Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy

Bioinformatics Identification of angiogenesis-related biomarkers and therapeutic targets in cerebral ischemia-reperfusion

Promoting vascular endothelial cell regeneration can enhance recovery from cerebral ischemia reperfusion injury (CIRI), but there is a lack of bioinformatic studies on angiogenesis-related biomarkers in CIRI. In this study, we utilized the GSE97537 and GSE61616 datasets from GEO to identify 181 angiogenesis-related genes (ARGs) and analyzed differentially expressed genes (DEGs) between CIRI and control groups. We converted ARGs to 169 rat homologues and intersected them with DEGs to find DE-ARGs. RF and XGBoost models were employed to identify five biomarkers (Stat3, Hmox1, Egfr, Col18a1, Ptgs2) and conducted GSEA on these biomarkers, revealing their enrichment in pathways such as ECM-receptor interaction and hematopoietic cell lineage. We also analyzed the immune microenvironment, finding significant differences in 21 immune cells between CIRI and control groups. Furthermore, we constructed lncRNA-miRNA-mRNA networks and drug-gene networks. Finally, biomarker expression was compared between the CIRI and control groups by qRT-PCR in tissue and blood samples. Overall, our bioinformatic exploration of angiogenesis-related biomarkers in CIRI provides new insights for the diagnosis and treatment of CIRI.

Identification of HIST1H2BH as the hub gene associated with multiple myeloma using integrated bioinformatics analysis

OBJECTIVES

Identifying the specific biomarkers and molecular signatures of MM might provide novel evidence for MM prognosis and targeted therapy.

METHODS

Bioinformatic analyses were performed through GEO and TCGA datasets. The differential expression of HIST1H2BH in MM sample was validated by the qRT-PCR. And the CCK-8 assay was performed to detect the proliferation activity of HIST1H2BH on MM cell lines.

RESULTS

A total of 793 DEGs were identified between bone marrow plasma cells from newly diagnosed myeloma and normal donors in GSE6477. Among them, four vital genes (HIST1H2AC, HIST1H2BH, CCND1 and TCF7L2) modeling were constructed. The increased HIST1H2BH expression was correlated with worse survival of MM based on TCGA datasets. The transcriptional expression of HIST1H2BH was significantly up-regulated in primary MM patients. And knockdown HIST1H2BH decreased the proliferation of MM cell lines.

CONCLUSIONS

We have identified up-regulated HIST1H2BH in MM patients associated with poor prognosis using integrated bioinformatical methods.

Establishment and validation of the prognostic risk model based on the anoikis-related genes in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a malignant condition in humans. Anoikis-related genes (ARGs) are crucial to cancer progression. Therefore, more studies on the relationship between ARGs and ESCC are warranted.

METHODS

The study acquired ESCC-related transcriptome data from TCGA. Differentially expressed ARGs (DE-ARGs) were obtained by differential analysis and candidates were filtered out by survival analysis. Prognostic genes were determined by Cox and LASSO regression. A risk model was constructed based on prognostic gene expressions. An immune infiltration study was done to explain how these genes contribute to ESCC development. The IC50 test was adopted to assess the clinical response of chemotherapy drugs. Single cell analysis was performed on the GSE145370 dataset. Moreover, the prognostic gene expressions were detected by qRT-PCR.

RESULTS

53 DE-ARGs were screened and four candidate genes including PBK, LAMC2, TNFSF10 and KL were obtained. Cox and LASSO regression identified the two prognostic genes, TNFSF10 and PBK. Immuno-infiltration analysis revealed positive associations of PBK with Macrophages M0 cells, and TNFSF10 with Macrophages M1 cells. The IC50 values of predicted drugs, in the case of Tozasertib 1096 and WIKI4 1940, were significantly variant between risk groups. Single cell analysis revealed that TNFSF10 and PBK levels were higher in epithelial cells than in other cells. The prognostic genes expression results by qRT-PCR were compatible with the dataset analysis.

CONCLUSION

The study established an ARG prognosis model of ESCC. It provided a reference for the research of ARGs in ESCC.

Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus

Aim

Fatty acid metabolism is pivotal for lipid synthesis, cellular signaling, and maintaining cell membrane integrity. However, its diagnostic significance in type 2 diabetes mellitus (T2DM) remains unclear.

Materials and methods

Three datasets and fatty acid metabolism-related genes were retrieved. Differential expression analysis, WGCNA, machine learning algorithms, diagnostic analysis, and validation were employed to identify key feature genes. Functional analysis, ceRNA network construction, immune microenvironment assessment, and drug prediction were conducted to explore the underlying molecular mechanisms.

Results

Six feature genes were identified with strong diagnostic performance and were involved in processes such as ribosome function and fatty acid metabolism. Immune cells, including dendritic cells, eosinophils, and neutrophils, may play a role in the progression of T2DM. ceRNA and drug-target network analysis revealed potential interactions, such as RP11-miR-29a-YTHDF3 and BPA-MSANTD1. The expression patterns of the feature genes, except for YTHDF3, were consistently upregulated in T2DM, aligning with trends observed in the training set.

Conclusion

This study investigated the potential molecular mechanisms of six fatty acid metabolism-related genes in T2DM, offering valuable insights that may guide future research and therapeutic development.

Identifying the prognostic significance of mitophagy-associated genes in multiple myeloma: a novel risk model construction

Multiple myeloma (MM) is a highly heterogeneous hematological malignancy that is currently incurable. Individualized therapeutic approaches based on accurate risk assessment are essential for improving the prognosis of MM patients. Nevertheless, current prognostic models for MM exhibit certain limitations and prognosis heterogeneity still an unresolved issue. Recent studies have highlighted the pivotal involvement of mitochondrial autophagy in the development and drug sensitivity of MM. This study seeks to conduct an integrative analysis of the prognostic significance and immune microenvironment of mitophagy-related signature in MM, with the aim of constructing a novel predictive risk model. GSE4581 and GSE47552 datasets were acquired from the Gene Expression Omnibus database. MM-differentially expressed genes (DEGs) were identified by limma between MM samples and normal samples in GSE47552. Mitophagy key module genes were obtained by weighted gene co-expression network analysis in the Cancer Genome Atlas (TCGA)-MM dataset. Mitophagy DEGs were identified by the overlap genes between MM-DEGs and mitophagy key module genes. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, and a risk model was subsequently constructed based on these prognostic genes. Subsequently, the MM samples were stratified into high- and low-risk groups based on their median risk scores. The validity of the risk model was further evaluated using the GSE4581 dataset. Moreover, a nomogram was developed using the independent prognostic factors identified from the risk score and various clinical indicators. Additionally, analyses were conducted on immune infiltration, immune scores, immune checkpoint, and chemotherapy drug sensitivity. The 17 mitophagy DEGs were obtained by intersection of 803 MM-DEGs and 1084 mitophagy key module genes. Five prognostic genes (CDC6, PRIM1, SNRPB, TOP2A, and ZNF486) were selected via LASSO and univariate cox regression analyses. The predictive performance of the risk model, which was constructed based on the five prognostic genes, demonstrated favorable results in both TCGA-MM and GSE4581 datasets as indicated by the receiver operating characteristic (ROC) curves. In addition, calibration curve, ROC curve, and decision curve analysis curve corroborated that the nomogram exhibited superior predictive accuracy for MM. Furthermore, immune analysis results indicated a significant difference in stromal scores of two risk groups categorized on median risk scores. And four immune checkpoints (CD274, CTLA4, LAG3, and PDCD1LG2) showed significant differences in different risk groups. The analysis of chemotherapy drug sensitivity revealed that etoposide and doxorubicin, which target TOP2A, exhibited superior treatment outcomes in the high-risk group. A novel prognostic model for MM was developed and validated, demonstrating significant potential in predicting patient outcomes and providing valuable guidance for personalized immunotherapy counseling.

