Development and validation of cuproptosis-related gene signature in the prognostic prediction of liver cancer

Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis

The task of predicting liver tumors is critical as part of medical image analysis and genomics area since diagnosis and prognosis are important in making correct medical decisions. Silent characteristics of liver tumors and interactions between genomic and imaging features are also the main sources of challenges toward reliable predictions. To overcome these hurdles, this study presents two integrated approaches namely, - Attention-Guided Convolutional Neural Networks (AG-CNNs), and Genomic Feature Analysis Module (GFAM). Spatial and channel attention mechanisms in AG-CNN enable accurate tumor segmentation from CT images while providing detailed morphological profiling. Evaluation with three control databases TCIA, LiTS, and CRLM shows that our model produces more accurate output than relevant literature with an accuracy of 94.5%, a Dice Similarity Coefficient of 91.9%, and an F1-Score of 96.2% for the Dataset 3. More considerably, the proposed methods outperform all the other methods in different datasets in terms of recall, precision, and Specificity by up to 10 percent than all other methods including CELM, CAGS, DM-ML, and so on.•Utilization of Attention-Guided Convolutional Neural Networks (AG-CNN) enhances tumor region focus and segmentation accuracy.•Integration of Genomic Feature Analysis (GFAM) identifies molecular markers for subtype-specific tumor classification.

The molecular mechanism and therapeutic landscape of copper and cuproptosis in cancer

Copper, an essential micronutrient, plays significant roles in numerous biological functions. Recent studies have identified imbalances in copper homeostasis across various cancers, along with the emergence of cuproptosis, a novel copper-dependent form of cell death that is crucial for tumor suppression and therapeutic resistance. As a result, manipulating copper levels has garnered increasing interest as an innovative approach to cancer therapy. In this review, we first delineate copper homeostasis at both cellular and systemic levels, clarifying copper's protumorigenic and antitumorigenic functions in cancer. We then outline the key milestones and molecular mechanisms of cuproptosis, including both mitochondria-dependent and independent pathways. Next, we explore the roles of cuproptosis in cancer biology, as well as the interactions mediated by cuproptosis between cancer cells and the immune system. We also summarize emerging therapeutic opportunities targeting copper and discuss the clinical associations of cuproptosis-related genes. Finally, we examine potential biomarkers for cuproptosis and put forward the existing challenges and future prospects for leveraging cuproptosis in cancer therapy. Overall, this review enhances our understanding of the molecular mechanisms and therapeutic landscape of copper and cuproptosis in cancer, highlighting the potential of copper- or cuproptosis-based therapies for cancer treatment.

Pump-free SERS microfluidic chip based on an identification-competition strategy for ultrasensitive and efficient simultaneous detection of liver cancer-related microRNAs

In this study, we present a pump-free SERS microfluidic chip capable of detecting liver cancer-related miR-21 and miR-155 concurrently with ultra-sensitivity and high efficiency. We employed a Fe3O4@cDNA-AuNPs@Raman reporter@H composite structure and a recognition competition strategy. When the target miRNAs (miR-21 and miR-155) are present in the test liquid, they specifically compete with the nucleic acid complementary strand(H) of Fe3O4@cDNA-AuNPs@Raman reporter@H, causing AuNPs to competitively detach from the surface of Fe3O4, resulting in a decrease in the SERS signal. Consequently, this pump-free SERS microfluidic chip enables the detection of the target miRNAs more rapidly and accurately in complex environments. This method offers an approach for the simultaneous and efficient detection of miRNAs and holds promising applications in the early diagnosis of liver cancer.

lncRNAs as prognostic markers and therapeutic targets in cuproptosis-mediated cancer

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in various cellular processes, including cancer progression and stress response. Recent studies have demonstrated that copper accumulation induces a unique form of cell death known as cuproptosis, with lncRNAs playing a key role in regulating cuproptosis-associated pathways. These lncRNAs may trigger cell-specific responses to copper stress, presenting new opportunities as prognostic markers and therapeutic targets. This paper delves into the role of lncRNAs in cuproptosis-mediated cancer, underscoring their potential as biomarkers and targets for innovative therapeutic strategies. A thorough review of scientific literature was conducted, utilizing databases such as PubMed, Google Scholar, and ScienceDirect, with search terms like 'lncRNAs,' 'cuproptosis,' and 'cancer.' Studies were selected based on their relevance to lncRNA regulation of cuproptosis pathways and their implications for cancer prognosis and treatment. The review highlights the significant contribution of lncRNAs in regulating cuproptosis-related genes and pathways, impacting copper metabolism, mitochondrial stress responses, and apoptotic signaling. Specific lncRNAs are potential prognostic markers in breast, lung, liver, ovarian, pancreatic, and gastric cancers. The objective of this article is to explore the role of lncRNAs as potential prognostic markers and therapeutic targets in cancers mediated by cuproptosis.

