stages of treatment, the effect of pantethine was limited by the development of immune cell exhaustion. Nevertheless, its activity was comparable with that of anti-PD1 treatment in sensitive tumors. In humans, VNN1 expression correlates with improved survival and immune cell infiltration in soft-tissue sarcomas, but not in osteosarcomas. Pantethine could be a potential therapeutic immunoadjuvant for the development of antitumor immunity.

Cuproptosis and its application in different cancers: an overview

Heavy metal ions are essential micronutrients for human health. They are also indispensable to maintaining health and regular operation of organs. Increasing or decreasing these metal ions will lead to cell death, such as ferroptosis. Tsvetkov et al. have recently proposed a novel cell death method called "Cuproptosis". Many researchers have linked this form of death to the diagnosis, prognosis, microenvironment infiltration, and prediction of immunotherapeutic efficacy of various tumors to better understand these tumors. Similarly, with the proposal of this mechanism, the killing effect of copper ionophores on cancer cells has come to our attention again. We introduced the mechanism of cuproptosis in detail and described the establishment of the corresponding prognostic model and risk score for uveal melanoma through cuproptosis. In addition, we describe the current progress in the study of cancer in other organs through cuproptosis and summarize the treatment of tumours by copper ionophore and its future research direction. With further research, the concept of cuproptosis may help us understand cancer and guide its clinical treatment.

Reprogramming Energy Metabolism with Synthesized PDK Inhibitors Based on Dichloroacetate Derivatives and Targeted Delivery Systems for Enhanced Cancer Therapy

In many types of cancers, pyruvate dehydrogenase kinase (PDK) is abnormally overexpressed and has become a promising target for cancer therapy. However, few highly effective inhibitors of PDK have been reported to date. Herein, we designed and synthesized a series of PDK inhibitors based on dichloroacetate (DCA) and arsenicals. Of the 27 compounds, 1f demonstrated PDK inhibition with high efficiency at a cellular level (IC50 = 2.0 μM) and an enzyme level (EC50 = 68 nM), far more effective than that of DCA. In silico, in vitro, and in vivo studies demonstrated that 1f inhibited PDK, shifted the energy metabolism from aerobic glycolysis to oxidative phosphorylation, and induced cell apoptosis. Moreover, new 1f-loaded nanoparticles were developed, and the administration of high-drug-loading nanoparticles (0.15 mg/kg) caused up to 90% tumor shrinkage without any apparent toxicity. Hence, this study provided a novel metabolic therapy for cancer treatment.

Cuproptosis and cuproptosis-related genes: Emerging potential therapeutic targets in breast cancer

Breast cancer is one of the most common malignant tumors in women worldwide, and thus, it is important to enhance its treatment efficacy [1]. Copper has emerged as a critical trace element that affects various intracellular signaling pathways, gene expression, and biological metabolic processes [2], thereby playing a crucial role in the pathogenesis of breast cancer. Recent studies have identified cuproptosis, a newly discovered type of cell death, as an emerging therapeutic target for breast cancer treatment, thereby offering new hope for breast cancer patients. Tsvetkov's research has elucidated the mechanism of cuproptosis and uncovered the critical genes involved in its regulation [3]. Manipulating the expression of these genes could potentially serve as a promising therapeutic strategy for breast cancer treatment. Additionally, using copper ionophores and copper complexes combined with nanomaterials to induce cuproptosis may provide a potential approach to eliminating drug-resistant breast cancer cells, thus improving the therapeutic efficacy of chemotherapy, radiotherapy, and immunotherapy and eventually eradicating breast tumors. This review aims to highlight the practical significance of cuproptosis-related genes and the induction of cuproptosis in the clinical diagnosis and treatment of breast cancer. We examine the potential of cuproptosis as a novel therapeutic target for breast cancer, and we explore the present challenges and limitations of this approach. Our objective is to provide innovative ideas and references for the development of breast cancer treatment strategies based on cuproptosis.

