Comprehensive Analysis of Alteration Landscape and Its Clinical Significance of Mitochondrial Energy Metabolism Pathway-Related Genes in Lung Cancers

Inhibition of the FOXO1-ROCK1 axis mitigates cardiomyocyte injury under chronic hypoxia in Tetralogy of Fallot by maintaining mitochondrial quality control

INTRODUCTION

Persistent chronic myocardial hypoxia causes disturbances in mitochondrial quality control (MQC), ultimately leading to increased cardiomyocyte injury in patients with Tetralogy of Fallot (TOF). The present study aimed to identify the key effector molecules of cardiomyocyte injury under chronic hypoxia in TOF.

METHODS

Clinical data from TOF patients were collected and whole transcriptome sequencing was performed on myocardial samples. Chronic hypoxia models were established in cardiac-specific knockout mice and cardiomyocytes, and a series of molecular experiments were used to determine the specific mechanisms involved.

RESULTS

Clinical cohort data and whole-transcriptome sequencing analysis of myocardial samples from TOF patients revealed that forkhead box O1 (FOXO1) plays an important role in chronic hypoxic cardiomyocyte injury. In a model of chronic hypoxia established in FOXO1 cardiac-specific knockout mice and FOXO1 gene-deficient cardiomyocytes, the AMPK signaling pathway regulates the expression of FOXO1, which in turn disrupts MQC by regulating the transcriptional activation of Rho-associated protein kinase 1 (ROCK1), and increasing the production of mitochondrial ROS, thereby exacerbating damage to cardiomyocytes. Excessive reactive oxygen species (ROS) production during MQC dysfunction further activates Cox7a2L to increase the assembly of the respiratory chain supercomplex. In addition, we found that miR-27b-3p partially binds to the 3' untranslated region of FOXO1 to exert a protective effect.

CONCLUSIONS

Maintenance of MQC under chronic hypoxia is achieved through a series of injury-protection mechanisms, suggesting that FOXO1 inhibition may be crucial for future mitigation of chronic hypoxic cardiomyocyte injury in TOF.

Cervicovaginal Metabolome and Tumor Characteristics for Endometrial Cancer Detection and Risk Stratification

PURPOSE

Endometrial cancer is highly prevalent and lacking noninvasive diagnostic techniques. Diagnosis depends on histological investigation of biopsy samples. Serum biomarkers for endometrial cancer have lacked sensitivity and specificity. The objective of this study was to investigate the cervicovaginal environment to improve the understanding of metabolic reprogramming related to endometrial cancer and identify potential biomarker candidates for noninvasive diagnostic and prognostic tests.

EXPERIMENTAL DESIGN

Cervicovaginal lavages were collected from 192 participants with endometrial cancer (n = 66) and non-malignant conditions (n = 108), and global untargeted metabolomics was performed. Using the metabolite data (n = 920), we completed a multivariate biomarker discovery analysis.

RESULTS

We analyzed grade 1/2 endometrioid carcinoma (n = 53) and other endometrial cancer subtypes (n = 13) to identify shared and unique metabolic signatures between the subtypes. When compared to non-malignant conditions, downregulation of proline (P < 0.0001), tryptophan (P < 0.0001), and glutamate (P < 0.0001) was found among both endometrial cancer groups, relating to key hallmarks of cancer including immune suppression and redox balance. Upregulation (q < 0.05) of sphingolipids, fatty acids, and glycerophospholipids was observed in endometrial cancer in a type-specific manner. Furthermore, cervicovaginal metabolites related to tumor characteristics, including tumor size and myometrial invasion.

CONCLUSIONS

Our findings provide insights into understanding the endometrial cancer metabolic landscape and improvement in diagnosis. The metabolic dysregulation described in this article linked specific metabolites and pathophysiological mechanisms including cellular proliferation, energy supply, and invasion of neighboring tissues. Furthermore, cervicovaginal metabolite levels related to tumor characteristics, which are used for risk stratification. Overall, development of noninvasive diagnostics can improve both the acceptability and accessibility of diagnosis.

Identification of lung adenocarcinoma subtypes based on mitochondrial energy metabolism-related genes

BACKGROUND

Identifying subtypes of lung adenocarcinoma (LUAD) patients based on mitochondrial energy metabolism and immunotherapy sensitivity is essential for precision cancer treatment.

