A novel cuproptosis-related prognostic lncRNA signature for predicting immune and drug therapy response in hepatocellular carcinoma

The immune landscape and prognostic analysis of CXCL8 immune-related genes in cervical squamous cell carcinoma

Cervical squamous cell carcinoma (CESC), one of the most common malignancies in women, imposes a significant burden on women's health worldwide. Despite extensive research, the molecular and pathogenic mechanisms of cervical squamous cell carcinoma and CESC remain unclear. This study aimed to explore the immune-related genes, immune microenvironment infiltration, and prognosis of CESC, providing a theoretical basis for guiding clinical treatment. Initially, by mining four gene sets and immune-related gene sets from public databases, 14 immune-related genes associated with CESC were identified. Through univariate and multivariate COX regression analyses, as well as lasso regression analysis, four CESC-independent prognostic genes were identified, and a prognostic model was constructed, dividing them into high and low-risk groups. The correlation between these genes and immune cells and immune functions were explored through ssGSEA enrichment analysis, revealing a close association between the high-risk group and processes such as angiogenesis and epithelial-mesenchymal transition. Furthermore, using public databases and qRT-PCR experiments, significant differences in CXCL8 expression between normal cervical cells and cervical cancer cells were discovered. Subsequently, a CXCL8 knockdown plasmid was constructed, and the efficiency of CXCL8 knockdown was validated in two CESC cell lines, MEG-01 and HCE-1. Through CCK-8, scratch, and Transwell assays, it was confirmed that CXCL8 knockdown could inhibit the proliferation, invasion, and migration abilities of CESC cells. Targeting CXCL8 holds promise for personalized therapy for CESC, providing a strong theoretical basis for achieving clinical translation.

Unraveling the significance of cuproptosis in hepatocellular carcinoma heterogeneity and tumor microenvironment through integrated single-cell sequencing and machine learning approaches

BACKGROUND

Hepatocellular carcinoma (HCC) exhibits pronounced heterogeneity, which significantly limits the effectiveness of precision therapies. A comprehensive understanding of the biological characteristics and molecular mechanisms underlying HCC cell subpopulations is crucial for improving prognostic predictions and refining treatment strategies.

METHODS

Single-cell RNA sequencing data were obtained from the GEO database and processed using the Seurat R package for quality control, including data filtering, batch effect correction, and dimensionality reduction via PCA and UMAP to visualize cell distribution and identify distinct subpopulations. Cell types were annotated using established marker genes and literature references. The GSVA method was applied to evaluate the activity of 18 programmed cell death pathways. Cell developmental trajectories were reconstructed using Monocle 2 and validated with cytoTRACE to assess differentiation potential. Metabolic pathway activity was analyzed using the scMetabolism package. Bulk RNA sequencing data from the TCGA cohort were integrated to identify prognosis-associated genes through univariate Cox regression. The malignant potential of tumor subpopulations was quantified using GSVA scoring. Weighted gene co-expression network analysis (WGCNA) was employed to identify cuproptosis-related genes. A risk scoring model was constructed using LASSO regression and multivariate Cox regression based on cuproptosis-related genes and marker genes of cuproptosis-characterized tumor cells. The model's performance was validated across TCGA, GEO, and ICGC datasets. Additionally, the relationships between risk scores, clinical characteristics, key signaling pathways, and immunotherapy responses were explored. Finally, a prognostic nomogram was developed to support clinical decision-making.

RESULTS

12 programmed cell death pathways were enriched in tumors, with cuproptosis defining HCC, particularly in the C2 subpopulation. GSVA highlighted high-risk patient enrichment in proliferation, DNA repair, and metabolism, reflecting aggressive malignancy. Developmental trajectory and metabolic analyses confirmed greater stemness and metabolic activity in C2. TCGA linked cuproptosis-related subpopulations to poor prognosis. The risk model stratified patients (validated in TCGA/GEO/ICGC), correlating with clinical grade, T-stage, survival (HR = 2.597, 95%CI 2.051-3.289, P < 0.05). The nomogram showed strong predictive power (C-index = 0.716), aiding clinical decisions.

CONCLUSION

The C2 subpopulation represents the most malignant subset of HCC cells, with cuproptosis serving as a defining characteristic of this subgroup. The risk scoring and nomogram models based on cuproptosis-related genes offer novel insights and a robust scientific foundation for prognostic prediction and personalized treatment in HCC patients. These findings highlight the potential of targeting cuproptosis and tumor microenvironment interactions to improve therapeutic outcomes in HCC.