Pan-cancer image segmentation based on feature pyramids and Mask R-CNN framework

BACKGROUND

Cancer, a disease with a high mortality rate, poses a great threat to patients' physical and mental health and can lead to huge medical costs and emotional damage. With the continuous development of artificial intelligence technologies, deep learning-based cancer image segmentation techniques are becoming increasingly important in cancer detection and accurate diagnosis. However, in segmentation tasks, there are differences in efficiency between large and small objects and limited segmentation effects on objects of individual sizes. The previous segmentation frameworks still have room for improvement in multi-scale collaboration when segmenting objects.

PURPOSE

This paper proposes a method to train a deep learning segmentation framework using a feature pyramid processing dataset to improve the average precision (AP) index, and realizes multi-scale cooperation in target segmentation.

OBJECTIVE

Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification (PanNuke) dataset was selected to include approximately 7500 pathology images with cells from 19 different types of tissues, including five classifications of cancer, non-cancer, inflammation, death, and connective tissue.

METHODS

First, the method uses whole-slide images in the pan-cancer histology dataset for nuclei instance segmentation and classification (PanNuke) dataset, combined with the mask region convolutional neural network (Mask R-CNN) segmentation framework and improved loss function to segment and detect each cellular tissue in cancerous sections. Second, to address the problem of non-synergistic object segmentation at different scales in cancerous tissue segmentation, a scheme using feature pyramids to process the dataset was adopted as part of the feature extraction module.

RESULTS

Extensive experimental results on this dataset show that the method in this paper yields 0.269 AP and a boost of about 4% compared to the original Mask R-CNN framework.

CONCLUSIONS

It is effective and feasible to use feature pyramid to process data set to improve the effect of medical image segmentation.

Identification of a novel prognostic cuproptosis-associated LncRNA signature for predicting prognosis and immunotherapy response in patients with esophageal cancer

Nowadays, effective prognostic models for esophageal cancer (ESCA) are still lacking. Long noncoding RNAs (lncRNAs) are commonly utilized as indicators for diagnosing cancer and forecasting patient outcomes. Cuproptosis is regulated by multiple genes and is crucial to the progression of ESCA. However, it is not yet clear what role the cuproptosis-associated lncRNAs (CuALs) play in ESCA. To tackle this problem, a prognostic signature incorporating three CuALs was created. This signature was constructed by the use of the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Subsequently, the signature effectively stratified ESCA samples into a high-risk group and a low-risk group. Those in the low-risk group demonstrated extended overall survival (OS), as well as increased infiltration of T cells, macrophages, and NK cells, suggesting a potentially enhanced response to immunotherapy. The ROC curve analysis demonstrated that this prognostic signature outperformed conventional clinical factors in predicting patient prognosis (AUC = 0.708). K-M survival analysis and correlation analysis identified UGDH-AS1 (a CuAL) as a protective factor positively associated with patient prognosis. The results of RT-qPCR and wound healing assays indicated that UGDH-AS1 is overexpressed in ESCA and could inhibit cancer cell migration. In general, the prognostic signature of CuALs demonstrated a robust capability in forecasting the immune environment and patient prognosis, highlighting its potential as a tool for enhancing personalized treatment strategies in ESCA.