Mitochondria's Role in the Maintenance of Cancer Stem Cells in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and is recognized as a major contributor to cancer-related mortality worldwide. Cancer stem cells (CSCs) are a tiny group of cancer cells that possess a significant ability to regenerate themselves, form tumors, and undergo differentiation. CSCs have a pivotal role in the initiation, spread, recurrence, and resistance to treatment of cancer. As a result, they are very susceptible to being targeted for therapeutic intervention. The potential to cure HCC may be achieved by efficiently targeting drugs that eradicate cancer stem cells. Mitochondria have a crucial function in granting drug resistance to cancer stem cells by means of mitochondrial metabolism, biogenesis, and dynamics. Dysfunction in mitochondrial metabolic processes, such as mitochondrial oxidative phosphorylation (OXPHOS), calcium signaling, and reactive oxygen species (ROS) generation, contributes to the initiation and progression of human malignancies, including HCC. ROS have both beneficial and detrimental effects depending on their concentration. Consequently, ROS have become a prominent subject in the study of the fundamental mechanisms of HCC. Furthermore, an imbalance in the process of creating new mitochondria is a characteristic feature of CSCs, and an increase in mitochondrial biogenesis is associated with the heightened resistance observed in CSCs. This article provides a detailed examination of the involvement of mitochondria in the preservation of CSCs, as well as the spread of HCC. A deeper understanding of how mitochondria participate in tumorigenesis and drug resistance could result in the discovery of novel cancer treatments.

Identification of key genes and diagnostic model associated with circadian rhythms and Parkinson's disease by bioinformatics analysis

Background

Circadian rhythm disruption is typical in Parkinson's disease (PD) early stage, and it plays an important role in the prognosis of the treatment effect in the advanced stage of PD. There is growing evidence that circadian rhythm genes can influence development of PD. Therefore, this study explored specific regulatory mechanism of circadian genes (C-genes) in PD through bioinformatic approaches.

Methods

Differentially expressed genes (DEGs) between PD and control samples were identified from GSE22491 using differential expression analysis. The key model showing the highest correlation with PD was derived through WGCNA analysis. Then, DEGs, 1,288 C-genes and genes in key module were overlapped for yielding differentially expressed C-genes (DECGs), and they were analyzed for LASSO and SVM-RFE for yielding critical genes. Meanwhile, from GSE22491 and GSE100054, receiver operating characteristic (ROC) was implemented on critical genes to identify biomarkers, and Gene Set Enrichment Analysis (GSEA) was applied for the purpose of exploring pathways involved in biomarkers. Eventually, immune infiltrative analysis was applied for understanding effect of biomarkers on immune microenvironment, and therapeutic drugs which could affect biomarkers expressions were also predicted. Finally, we verified the expression of the genes by q-PCR.

Results

Totally 634 DEGs were yielded between PD and control samples, and MEgreen module had the highest correlation with PD, thus it was defined as key model. Four critical genes (AK3, RTN3, CYP4F2, and LEPR) were identified after performing LASSO and SVM-RFE on 18 DECGs. Through ROC analysis, AK3, RTN3, and LEPR were identified as biomarkers due to their excellent ability to distinguish PD from control samples. Besides, biomarkers were associated with Parkinson's disease and other functional pathways.

Conclusion

Through bioinformatic analysis, the circadian rhythm related biomarkers were identified (AK3, RTN3 and LEPR) in PD, contributing to studies related to PD treatment.

To Develop Biomarkers for Diabetic Nephropathy Based on Genes Related to Fibrosis and Propionate Metabolism and Their Functional Validation

Propionate metabolism is important in the development of diabetes, and fibrosis plays an important role in diabetic nephropathy (DN). However, there are no studies on biomarkers related to fibrosis and propionate metabolism in DN. Hence, the current research is aimed at evaluating biomarkers associated with fibrosis and propionate metabolism and to explore their effect on DN progression. The GSE96804 (DN : control = 41 : 20) and GSE104948 (DN : control = 7 : 18) DN-related datasets and 924 propionate metabolism-related genes (PMRGs) and 656 fibrosis-related genes (FRGs) were acquired from the public database. First, DN differentially expressed genes (DN-DEGs) between the DN and control samples were sifted out via differential expression analysis. The PMRG scores of the DN samples were calculated based on PMRGs. The samples were divided into the high and low PMRG score groups according to the median scores. The PM-DEGs between the two groups were screened out. Second, the intersection of DN-DEGs, PM-DEGs, and FRGs was taken to yield intersected genes. Random forest (RF) and recursive feature elimination (RFE) analyses of the intersected genes were performed to sift out biomarkers. Then, single gene set enrichment analysis was conducted. Finally, immunoinfiltrative analysis was performed, and the transcription factor (TF)-microRNA (miRNA)-mRNA regulatory network and the drug-gene interaction network were constructed. There were 2633 DN-DEGs between the DN and control samples and 515 PM-DEGs between the high and low PMRG score groups. In total, 10 intersected genes were gained after taking the intersection of DN-DEGs, PM-DEGs, and FRGs. Seven biomarkers, namely, SLC37A4, ACOX2, GPD1, angiotensin-converting enzyme 2 (ACE2), SLC9A3, AGT, and PLG, were acquired via RF and RFE analyses, and they were found to be involved in various mechanisms such as glomerulus development, fatty acid metabolism, and peroxisome. The seven biomarkers were positively correlated with neutrophils. Moreover, 8 TFs, 60 miRNAs, and 7 mRNAs formed the TF-miRNA-mRNA regulatory network, including USF1-hsa-mir-1296-5p-AGT and HIF1A-hsa-mir-449a-5p-ACE2. The drug-gene network contained UROKINASE-PLG, ATENOLOL-AGT, and other interaction relationship pairs. Via bioinformatic analyses, the risk of fibrosis and propionate metabolism-related biomarkers in DN were explored, thereby providing novel ideas for research related to DN diagnosis and treatment.