Cuproplasia and cuproptosis in hepatocellular carcinoma: mechanisms, relationship and potential role in tumor microenvironment and treatment

BACKGROUND

Hepatocellular carcinoma (HCC) is the main phenotype of liver cancer with a poor prognosis. Copper is vital in liver function, and HCC cells rely on it for growth and metastasis, leading to cuproplasia. Excessive copper can induce cell death, termed cuproptosis. Tumor microenvironment (TME) is pivotal in HCC, especially in immunotherapy, and copper is closely related to the TME pathogenesis. However, how these two mechanisms contribute to the TME is intriguing.

MAIN BODY

We conducted the latest progress literature on cuproplasia and cuproptosis in HCC, and summarized their specific roles in TME and treatment strategies. The mechanisms of cuproplasia and cuproptosis and their relationship and role in TME have been deeply summarized. Cuproplasia fosters TME formation, angiogenesis, and metastasis, whereas cuproptosis may alleviate mitochondrial dysfunction and hypoxic conditions in the TME. Inhibiting cuproplasia and enhancing cuproptosis in HCC are essential for achieving therapeutic efficacy in HCC.

CONCLUSION

An in-depth analysis of cuproplasia and cuproptosis mechanisms within the TME of HCC unveils their opposing nature and their impact on copper regulation. Grasping the equilibrium between these two factors is crucial for a deeper understanding of HCC mechanisms to shed light on novel directions in treating HCC.

Deciphering m6A signatures in hepatocellular carcinoma: Single-cell insights, immune landscape, and the protective role of IGFBP3

RNA m6 methyladenosine (m6A) modifications impact tumor biology and immune processes, particularly in hepatocellular malignant tumors. Using a consensus clustering algorithm on 371 hepatocellular carcinoma (HCC) samples, we identified three m6A-modified subtypes and correlated them with positive tumor microenvironment (TME) markers for distinct immune phenotypes. Stratifying patients based on m6A scores revealed a low presentation group with better immune penetration, lower tumor mutation load, and increased expression of immune checkpoint markers like CTLA-4 and PD-1, suggesting enhanced responsiveness to immunization therapy. A machine-learning model of 23 m6A genes was constructed. Single-cell analysis revealed a surprising enrichment of IGFBP3 in astrocytes, prompting the exploration of associated signaling pathways. Experimental verification shows that IGFBP3 is significantly enhanced in normal tissues, while immunohistochemical analysis shows that its expression is lower in tumor tissues, indicating its protective effect in HCC and a good prognosis. Importantly, high IGFBP3 expression is associated with better outcomes in patients receiving immunotherapy. Moreover, cytotoxic T lymphocyte (CTL) experiments have confirmed that high expression of IGFBP3 is associated with stronger T cell-killing ability. In summary, the comprehensive evaluation of m6A modification, immune characteristics, and single-cell analysis in this study not only revealed the TME of HCC but also made significant contributions to the progress of personalized HCC immunotherapy targeting IGFBP3. This study provides a solid theoretical foundation for clinical translation and emphasizes its potential impact on developing effective treatment strategies.

The role and mechanism of hepatocyte nuclear factor 1β in the occurrence and development of different human tumors: A pan-cancer analysis

Carcinomatosis is one of the leading threats to human public fitness. HNF1B is a critical transcription element in vertebrate proliferation and oncogenesis, which has been shown to play roles in reactive oxygen species (ROS) metabolism. Our previous results have identified HNF1B as a tumor suppressor that could inhibit the malignant progression of prostate cancer. Yet there is no pan-carcinomatosis analysis of HNF1B, which could help us better understand common and unique underlying mechanisms in mankind knubs to enhance novel and competent treatment. Here, in our research, we evaluated the utterance pattern and explored the function of HNF1B in 33 knub categories using the data from the Cancer Genome Atlas Program (TCGA), Gene Expression Omnibus (GEO), and CLNICAL PROTEOMICTUMOR ANALYSIS CONSORTIUM (CPTAC) dataset. We found different HNF1B roles in various cancer types. HNF1B was upregulated in CHOL, STAD, KIRP, and THCA, and was downregulated in GBM, KICH, COAD, KIRC, LUSC, SARC, PAAD, and TGCT. Prognostic analyses indicated that higher HNF1B displayed better illness outcomes in BLCA, READ, and PRAD, while poorer outcomes in LUSC and THYM. HNF1B mutation was most frequent in endometrial cancer but was not associated with disease prognosis. It was discovered that HNF1B utterance relevant to endothelial cell penetration status in BLCA, ESCA, LUAD, LUSC, and TGCT, and carcinomatosis-associated fibroblast infiltration was observed in ESCA, KIRC, LIHC, and TGCT. Moreover, functional enrichment analysis disclosed that metabolism-related functions were implicated in the function of HNF1B. Taken together, our pan- carcinomatosis analysis showed the complicated roles of HNF1B in a variety of carcinomatoses, being able to improve the extensive comprehension of HNF1B's role in tumorigenesis.