Use of a ferroptosis-related gene signature to construct diagnostic and prognostic models for assessing immune infiltration in metabolic dysfunction-associated fatty liver disease

Introduction: Metabolic dysfunction-associated fatty liver disease (MAFLD), a serious health problem worldwide, can involve ferroptosis. This study aimed to comprehensively analyze the ferroptosis-related genes associated with MAFLD. Methods: Ferroptosis-related differentially expressed genes (FRDEGs) were identified in patients with MAFLD and healthy individuals. Gene ontology functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and gene set enrichment analysis (GSEA) were used to analyze the relevant action pathways of the FRDEGs. The Encyclopedia of RNA Interactomes, CHIPBase, and comparative toxicogenomics databases were used to build mRNA-miRNA, mRNA-transcription factor (TF), and mRNA-drug interaction networks, respectively. A diagnostic model was constructed and bioinformatics analysis methods, such as least absolute shrinkage and selection operator regression analysis, Cox regression analysis, nomogram-based analysis, consensus clustering analysis, and single-sample GSEA, were used to systematically investigate the prognostic values and immunologic characteristics. Results: A total of 13 FRDEGs were obtained and eight were used to construct a diagnostic model and perform a prognostic analysis. Hub genes were also used to construct mRNA-miRNA and mRNA-TF interaction networks and potential drug or molecular compounds. Two MAFLD subtypes were identified: cluster2, which represents an "immunoactive" type, and cluster1, which represents an "immunosuppressive" type; a significant correlation was observed between the immune cell contents and the expression of three FRDEGs (NR4A1, FADS2, and SCD). Conclusion: A ferroptosis-related gene signature was constructed to diagnose MAFLD-associated steatohepatitis, predict the prognosis of MAFLD patients, and analyze the immunologic characteristics of MAFLD. Our findings may provide insights into developing innovative MAFLD treatment techniques.

Prognostic analyses of genes associated with anoikis in breast cancer

Breast cancer (BRCA) is the most diagnosed cancer worldwide and is responsible for the highest cancer-associated mortality among women. It is evident that anoikis resistance contributes to tumour cell metastasis, and this is the primary cause of treatment failure for BRCA. However, anoikis-related gene (ARG) expression profiles and their prognostic value in BRCA remain unclear. In this study, a prognostic model of ARGs based on The Cancer Genome Atlas (TCGA) database was established using a least absolute shrinkage and selection operator analysis to evaluate the prognostic value of ARGs in BRCA. The risk factor graph demonstrated that the low-risk group had longer survival than the high-risk group, implying that the prognostic model had a good performance. We identified 11 ARGs that exhibited differential expression between the two risk groups in TCGA and Gene Expression Omnibus databases. Through Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes enrichment analyses, we revealed that the screened ARGs were associated with tumour progression and metastasis. In addition, a protein-protein interaction network showed potential interactions among these ARGs. Furthermore, gene set enrichment analysis suggested that the Notch and Wnt signalling pathways were overexpressed in the high-risk group, and gene set variation analysis revealed that 38 hallmark genes differed between the two groups. Moreover, Kaplan-Meier survival curves and receiver operating characteristic curves were used to identify five ARGs (CD24, KRT15, MIA, NDRG1, TP63), and quantitative polymerase chain reaction was employed to assess the differential expression of these ARGs. Univariate and multivariate Cox regression analyses were then performed for the key ARGs, with the best prediction of 3 year survival. In conclusion, ARGs might play a crucial role in tumour progression and serve as indicators of prognosis in BRCA.

Glycolytic shift during West Nile virus infection provides new therapeutic opportunities

BACKGROUND

Viral rewiring of host bioenergetics and immunometabolism may provide novel targets for therapeutic interventions against viral infections. Here, we have explored the effect on bioenergetics during the infection with the mosquito-borne flavivirus West Nile virus (WNV), a medically relevant neurotropic pathogen causing outbreaks of meningitis and encephalitis worldwide.