Identification and validation of diagnostic genes associated with neutrophil extracellular traps of type 2 diabetes mellitus

Background

Neutrophil extracellular traps (NETs) cause delayed wound closed up in type 2 diabetes mellitus (T2DM), but the specific regulatory mechanism of NETs-related genes (NETs-RGs) in T2DM is unclear.

Methods

We acquired GSE21321 and GSE15932 datasets from gene expression omnibus (GEO) database. First, differentially expressed genes (DEGs) between T2DM and control samples of GSE21321 dataset were sifted out by differential expression analysis. NETs scores were calculated for all samples in GSE21321 dataset, and key module genes associated with NETs scores were screened by constructing co-expression network. Then, DEGs and key module genes were intersected to yield intersection genes, and candidate genes were identified by constructing a protein protein interaction (PPI) network. Least absolute shrinkage and selection operator (LASSO) regression analysis was implemented on candidate genes to screen out diagnostic genes, and they were subjected to single sample gene set enrichment analysis (ssGSEA). Finally, immune characteristic analysis was carried out, and we constructed the gene-drug and transcription factor (TF)-miRNA-mRNA networks. Besides, we validated the expression of diagnostic genes by quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In total, 23 candidate genes were gained by PPI analysis. The 5 diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and calcium voltage-gated channel subunit alpha1 C (CACNA1C), were identified via LASSO analysis, and they were involved in carboxylic acid transport, axonogenesis, etc. M2 Macrophage, Monocyte, Natural killer (NK) cell, and Myeloid dendritic cells (DC) were remarkably different between T2DM and control samples. Diagnostic genes had the strongest and the most significant positive correlation with B cells. The gene-drug network included CACNA1C-Isradipine, CACNA1C-Benidipine and other relationship pairs. Totally 76 nodes and 44 edges constituted the TF-miRNA-mRNA network, including signal transducer and activator of transcription 1(STAT1) -hsa-miR-3170-AGER, CCCTC-binding factor (CTCF)-hsa-miR-455-5p-CACNA1C, etc. Moreover, qRT-PCR suggested that the expression trends of FGF1 and AGER were in keeping with the results of bioinformatic analysis. FGF1 and AGER were markedly regulated downwards in the T2DM group.

Conclusion

Through bioinformatic analysis, we identified NETs-related diagnostic genes (ITIH3, FGF1, NRCAM, AGER, CACNA1C) in T2DM, and explored their mechanism of action from different aspects, providing new ideas for the studies related to diagnosis and treatment of T2DM.

Immune cell related signature predicts prognosis in esophageal squamous cell carcinoma based on single-cell and bulk-RNA sequencing

Background

It has been reported that tumor immune microenvironment performs a vital role in tumor progress. However, acting mechanism of immune cell related genes (IRGs) in esophageal squamous cell carcinoma (ESCC) is uncertain.

Methods

TCGA-ESCC, GSE23400, GSE26886, GSE75241, and GSE196756 datasets were gained via public databases. First, differentially expressed genes (DEGs) between ESCC and control samples from GSE23400, GSE26886, and GSE75241 were screened out by differential expression analysis, and overlapping DEGs were identified. Single-cell transcriptome data of GSE196756 were applied to explore immune cells that might be involved in regulation of ESCC. Then, weighted gene co-expression network analysis was applied to screen IRGs. Next, differentially expressed IRGs (DE-IRGs) were identified by overlapping IRGs and DEGs, and were incorporated into univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox to acquire prognosis-related genes, and ESCC samples were grouped into high-/low-risk groups on the basis of median risk score. Finally, the role of prognosis model in immunotherapy was analyzed.

Results

Totally 248 DEGs were yielded by overlapping 3,915 DEGs in GSE26886, 459 DEGs in GSE23400, and 1,641 DEGs in GSE75241. Single-cell analysis found that B cells, dendritic cells, monocytes, neutrophils, natural killer cells, and T cells were involved in ESCC development. Besides, MEred, MEblack, MEpink, MEblue and MEbrown modules were considered as key modules because of their highest correlations with immune cell subtypes. A total of 154 DE-IRGs were yielded by taking intersection of DEGs and genes in key modules. Moreover, CTSC, ALOX12, and RMND5B were identified as prognosis-related genes in ESCC. Obviously, Exclusion and TIDE scores were notably lower in high-risk group than in the other one, indicating that high-risk group was more responsive to immunotherapy.

Conclusions

Through bioinformatic analysis, we identified a prognosis model consisting of IRGs (CTSC, ALOX12, and RMND5B) in ESCC, providing new ideas for studies related to treatment and prognosis of ESCC.

Pan-Cancer Analysis of the Prognostic and Immunological Role of TOMM40 to Identify Its Function in Breast Cancer

Breast cancer (BRCA) is currently the most commonly diagnosed malignancy in women worldwide. Previous studies have demonstrated that mitophagy is important for the prevention and treatment of BRCA. However, few studies have focused on the individual mitochondrial autophagy-related genes (MARG) in human cancers. Based on bioinformatics analyses, TOMM40 was identified as a prognostic DEMARG (PDEMARGs); Kaplan-Meier (KM) survival analysis also indicates that TOMM40 can be useful as a prognostic indicator in BRCAs, with patients in the high expression group having a poorer prognosis. For 20 distinct cancer kinds, there were appreciable differences in the expression of TOMM40 between tumor and normal tissues; in addition, in 21 different cancer types, there were associations between the expression profile of TOMM40 and patient prognosis. Gene Set Enrichment Analysis (GSEA), functional enrichment analysis, and immunological and drug sensitivity analyses of TOMM40 have indicated its biological significance in pan-cancers. Knockdown of TOMM40 in MDA-MB-231 cells inhibited their proliferation, migration, and invasiveness. In conclusion, we found that TOMM40 has prognostic value in 21 cancers, including breast cancer, by bioinformatics analysis. Based on immune correlation analysis, TOMM40 may also be a potential immunotherapeutic target for the treatment of BRCA. Therefore, our results may provide researchers to further explore the role of MARGs, especially TOMM40, in the developmental process of breast cancer, which may provide new directions and targets for the improvement of prognosis of breast cancer patients and their treatment.