A Novel Signature Combing Cuproptosis- and Ferroptosis-Related Genes in Nonalcoholic Fatty Liver Disease

OBJECTIVES

To identify cuproptosis- and ferroptosis-related genes involved in nonalcoholic fatty liver disease and to determine the diagnostic value of hub genes.

METHODS

The gene expression dataset GSE89632 was retrieved from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between the non-alcoholic steatohepatitis (NASH) group and the healthy group using the 'limma' package in R software and weighted gene co-expression network analysis. Gene ontology, kyoto encyclopedia of genes and genomes pathway, and single-sample gene set enrichment analyses were performed to identify functional enrichment of DEGs. Ferroptosis- and cuproptosis-related genes were obtained from the FerrDb V2 database and available literatures, respectively. A combined signature for cuproptosis- and ferroptosis-related genes, called CRF, was constructed using the STRING database. Hub genes were identified by overlapping DEGs, WGCNA-derived key genes, and combined signature CRF genes, and validated using the GSE109836 and GSE227714 datasets and real-time quantitative polymerase chain reaction. A nomogram of NASH diagnostic model was established utilizing the 'rms' package in R software based on the hub genes, and the diagnostic value of hub genes was assessed using receiver operating characteristic curve analysis. In addition, immune cell infiltration in NASH versus healthy controls was examined using the CIBERSORT algorithm. The relationships among various infiltrated immune cells were explored with Spearman's correlation analysis.

RESULTS

Analysis of GSE89632 identified 236 DEGs between the NASH group and the healthy group. WGCNA highlighted 8 significant modules and 11,095 pivotal genes, of which 330 genes constituted CRF. Intersection analysis identified IL6, IL1B, JUN, NR4A1, and PTGS2 as hub genes. The hub genes were all downregulated in the NASH group, and this result was further verified by the NASH validation dataset and real-time quantitative polymerase chain reaction. Receiver operating characteristic curve analysis confirmed the diagnostic efficacy of these hub genes with areas under the curve of 0.985, 0.941, 1.000, 0.967, and 0.985, respectively. Immune infiltration assessment revealed that gamma delta T cells, M1 macrophages, M2 macrophages, and resting mast cells were predominantly implicated.

CONCLUSIONS

Our investigation underscores the significant association of cuproptosis- and ferroptosis-related genes, specifically IL6, IL1B, JUN, NR4A1, and PTGS2, with NASH. These findings offer novel insights into the pathogenesis of NASH, potentially guiding future diagnostic and therapeutic strategies.

Copper homeostasis and cuproptosis in health and disease

Copper is a vital trace element in human physiology, essential for the synthesis of numerous crucial metabolic enzymes and facilitation of various biological processes. Regulation of copper levels within a narrow range is imperative for maintaining metabolic homeostasis. Numerous studies have demonstrated the significant roles of copper homeostasis and cuproptosis in health and disease pathogenesis. However, a comprehensive and up-to-date systematic review in this domain remains absent. This review aims to consolidate recent advancements in understanding the roles of cuproptosis and copper homeostasis in health and disease, focusing on the underlying mechanisms and potential therapeutic interventions. Dysregulation of copper homeostasis, manifesting as either copper excess or deficiency, is implicated in the etiology of various diseases. Cuproptosis, a recently identified form of cell death, is characterized by intracellular copper overload. This phenomenon mediates a diverse array of evolutionary processes in organisms, spanning from health to disease, and is implicated in genetic disorders, liver diseases, neurodegenerative disorders, and various cancers. This review provides a comprehensive summary of the pathogenic mechanisms underlying cuproptosis and copper homeostasis, along with associated targeted therapeutic agents. Furthermore, it explores future research directions with the potential to yield significant advancements in disease treatment, health management, and disease prevention.

Novel biomarkers and drug correlations of non-canonical WNT signaling in prostate and breast cancer

Prostate cancer (PCa) and breast cancer (BC) present formidable challenges in global cancer-related mortality, necessitating effective management strategies. The present study explores non-canonical Wnt signaling in PCa and BC, aiming to identify biomarkers and assess their clinical and therapeutic implications. Co-expression analyses reveal distinct gene patterns, with five overlapping genes (SULF1, ALG3, IL16, PLXNA2 and RASGFR2) exhibiting divergent expression in both cancers. Clinical relevance investigations demonstrate correlations with TNM stages and biochemical recurrence. Drug correlation analyses unveil potential therapeutic avenues, indicating that Wnt5a and ROR2 expressions are related to MEK inhibitor sensitivity in cancers. Meanwhile, further correlation analyses were conducted between drugs and the other novel non-canonical WNT genes (ALG3, IL16, SULF1, PLXNA2, and RASGRF2). Our findings contribute to understanding non-canonical Wnt signaling, offering insights into cancer progression and potential personalized treatment approaches.