METHODS

LUAD subtypes were identified using unsupervised consensus clustering, and results were subjected to immune and tumor mutation analyses. DEGs between subtypes were identified by differential analysis. Functional enrichment and PPI network analyses were conducted. Patients were classified into high and low expression groups based on the expression of the top 10 hub genes, and survival analysis was performed. Drugs sensitive to feature genes were screened based on the correlation between hub gene expression and drug IC50 value. qRT-PCR and western blot were used for gene expression detection, and CCK-8 and flow cytometry were for cell viability and apoptosis analysis.

RESULTS

Cluster-1 had significantly higher overall survival and a higher degree of immunoinfiltration and immunophenotypic score, but a lower TIDE score, DEPTH score, and TMB. Enrichment analysis showed that pathways and functions of DEGs between two clusters were mainly related to the interaction of receptor ligands with intracellular proteases. High expression of hub genes corresponded to lower patient survival rates. The predicted drugs with high sensitivity to feature genes were CDK1: Ribavirin (0.476), CCNB2: Hydroxyurea (0.474), Chelerythrine (0.470), and KIF11: Ribavirin (0.471). KIF11 and CCNB2 were highly expressed in LUAD cells and promoted cell viability and inhibited cell apoptosis.

CONCLUSION

This study identified two subtypes of LUAD, with cluster-1 being more suitable for immunotherapy. These results provided a reference for the development of precision immunotherapy for LUAD patients.

Prognostic relevance and validation of ARPC1A in the progression of low-grade glioma

Low-grade glioma (LGG) is a grade II-III glioma accompanied by distinct clinical and molecular characteristics and the studies related to its prognosis are still unclear. The objective of this study is to explore the involvement of mitochondrial-related genes SLBP, COMMD7, LSM4, TOMM34, RPP40, FKBP1A, ARPC1A, and TBCA for the prognosis of LGG. We detected differences in the expression of some of the genes by analyzing the bioinformatics dataset and combining it with RT-PCR experiments. Subsequently, a nomogram was constructed and validated for the clinical relevance of risk factors such as age, WHO grade, IDH mutation status, Ch.1p19q co-deletion status, and high and low expression of ARPC1A to predict the 1-, 3-, 5-year overall survival and prognostic relevance of ARPC1A. Gene set enrichment analysis was performed for the relevant datasets pertinent to the expression of ARPC1A to elucidate the cancer-promoting pathways involved in the LGG through KEGG and GO analysis. Transfection assays, CCK-8 assays, and flow cytometry were used to determine the proliferation rate, and apoptosis rate of the HS683 and SW1783 cell lines respectively. Western blotting was used to examine the involvement of the cancer-promoting activity of ARPC1A through MAPK signaling. In this study, the prognostic value of ARPC1A in LGG was found by bioinformatics analysis combined with experimental approach analysis and may be a significant independent risk factor. ARPC1A fosters a higher LGG proliferation rate that may control the MAP kinase signaling and could be a prominent biomarker for LGG. Future studies are warranted to explore its clinical implications.

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.

Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach

Objective: Triple-negative breast cancer (TNBC) poses significant diagnostic challenges due to its aggressive nature. This research develops an innovative deep learning (DL) model based on the latest multi-omics data to enhance the accuracy of TNBC subtype and prognosis prediction. The study focuses on addressing the constraints of prior studies by showcasing a model with substantial advancements in data integration, statistical performance, and algorithmic optimization. Methods: Breast cancer-related molecular characteristic data, including mRNA, miRNA, gene mutations, DNA methylation, and magnetic resonance imaging (MRI) images, were retrieved from the TCGA and TCIA databases. This study not only compared single-omics with multi-omics machine learning models but also applied Bayesian optimization to innovatively optimize the neural network structure of a DL model for multi-omics data. Results: The DL model for multi-omics data significantly outperformed single-omics models in subtype prediction, achieving a 98.0% accuracy in cross-validation, 97.0% in the validation set, and 91.0% in an external test set. Additionally, the MRI radiomics model showed promising performance, especially with the training set; however, a decrease in performance during transfer testing underscored the advantages of the DL model for multi-omics data in data consistency and digital processing. Conclusion: Our multi-omics DL model presents notable innovations in statistical performance and transfer learning capability, bearing significant clinical relevance for TNBC classification and prognosis prediction. While the MRI radiomics model proved effective, it requires further optimization for cross-dataset application to enhance accuracy and consistency. Our findings offer new insights into improving TNBC classification and prognosis through multi-omics data and DL algorithms.