Mitochondria in Mesenchymal Stem Cells: Key to Fate Determination and Therapeutic Potential

Mesenchymal stem cells (MSCs) have become popular tool cells in the field of transformation and regenerative medicine due to their function of cell rescue and cell replacement. The dynamically changing mitochondria serve as an energy metabolism factory and signal transduction platform, adapting to different cell states and maintaining normal cell activities. Therefore, a clear understanding of the regulatory mechanism of mitochondria in MSCs is profit for more efficient clinical transformation of stem cells. This review highlights the cutting-edge knowledge regarding mitochondrial biology from the following aspects: mitochondrial morphological dynamics, energy metabolism and signal transduction. The manuscript mainly focuses on mitochondrial mechanistic insights in the whole life course of MSCs, as well as the potential roles played by mitochondria in MSCs treatment of transplantation, for seeking pivotal targets of stem cell fate regulation and stem cell therapy.

Identification of m6A-associated autophagy genes in non-alcoholic fatty liver

Background

Studies had shown that autophagy was closely related to nonalcoholic fat liver disease (NAFLD), while N6-methyladenosine (m6A) was involved in the regulation of autophagy. However, the mechanism of m6A related autophagy in NAFLD was unclear.

Methods

The NAFLD related datasets were gained via the Gene Expression Omnibus (GEO) database, and we also extracted 232 autophagy-related genes (ARGs) and 37 m6A. First, differentially expressed ARGs (DE-ARGs) and differentially expressed m6A (DE-m6A) were screened out by differential expression analysis. DE-ARGs associated with m6A were sifted out by Pearson correlation analysis, and the m6A-ARGs relationship pairs were acquired. Then, autophagic genes in m6A-ARGs pairs were analyzed for machine learning algorithms to obtain feature genes. Further, we validated the relationship between feature genes and NAFLD through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB). Finally, the immuno-infiltration analysis was implement, and we also constructed the TF-mRNA and drug-gene networks.

Results

There were 19 DE-ARGs and four DE-m6A between NAFLD and normal samples. The three m6A genes and five AGRs formed the m6A-ARGs relationship pairs. Afterwards, genes obtained from machine learning algorithms were intersected to yield three feature genes (TBK1, RAB1A, and GOPC), which showed significant positive correlation with astrocytes, macrophages, smooth muscle, and showed significant negative correlation with epithelial cells, and endothelial cells. Besides, qRT-PCR and WB indicate that TBK1, RAB1A and GOPC significantly upregulated in NAFLD. Ultimately, we found that the TF-mRNA network included FOXP1-GOPC, ATF1-RAB1A and other relationship pairs, and eight therapeutic agents such as R-406 and adavosertib were predicted based on the TBK1.

Conclusion

The study investigated the potential molecular mechanisms of m6A related autophagy feature genes (TBK1, RAB1A, and GOPC) in NAFLD through bioinformatic analyses and animal model validation. However, it is critical to note that these findings, although consequential, demonstrate correlations rather than cause-and-effect relationships. As such, more research is required to fully elucidate the underlying mechanisms and validate the clinical relevance of these feature genes.

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning

Background

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.

Materials and methods

LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.

Results

Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.

Conclusion

In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.

Comprehensive analysis of mitophagy-related subtypes of breast cancer and the association with immune related characteristics

Breast cancer (BRCA) is a common neoplasm characterized by high levels of molecular heterogeneity. Previous studies have noted the importance of mitophagy for the progression and prognosis of BRCA. However, little was found in the similarity and difference of mitophagy-related gene expression patterns of BRCA. This study intended to investigate the differences in functional activation, somatic mutation, and immune-related characteristics among different subtypes of BRCA associated with mitophagy. Based on bioinformatics analysis, we systematically examined the heterogeneity of breast cancer concerning mitophagy and observed two distinct subtypes with different tumor microenvironments and prognoses. BRCA samples from TCGA database were divided into two subtypes based on the expression of 29 mitophagy-related genes by ConsensusClusterPlus algorithm. Two mitophagy-related subtypes with marked prognostic discrepancies were significantly correlated with race, intrinsic subtype grouped based on PAM50 subtype purity and BRCA Pathology. The results of GSVA and immune microenvironment analysis showed significant differences in cancer-related and immune-related features between the two subtypes. METABRIC datasets were extracted to validate the immune characteristics scoring and the expression of immune checkpoints between different subtypes based on the medium value of TCGA-Mitophagy score. It is noteworthy that the present study is the first to demonstrate a new classification based on the mitophagy of breast cancer, which comes up with a new perspective for the assessment and prognoses of BRCA.

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.

Identification and study of cuproptosis-related genes in prognostic model of multiple myeloma

BACKGROUND

Multiple myeloma (MM) is a highly heterogeneous disease. Cuproptosis is a novel mode of death that is closely associated with several diseases, such as hepatocellular carcinoma. However, its role in MM is unknown.

METHODS

MM transcriptomic and clinical data were obtained from UCSC Xena and gene expression omnibus (GEO) databases. Following MM samples were divided into different subtypes based on the cuproptosis genes, the differentially expressed genes (DEGs) among different subtypes, namely, candidate cuproptosis related genes were analyzed by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression to construct a cuproptosis-related risk model. After the independent prognostic analysis was performed, a nomogram was constructed. Finally, Functional enrichment analysis and immune infiltration analysis were performed in the high- and low-risk groups, potential therapeutic agents were then predicted.

RESULTS

The 784 MM samples in UCSC Xena cohorts were divided into three different subtypes, and 4 out of 346 candidate cuproptosis related genes, namely CDKN2A, BCL3, KCNA3 and TTC14 were used to construct a risk model. Risk score was considered a reliable independent prognostic factor for MM patients. It was investigated that the pathway of cell cycle was significantly enriched in the high-risk group. In addition, immune score, ESTIMATE score and cytolytic activity were significantly different between different risk groups, as well as 13 immune cells such as memory B cells. Nine drugs were predicted in our study.

CONCLUSION

A cuproptosis-related prognostic model was constructed, which may have a potential guiding role in the treatment of MM.