Identification of a novel prognostic cuproptosis-associated LncRNA signature for predicting prognosis and immunotherapy response in patients with esophageal cancer

Nowadays, effective prognostic models for esophageal cancer (ESCA) are still lacking. Long noncoding RNAs (lncRNAs) are commonly utilized as indicators for diagnosing cancer and forecasting patient outcomes. Cuproptosis is regulated by multiple genes and is crucial to the progression of ESCA. However, it is not yet clear what role the cuproptosis-associated lncRNAs (CuALs) play in ESCA. To tackle this problem, a prognostic signature incorporating three CuALs was created. This signature was constructed by the use of the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Subsequently, the signature effectively stratified ESCA samples into a high-risk group and a low-risk group. Those in the low-risk group demonstrated extended overall survival (OS), as well as increased infiltration of T cells, macrophages, and NK cells, suggesting a potentially enhanced response to immunotherapy. The ROC curve analysis demonstrated that this prognostic signature outperformed conventional clinical factors in predicting patient prognosis (AUC = 0.708). K-M survival analysis and correlation analysis identified UGDH-AS1 (a CuAL) as a protective factor positively associated with patient prognosis. The results of RT-qPCR and wound healing assays indicated that UGDH-AS1 is overexpressed in ESCA and could inhibit cancer cell migration. In general, the prognostic signature of CuALs demonstrated a robust capability in forecasting the immune environment and patient prognosis, highlighting its potential as a tool for enhancing personalized treatment strategies in ESCA.

Copper metabolism and cuproptosis in human malignancies: Unraveling the complex interplay for therapeutic insights

Copper, a vital trace element, orchestrates diverse cellular processes ranging from energy production to antioxidant defense and angiogenesis. Copper metabolism and cuproptosis are closely linked in the context of human diseases, with a particular focus on cancer. Cuproptosis refers to a specific type of copper-mediated cell death or copper toxicity triggered by disruptions in copper metabolism within the cells. This phenomenon encompasses a spectrum of mechanisms, such as oxidative stress, mitochondrial dysfunction, endoplasmic reticulum stress, and perturbations in metal ion equilibrium. Mechanistically, cuproptosis is driven by copper binding to the lipoylated enzymes within the tricarboxylic acid (TCA) cycle. This interaction participates in protein aggregation and proteotoxic stress, ultimately culminating in cell death. Targeting copper metabolism and its associated pathways in cancer cells hold therapeutic potential by selectively targeting and eliminating cancerous cells. Strategies to modulate copper levels, enhance copper excretion, or interfere with cuproptotic pathways are being explored to identify novel therapeutic targets for cancer therapy and improve patient outcomes. Understanding the relationship between cuproptosis and copper metabolism in human malignancies remains an active area of research. This review provides a comprehensive overview of the association among copper metabolism, copper homeostasis, and carcinogenesis, explicitly emphasizing the cuproptosis mechanism and its implications for cancer pathogenesis. Additionally, we emphasize the therapeutic aspects of targeting copper and cuproptosis for cancer treatment.

PANoptosis-related genes function as efficient prognostic biomarkers in colon adenocarcinoma

BACKGROUND

PANoptosis is a newly discovered cell death type, and tightly associated with immune system activities. To date, the mechanism, regulation and application of PANoptosis in tumor is largely unknown. Our aim is to explore the prognostic value of PANoptosis-related genes in colon adenocarcinoma (COAD).

METHODS

Analyzing data from The Cancer Genome Atlas-COAD (TCGA-COAD) involving 458 COAD cases, we concentrated on five PANoptosis pathways from the Molecular Signatures Database (MSigDB) and a comprehensive set of immune-related genes. Our approach involved identifying distinct genetic COAD subtype clusters and developing a prognostic model based on these parameters.

RESULTS

The research successfully identified two genetic subtype clusters in COAD, marked by distinct profiles in PANoptosis pathways and immune-related gene expression. A prognostic model, incorporating these findings, demonstrated significant predictive power for survival outcomes, underscoring the interplay between PANoptosis and immune responses in COAD.

CONCLUSION

This study enhances our understanding of COAD's genetic framework, emphasizing the synergy between cell death pathways and the immune system. The development of a prognostic model based on these insights offers a promising tool for personalized treatment strategies. Future research should focus on validating and refining this model in clinical settings to optimize therapeutic interventions in COAD.

Peptide fractions from Sacha inchi induced apoptosis in HepG2 cells via P53 activation and a mitochondria-mediated pathway

BACKGROUND

Sacha inchi is known for its high protein content and medicinal properties. Bioactive peptides have been reported to have therapeutic potential in various human diseases. However, there is a lack of research evaluating the pharmacological value of peptides derived from Sacha inchi. Therefore, this study aimed to investigate the anti-hepatoma effect of Sacha inchi peptides (SPs) and their underlying mechanism.