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment

Background

Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene signature (HPRGS) and explore its clinical application value.

Methods

TCGA-LIHC cohort was used for training, and the LIRI-JP cohort and HCC cDNA microarray were used for validation. Machine learning algorithms constructed a prognostic gene label for HCC. Kaplan-Meier (K-M), ROC curve, multiple analyses, algorithms, and online databases were used to analyze differences between high- and low-risk populations. A nomogram was constructed to facilitate clinical application.

Results

We identified 119 differential genes based on transcriptome sequencing data from five independent HCC cohorts, and 53 of these genes were associated with overall survival (OS). Using 101 machine learning algorithms, the 10 most prognostic genes were selected. We constructed an HCC HPRGS with four genes (SOCS2, LCAT, ECT2, and TMEM106C). Good predictive performance of the HPRGS was confirmed by ROC, C-index, and K-M curves. Mutation analysis showed significant differences between the low- and high-risk patients. The low-risk group had a higher response to transcatheter arterial chemoembolization (TACE) and immunotherapy. Treatment response of high- and low-risk groups to small-molecule drugs was predicted. Linifanib was a potential drug for high-risk populations. Multivariate analysis confirmed that HPRGS were independent prognostic factors in TCGA-LIHC. A nomogram provided a clinical practice reference.

Conclusion

We constructed an HPRGS for HCC, which can accurately predict OS and guide the treatment decisions for patients with HCC.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

Prognostic analysis of hepatocellular carcinoma based on cuproptosis -associated lncRNAs

OBJECTIVES

Cuproptosis represents an innovative type of cell death, distinct from apoptosis, driven by copper dependency, yet the involvement of copper apoptosis-associated long non-coding RNAs (CRLncRNAs) in hepatocellular carcinoma (HCC) remains unclear. This study is dedicated to unveiling the role and significance of these copper apoptosis-related lncRNAs within the context of HCC, focusing on their impact on both the development of the disease and its prognosis.

METHODS

We conducted an analysis of gene transcriptomic and clinical data for HCC cases by sourcing information from The Cancer Genome Atlas database. By incorporating cuproptosis-related genes, we established prognostic features associated with cuproptosis-related lncRNAs. Furthermore, we elucidated the mechanism of cuproptosis-related lncRNAs in the prognosis and treatment of HCC through comprehensive approaches, including Lasso and Cox regression analyses, survival analyses of samples, as well as examinations of tumor mutation burden and immune function.

RESULTS

We developed a prognostic model featuring six cuproptosis-related lncRNAs: AC026412.3, AC125437.1, AL353572.4, MKLN1-AS, TMCC1-AS1, and SLC6A1-AS1. This model demonstrated exceptional prognostic accuracy in both training and validation cohorts for patients with tumors, showing significantly longer survival times for those categorized in the low-risk group compared to the high-risk group. Additionally, our analyses, including tumor mutation burden, immune function, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and drug sensitivity, further elucidated the potential mechanisms through which cuproptosis-associated lncRNAs may influence disease outcome.

CONCLUSIONS

The model developed using cuproptosis-related long non-coding RNAs (lncRNAs) demonstrates promising predictive capabilities for both the prognosis and immunotherapy outcomes of tumor patients. This could play a crucial role in patient management and the optimization of immunotherapeutic strategies, offering valuable insights for future research.

Integrated bioinformatics and experiment revealed that cuproptosis is the potential common pathogenesis of three kinds of primary cardiomyopathy

Cuproptosis is a recently reported new mode of programmed cell death which might be a potential co-pathogenesis of three kinds of primary cardiomyopathy. However, no investigation has reported a clear relevance between primary cardiomyopathy and cuproptosis. In this study, the differential cuproptosis-related genes (CRGs) shared by three kinds of primary cardiomyopathy were identified in training sets. As a result, four CRGs shared by three kinds of primary cardiomyopathy were acquired and they were mainly related to biological processes such as cell death and immuno-inflammatory response through differential analysis, correlation analysis, GSEA, GSVA and immune cell infiltration analysis. Then, three key CRGs (K-CRGs) with high diagnostic value were identified by LASSO regression. The results of nomogram, machine learning, ROC analysis, calibration curve and decision curve indicated that the K-CRGs exhibited outstanding performance in the diagnosis of three kinds of primary cardiomyopathy. After that, in each disease, two molecular subtypes clusters were distinguished. There were many differences between different clusters in the biological processes associated with cell death and immunoinflammation and K-CRGs had excellent molecular subtype identification efficacy. Eventually, results from validation datasets and in vitro experiments verified the role of K-CRGs in diagnosis of primary cardiomyopathy, identification of primary cardiomyopathic molecular subtypes and pathogenesis of cuproptosis. In conclusion, this study found that cuproptosis might be the potential common pathogenesis of three kinds of primary cardiomyopathy and K-CRGs might be promising biomarkers for the diagnosis and molecular subtypes identification of primary cardiomyopathy.