An integrative analysis reveals the prognostic value and potential functions of MTMR2 in hepatocellular carcinoma

Abnormal expression of myotubularin-related protein 2 (MTMR2) has been identified in certain types of cancer, leading to varying effects on tumor genesis and progression. However, the various biological significances of MTMR2 in hepatocellular carcinoma (HCC) have not been systematically and comprehensively studied. The aim of this study was to explore the role of MTMR2 in HCC. We obtained the raw data from Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Afterward, we analyzed the data using R and cBioPortal. We investigated the connection between MTMR2 and its expression, prognosis, clinical significance, methylation, genetic alterations, tumor microenvironment (TME), tumor mutation burden (TMB), and drug reactivity in HCC patients. MTMR2 expression levels in HCC cells were validated through western blotting and RT-qPCR. MTMR2 exhibits high levels of expression across a wide range of cancer types, including HCC. MTMR2 is diagnostically valuable in detecting HCC, with its up-regulated expression often being indicative of poor prognosis among HCC patients. The in vitro experiments confirmed elevated MTMR2 expression in HepG2, HUH-7, and MHCC-97H cells. Univariate and multivariate Cox analysis demonstrated that MTMR2 was an independent prognostic factor in HCC patients. The cg20195272 site has the highest degree of methylation in MTMR2, and it is positively correlated with MTMR2 expression. Patients with high levels of methylation at the cg20195272 site show poor prognosis. Analysis of the TME indicates that high expression of MTMR2 is associated with elevated ESTIMATE score and that MTMR2 expression correlates positively with infiltration by resting memory CD4 T cells, activated dendritic cells, as well as several immune checkpoints. There is a negative correlation between MTMR2 expression and TMB, and drug sensitivity analyses have shown that higher MTMR2 expression is associated with lower IC50 values. This study indicates that increased expression of MTMR2 may play a crucial role in the occurrence, progression, diagnosis, prognostic prediction and drug therapy of HCC.

Proposing a novel molecular subtyping scheme for predicting distant recurrence-free survival in breast cancer post-neoadjuvant chemotherapy with close correlation to metabolism and senescence

Background

High relapse rates remain a clinical challenge in the management of breast cancer (BC), with distant recurrence being a major driver of patient deterioration. To optimize the surveillance regimen for distant recurrence after neoadjuvant chemotherapy (NAC), we conducted a comprehensive analysis using bioinformatics and machine learning approaches.

Materials and methods

Microarray data were retrieved from the GEO database, and differential expression analysis was performed with the R package 'Limma'. We used the Metascape tool for enrichment analyses, and 'WGCNA' was utilized to establish co-expression networks, selecting the soft threshold power with the 'pickSoftThreshold' algorithm. We integrated ten machine learning algorithms and 101 algorithm combinations to identify key genes associated with distant recurrence in BC. Unsupervised clustering was performed with the R package 'ConsensusCluster Plus'. To further screen the key gene signature of residual cancer burden (RCB), multiple knockdown studies were analyzed with the Genetic Perturbation Similarity Analysis (GPSA) database. Single-cell RNA sequencing (scRNA-seq) analysis was conducted through the Tumour Immune Single-cell Hub (TISCH) database, and the XSum algorithm was used to screen candidate small molecule drugs based on the Connectivity Map (CMAP) database. Molecular docking processes were conducted using Schrodinger software. GMT files containing gene sets associated with metabolism and senescence were obtained from GSEA MutSigDB database. The GSVA score for each gene set across diverse samples was computed using the ssGSEA function implemented in the GSVA package.

Results

Our analysis, which combined Limma, WGCNA, and machine learning approaches, identified 16 RCB-relevant gene signatures influencing distant recurrence-free survival (DRFS) in BC patients following NAC. We then screened GATA3 as the key gene signature of high RCB index using GPSA analysis. A novel molecular subtyping scheme was developed to divide patients into two clusters (C1 and C2) with different distant recurrence risks. This molecular subtyping scheme was found to be closely associated with tumor metabolism and cellular senescence. Patients in cluster C2 had a poorer DRFS than those in cluster C1 (HR: 4.04; 95% CI: 2.60-6.29; log-rank test p < 0.0001). High GATA3 expression, high levels of resting mast cell infiltration, and a high proportion of estrogen receptor (ER)-positive patients contributed to better DRFS in cluster C1. We established a nomogram based on the N stage, RCB class, and molecular subtyping. The ROC curve for 5-year DRFS showed excellent predictive value (AUC=0.91, 95% CI: 0.95-0.86), with a C-index of 0.85 (95% CI: 0.81-0.90). Entinostat was identified as a potential small molecule compound to reverse high RCB after NAC. We also provided a comprehensive review of the EDCs exposures that potentially impact the effectiveness of NAC among BC patients.

Conclusion

This study established a molecular classification scheme associated with tumor metabolism and cancer cell senescence to predict RCB and DRFS in BC patients after NAC. Furthermore, GATA3 was identified and validated as a key gene associated with BC recurrence.

Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

Purpose

The accurate prediction of non-cirrhotic hepatocellular carcinoma (NCHCC) risk facilitates improved surveillance strategy and decreases cancer-related mortality. This study aimed to explore the correlation between immunogenic cell death (ICD) and NCHCC prognosis using The Cancer Genome Atlas (TCGA) datasets, and the potential prognostic value of ICD-related genes in NCHCC.

Methods

Clinical and transcriptomic data of patients with NCHCC patients were retrieved from TCGA database. Weighted gene co-expression network analysis was performed to obtain the NCHCC phenotype-related module genes. Consensus clustering analysis was performed to classify the patients into two clusters based on intersection genes among differentially expressed genes (DEGs) between cancer and adjacent tissues, NCHCC phenotype-related genes, and ICD-related genes. NCHCC-derived tissue microarray was used to evaluate the correlation of the expression levels of key genes with NCHCC prognosis using immunohistochemical staining.

Results

Cox regression analyses were performed to construct a prognostic risk score model comprising three genes (TMC7, GRAMD1C, and GNPDA1) based on DEGs between two clusters. The model stratified patients with NCHCC into two risk groups. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. Univariable and multivariable Cox regression analyses revealed that these signature genes are independent predictors of OS. Functional analysis revealed differential immune status between the two risk groups. Next, a nomogram was constructed, which demonstrated the potent distinguishing ability of the developed model based on receiver operating characteristic curves. In vitro functional validation revealed that the migration and invasion abilities of HepG2 and Huh7 cells were upregulated upon GRAMD1C knockdown but downregulated upon TMC7 knockdown.

Conclusion

This study developed a prognostic model comprising three genes, which can aid in predicting the survival of patients with NCHCC and guide the selection of drugs and molecular markers for NCHCC.

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignant tumor with high recurrence and mortality rates. This study aimed to develop and validate a prognostic model for patients with UCEC based on cuproptosis-related long non-coding RNA (lncRNA) signature.