RESULTS

A systematic literature search and meta-analysis pointed to a misbalance of glucose homeostasis in the central nervous system of WNV patients. Real-time bioenergetic analyses confirmed upregulation of aerobic glycolysis and a reduction of mitochondrial oxidative phosphorylation during viral replication in cultured cells. Transcriptomics analyses in neural tissues from experimentally infected mice unveiled a glycolytic shift including the upregulation of hexokinases 2 and 3 (Hk2 and Hk3) and pyruvate dehydrogenase kinase 4 (Pdk4). Treatment of infected mice with the Hk inhibitor, 2-deoxy-D-glucose, or the Pdk4 inhibitor, dichloroacetate, alleviated WNV-induced neuroinflammation.

CONCLUSIONS

These results highlight the importance of host energetic metabolism and specifically glycolysis in WNV infection in vivo. This study provides proof of concept for the druggability of the glycolytic pathway for the future development of therapies to combat WNV pathology.

Development of a thyroid cancer prognostic model based on the mitophagy-associated differentially expressed genes

BACKGROUND

The prevalence of thyroid cancer (ThyC), a frequent malignant tumor of the endocrine system, has been rapidly increasing over time. The mitophagy pathway is reported to play a critical role in ThyC onset and progression in many studies. This research aims to create a mitophagy-related survival prediction model for ThyC patients.

METHODS

Genes connected to mitophagy were found in the GeneCards database. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided information on the expression patterns of ThyC-related genes. To identify differentially expressed genes (DEGs), R software was employed. The prognostic significance of each DEG was assessed using the prognostic K-M curve. The prognostic model was built using LASSO, ROC, univariate, and multivariate Cox regression analyses. Finally, a nomogram model was developed to predict the survival outcome of ThyC patients in the clinical setting.

RESULTS

Through differential analysis, functional enrichment analysis, and protein-protein interaction (PPI) network analysis, we screened 10 key genes related to mitophagy in ThyC. The risk model was eventually developed using LASSO and Cox regression analyses based on the six DEGs related to mitophagy. An altered expression level of a mitophagy-related prognostic gene, GGCT, was found to be the most significant one, according to the KM survival curve analysis. An immunohistochemical (IHC) investigation revealed that ThyC tissues expressed higher levels of GGCT than normal thyroid tissues. The ROC curve verified the satisfactory performance of the model in survival prediction. Multivariate Cox regression analysis showed that the pathological grade, residual tumor volume, and initial tumor lesion type were significantly linked to the prognosis. Finally, we created a nomogram to predict the overall survival rate of ThyC patients at 3-, 5-, and 7- year time points.

CONCLUSION

The nomogram risk prediction model was developed to precisely predict the survival rate of ThyC patients. The model was validated based on the most significant DEG GGCT gene expression in ThyC. This model may serve as a guide for the creation of precise treatment plans for ThyC patients.

Identification and validation of a prognostic signature of cuproptosis-related genes for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a highly lethal form of cancer. Cuproptosis is a recently discovered form of regulated cell death. However, its significance in ESCC remains largely unknown. In this study, we observed significant expression differences in most of the 12 cuproptosis-related genes (CRGs) in the TCGA-ESCC dataset, which was validated using GSE20347, GSE38129, and individual ESCC datasets. We were able to divide patients in the TCGA-ESCC cohort into two subgroups based on disease, and found significant differences in survivor outcomes and biological functions between these subgroups. Additionally, we identified 11 prognosis-related genes from the 12 CRGs using LASSO COX regression analysis and constructed a CRGs signature for ESCC. Patients were categorized into high- and low-risk subgroups based on their median risk score, with those in the high-risk subgroup having significantly worse overall survival than those in the low-risk subgroup. The CRGs signature was also highly accurate in predicting prognosis and survival outcomes. Univariate and multivariate Cox regression analyses revealed that 8 of the 11 CRGs were independent prognostic factors for predicting survival in ESCC patients. Furthermore, our nomogram performed well and could serve as a useful tool for predicting prognosis. Finally, our risk model was found to be relevant to the sensitivity of targeted agents and immune infiltration. Functional enrichment analysis demonstrated that the risk model was associated with biological pathways of tumor migration and invasion. In summary, our study may provide a promising prognostic signature based on CRGs and offers potential targets for personalized therapy.