RESULTS

The study found that treatment with SPs significantly reduced the proliferation of HepG2 cells by inducing apoptosis and arresting the cell cycle at the G0/G1 phase. SPs also induced HepG2 cell apoptosis by increasing the levels of proteins such as Bax, Caspase-3 and P53. The study identified nine novel peptides in SPs, of which LLEPDVR, ALVEKAKAS and TGDGSLRPY exhibited higher cell proliferative inhibition rates compared to other peptides.

CONCLUSION

The findings of this study suggest that Sacha inchi peptides have potential pharmacological effects in the treatment of liver cancer. SPs effectively suppress the cell cycle and facilitate cell apoptosis, indicating their anti-hepatoma effect. The novel peptides identified in SPs may have therapeutic value for liver cancer treatment. © 2023 Society of Chemical Industry.

An effective prognostic model in colon adenocarcinoma composed of cuproptosis-related epigenetic regulators

Background: Colorectal adenocarcinoma (COAD) is a common malignant tumor with little effective prognostic markers. Cuproptosis is a newly discovered mode of cell death that may be related to epigenetic regulators. This study aimed to explore the association between epigenetic regulators and cuproptosis, and to establish a prognostic prediction model for COAD based on epigenetic regulators associated with cuproptosis (EACs). Methods: RNA sequencing data and clinical data of 524 COAD patients were obtained from the TCGA-COAD database, cuproptosis-related genes were from the FerrDb database, and epigenetic-related genes were from databases such as GO and EpiFactors. LASSO regression analysis and other methods were used to screen out epigenetic regulators associated with cuproptosis and prognosis. The risk score of each patient was calculated and the patients were divided into high-risk group and low-risk group. Next, the survival difference, functional enrichment analyses, tumor mutation burden, chemotherapy drug sensitivity and other indicators between the two groups were compared and analyzed. Results: We found 716 epigenetic regulators closely related to cuproptosis, among which 35 genes were related to prognosis of COAD. We further screened out 7 EACs from the 35 EACs to construct a prognostic prediction model. We calculated the risk score of each patient based on these 7 genes, and divided the patients into high-risk group and low-risk group. We found that the overall survival rate and progression-free survival rate of the high-risk group were significantly lower than those of the low-risk group. This model showed good predictive ability in the training set, test set and overall data set. We also constructed a prognostic prediction model based on risk score and other clinical features, and drew the corresponding Nomogram. In addition, we found significant differences between the high-risk group and the low-risk group in tumor mutation burden, chemotherapy drug sensitivity and other clinical aspects. Conclusion: We established an effective predictive prediction model for COAD based on EACs, revealing the association between epigenetic regulators and cuproptosis in COAD. We hope that this model can not only facilitate the treatment decision of COAD patients, but also promote the research progress in the field of cuproptosis.

Cuproptosis- and m6A-Related lncRNAs for Prognosis of Hepatocellular Carcinoma

Cuproptosis and N6-methyladenosine (m6A) have potential as prognostic predictors in cancer patients, but their roles in hepatocellular carcinoma (HCC) are unclear. This study aimed to screen a total of 375 HCC samples were retrieved from the TCGA database, and lncRNAs related to cuproptosis and m6A were obtained through correlation analysis. To construct a risk assessment model, univariate Cox regression analysis and LASSO Cox regression were employed. Analyze the regulatory effect of relevant risk assessment models on tumor mutation load (TMB) and immune microenvironment. A total of five lncRNAs (AC007405.3, AL031985.3, TMCC1-AS1, MIR210HG, TMEM220-AS1) with independent overall survival-related risk models were obtained by LASSO survival regression. TP53 and CTNNB1 were the three genes found to have the most mutations in high-risk group patients. The high-risk group with low TMB had the worst survival, whereas the low-risk group with high TMB had the best survival. KEGG pathway analysis revealed that the high-risk group was enriched with cell cycle, oocyte meiosis, cell senescence, and glycolysis/glucose production pathways. We constructed a reliable cuproptosis- and m6A-related lncRNA model for the prognosis of HCC. The model may provide new insights into managing HCC patients, but further research is needed to validate it.