Methods

Transcriptome and clinical UCEC data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis was conducted to screen out the cuproptosis-related lncRNAs, and univariate regression analysis was performed to determine prognostic factors associated with overall survival (OS). A cuproptosis-related lncRNA risk model was constructed through least absolute shrinkage and selection operator (LASSO) regression and cross-validation. The accuracy and reliability of the model were verified through Kaplan-Meier (KM), proportional hazards model (Cox) regression, nomogram, principal component analysis (PCA), and stage analysis. Gene Ontology (GO) enrichment, immune function, and tumor mutation burden (TMB) analyses were conducted between low-risk and high-risk groups, and antineoplastic drugs were predicted.

Results

By correlation analysis, 155 cuproptosis-related lncRNAs were acquired, and 9 lncRNAs were identified as independent prognostic factors. A 6-cuproptosis-related lncRNA model was established. The results revealed that patients in the high-risk group were more inclined to have a poor OS than those in the low-risk group. Risk score was an independent prognostic factor and had a high accuracy and predictive value. The extracellular structure and anchored components of membrane-related GO terms were significantly enriched. Immune function and TMB results were assumed to be different from each other, which might explain a better outcome in the low-risk group than that in the high-risk group. Eighteen compounds were predicted as chemotherapy drugs with high half maximal inhibitory concentration (IC50) in the high-risk group.

Conclusions

We successfully developed a cuproptosis-related lncRNA risk model for the prediction of prognosis, while simultaneously providing insights on new approaches for immunotherapy and chemotherapy for patients with UCEC.

Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer

BACKGROUND

Ovarian cancer (OC) is the most malignant tumor with the worst prognosis in female reproductive system. Mitophagy and long non-coding RNAs (lncRNAs) play pivotal roles in tumorigenesis, development, and drug resistance. The effects of mitophagy-related lncRNAs on OC prognosis and therapeutic response remain unelucidated.

METHODS

We retrieved OC-related RNA sequence, copy number variation, somatic mutation, and clinicopathological information from The Cancer Genome Atlas database and mitophagy-related gene sets from the Reactome database. Pearson's correlation analysis was used to distinguish mitophagy-related lncRNAs. A prognostic lncRNA signature was constructed using UniCox, LASSO, and forward stepwise regression analysis. Individuals with a risk score above or below the median were classified as high- or low-risk groups, respectively. The risk model was analyzed using the Kaplan-Meier estimator, receiver operating characteristic curve, decision curve analysis, and Cox regression analysis and validated using an internal dataset. LINC00174 was validated in clinical samples and OC cell lines. We also reviewed reports on the role of LINC00174 in cancer. Subsequently, a nomogram model was constructed. Furthermore, the Genomics of Drug Sensitivity in Cancer database was used to explore the relationship between the risk model and anti-tumor drug sensitivity. Gene set variation analysis was performed to assess potential differences in biological functions between the two groups. Finally, a lncRNA prognostic signature-related competing endogenous RNA (ceRNA) network was constructed.

RESULTS

The prognostic signature showed that patients in the high-risk group had a poorer prognosis. The nomogram exhibited satisfactory accuracy and predictive potential. LINC00174 mainly acts as an oncogene in cancer and is upregulated in OC; its knockdown inhibited the proliferation and migration, and promoted apoptosis of OC cells. High-risk patients were more insensitive to cisplatin and olaparib than low-risk patients. The ceRNA network may help explore the potential regulatory mechanisms of lncRNAs.

CONCLUSION

The mitophagy-related lncRNA signature can help estimate the survival and drug sensitivity, the ceRNA network may provide novel therapeutic targets for patients with OC.

Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis

Background

As yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.

Materials and methods

The gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package "WGCNA". Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using "Limma" R package. The "randomForest" packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by "ConsensusClusterPlus"R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package "pheatmap". Diagnostic parameter capability was evaluated by ROC curve. The"XSum"packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.

Results

Via WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The"XSum"algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of -7.400 kcal/mol, -7.191 kcal/mol and -6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.

Conclusion

Using WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.

The role of ferroptosis-related genes in airway epithelial cells of asthmatic patients based on bioinformatics

It has been reported that airway epithelial cells and ferroptosis have certain effect on asthma. However, the action mechanism of ferroptosis-related genes in airway epithelial cells of asthmatic patients is still unclear. Firstly, the study downloaded the GSE43696 training set, GSE63142 validation set and GSE164119 (miRNA) dataset from the gene expression omnibus database. 342 ferroptosis-related genes were downloaded from the ferroptosis database. Moreover, differentially expressed genes (DEGs) between asthma and control samples in the GSE43696 dataset were screened by differential analysis. Consensus clustering analysis was performed on asthma patients to classify clusters, and differential analysis was performed on clusters to obtain inter-cluster DEGs. Asthma-related module was screened by weighted gene co-expression network analysis. Then, DEGs between asthma and control samples, inter-cluster DEGs and asthma-related module were subjected to venn analysis for obtaining candidate genes. The last absolute shrinkage and selection operator and support vector machines were respectively applied to the candidate genes to screen for feature genes, and functional enrichment analysis was performed. Finally, a competition endogenetic RNA network was constructed and drug sensitivity analysis was conducted. There were 438 DEGs (183 up-regulated and 255 down-regulated) between asthma and control samples. 359 inter-cluster DEGs (158 up-regulated and 201 down-regulated) were obtained by screening. Then, the black module was significantly and strongly correlated with asthma. The venn analysis yielded 88 candidate genes. 9 feature genes (NAV3, ITGA10, SYT4, NOX1, SNTG2, RNF182, UPK1B, POSTN, SHISA2) were screened and they were involved in proteasome, dopaminergic synapse etc. Besides, 4 mRNAs, 5 miRNAs, and 2 lncRNAs collectively formed competition endogenetic RNA regulatory network, which included RNF182-hsa-miR-455-3p-LINC00319 and so on. The predicted therapeutic drug network map contained NAV3-bisphenol A and other relationship pairs. The study investigated the potential molecular mechanisms of NAV3, ITGA10, SYT4, NOX1, SNTG2, RNF182, UPK1B, POSTN, SHISA2 in airway epithelial cells of asthmatic patients through bioinformatics analysis, providing a reference for the research of asthma and ferroptosis.