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Pathological and pharmacological functions of the metabolites of polyunsaturated fatty acids mediated by cyclooxygenases, lipoxygenases, and cytochrome P450s in cancers

Oxylipins have garnered increasing attention because they were consistently shown to play pathological and/or pharmacological roles in the development of multiple cancers. Oxylipins are the metabolites of polyunsaturated fatty acids via both enzymatic and nonenzymatic pathways. The enzymes mediating the metabolism of PUFAs include but not limited to lipoxygenases (LOXs), cyclooxygenases (COXs), and cytochrome P450s (CYPs) pathways, as well as the down-stream enzymes. Here, we systematically summarized the pleiotropic effects of oxylipins in different cancers through pathological and pharmacological aspects, with specific reference to the enzyme-mediated oxylipins. We discussed the specific roles of oxylipins on cancer onset, growth, invasion, and metastasis, as well as the expression changes in the associated metabolic enzymes and the associated underlying mechanisms. In addition, we also discussed the clinical application and potential of oxylipins and related metabolic enzymes as the targets for cancer prevention and treatment. We found the specific function of most oxylipins in cancers, especially the underlying mechanisms and clinic applications, deserves and needs further investigation. We believe that research on oxylipins will provide not only more therapeutic targets for various cancers but also dietary guidance for both cancer patients and healthy humans.

Regulation of mitochondrial metabolism by autophagy supports leptin-induced cell migration

Leptin is an adipokine secreted by adipose tissue, which promotes tumor progression by activating canonical signaling pathways such as MAPK/ERK. Recent studies have shown that leptin induces autophagy, and this process is involved in leptin-induced characteristics of malignancy. Autophagy is an intracellular degradation process associated with different hallmarks of cancer, such as cell survival, migration, and metabolic reprogramming. However, its relationship with metabolic reprogramming has not been clearly described. The purpose of this study was to determine the role of leptin-induced autophagy in cancer cell metabolism and its association with cellular proliferation and migration in breast cancer cells. We used ER+/PR+ and triple-negative breast cancer cell lines treated with leptin, autophagy inhibition, or mitochondrial metabolism inhibitors. Our results show that leptin induces autophagy, increases proliferation, mitochondrial ATP production and mitochondrial function in ER+/PR+ cells. Importantly, autophagy was required to maintain metabolic changes and cell proliferation driven by leptin. In triple-negative cells, leptin did not induce autophagy or cell proliferation but increased glycolytic and mitochondrial ATP production, mitochondrial function, and cell migration. In triple negative cells, autophagy was required to support metabolic changes and cell migration, and autophagy inhibition decreased cellular migration similar to mitochondrial inhibitors. In conclusion, leptin-induced autophagy supports mitochondrial metabolism in breast cancer cells as well as glycolysis in triple negative cells. Importantly, leptin-induced mitochondrial metabolism promoted cancer cell migration.

Expeditious profiling of polycyclic aromatic hydrocarbons transport and obstruction mechanisms in crop xylem sap proteins via proteomics and molecular docking

Polycyclic aromatic hydrocarbons (PAHs) pose significant environmental risks due to their toxicity and carcinogenic properties. This research seeks to pinpoint protein targets in crop xylem sap related to PAH contamination and delve into their protein-ligand interactions using computational tools. Proteomic assessment revealed differentially expressed proteins (DEPs), which were subjected to virtual high-throughput screening. Notably, the phenanthrene's influence on xylem sap proteins in maize and wheat was more pronounced than in soybean, with DEPs expression peak at 24 h post-treatment. Maize DEPs were predominantly associated with lipid biosynthesis. Phenanthrene impacted cell membrane hydrophobicity, limiting PAH adsorption and decreasing its concentration in maize xylem sap. Wheat DEPs exhibited an increase in ABC transporters after 24 h of phenanthrene exposure. ABC transporters interacted with stress-responsive proteins like C6TIY1-Co-chaperone p23 and others that either facilitate or inhibit PAH transport, including Indeno[1,2,3-cd]Pyrene and C6TIY1-Co-chaperone protein p23. Both maize and wheat created high-affinity complexes between specific proteins and PAHs, influencing their transport. This study provides insights into the mechanisms of PAH regulation and movement within plant xylem.

The mitochondrial energy metabolism pathway-related signature predicts prognosis and indicates immune microenvironment infiltration in osteosarcoma

BACKGROUND

Abnormalities in the mitochondrial energy metabolism pathways are closely related to the occurrence and development of many cancers. Furthermore, abnormal genes in mitochondrial energy metabolism pathways may be novel targets and biomarkers for the diagnosis and treatment of osteosarcoma. In this study, we aimed to establish a mitochondrial energy metabolism-related gene signature for osteosarcoma prognosis.