Novel cuproptosis-related lncRNAs can predict the prognosis of patients with multiple myeloma

Background

Cuproptosis-related long-stranded non-coding RNAs (lncRNAs) have several implications for the prognosis of multiple myeloma (MM). This research aimed to construct a prognostic risk model for MM patients and explore the potential signaling pathways in the risk group.

Methods

Cuproptosis-related lncRNAs were obtained from the co-expression analysis of cuproptosis-related genes and lncRNAs. Subsequently, twelve cuproptosis-related lncRNAs were selected to construct a prognostic risk model of MM patients by the least absolute shrinkage and selection operator (LASSO) regression. Then, the clinical data of these patients were randomly divided into the training group and the testing group. Next, patients were divided into the low- and high-risk groups according to the median risk score. The Kaplan-Meier survival analysis was performed to clarify the prognostic differences between risk subtypes. Besides, the Cox analysis was conducted to identify whether the risk score can be used as an independent prognostic factor. In addition, the receiver operating characteristic (ROC) curve analysis and the concordance index (C-index) curve analysis were performed to elucidate the value of risk score as a prognostic indicator. Finally, the differential risk analysis and functional enrichment analysis were carried out to identify the potential signaling pathways in the low- and high-risk groups.

Results

The results demonstrated that the overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group. There were significant differences in the expression of genes in MM patients between the high- and low-risk groups. The Gene Ontology (GO) analysis results showed that the differentially expressed risk-related genes (DERGs) were mainly concentrated on the collagen-containing extracellular matrix. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results, the DERGs may be related to the neuroactive ligand-receptor interaction and mitogen-activated protein kinase (MAPK) signaling pathway, indicating that they may be involved in the progression of tumors.

Conclusions

The findings of this study suggest that cuproptosis-related lncRNAs may be effective biomarkers for predicting the prognosis of MM patients, which is anticipated to contribute to the improvement of clinical outcomes.

Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques

Background

To identify novel gene combinations and to develop an early diagnostic model for Polycystic Ovary Syndrome (PCOS) through the integration of artificial neural networks (ANN) and random forest (RF) methods.

Methods

We retrieved and processed gene expression datasets for PCOS from the Gene Expression Omnibus (GEO) database. Differential expression analysis of genes (DEGs) within the training set was performed using the "limma" R package. Enrichment analyses on DEGs using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and immune cell infiltration. The identification of critical genes from DEGs was then performed using random forests, followed by the developing of new diagnostic models for PCOS using artificial neural networks.

Results

We identified 130 up-regulated genes and 132 down-regulated genes in PCOS compared to normal samples. Gene Ontology analysis revealed significant enrichment in myofibrils and highlighted crucial biological functions related to myofilament sliding, myofibril, and actin-binding. Compared with normal tissues, the types of immune cells expressed in PCOS samples are different. A random forest algorithm identified 10 significant genes proposed as potential PCOS-specific biomarkers. Using these genes, an artificial neural network diagnostic model accurately distinguished PCOS from normal samples. The diagnostic model underwent validation using the independent validation set, and the resulting area under the receiver operating characteristic curve (AUC) values was consistent with the anticipated outcomes.

Conclusion

Utilizing unique gene combinations, this research created a diagnostic model by merging random forest techniques with artificial neural networks. The AUC indicated a notably superior performance of the diagnostic model.

Clinical Significance of Non-Coding RNA Regulation of Programmed Cell Death in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a widely prevalent and malignantly progressive tumor. Most patients are typically diagnosed with HCC at an advanced stage, posing significant challenges in the execution of curative surgical interventions. Non-coding RNAs (ncRNAs) represent a distinct category of RNA molecules not directly involved in protein synthesis. However, they possess the remarkable ability to regulate gene expression, thereby exerting significant regulatory control over cellular processes. Notably, ncRNAs have been implicated in the modulation of programmed cell death (PCD), a crucial mechanism that various therapeutic agents target in the fight against HCC. This review summarizes the clinical significance of ncRNA regulation of PCD in HCC, including patient diagnosis, prognosis, drug resistance, and side effects. The aim of this study is to provide new insights and directions for the diagnosis and drug treatment strategies of HCC.

LncRNAs and regulated cell death in tumor cells

Regulated Cell Death (RCD) is a mode of cell death that occurs through drug or genetic intervention. The regulation of RCDs is one of the significant reasons for the long survival time of tumor cells and poor prognosis of patients. Long non-coding RNAs (lncRNAs) which are involved in the regulation of tumor biological processes, including RCDs occurring on tumor cells, are closely related to tumor progression. In this review, we describe the mechanisms of eight different RCDs which contain apoptosis, necroptosis, pyroptosis, NETosis, entosis, ferroptosis, autosis and cuproptosis. Meanwhile, their respective roles in the tumor are aggregated. In addition, we outline the literature that is related to the regulatory relationships between lncRNAs and RCDs in tumor cells, which is expected to provide new ideas for tumor diagnosis and treatment.