A glycolysis-related gene signatures in diffuse large B-Cell lymphoma predicts prognosis and tumor immune microenvironment

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma which that highly aggressive and heterogeneous. Glycolysis has been implicated in the regulation of tumor microenvironment (TME) and development. In this study, we aimed to establish a glycolysis-related prognostic model for the risk stratification, prognosis prediction, and immune landscape evaluation in patients with DLBCL. Methods: Three independent datasets GSE181063, GSE10846, and GSE53786 containing gene expression profiles and clinical data were downloaded from the Gene Expression Omnibus (GEO) database. The glycolysis-related prognostic model was developed with Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression and validated. A nomogram integrating clinical factors and glycolytic risk scores was constructed. The composition of the TME was analyzed with the ESTIMATE algorithm and single-sample gene set enrichment analysis (ssGSEA). Results: A glycolytic risk model containing eight genes was developed. The area under the receiver operating characteristic (ROC) curve (AUC) for the 1-, 3-, and 5-year was 0.718, 0.695, and 0.688, respectively. Patients in the high-risk group had significantly lower immune scores, elevated tumor purity, and poorer survival compared with those in the low-risk group. The nomogram constructed based on glycolytic risk score, age, Eastern Cooperative Oncology Group performance status (ECOG-PS), use of rituximab, and cell of origin (COO) displayed better prediction performance compared with the International Prognostic Index (IPI) in DLBCL. The glycolytic risk score was negatively correlated with the infiltration level of activated CD8 T cells, activated dendritic cells, natural killer cells, and macrophages and immune checkpoint molecules including PD-L2, CTLA4, TIM-3, TIGIT, and B7-H3. Conclusion: These results suggested that the glycolytic risk model could accurately and stably predict the prognosis of patients with DLBCL and might unearth the possible explanation for the glycolysis-related poor prognosis.

Identification and development of TP53 mutation-associated Long non-coding RNAs signature for optimized prognosis assessment and treatment selection in hepatocellular carcinoma

BACKGROUND

The TP53 gene is estimated to be mutated in over 50% of tumors, with the majority of tumors exhibiting abnormal TP53 signaling pathways. However, the exploration of TP53 mutation-related LncRNAs in Hepatocellular carcinoma (HCC) remains incomplete. This study aims to identify such LncRNAs and enhance the prognostic accuracy for Hepatoma patients.

MATERIAL AND METHODS

Differential gene expression was identified using the "limma" package in R. Prognosis-related LncRNAs were identified via univariate Cox regression analysis, while a prognostic model was crafted using multivariate Cox regression analysis. Survival analysis was conducted using Kaplan-Meier curves. The precision of the prognostic model was assessed through ROC analysis. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were executed on the TCGA dataset via the TIDE database. Fractions of 24 types of immune cell infiltration were obtained from NCI Cancer Research Data Commons using deconvolution techniques. The protein expression levels encoded by specific genes were obtained through the TPCA database.

RESULTS

In this research, we have identified 85 LncRNAs associated with TP53 mutations and developed a corresponding signature referred to as TP53MLncSig. Kaplan-Meier analysis revealed a lower 3-year survival rate in high-risk patients (46.9%) compared to low-risk patients (74.2%). The accuracy of the prognostic TP53MLncSig was further evaluated by calculating the area under the ROC curve. The analysis yielded a 5-year ROC score of 0.793, confirming its effectiveness. Furthermore, a higher score for TP53MLncSig was found to be associated with an increased response rate to immune checkpoint blocker (ICB) therapy (p = .005). Patients possessing high-risk classification exhibited lower levels of P53 protein expression and higher levels of genomic instability.

CONCLUSION

The present study aimed to identify and validate LncRNAs associated with TP53 mutations. We constructed a prognostic model that can predict chemosensitivity and response to ICB therapy in HCC patients. This novel approach sheds light on the role of LncRNAs in TP53 mutation and provides valuable resources for analyzing patient prognosis and treatment selection.

A Comprehensive Analysis of the Effects of Key Mitophagy Genes on the Progression and Prognosis of Lung Adenocarcinoma

The aim of our study was to perform a comprehensive analysis of the gene expression, copy number variation (CNV) and mutation of key mitophagy genes in the progression and prognosis of lung adenocarcinoma (LUAD). We obtained the data from The Cancer Genome Atlas (TCGA). Clustering analysis was performed to stratify the mitophagy related groups. The least absolute shrinkage and selection operator (LASSO) based cox model was used to select hub survival genes. An independent validation cohort was retrieved from Gene Expression Omnibus database. We found 24 out of 27 mitophagy genes were aberrantly expressed between tumor and normal samples. CNV gains were associated with higher expression of mitophagy genes in 23 of 27 mitophagy genes. The clustering analysis identified high and low risk mitophagy groups with distinct survival differences. The high risk mitophagy groups had higher tumor mutation burden, stemness phenotype, total CNVs and lower CD4+ T cells infiltration. Drugs targeted to high risk mitophagy groups were identified including the PI3K/AKT/mTOR inhibitor, HDAC inhibitor and chemotherapy agents such as cisplatin and gemcitabine. In addition, the differentially expressed genes (DEGs) were identified between mitophagy groups. Further univariate Cox analysis of each DEG and subsequent LASSO-based Cox model revealed a mitophagy-related prognostic signature. The risk score model of this signature showed a strong ability to predict the overall survival of LUAD patients in training and validation datasets. In conclusion, the mitophagy genes played an important role in the progression and prognosis of LUAD, which might provide useful information for the treatment of LUAD.

Integrative analyses of a mitophagy-related gene signature for predicting prognosis in patients with uveal melanoma

We aimed to create a mitophagy-related risk model via data mining of gene expression profiles to predict prognosis in uveal melanoma (UM) and develop a novel method for improving the prediction of clinical outcomes. Together with clinical information, RNA-seq and microarray data were gathered from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ConsensusClusterPlus was used to detect mitophagy-related subgroups. The genes involved with mitophagy, and the UM prognosis were discovered using univariate Cox regression analysis. In an outside population, a mitophagy risk sign was constructed and verified using least absolute shrinkage and selection operator (LASSO) regression. Data from both survival studies and receiver operating characteristic (ROC) curve analyses were used to evaluate model performance, a bootstrap method was used test the model. Functional enrichment and immune infiltration were examined. A risk model was developed using six mitophagy-related genes (ATG12, CSNK2B, MTERF3, TOMM5, TOMM40, and TOMM70), and patients with UM were divided into low- and high-risk subgroups. Patients in the high-risk group had a lower chance of living longer than those in the low-risk group (p < 0.001). The ROC test indicated the accuracy of the signature. Moreover, prognostic nomograms and calibration plots, which included mitophagy signals, were produced with high predictive performance, and the risk model was strongly associated with the control of immune infiltration. Furthermore, functional enrichment analysis demonstrated that several mitophagy subtypes may be implicated in cancer, mitochondrial metabolism, and immunological control signaling pathways. The mitophagy-related risk model we developed may be used to anticipate the clinical outcomes of UM and highlight the involvement of mitophagy-related genes as prospective therapeutic options in UM. Furthermore, our study emphasizes the essential role of mitophagy in UM.