METHODS

We first obtained differentially expressed genes based on the metastatic status of 84 patients with osteosarcoma from the TARGET database. After Venn analysis of differentially expressed genes and mitochondrial energy metabolism pathway-related genes (MMRGs), 2 key genes were obtained using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. Next, we used these 2 genes to establish a prognostic signature. Subsequent analyses elucidated the correlation between these 2 key genes with clinical features and 28 types of immune cells. Pathway changes in osteosarcoma pathogenesis under different metastatic states were clarified using gene set enrichment analysis (GSEA) of differentially expressed genes.

RESULTS

A gene signature composed of 2 key prognosis-related genes (KCNJ5 and PFKFB2) was identified. A risk score was calculated based on the gene signature, which divided osteosarcoma patients into low- or high-risk groups that showed good and poor prognosis, respectively. High expression of these 2 key genes is associated with low-risk group in patients with osteosarcoma. We constructed an accurate nomogram to help clinicians assess the survival time of patients with osteosarcoma. The results of immune cell infiltration level showed that the high-risk group had lower levels of immune cell infiltration. GSEA revealed changes in immune regulation and hypoxia stress pathways in osteosarcoma under different metastatic states.

CONCLUSION

Our study identified an excellent gene signature that could be helpful in improving the prognosis of patients with osteosarcoma.

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

Human Aldehyde Dehydrogenases: A Superfamily of Similar Yet Different Proteins Highly Related to Cancer

The superfamily of human aldehyde dehydrogenases (hALDHs) consists of 19 isoenzymes which are critical for several physiological and biosynthetic processes and play a major role in the organism's detoxification via the NAD(P) dependent oxidation of numerous endogenous and exogenous aldehyde substrates to their corresponding carboxylic acids. Over the last decades, ALDHs have been the subject of several studies as it was revealed that their differential expression patterns in various cancer types are associated either with carcinogenesis or promotion of cell survival. Here, we attempt to provide a thorough review of hALDHs' diverse functions and 3D structures with particular emphasis on their role in cancer pathology and resistance to chemotherapy. We are especially interested in findings regarding the association of structural features and their changes with effects on enzymes' functionalities. Moreover, we provide an updated outline of the hALDHs inhibitors utilized in experimental or clinical settings for cancer therapy. Overall, this review aims to provide a better understanding of the impact of ALDHs in cancer pathology and therapy from a structural perspective.

The role of sperm-specific glyceraldehyde-3-phosphate dehydrogenase in the development of pathologies-from asthenozoospermia to carcinogenesis

The review considers various aspects of the influence of the glycolytic enzyme, sperm-specific glyceraldehyde-3-phosphate dehydrogenase (GAPDS) on the energy metabolism of spermatozoa and on the occurrence of several pathologies both in spermatozoa and in other cells. GAPDS is a unique enzyme normally found only in mammalian spermatozoa. GAPDS provides movement of the sperm flagellum through the ATP formation in glycolytic reactions. Oxidation of cysteine residues in GAPDS results in inactivation of the enzyme and decreases sperm motility. In particular, reduced sperm motility in diabetes can be associated with GAPDS oxidation by superoxide anion produced during glycation reactions. Mutations in GAPDS gene lead in the loss of motility, and in some cases, disrupts the formation of the structural elements of the sperm flagellum, in which the enzyme incorporates during spermiogenesis. GAPDS activation can be used to increase the spermatozoa fertility, and inhibitors of this enzyme are being tried as contraceptives. A truncated GAPDS lacking the N-terminal fragment of 72 amino acids that attaches the enzyme to the sperm flagellum was found in melanoma cell lines and then in specimens of melanoma and other tumors. Simultaneous production of the somatic form of GAPDH and sperm-specific GAPDS in cancer cells leads to a reorganization of their energy metabolism, which is accompanied by a change in the efficiency of metastasis of certain forms of cancer. Issues related to the use of GAPDS for the diagnosis of cancer, as well as the possibility of regulating the activity of this enzyme to prevent metastasis, are discussed.