A five-cuproptosis-related LncRNA Signature: predicting prognosis, assessing immune function & drug sensitivity in lung squamous cell carcinoma

Lung squamous cell carcinoma has so far lacked effective targets for diagnosis and treatment. In cancer research, long noncoding RNAs (LncRNAs) emerge as novel therapeutic targets and biomarkers. Cuprophosis is a new death type involving multiple biological processes in tumor cells. Here, we aimed to explore whether Cuprophosis-related lncRNAs could be used to predict prognosis, assess immune function, and test drug sensitivity in LUSC patients. The Cancer Genome Map (TCGA) was used to obtain genome and clinical data, and Cuprophosis-relevant genes were found in the literature. A cuproptosis-related lncRNA risk model was built using co-expression analysis, univariate/multivariate Cox regression, and LASSO analysis. The survival analysis was used to assess the model's prognostic value. The univariate and multivariate Cox regression analyses were performed to determine whether risk score, age, gender, or clinical stages could be used as independent prognostic factors. Gene Set Enrichment Analysis and mutation analysis were performed on differentially expressed mRNA between high-risk and low-risk groups. The (TIDE) algorithm was used to conduct immunological functional analysis and drug sensitivity testing. Five cuproptosis-related LncRNAs were identified, and the selected LncRNAs constructed a prognosis model. According to the Kaplan-Meier survival analysis, the overall survival time for patients in the high-risk group was shorter than for those in the low-risk group. For LUSC patients, the risk score serves as an independent prognostic indicator. The GO and KEGG enrichment analysis revealed that the differentially expressed mRNAs between the high- and low-risk groups were enriched in several immune-related processes. The enrichment score of differentially expressed mRNAs in the high-risk group is higher than that of the low-risk group in multiple immune function pathways, including the IFN-γ and MHC I pathways. The Tumor Immune Dysfunction and Exclusion (TIDE) test revealed that the high-risk group was more likely to experience immune escape. The drug sensitivity analysis showed that patients with low-risk ratings were likely to respond to GW441756 and Salubrinal. In contrast, patients with higher risk scores were more responsive to dasatinib and Z-LLNIe CHO. The 5-Cuprophosis-related lncRNA signature can be used to predict prognosis, assess immune function, and test drug sensitivity in LUSC patients.

Cuproptosis-Related Ferroptosis genes for Predicting Prognosis in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a main subtype of kidney cancers. Cuproptosis and ferroptosis are correlated with immune infiltration and prognosis in tumors. However, the role of Cuproptosis-related Ferroptosis genes (CRFGs) in KIRC has rarely been fully understood. Therefore, we constructed a prognostic signature based on different expression of CRFGs in KIRC. All raw data of this study were extracted from public TCGA datasets. Cuproptosis and Ferroptosis genes were collected from the previous research. Finally, a total of 36 significantly different CRFGs were identified from TCGA-KIRC cohort. Six-gene signature (TRIB3, SLC2A3, PML, CD44, CDKN2A and MIOX) was identified by LASSO Cox regression based on the significantly different CRFGs. The CRFGs signature was correlated with worse overall survival and the AUC was 0.750. Functional enrichment indicated that CRFGs were mainly enriched in metabolism, drug resistance, tumor immunity pathways. Besides, the IC50 and immune checkpoint differentially expressed between different groups. The proposed 6-CRFGs signature is a promising biomarker to predict clinical outcomes and therapeutic responses for KIRC patient.

Regulatory roles of copper metabolism and cuproptosis in human cancers

Copper is an essential micronutrient for human body and plays a vital role in various biological processes including cellular respiration and free radical detoxification. Generally, copper metabolism in the body is in a stable state, and there are specific mechanisms to regulate copper metabolism and maintain copper homeostasis. Dysregulation of copper metabolism may have a great connection with various types of diseases, such as Wilson disease causing copper overload and Menkes disease causing copper deficiency. Cancer presents high mortality rates in the world due to the unlimited proliferation potential, apoptosis escape and immune escape properties to induce organ failure. Copper is thought to have a great connection with cancer, such as elevated levels in cancer tissue and serum. Copper also affects tumor progression by affecting angiogenesis, metastasis and other processes. Notably, cuproptosis is a novel form of cell death that may provide novel targeting strategies for developing cancer therapy. Copper chelators and copper ionophores are two copper coordinating compounds for the treatment of cancer. This review will explore the relationship between copper metabolism and cancers, and clarify copper metabolism and cuproptosis for cancer targeted therapy.