A Mitophagy-Related Gene Signature for Subtype Identification and Prognosis Prediction of Hepatocellular Carcinoma

Globally, hepatocellular carcinoma (HCC) is the sixth most common cancer. In this study, the correlation between mitophagy and HCC prognosis was evaluated using data from The Cancer Genome Atlas (TCGA). Clinical and transcriptomic data of HCC patients were downloaded from TCGA dataset, and mitophagy-related gene (MRG) datasets were obtained from the Molecular Signature Database. Then, a consensus clustering analysis was performed to classify the patients into two clusters. Furthermore, tumor prognosis, clinicopathological features, functional analysis, immune infiltration, immune checkpoint (IC)-related gene expression level, tumor stem cells, ferroptosis status, and N6-methyladenosine analysis were compared between the two clusters. Finally, a mitophagy-related signature was developed. Two clusters (C1 and C2) were identified using the consensus clustering analysis based on the MRG signature. Patients with the C1 subtype exhibited upregulated pathways with better liver function, downregulated cancer-related pathways, lower cancer stem cell scores, lower Tumor Immune Dysfunction and Exclusion scores (TIDE), different ferroptosis status, and better prognosis compared with the patients with the C2 subtype. The C2 subtype was characterized by the increased grade of HCC, as well as the increased number of immune-related pathways and m⁶A-related genes. Higher immune scores were also observed for the C2 subtype. A signature containing four MRGs (PGAM5, SQSTM1, ATG9A, and GABARAPL1) which can accurately predict the prognosis of HCC patients was then identified. This four-gene signature exhibited a predictive effect in five other cancer types, namely glioma, uveal melanoma, acute myeloid leukemia, adrenocortical carcinoma, and mesothelioma. The mitophagy-associated subtypes of HCC were closely related to the immune microenvironment, immune checkpoint-related gene expression, cancer stem cells, ferroptosis status, m⁶A, prognosis, and HCC progression. The established MRG signature could predict prognosis in patients with HCC.

Identification and Validation of Genomic Subtypes and a Prognostic Model Based on Antigen-Presenting Cells and Tumor Microenvironment Infiltration Characteristics in Hepatocellular Carcinoma

Currently, the prognosis of hepatocellular carcinoma (HCC) is poor, and there is a lack of effective targeted therapy. As key mediators of the immune response, the prognostic value of antigen-presenting cells (APCs) in HCC still remains unclear. In this study, we aimed to identify APC-related genomic subtypes and develop a novel prognostic model in HCC. Our results indicated that overall survival (OS) and the level of immune infiltration significantly differed between different APC clusters. By analyzing the gene expression profile between APC clusters, APC-related genomic subtypes were identified. There was a significant difference in OS and tumor microenvironment infiltration in HCC patients with different genomic subtypes. With the aid of genomic subtypes, significantly differentially expressed genes were screened to generate a novel prognostic model. The risk score of the model had a significant positive correlation with APCs and was associated with immune checkpoint expressions. Through the clinical cohort collected from the First Affiliated Hospital of Wenzhou Medical University, the prognostic value of the risk score was further validated. Moreover, after the risk score and clinical characteristics were combined, a nomogram was constructed to evaluate the prognosis for HCC patients. In conclusion, we mainly identified the APC-related genomic subtypes and generated a novel prognostic model to improve the prognostic prediction and targeted therapy for HCC patients.

Necroptosis-Related LncRNAs Signature and Subtypes for Predicting Prognosis and Revealing the Immune Microenvironment in Breast Cancer

Purpose

Necroptosis is a mode of programmed cell death that overcomes apoptotic resistance. We aimed to construct a steady necroptosis-related signature and identify subtypes for prognostic and immunotherapy sensitivity prediction.

Methods

Necroptosis-related prognostic lncRNAs were selected by co-expression analysis, and were used to construct a linear stepwise regression model via univariate and multivariate Cox regression, along with least absolute shrinkage and selection operator (LASSO). Quantitative reverse transcription polymerase chain reaction (RT-PCR) was used to measure the gene expression levels of lncRNAs included in the model. Based on the riskScore calculated, we separated patients into high- and low-risk groups. Afterwards, we performed CIBERSORT and the single-sample gene set enrichment analysis (ssGSEA) method to explore immune infiltration status. Furthermore, we investigated the relationships between the signature and immune landscape, genomic integrity, clinical characteristics, drug sensitivity, and immunotherapy efficacy.

Results

We constructed a robust necroptosis-related 22-lncRNA model, serving as an independent prognostic factor for breast cancer (BRCA). The low-risk group seemed to be the immune-activated type. Meanwhile, it showed that the higher the tumor mutation burden (TMB), the higher the riskScore. PD-L1-CTLA4 combined immunotherapy seemed to be a promising treatment strategy. Lastly, patients were assigned to 4 clusters to better discern the heterogeneity among patients.

Conclusions

The necroptosis-related lncRNA signature and molecular clusters indicated superior predictive performance in prognosis and the immune microenvironment, which may also provide guidance to drug regimens for immunotherapy and provide novel insights into precision medicine.

Comprehensive Analysis of a Ferroptosis Pattern and Associated Prognostic Signature in Acute Myeloid Leukemia

Ferroptosis is a widespread form of programmed cell death. The environment of cancer cells makes them vulnerable to ferroptosis, including AML cells, yet the specific association between ferroptosis and AML outcome is little known. In this study, we utilized ferroptosis-related genes to distinguish two subtypes in TCGA cohort, which were subsequently validated in independent AML cohorts. The subtypes were linked with tumor-related immunological abnormalities, mutation landscape and pathway dysregulation, and clinical outcome. Further, we developed a 13-gene prognostic model for AML from DEG analysis in the two subtypes. A risk score was calculated for each patient, and then the overall group was stratified into high- and low-risk groups; the higher risk score correlated with short survival. The model was validated in both independent AML cohorts and pan-cancer cohorts, which demonstrated robustness and extended the usage of the model. A nomogram was constructed that integrated risk score, FLT3-ITD, TP53, and RUNX1 mutations, and age. This model had the additional value of discriminating the sensitivity of several chemotherapeutic drugs and ferroptosis inducers in the two risk groups, which increased the translational value of this model as a potential tool in clinical management. Through integrated analysis of ferroptosis pattern and its related model, our work shed new light on the relationship between ferroptosis and AML, which may facilitate clinical application and therapeutics.