Characteristics of Autophagy-Related Genes, Diagnostic Models, and Their Correlation with Immune Infiltration in Keratoconus

Purpose

Keratoconus (KTCN) is one of the most common degenerative keratopathies, significantly affecting vision and even leading to blindness. This study identifies potential biomarkers of KTCN based on the characterization of autophagy-related genes (ARGs) and the construction of a diagnostic model; and explores their relevance to immune infiltrating cells in KTCN.

Methods

Gene Expression Omnibus (GEO) data were downloaded and ARGs were acquired from GeneCards and Molecular Signatures Database (MSigDB). Autophagy-related differential expression genes (ARDEGs) were discovered through the integration of differentially expressed genes (DEGs) with ARGs, while hub genes of KTCN were discovered by protein-protein interaction (PPI) network analysis. The probable biological roles of these hub ARDEGs were examined using functional enrichment analysis, and a KTCN diagnostic model was generated using the least absolute shrinkage and selection operator (LASSO) regression analysis. We also employed the CIBERSORTx and ssGSEA algorithms to identify potential regulatory pathways to compare the abundance of immune cell infiltrates and their association with hub genes. Finally, the hub gene expression levels were confirmed using validation datasets as well as blood samples from KTCN and healthy individuals.

Results

In this study, we identified 12 hub ARDEGs, of which 9 genes were substantially distinct between KTCN patients and normal groups. The LASSO risk score was used to generate the nomogram, and the calibration curve evaluated the model's effective diagnostic performance (C index of 0.961). Patients with KTCN had greater percentages of M2 Macrophages and Gamma delta T cells, according to CIBERSORTx and ssGSEA. The outcomes of the bioinformatics analysis were supported by the DDIT3 and BINP3 expression levels in KTCN patients and healthy controls, according to the qRT-PCR data.

Conclusion

Five biomarkers (CFTR, PLIN2, DDIT3, BAG3, and BNIP3) and diagnostic models offer fresh perspectives on identifying and managing KTCN.

Hexokinase 2-mediated gene expression via histone lactylation is required for hepatic stellate cell activation and liver fibrosis

Lactate was implicated in the activation of hepatic stellate cells (HSCs). However, the mechanism by which lactate exerts its effect remains elusive. Using RNA-seq and CUT&Tag chromatin profiling, we found that induction of hexokinase 2 (HK2) expression in activated HSCs is required for induced gene expression by histone lactylation but not histone acetylation. Inhibiting histone lactylation by Hk2 deletion or pharmacological inhibition of lactate production diminishes HSC activation, whereas exogenous lactate but not acetate supplementation rescues the activation phenotype. Thus, lactate produced by activated HSCs determines the HSC fate via histone lactylation. We found that histone acetylation competes with histone lactylation, which could explain why class I HDAC (histone deacetylase) inhibitors impede HSC activation. Finally, HSC-specific or systemic deletion of HK2 inhibits HSC activation and liver fibrosis in vivo. Therefore, we provide evidence that HK2 may be an effective therapeutic target for liver fibrosis.

Unraveling the therapeutic mechanisms of dichloroacetic acid in lung cancer through integrated multi-omics approaches: metabolomics and transcriptomics