Cuproptosis: mechanisms and links with cancers

Cuproptosis was a copper-dependent and unique kind of cell death that was separate from existing other forms of cell death. The last decade has witnessed a considerable increase in investigations of programmed cell death, and whether copper induced cell death was an independent form of cell death has long been argued until mechanism of cuproptosis has been revealed. After that, increasing number of researchers attempted to identify the relationship between cuproptosis and the process of cancer. Thus, in this review, we systematically detailed the systemic and cellular metabolic processes of copper and the copper-related tumor signaling pathways. Moreover, we not only focus on the discovery process of cuproptosis and its mechanism, but also outline the association between cuproptosis and cancers. Finally, we further highlight the possible therapeutic direction of employing copper ion ionophores with cuproptosis-inducing functions in combination with small molecule drugs for targeted therapy to treat specific cancers.

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.

Development and validation of a cuproptosis-associated prognostic model for diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous disease. Therefore, more reliable biomarkers are required to better predict the prognosis of DLBCL. Cuproptosis is a novel identified form of programmed cell death (PCD) that is different from oxidative stress-related cell death (e.g., apoptosis, ferroptosis, and necroptosis) by Tsvetkov and colleagues in a recent study released in Science. Cuproptosis is copper-dependent PCD that is closely tied to mitochondrial metabolism. However, the prognostic value of cuproptosis-related genes (CRGs) in DLBCL remains to be further elucidated. In the present study, we systematically evaluated the molecular changes of CRGs in DLBCL and found them to be associated with prognosis. Subsequently, based on the expression profiles of CRGs, we characterized the heterogeneity of DLBCL by identifying two distinct subtypes using consensus clustering. Two isoforms exhibited different survival, biological functions, chemotherapeutic drug sensitivity, and immune microenvironment. After identifying differentially expressed genes (DEGs) between CRG clusters, we built a prognostic model with the Least absolute shrinkage and selection operator (LASSO) Cox regression analysis and validated its prognostic value by Cox regression analysis, Kaplan-Meier curves, and receiver operating characteristic (ROC) curves. In addition, the risk score can predict clinical characteristics, levels of immune cell infiltration, and prognosis. Furthermore, a nomogram incorporating clinical features and risk score was generated to optimize risk stratification and quantify risk assessment. Compared to the International Prognostic Index (IPI), the nomogram has demonstrated more accuracy in survival prediction. Furthermore, we validated the prognostic gene expression levels through external experiments. In conclusion, cuproptosis-related gene signature can serve as a potential prognostic predictor in DLBCL patients and may provide new insights into cancer therapeutic targets.

The Cuproptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Immune Cell Infiltration in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC), ranking as one of the most common malignant tumors, is one of the leading causes of cancer death, with a poor prognosis. Cuproptosis, a novel programmed cell death modality that has just been confirmed recently, may play an important role in HCC prognosis. Long noncoding RNA (LncRNA) is a key participant in tumorigenesis and immune responses. It may be of great significance to predict HCC based on cuproptosis genes and their related LncRNA.

Methods

The sample data on HCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Combined with cuproptosis-related genes collected from the literature search, expression analysis was carried out to find cuproptosis genes and their related LncRNAs significantly expressed in HCC. The prognostic model was constructed by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression. The feasibility of these signature LncRNAs used for the evaluation of the overall survival rate in HCC patients as independent factors was investigated. The expression profile of cuproptosis, immune cell infiltration, and the status of somatic mutation were analyzed and compared.

Results

A prognostic model of HCC consisting of seven cuproptosis gene-related LncRNA signatures was constructed. Multiple verification methods have showed that this model can accurately predict the prognosis of HCC patients. It was showed that the classified high-risk group under the risk score of this model had worse survival status, more significant expression of the immune function, and higher mutation frequency. During the analysis, the cuproptosis gene CDKN2A was found to be most closely related to LncRNA DDX11-AS1 in the expression profile of HCC patients.

Conclusion

The cuproptosis-related signature LncRNA in HCC was identified, on the basis of which a model was constructed, and it was verified that it can be used to predict the prognosis of HCC patients. The potential role of these cuproptosis-related signature LncRNAs as new targets for disease therapy in antagonizing HCC development was discussed.