Construction of a prognostic model based on genes associated with mitochondrial energy metabolic pathway in colon adenocarcinoma and its clinical significance

Mitochondria are the main sites of oxidative metabolism and energy release of sugars, fats and amino acids in the body. According to studies, malignant tumor occurrence and development have been linked to abnormal mitochondrial energy metabolism (MEM). However, the feasible role of abnormal MEM in colon adenocarcinoma (COAD) is poorly understood. In this work, we obtained COAD patient data from The Cancer Genome Atlas (TCGA) as the training set, and GSE103479 from Gene Expression Omnibus (GEO) as the validation set. Combined with the mitochondrial energy metabolic pathway (MEMP)-related genes in Kyoto Encyclopedia of Genes and Genomes (KEGG) database, a risk prognostic model was constructed by utilizing Cox regression analysis to identify 6 feature genes (CYP4A11, PGM2, PKLR, PPARGC1A, CPT2 and ACAT2) that were significantly associated with MEMP in COAD. By stratifying the samples based on riskscore, two distinct groups, namely the high- and low-risk groups, were identified. The model demonstrated accurate assessment of the prognosis risk in COAD patients and exhibited independent prognostic capability, as evidenced by the survival curve and receiver operating characteristic (ROC) curve analysis. A nomogram was plotted based on clinical information and riskscore. We proved it could predict the survival time of COAD patients effectively combined with the calibration curve of risk prediction. Subsequently, based on the immune evaluation and mutation frequency analysis performed on COAD patients, patients in high-risk group had observably higher immune scores, immune activity and PDCD1 expression level than low-risk group. In general, the prognostic model developed using MEMP-related genes served as a valuable biomarker for forecasting the prognosis of COAD patients, which offered a reference for the prognosis evaluation and clinical cure of COAD patients.

Dissecting the alternation landscape of mitochondrial metabolism-related genes in lung adenocarcinoma and their latent mechanisms

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer with high incidence and unsatisfactory prognosis. The majority of LUAD patients eventually succumb to local and/or distinct metastatic recurrence. Genomic research of LUAD has broadened our understanding of this disease's biology and improved target therapies. However, the alternation landscape and characteristics of mitochondrial metabolism-related genes (MMRGs) in LUAD progression remain poorly understood. We performed a comprehensive analysis to identify the function and mechanism of MMRGs in LUAD based on the TCGA and GEO databases, which might offer therapeutic values for clinical researchers. Then, we figured out three hub prognosis-associated MMRGs (also termed as PMMRGs: ACOT11, ALDH2, and TXNRD1) that were engaged in the evolution of LUAD. To investigate the correlation between clinicopathological characteristics and MMRGs, we divided LUAD samples into two clusters (C1 and C2) based on key MMRGs. In addition, important pathways and the immune infiltration landscape affected by LUAD clusters were also delineated. Further, we nominated potential regulatory mechanisms underlying the MMRGs in LUAD development and progression. In conclusion, our integrative analysis enables a more comprehensive understanding of the mutation landscape of MMRGs in LUAD and provides an opportunity for more precise treatment.

Integrated analysis identifies oxidative stress-related lncRNAs associated with progression and prognosis in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most common cancers in the world. Oxidative stress reactions have been reportedly associated with oncogenesis and tumor progression. By analyzing mRNA expression data and clinical information from The Cancer Genome Atlas (TCGA), we aimed to construct an oxidative stress-related long noncoding RNA (lncRNA) risk model and identify oxidative stress-related biomarkers to improve the prognosis and treatment of CRC.

RESULTS

Differentially expressed oxidative stress-related genes (DEOSGs) and oxidative stress-related lncRNAs were identified by using bioinformatics tools. An oxidative stress-related lncRNA risk model was constructed based on 9 lncRNAs (AC034213.1, AC008124.1, LINC01836, USP30-AS1, AP003555.1, AC083906.3, AC008494.3, AC009549.1, and AP006621.3) by least absolute shrinkage and selection operator (LASSO) analysis. The patients were then divided into high- and low-risk groups based on the median risk score. The high-risk group had a significantly worse overall survival (OS) (p < 0.001). Receiver operating characteristic (ROC) and calibration curves displayed the favorable predictive performance of the risk model. The nomogram successfully quantified the contribution of each metric to survival, and the concordance index and calibration plots demonstrated its excellent predictive capacity. Notably, different risk subgroups showed significant differences in terms of their metabolic activity, mutation landscape, immune microenvironment and drug sensitivity. Specifically, differences in the immune microenvironment implied that CRC patients in certain subgroups might be more responsive to immune checkpoint inhibitors.

CONCLUSIONS

Oxidative stress-related lncRNAs can predict the prognosis of CRC patients, which provides new insight for future immunotherapies based on potential oxidative stress targets.

Development and validation of a mitochondrial energy metabolism-related risk model in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers and ranks third inmortality. Mitochondria are the energy manufacturers of cells. Disruption of mitochondrial energy metabolism pathways is strongly correlated with the onset and progression of HCC. Aberrant genes in mitochondrial energy metabolism pathways may represent a unique diagnostic and therapeutic targets that act as indicators for HCC.