Objective: The aim of this study was to investigate the molecular mechanisms underlying the therapeutic effects of dichloroacetic acid (DCA) in lung cancer by integrating multi-omics approaches, as the current understanding of DCA's role in cancer treatment remains insufficiently elucidated. Methods: We conducted a comprehensive analysis of publicly available RNA-seq and metabolomic datasets and established a subcutaneous xenograft model of lung cancer in BALB/c nude mice (n = 5 per group) treated with DCA (50 mg/kg, administered via intraperitoneal injection). Metabolomic profiling, gene expression analysis, and metabolite-gene interaction pathway analysis were employed to identify key pathways and molecular players involved in the response to DCA treatment. In vivo evaluation of DCA treatment on tumor growth and MIF gene expression was performed in the xenograft model. Results: Metabolomic profiling and gene expression analysis revealed significant alterations in metabolic pathways, including the Warburg effect and citric acid cycle, and identified the MIF gene as a potential therapeutic target in lung cancer. Our analysis indicated that DCA treatment led to a decrease in MIF gene expression and an increase in citric acid levels in the treatment group. Furthermore, we observed a potential interaction between citric acid and the MIF gene, suggesting a novel mechanism underlying the therapeutic effects of DCA in lung cancer. Conclusion: This study underscores the importance of integrated omics approaches in deciphering the complex molecular mechanisms of DCA treatment in lung cancer. The identification of key metabolic pathways and the novel finding of citric acid elevation, together with its interaction with the MIF gene, provide promising directions for the development of targeted therapeutic strategies and improving clinical outcomes for lung cancer patients.

Protein kinases on carbon metabolism: potential targets for alternative chemotherapies against toxoplasmosis

The apicomplexan parasite Toxoplasma gondii is the causative agent of toxoplasmosis, a global disease that significantly impacts human health. The clinical manifestations are mainly observed in immunocompromised patients, including ocular damage and neuronal alterations leading to psychiatric disorders. The congenital infection leads to miscarriage or severe alterations in the development of newborns. The conventional treatment is limited to the acute phase of illness, without effects in latent parasites; consequently, a cure is not available yet. Furthermore, considerable toxic effects and long-term therapy contribute to high treatment abandonment rates. The investigation of exclusive parasite pathways would provide new drug targets for more effective therapies, eliminating or reducing the side effects of conventional pharmacological approaches. Protein kinases (PKs) have emerged as promising targets for developing specific inhibitors with high selectivity and efficiency against diseases. Studies in T. gondii have indicated the presence of exclusive PKs without homologs in human cells, which could become important targets for developing new drugs. Knockout of specific kinases linked to energy metabolism have shown to impair the parasite development, reinforcing the essentiality of these enzymes in parasite metabolism. In addition, the specificities found in the PKs that regulate the energy metabolism in this parasite could bring new perspectives for safer and more efficient therapies for treating toxoplasmosis. Therefore, this review provides an overview of the limitations for reaching an efficient treatment and explores the role of PKs in regulating carbon metabolism in Toxoplasma, discussing their potential as targets for more applied and efficient pharmacological approaches.

Excess Dietary Sugar Alters Colonocyte Metabolism and Impairs the Proliferative Response to Damage

BACKGROUND & AIMS

The colonic epithelium requires continuous renewal by crypt resident intestinal stem cells (ISCs) and transit-amplifying (TA) cells to maintain barrier integrity, especially after inflammatory damage. The diet of high-income countries contains increasing amounts of sugar, such as sucrose. ISCs and TA cells are sensitive to dietary metabolites, but whether excess sugar affects their function directly is unknown.

METHODS

Here, we used a combination of 3-dimensional colonoids and a mouse model of colon damage/repair (dextran sodium sulfate colitis) to show the direct effect of sugar on the transcriptional, metabolic, and regenerative functions of crypt ISCs and TA cells.

RESULTS

We show that high-sugar conditions directly limit murine and human colonoid development, which is associated with a reduction in the expression of proliferative genes, adenosine triphosphate levels, and the accumulation of pyruvate. Treatment of colonoids with dichloroacetate, which forces pyruvate into the tricarboxylic acid cycle, restored their growth. In concert, dextran sodium sulfate treatment of mice fed a high-sugar diet led to massive irreparable damage that was independent of the colonic microbiota and its metabolites. Analyses on crypt cells from high-sucrose-fed mice showed a reduction in the expression of ISC genes, impeded proliferative potential, and increased glycolytic potential without a commensurate increase in aerobic respiration.

CONCLUSIONS

Taken together, our results indicate that short-term, excess dietary sucrose can directly modulate intestinal crypt cell metabolism and inhibit ISC/TA cell regenerative proliferation. This knowledge may inform diets that better support the treatment of acute intestinal injury.