Insights into prognosis and immune infiltration of cuproptosis-related genes in breast cancer

Introduction

Breast cancer (BC) has been ranking first in incidence and the leading cause of death among female cancers worldwide based on the latest report. Regulated cell death (RCD) plays a significant role in tumor initiation and provides an important target of cancer treatment. Cuproptosis, a novel form of RCD, is ignited by mitochondrial stress, particularly the lipoylated mitochondrial enzymes aggregation. However, the role of cuproptosis-related genes (CRGs) in tumor generation and progression remains unclear.

Methods

In this study, the mRNA expression data of CRGs in BC and normal breast tissue were extracted from TCGA database, and protein expression patterns of these CRGs were analyzed using UALCAN. The prognostic values of CRGs in BC were explored by using KaplanMeier plotter and Cox regression analysis. Genetic mutations profiles were evaluated using the cBioPortal database. Meanwhile, we utilized CIBERSORT and TIMER 2.0 database to perform the correlation analysis between CRGs and immune cell infiltration.

Results

Our results indicated that CRGs expression is significantly different in BC and normal breast tissues. Then we found that upregulated PDHA1 expression was associated with worse endpoint of BC. Moreover, we also performed immune infiltration analysis of CRGs, and demonstrated that PDHA1 expression was closely related to the infiltration levels of CD4+ memory T cell, macrophage M0 and M1 cell and mast cell in BC.

Conclusions

Our results demonstrated the prognostic and immunogenetic values of PDHA1 in BC. Therefore, PDHA1 can be an independent prognostic biomarker and potential target for immunotherapy of BC.

Cuproptosis-Related lncRNA Gene Signature Establishes a Prognostic Model of Gastric Adenocarcinoma and Evaluate the Effect of Antineoplastic Drugs

BACKGROUND

One of the most frequent malignancies of the digestive system is stomach adenocarcinoma (STAD). Recent research has demonstrated how cuproptosis (copper-dependent cell death) differs from other cell death mechanisms that were previously understood. Cuproptosis regulation in tumor cells could be a brand-new treatment strategy. Our goal was to create a cuproptosis-related lncRNA signature. Additionally, in order to evaluate the possible immunotherapeutic advantages and drug sensitivity, we attempted to study the association between these lncRNAs and the tumor immune microenvironment of STAD tumors.

METHODS

The TCGA database was accessed to download the RNA sequencing data, genetic mutations, and clinical profiles for TCGA STAD. To locate lncRNAs related to cuproptosis and build risk-prognosis models, three techniques were used: co-expression network analysis, Cox-regression techniques, and LASSO techniques. Additionally, an integrated methodology was used to validate the models' predictive capabilities. Then, using GO and KEGG analysis, we discovered the variations in biological functions between each group. The link between the risk score and various medications for STAD treatment was estimated using the tumor mutational load (TMB) and tumor immune dysfunction and rejection (TIDE) scores.

RESULT

We gathered 22 genes linked to cuproptosis based on the prior literature. Six lncRNAs related to cuproptosis were used to create a prognostic marker (AC016394.2, AC023511.1, AC147067.2, AL590705.3, HAGLR, and LINC01094). After that, the patients were split into high-risk and low-risk groups. A statistically significant difference in overall survival between the two groups was visible in the survival curves. The risk score was demonstrated to be an independent factor affecting the prognosis by both univariate and multivariate Cox regression analysis. Different risk scores were substantially related to the various immunological states of STAD patients, as further evidenced by immune cell infiltration and ssGSEA analysis. The two groups had differing burdens of tumor mutations. In addition, immunotherapy was more effective for STAD patients in the high-risk group than in the low-risk group, and risk scores for STAD were substantially connected with medication sensitivity.

CONCLUSIONS

We discovered a marker for six cuproptosis-associated lncRNAs linked to STAD as prognostic predictors, which may be useful biomarkers for risk stratification, evaluation of possible immunotherapy, and assessment of treatment sensitivity for STAD.

Identification and validation of cuproptosis-related LncRNA signatures as a novel prognostic model for head and neck squamous cell cancer

Background

Head and neck squamous cell cancer (HNSCC) is a common malignant cancer. We aimed to explore prognostic cuproptosis-related lncRNAs (CRLs) and prognostic risk models for HNSCC.

Methods

The transcriptome profiles and clinical data were obtained from the TCGA database, and 19-cuproptosis-related genes (CRGs) were acquired from previous studies. Then, the prognostic model based on seven CRLs was established. We analysed its value to evaluate the prognosis, drug sensitivity, and tumour immune functions of patients with HNSCC. Finally, we used quantitative reverse transcription polymerase chain reaction (qRT‒PCR) to validate the seven CRLs.