METHODS

Gene expression data from 374 HCC patients and 50 controls were acquired from TCGA database. A total of 188 mitochondrial energy metabolism-related genes (MMRGs) were obtained from KEGG PATHWAY database. A total of 368 patients with survival data were randomly split into training and validation groups in a 7: 3 ratio. Prognosis-related MMRGs were selected by univariate Cox and LASSO analyses. Kaplan-Meier and ROC curves were employed to analyze the model precision, whereas the validation set was used for model verification. Furthermore, clinical examinations, immune infiltration analysis, GSVA, and immunotherapy analysis were conducted in the high- and low-risk groups. Finally, the risk model was combined with the clinical variables of HCC patients to perform univariate and multivariate Cox regression analyses to obtain independent risk indicators and draw a nomogram. Therefore, we evaluated the accuracy of the predictions using calibration curves.

RESULTS

A total of 6032 differentially expressed genes (DEGs) were detected in the HCC and control samples. After overlapping DEGs with 188 MMRGs, 42 mitochondrial energy metabolism-related DEGs (DEMMRGs) were identified. A 17 specific genes-based risk score model of HCC was created, which revealed effectiveness in each TCGA training and validation dataset. Moreover, patients categorized by risk scores exhibited distinct immune infiltration status, immunotherapy responsiveness, and functional properties. Finally, univariate and multivariate Cox regression analyses revealed that risk score and stage T were independent predictive variables. Based on the T stage and risk score, a nomogram for estimating the survival of HCC patients was created. The calibration curves demonstrated that the prediction model had a high level of accuracy.

CONCLUSIONS

Our study constructed a mitochondrial energy metabolism-related risk model, that may be utilized to anticipate HCC prognosis and represent the immunological microenvironment of HCC.

Comprehensive analysis reveals signal and molecular mechanism of mitochondrial energy metabolism pathway in pancreatic cancer

Pancreatic cancer (PAAD) is one of the most malignant tumors with the worst prognosis. The abnormalities in the mitochondrial energy metabolism pathway are intimately correlated with the occurrence and progression of cancer. For the diagnosis and treatment of pancreatic cancer, abnormal genes in the mitochondrial energy metabolism system may offer new targets and biomarkers. In this study, we compared the dysregulated mitochondrial energy metabolism-associated pathways in PAAD based on pancreatic cancer samples in the Cancer Genome Atlas (TCGA) database and normal pancreas samples from the Genotype Tissue Expression project (GTEx) database. Then identified 32 core genes of mitochondrial energy metabolism pathway-related genes (MMRG) were based on the gene set enrichment analysis (GSEA). We found most of these genes were altered among different clinical characteristic groups, and showed significant prognostic value and association with immune infiltration, suggesting critical roles of MMRG involve tumor genesis of PAAD. Therefore, we constructed a four-gene (LDHA, ALDH3B1, ALDH3A1, and ADH6) prognostic biomarker after eliminating redundant factors, and confirming its efficiency and independence. Further analysis indicated the potential therapeutic compounds based on the mitochondrial energy metabolism-associated prognostic biomarker. All of the above analyses dissected the critical role of mitochondrial energy metabolism signaling in pancreatic cancer and gave a better understanding of the clinical intervention of PAAD.

Oxidative Stress and Air Pollution: Its Impact on Chronic Respiratory Diseases

Redox regulation participates in the control of various aspects of metabolism. Reactive oxygen and nitrogen species participate in many reactions under physiological conditions. When these species overcome the antioxidant defense system, a distressed status emerges, increasing biomolecular damage and leading to functional alterations. Air pollution is one of the exogenous sources of reactive oxygen and nitrogen species. Ambient airborne particulate matter (PM) is important because of its complex composition, which includes transition metals and organic compounds. Once in contact with the lungs' epithelium, PM components initiate the synthesis of inflammatory mediators, macrophage activation, modulation of gene expression, and the activation of transcription factors, which are all related to the physiopathology of chronic respiratory diseases, including cancer. Even though the pathophysiological pathways that give rise to the development of distress and biological damage are not fully understood, scientific evidence indicates that redox-dependent signaling pathways are involved. This article presents an overview of the redox interaction of air pollution inside the human body and the courses related to chronic respiratory diseases.

Comprehensive analysis of the prognosis, tumor microenvironment, and immunotherapy response of SDHs in colon adenocarcinoma

Background

Succinate dehydrogenase (SDH), one of the key enzymes in the tricarboxylic acid cycle, is mainly found in the mitochondria. SDH consists of four subunits encoding SDHA, SDHB, SDHC, and SDHD. The biological function of SDH is significantly related to cancer progression. Colorectal cancer (CRC) is one of the most common malignant tumors globally, whose most common histological subtype is colon adenocarcinoma (COAD). However, the correlation between SDH factors and COAD remains unclear.