Combining WGCNA and machine learning to construct basement membrane-related gene index helps to predict the prognosis and tumor microenvironment of HCC patients and verifies the carcinogenesis of key gene CTSA

Hepatocellular carcinoma (HCC) is a malignant tumor with high recurrence and metastasis rates and poor prognosis. Basement membrane is a ubiquitous extracellular matrix and is a key physical factor in cancer metastasis. Therefore, basement membrane-related genes may be new targets for the diagnosis and treatment of HCC. We systematically analyzed the expression pattern and prognostic value of basement membrane-related genes in HCC using the TCGA-HCC dataset, and constructed a new BMRGI based on WGCNA and machine learning. We used the HCC single-cell RNA-sequencing data in GSE146115 to describe the single-cell map of HCC, analyzed the interaction between different cell types, and explored the expression of model genes in different cell types. BMRGI can accurately predict the prognosis of HCC patients and was validated in the ICGC cohort. In addition, we also explored the underlying molecular mechanisms and tumor immune infiltration in different BMRGI subgroups, and confirmed the differences in response to immunotherapy in different BMRGI subgroups based on the TIDE algorithm. Then, we assessed the sensitivity of HCC patients to common drugs. In conclusion, our study provides a theoretical basis for the selection of immunotherapy and sensitive drugs in HCC patients. Finally, we also considered CTSA as the most critical basement membrane-related gene affecting HCC progression. In vitro experiments showed that the proliferation, migration and invasion abilities of HCC cells were significantly impaired when CTSA was knocked down.

Lactobacillus brevis alleviates the progress of hepatocellular carcinoma and type 2 diabetes in mice model via interplay of gut microflora, bile acid and NOTCH 1 signaling

Type 2 diabetes (T2DM) clinically exhibits a higher incidence of hepatocellular carcinoma (HCC), contributing to a lousy prognosis in patients harboring both diseases. Microflora-based therapy draws attention with low side effects. Accumulating evidence shows that Lactobacillus brevis can improve blood glucose and body weight of the T2DM mice model and reduce several cancer incidences. However, the therapeutic effect of Lactobacillus brevis in affecting the prognosis of T2DM+HCC remains unknown. In this study, we aim to explore this question via an established T2DM+HCC mice model. We observed a significant alleviation after the probiotic intervention. Lactobacillus brevis improves blood glucose and insulin resistance and ameliorates Mechanically. Combined with a multi-omics approach including 16SrDNA, GC-MS, and RNA-seq, we identified distinct intestinal microflora composition and metabolites after Lactobacillus brevis intervention. Furthermore, we found that Lactobacillus brevis delayed disease progression by regulating MMP9 and NOTCH 1 signaling pathways, potentially through gut microflora and BA interaction. This study indicates that Lactobacillus brevis may improve the prognosis of T2DM + HCC, providing novel therapeutic opportunities via targeting intestinal flora for patients with T2DM+HCC.

Transcriptomic profiling identifies differentially expressed genes and related pathways associated with wound healing and cuproptosis-related genes in Ganxi goats

Introduction

Wound healing is very important for the maintenance of immune barrier integrity, which has attracted wide attention in past 10 years. However, no studies on the regulation of cuproptosis in wound healing have been reported.

Methods

In this study, the skin injury model was constructed in Gnxi goats, and the function, regulatory network and hub genes of the skin before and after the injury were comprehensively analyzed by transcriptomics.

Results

The results showed that there were 1,438 differentially expressed genes (DEGs), genes up-regulated by 545 and genes down-regulated by 893, which were detected by comparing day 0 and day 5 posttraumatic skin. Based on GO-KEGG analysis, DEGs that were up-regulated tended to be enriched in lysosome, phagosome, and leukocyte transendothelial migration pathways, while down-regulated DEGs were significantly enriched in adrenergic signaling in cardiomyocytes and calcium signaling pathway. There were 166 overlapped genes (DE-CUGs) between DEGs and cuproptosis-related genes, with 72 up-regulated DE-CUGs and 94 down-regulated DE-CUGs. GOKEGG analysis showed that up-regulated DE-CUGs were significantly enriched in ferroptosis, leukocyte transendothelial migration and lysosome pathways, while down-regulated DE-CUGs were significantly enriched in Apelin signaling pathway and tyrosine metabolism pathways. By constructing and analyzing of protein-protein interaction (PPI) networks of DEGs and DE-CUGs, 10 hub DEGs (ENSCHIG00000020079, PLK1, AURKA, ASPM, CENPE, KIF20A, CCNB2, KIF2C, PRC1 and KIF4A) and 10 hub DE-CUGs (MMP2, TIMP1, MMP9, MMP14, TIMP3, MMP1, EDN1, GCAT, SARDH, and DCT) were obtained, respectively.