Results

We established a 7-CRL signature. Kaplan‒Meier (K–M) curve analysis demonstrated a significantly preferable prognosis in the low-risk group. Multivariate Cox regression analysis revealed that the risk score could serve as an independent prognostic factor. Nomogram, ROC curve, and principal component analysis indicated that the signature presented significant predictive capability. Moreover, most of the high-risk group showed lower levels of IC50 for certain chemotherapy drugs, such as cisplatin, cytarabine, docetaxel, doxorubicin, etoposide, gemcitabine, methotrexate, paclitaxel, and dasatinib. Finally, the expression of AP001372.2, MIR9-3HG, AL160314.2, POLH-AS1, and AL109936.2 was upregulated, while AC090587.1 and WDFY3-AS2 were downregulated in HNSCC cell lines compared with normal cell lines by qRT‒PCR.

Conclusions

The 7-CRL signature was presented to be a novel biomarker for predicting prognosis for HNSCC.

Analysis of the correlation between non-alcoholic fatty liver disease and the risk of colorectal neoplasms

This study aims at assessing the potential association between non-alcoholic fatty liver disease (NAFLD) and colorectal neoplasms (CRN). PubMed, Cochrane Library, and Embase were searched for cohort studies. 14 cohort studies with a total population of 38,761,773 were included for meta-analysis after selection. The results showed that NAFLD is related to an increased risk of CRN (OR = 1.23; 95% CI: 1.14-1.32; I2 = 70.7%, p < 0.001). In the subgroup analysis, NAFLD were found to be the independent risk factor of colorectal adenoma (CRA) (OR = 1.29; 95% CI = 1.15-1.45; I2 = 66.4%) and colorectal cancer (CRC) (OR = 1.13; 95% CI = 1.12-1.15; I2 = 69.4%). There is no close correlation between smoking status of NAFLD patients and CRN. Interestingly, bioinformatics analysis revealed that there were overlap of dysregulated gene sets among NAFLD, CRC, and two recently identified regulated cell death types, ferroptosis and cuproptosis, respectively. Our meta- and bioinformatics analysis shows that NAFLD increases the risk of CRN. Ferroptosis and cuproptosis may be the critical links between NAFLD and CRN, respectively. These findings here support that NAFLD is necessary to be considered as an emerging risk factor for CRN.

A prognostic signature of cuproptosis and TCA-related genes for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common malignant tumor of the liver. Cuproptosis is a newly defined form of cell death. Copper ion induces cell death by binding to the tricarboxylic acid cycle (TCA). The effect of cuproptosis-related and TCA-related genes on the clinical prognosis of HCC is still unclear. In this study, we explores the genetic changes of cuproptosis-related genes that affect the TCA process and their potential therapeutic value in HCC patients.

Methods

The cuproptosis and TCA-related genes were obtained from cuproptosis-related articles and the molecular signatures database. The prognosis signatures of eight related genes were constructed using the last absolute shrinkage and selection operator (LASSO), and Receiver Operating Characteristic (ROC) curves were used to evaluate the signature. In addition, we analyzed downstream functional enrichment and immune infiltration to explore cuproptosis-inducing drugs and immunotherapeutic responses. All these analyses were validated using multiple datasets of the International Cancer Genome Consortium (ICGC).

Results

TCA and copper malnutrition-related genes (CDKN2A, IDH1, OGDHL, IDH3G, IDH3B, GLS, DLAT, LIPT1) were finally included. According to the risk score, they were divided into high-risk and low-risk groups. Survival analysis showed that the overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. We established a risk prognostic feature to predict the OS of patients with HCC. Based on this feature and the clinical stage, we constructed a nomogram. Functional enrichment analysis revealed pathways related to organelle division and the cell cycle. Different risk scores had different immune abundances in immune cells (including macrophages and regulatory T-cells) and immune pathways (including antigen-presenting cells co-stimulation). Moreover, the drug sensitivity of eleschomol and PD-L1 in the high-risk group was better than that in the low-risk group. The status of TP53 somatic mutation was also closely related to the risk score.

Conclusion

In this study, we established a new prediction signature of eight genes related to cuproptosis and the TCA process, which can effectively predict the prognosis of HCC patients.