Methods

The data on pan-cancer was obtained from The Cancer Genome Atlas (TCGA) database. Kaplan-Meier survival analysis showed the prognostic ability of SDHs. The cBioPortal database reflected genetic variations of SDHs. The correlation analysis was conducted between SDHs and mitochondrial energy metabolism genes (MMGs) and the protein-protein interaction (PPI) network was built. Consequently, Univariate and Multivariate Cox Regression Analysis on SDHs and other clinical characteristics were conducted. A nomogram was established. The ssGSEA analysis visualized the association between SDHs and immune infiltration. Immunophenoscore (IPS) explored the correlation between SDHs and immunotherapy, and the correlation between SDHs and targeted therapy was investigated through Genomics of Drug Sensitivity in Cancer. Finally, qPCR and immunohistochemistry detected SDHs' expression.

Results

After assessing SDHs differential expression in pan-cancer, we found that SDHB, SDHC, and SDHD benefit COAD patients. The cBioPortal database demonstrated that SDHA was the top gene in mutation frequency rank. Correlation analysis mirrored a strong link between SDHs and MMGs. We formulated a nomogram and found that SDHB, SDHC, SDHD, and clinical characteristics correlated with COAD patients' survival. For T helper cells, Th2 cells, and Tem, SDHA, SDHB, SDHC, and SDHD were significantly enriched in the high expression group. Moreover, COAD patients with high SDHA expression were more suitable for immunotherapy. And COAD patients with different SDHs' expression have different sensitivity to targeted drugs. Further verifying the gene and protein expression levels of SDHs, we found that the tissues were consistent with the bioinformatics analysis.

Conclusions

Our study analyzed the expression and prognostic value of SDHs in COAD, explored the pathway mechanisms involved, and the immune cell correlations, indicating that SDHs might be biomarkers for COAD patients.

Targeting the mitochondria in chronic respiratory diseases

Mitochondria are one of the basic essential components for eukaryotic life survival. It is also the source of respiratory ATP. Recently published studies have demonstrated that mitochondria may have more roles to play aside from energy production. There is an increasing body of evidence which suggest that mitochondrial activities involved in normal and pathological states contribute to significant impact to the lung airway morphology and epithelial function in respiratory diseases such as asthma, COPD, and lung cancer. This review summarizes the pathophysiological pathways involved in asthma, COPD, lung cancer and highlights potential treatment strategies that target the malfunctioning mitochondria in such ailments. Mitochondria are responsive to environmental stimuli such as infection, tobacco smoke, and inflammation, which are essential in the pathogenesis of respiratory diseases. They may affect mitochondrial shape, protein production and ultimately cause dysfunction. The impairment of mitochondrial function has downstream impact on the cytosolic components, calcium control, response towards oxidative stress, regulation of genes and proteins and metabolic activities. Several novel compounds and alternative medicines that target mitochondria in asthma and chronic lung diseases have been discussed here. Moreover, mitochondrial enzymes or proteins that may serve as excellent therapeutic targets in COPD are also covered. The role of mitochondria in respiratory diseases is gaining much attention and mitochondria-based treatment strategies and personalized medicine targeting the mitochondria may materialize in the near future. Nevertheless, more in-depth studies are urgently needed to validate the advantages and efficacy of drugs that affect mitochondria in pathological states.

Development of a Hallmark Pathway-Related Gene Signature Associated with Immune Response for Lower Grade Gliomas

Although some biomarkers have been used to predict prognosis of lower-grade gliomas (LGGs), a pathway-related signature associated with immune response has not been developed. A key signaling pathway was determined according to the lowest adjusted p value among 50 hallmark pathways. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox analyses were performed to construct a pathway-related gene signature. Somatic mutation, drug sensitivity and prediction of immunotherapy analyses were conducted to reveal the value of this signature in targeted therapies. In this study, an allograft rejection (AR) pathway was considered as a crucial signaling pathway, and we constructed an AR-related five-gene signature, which can independently predict the prognosis of LGGs. High-AR LGG patients had higher tumor mutation burden (TMB), Immunophenscore (IPS), IMmuno-PREdictive Score (IMPRES), T cell-inflamed gene expression profile (GEP) score and MHC I association immunoscore (MIAS) than low-AR patients. Most importantly, our signature can be validated in four immunotherapy cohorts. Furthermore, IC50 values of the six classic chemotherapeutic drugs were significantly elevated in the low-AR group compared with the high-AR group. This signature might be regarded as an underlying biomarker in predicting prognosis for LGGs, possibly providing more therapeutic strategies for future clinical research.