Discussion

This study revealed the hub genes and important wound healing pathways in Ganxi goats, and identified the correlation between wound healing and cuproptosis for the first time, and found that MMP2, TIMP1, MMP9, and EDN1 were the core genes associated. This study enriched the transcriptome data of wound healing in Ganxi goats and expanded the research direction of cuproptosis.

Bioinformatics analysis and identification of hub genes associated with female acute myocardial infarction patients by using weighted gene co-expression networks

To explore potential biomarkers of acute myocardial infarction (AMI) in females by using bioinformatics analysis. In this study, we explored potential biomarkers of AMI in females using bioinformatics analysis. We screened a total of 186 differentially expressed genes from the Gene Expression Omnibus. In the study, we found that weighted gene co-expression network analysis explored the co-expression network of genes and identified key modules. Simultaneously, we chose brown modules as key modules related to AMI. In this study, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that genes in the brown module were mainly enriched in "heparin" and 'complementation and coagulation cascade. Based on the protein-protein interaction network, we identified S100A9, mitogen-activated protein kinase (MAPK) 3, MAPK1, MMP3, interleukin (IL)-17A, and HSP90AB1 as hub gene sets. Whereas, polymerase chain reaction results showed that S100A9, MAPK3, MAPK1, MMP3, IL-17A, and HSP90AB1 were highly expressed compared with the control group. The IL-17 signaling pathway associated with an inflammatory response may be a potential biomarker and target for the treatment of women with myocardial infarction.

Identification of cuproptosis hub genes contributing to the immune microenvironment in ulcerative colitis using bioinformatic analysis and experimental verification

Instruction

Ulcerative colitis (UC) can cause a variety of immune-mediated intestinal dysfunctions and is a significant model of inflammatory bowel disease (IBD). Colorectal cancer (CRC) mostly occurs in patients with ulcerative colitis. Cuproptosis is a type of procedural death that is associated with different types of diseases to various degrees.

Methods

We used a combination of bioinformatic prediction and experimental verification to study the correlation between copper poisoning and UC. We used the Gene Expression Omnibus database to obtain disease gene expression data and then identified relevant genes involved in various expression levels in normal and UC samples. The Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed to cluster the genes that are highly responsible and find the central interaction in gene crosstalk. Notably, DLD, DLAT, and PDHA1 were present in high-scoring PPI networks. In addition, hub gene expression information in UC tissues was integrated to estimate the relationship between UC copper poisoning and the immune environment.

Results

In our study, the expression of DLD, DLAT, and PDHA1 in UC tissues was lower than that in normal tissues. The key genes associated with cuproptosis have therapeutic effects on immune infiltration. We verified the expression of DLD, DLAT, and PDHA1 using real-time quantitative polymerase chain reaction in mouse models of UC induced by DSS.

Discussion

Notably, this study clearly indicates that bioinformatic analysis performed to verify the experimental methods provides evidence that cuproptosis is associated with UC. This finding suggests that immune cell infiltration in UC patients is associated with cuproptosis. The key genes associated with cuproptosis can be helpful for discovering the molecular mechanism of UC, thus facilitating the improvement of UC treatment and preventing the associated CRC.

A Prognostic Cuproptosis-Related LncRNA Signature for Colon Adenocarcinoma

Background

Cuproptosis, a recently discovered form of cell death, is caused by copper levels exceeding homeostasis thresholds. Although Cu has a potential role in colon adenocarcinoma (COAD), its role in the development of COAD remains unclear.

Methods

In this study, 426 patients with COAD were extracted from the Cancer Genome Atlas (TCGA) database. The Pearson correlation algorithm was used to identify cuproptosis-related lncRNAs. Using the univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) was used to select cuproptosis-related lncRNAs associated with COAD overall survival (OS). A risk model was established based on the multivariate Cox regression analysis. A nomogram model was used to evaluate the prognostic signature based on the risk model. Finally, mutational burden and sensitivity analyses of chemotherapy drugs were performed for COAD patients in the low- and high-risk groups.

Result

Ten cuproptosis-related lncRNAs were identified and a novel risk model was constructed. A signature based on ten cuproptosis-related lncRNAs was an independent prognostic predictor for COAD. Mutational burden analysis suggested that patients with high-risk scores had higher mutation frequency and shorter survival.

Conclusion

Constructing a risk model based on the ten cuproptosis-related lncRNAs could accurately predict the prognosis of COAD patients, providing a fresh perspective for future research on COAD.