An integrated bioinformatic investigation of mitochondrial energy metabolism genes in colon adenocarcinoma followed by preliminary validation of CPT2 in tumor immune infiltration

Background

The prognosis for colon adenocarcinoma (COAD) today remains poor. Changes in mitochondria-related genes and metabolic reprogramming are related to tumor growth, metastasis, and immune evasion and are key factors in tumor genesis and development.

Methods

TCGA database was used to analyze the differentially expressed mitochondrial energy metabolism pathway-related genes (MMRGs) in COAD patients, and the mutation of MMRG in tumor cells, the biological processes involved, and the correlation with tumor immunity were also analyzed. Then, MMRG and MMRG-related genes were used to divide COAD patients into different subtypes, and immunocorrelation analysis and survival analysis were performed. Finally, univariate regression analysis and LASSO regression analysis were used to construct a prognostic risk model for COAD patients, which was verified by the GEO database and evaluated by Kaplan–Meier (K-M) and receiver operating characteristic (ROC) curves, and the correlation between the risk model and immunity and clinical subtypes based on MMRG was analyzed.

Results

In this study, the MMRG patterns and tumor immune microenvironment characteristics in COAD patients were systematically evaluated by clustering the expression of 188 MMRGs. We identified two subtypes of COAD with different clinical and immunological characteristics. Eight of the 28 differentially expressed MMRG genes were used to construct risk scores. ROC and K-M curves suggested that the risk model could well predict the prognosis of COAD patients, and the risk model was related to immune cell infiltration and immune function.

Conclusions

The two COAD subtypes identified by MMRG are helpful for the clinical differentiation of patients with different prognoses and tumor progressions, and the risk score can assist the clinical evaluation of patient prognosis. Our results suggest that CPT2 contributes to the recruitment and regulation of neutrophils in COAD. CPT2 may act as a valuable biomarker for COAD immunotherapy.

Evaluating the Role of lncRNAs in the Incidence of Cardiovascular Diseases in Androgenetic Alopecia Patients

Hair loss occurs in patients with Androgenetic Alopecia (AGA). The pattern of hair loss is different between men and women. The main cause of hair loss is increased cell apoptosis and decreased regeneration, proliferation and differentiation processes in hair follicles. Long Non-Coding RNAs (lncRNAs) are one of the most important molecules that regulate the processes of apoptosis, regeneration, proliferation and differentiation in hair follicles. Since studies have shown that lncRNAs can be effective in the development of cardiotoxicity and induction of cardiovascular disease (CVD); so effective lncRNAs in the regulation of regeneration, proliferation, differentiation and apoptosis of hair follicles can be involved in the development of CVD in AGA patients with. Therefore, this study investigated the lncRNAs involved in increasing apoptosis and reducing the processes of regeneration, proliferation and differentiation of hair follicles. The aim of the current study was to evaluate the role of lncRNAs as a risk factor in the incidence of CVD in AGA patients; it will help to design treatment strategies by targeting signaling pathways without any cardiotoxicity complications.

Discovery of Proteins Responsible for Resistance to Three Chemotherapy Drugs in Breast Cancer Cells Using Proteomics and Bioinformatics Analysis

Chemoresistance is a daunting obstacle to the effective treatment of breast cancer patients receiving chemotherapy. Although the mechanism of chemotherapy drug resistance has been explored broadly, the precise mechanism at the proteome level remains unclear. Especially, comparative studies between widely used anticancer drugs in breast cancer are very limited. In this study, we employed proteomics and bioinformatics approaches on chemoresistant breast cancer cell lines to understand the underlying resistance mechanisms that resulted from doxorubicin (DR), paclitaxel (PR), and tamoxifen (TAR). In total, 10,385 proteins were identified and quantified from three TMT 6-plex and one TMT 10-plex experiments. Bioinformatics analysis showed that Notch signaling, immune response, and protein re-localization processes were uniquely associated with DR, PR, and TAR resistance, respectively. In addition, proteomic signatures related to drug resistance were identified as potential targets of many FDA-approved drugs. Furthermore, we identified potential prognostic proteins with significant effects on overall survival. Representatively, PLXNB2 expression was associated with a highly significant increase in risk, and downregulation of ACOX3 was correlated with a worse overall survival rate. Consequently, our study provides new insights into the proteomic aspects of the distinct mechanisms underlying chemoresistance in breast cancer.