Copper Death Inducer, FDX1, as a Prognostic Biomarker Reshaping Tumor Immunity in Clear Cell Renal Cell Carcinoma

BACKGROUND

Progress in the diagnosis and treatment of clear cell renal cell carcinoma (ccRCC) has significantly prolonged patient survival. However, ccRCC displays an extreme heterogenous characteristic and metastatic tendency, which limit the benefit of targeted or immune therapy. Thus, identifying novel biomarkers and therapeutic targets for ccRCC is of great importance.

METHOD

Pan cancer datasets, including the expression profile, DNA methylation, copy number variation, and single nucleic variation, were introduced to decode the aberrance of copper death regulators (CDRs). Then, FDX1 was systematically analyzed in ccRCC to evaluate its impact on clinical characteristics, prognosis, biological function, immune infiltration, and therapy response. Finally, in vivo experiments were utilized to decipher FDX1 in ccRCC malignancy and its role in tumor immunity.

RESULT

Copper death regulators were identified at the pancancer level, especially in ccRCC. FDX1 played a protective role in ccRCC, and its expression level was significantly decreased in tumor tissues, which might be regulated via CNV events. At the molecular mechanism level, FDX1 positively regulated fatty acid metabolism and oxidative phosphorylation. In addition, FDX1 overexpression restrained ccRCC cell line malignancy and enhanced tumor immunity by increasing the secretion levels of IL2 and TNFγ.

CONCLUSIONS

Our research illustrated the role of FDX1 in ccRCC patients' clinical outcomes and its impact on tumor immunity, which could be treated as a promising target for ccRCC patients.

Prognostic and immune correlation evaluation of a novel cuproptosis-related genes signature in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the world's malignant tumors with high morbidity and mortality. Cuproptosis is a novel form of cell death. However, the prognostic evaluation and immune relevance of cuproptosis-related genes (CRGs) in HCC are largely unknown. In our study, we constructed a prognostic model of CRGs in HCC and performed immune infiltration, functional analysis, immune checkpoint and drug sensitivity analysis. Systematically elaborated the prognostic and immune correlation of CRGs in HCC. The results showed that 15 CRGs were up-regulated or down-regulated in HCC, and the mutation frequency of CRGs reached 10.33% in HCC, with CDKN2A having the highest mutation frequency. These 19 CRGs were mainly involved in the mitochondrion, immune response and metabolic pathways. Five selected genes (CDKN2A, DLAT, DLST, GLS, PDHA1) were involved in constructing a prognostic CRGs model that enables the overall survival in HCC patients to be predicted with moderate to high accuracy. Prognostic CRGs, especially CDKN2A, the independent factor of HCC prognosis, may be closely associated with immune-cell infiltration, tumor mutation burden (TMB), microsatellite instability(MSI), and immune checkpoints. CD274, CTLA4, LAG3, PDCD1, PDCD1LG2 and SIGLEC15 may be identified as potential therapeutic targets and CD274 correlated highly with prognostic genes. Quantitative Real-Time PCR (qRT-PCR) and immunohistochemical were performed to validate the mRNA and protein expression levels of CDKN2A in adjacent normal tissues and HCC tissues, and the results were consistent with gene difference analysis. In conclusion, CRGs, especially CDKN2A, may serve as potential prognostic predictors in HCC patients and provide novel insights into cancer therapy.

A Novel Cuproptosis-Related Signature Identified DLAT as a Prognostic Biomarker for Hepatocellular Carcinoma Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common type of liver cancers, with more than a million cases per year by 2025. Cuproptosis is a novel form of programmed cell death, and is caused by mitochondrial lipoylation and destabilization of iron-sulfur proteins triggered by copper, which was considered as a key player in various biological processes. However, the roles of cuproptosis-related genes (CRGs) in HCC remain largely unknown.

METHODS

In the present study, we constructed and validated a four CRGs signature for predicting the overall survival (OS) of HCC patients in both The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases.

RESULTS

Patients with high CRGs risk score showed shorter OS than those with low CRGs risk score. Functional analysis suggested that the CRGs-based prognostic signature was associated with metabolism remodeling which facilitated liver cancer progression. In addition, reduced infiltration of CD8⁺ T cells and increased macrophages were found in HCCs from patients with high CRGs risk score. As one of the four CRGs, higher expression of dihydrolipoamide S-acetyltransferase (DLAT) was accompanied by higher expression of program death ligand 1 (PD-L1) in HCC. Further, we confirmed that DLAT was up-regulated and correlated with poor prognosis in a clinical HCC cohort.

CONCLUSION

In conclusion, our study constructed a four CRGs signature prognostic model and identified DLAT as an independent prognostic factor for HCC, thus providing new clues for understanding the association between cuproptosis and HCC.