Cuproptosis-related lncRNA predict prognosis and immune response of lung adenocarcinoma

Transcriptomic profiling and risk assessment in bladder cancer: Insights from copper death-related genes

BACKGROUND

The study aimed to investigate the role of copper death-related genes (CRGs) in bladder cancer (BC) for improved prognosis assessment.

METHODS

Multi-omics techniques were utilized to analyze CRG expression in BC tissues from TCGA and GEO databases. Consensus clustering categorized patients into molecular subtypes based on clinical characteristics and immune cell infiltration.

RESULTS

An innovative risk assessment model identified eight critical genes associated with BC risk. In vitro and in vivo experiments validated LIPT1's significant impact on copper-induced cell death, proliferation, migration, and invasion in BC.

CONCLUSION

This multi-omics analysis elucidates the pivotal role of CRGs in BC progression, suggesting enhanced risk assessment through molecular subtype categorization and identification of key genes like LIPT1. Insights into these mechanisms offer the potential for improved diagnosis and treatment strategies for BC patients.

Prognostic model development and clinical correlation of eight key genes in skin cutaneous melanoma

Cutaneous melanoma (SKCM) is a challenging and increasingly prevalent cancer with limited effective treatments. In our extensive study of 342 SKCM samples, we developed a prognostic model identifying eight key genes-CASPASE7CLEAVEDD198, FOXO3A, Melanoma gp100, CD171, 1433ZETA, SRC, P21, and CABL-linked to SKCM prognosis. Statistical analysis indicated significant differences in clinical outcomes between low and high-risk groups, corroborated by principal component analysis (PCA). Survival analysis and receiver operating characteristic (ROC) curve analysis confirmed the model's predictive accuracy for SKCM prognosis. Additionally, we observed notable correlations between the expression levels of genes related to prognosis and clinical characteristics. Our research offers crucial insights into SKCM prognosis, suggesting potential diagnostic markers and personalized treatment targets.

Cuproptosis in cancers: Function and implications from bench to bedside

Copper, an indispensable micronutrient, is implicated in numerous vital biological processes and is essential for all physiological activities. Recently, the discovery of a novel type of copper-dependent cell death, known as cuproptosis, has shed light on its role in cancer development. Extensive research is currently underway to unravel the mechanisms underlying cuproptosis and its correlation with various cancer types. In this review, we summarize the findings regarding the roles and mechanisms of cuproptosis in various cancer types, including colorectal cancer, lung cancer, gastric cancer, breast cancer, liver cancer and cutaneous melanoma. Furthermore, the effects of copper-related agents such as copper chelators and copper ionophores on cell proliferation, apoptosis, angiogenesis, tumor immunity, and chemotherapy resistance have been explored in cancer preclinical and clinical trials. These insights provide promising avenues for the development of prospective anticancer drugs aimed at inducing cuproptosis.

Cuproptosis-Related lncRNA Predict Prognosis and Immune Response of LUAD

Background

Lung cancer is the leading cause of cancer deaths worldwide, primarily due to lung adenocarcinoma (LUAD). However, the heterogeneity of programmed cell death results in varied prognostic and predictive outcomes. This study aimed to develop an LUAD evaluation marker based on cuproptosis-related lncRNAs.

Methods

First, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate, LASSO, and multivariate Cox regression analyses were conducted to construct cuproptosis-associated lncRNA models. LUAD patients were categorized into high-risk and low-risk groups using prognostic risk values. Kaplan-Meier analysis, PCA, GSEA, and nomograms were employed to evaluate and validate the results.

Results

7 cuproptosis-related lncRNAs were identified, and a risk model was created. High-risk tumors exhibited cuproptosis-related gene alterations in 95.54% of cases, while low-risk tumors showed alterations in 85.65% of cases, mainly involving TP53. The risk value outperformed other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival. The cuproptosis-related lncRNA-based risk model demonstrated high validity for LUAD evaluation, potentially influencing individualized treatment approaches. Expression analysis of four candidate cuproptosis-related lncRNAs (AL606834.1, AL161431.1, AC007613.1, and LINC02835) in LUAD tissues and adjacent normal tissues revealed significantly higher expression levels of AL606834.1 and AL161431.1 in LUAD tissues, positively correlating with tumor stage, lymph node metastasis, and histopathological grade. Conversely, AC007613.1 and LINC02835 exhibited lower expression levels, negatively correlating with these factors. High expression of AL606834.1 and AL161431.1 indicated poor prognosis, while low expression of AC007613.1 and LINC02835 was associated with unfavorable outcomes. Univariate and multivariate analyses confirmed these lncRNAs as independent risk factors for LUAD prognosis.

Conclusion

The 4 cuproptosis-related (lncRNAsAL606834.1, AL161431.1, AC007613.1, and LINC02835) can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

Challenges of Regulated Cell Death: Implications for Therapy Resistance in Cancer

Regulated cell death, a regulatory form of cell demise, has been extensively studied in multicellular organisms. It plays a pivotal role in maintaining organismal homeostasis under normal and pathological conditions. Although alterations in various regulated cell death modes are hallmark features of tumorigenesis, they can have divergent effects on cancer cells. Consequently, there is a growing interest in targeting these mechanisms using small-molecule compounds for therapeutic purposes, with substantial progress observed across various human cancers. This review focuses on summarizing key signaling pathways associated with apoptotic and autophagy-dependent cell death. Additionally, it explores crucial pathways related to other regulated cell death modes in the context of cancer. The discussion delves into the current understanding of these processes and their implications in cancer treatment, aiming to illuminate novel strategies to combat therapy resistance and enhance overall cancer therapy.

Unveiling the role of copper metabolism and STEAP2 in idiopathic pulmonary fibrosis molecular landscape

Idiopathic pulmonary fibrosis (IPF) is a debilitating interstitial lung disease characterized by progressive fibrosis and poor prognosis. Despite advancements in treatment, the pathophysiological mechanisms of IPF remain elusive. Herein, we conducted an integrated bioinformatics analysis combining clinical data and carried out experimental validations to unveil the intricate molecular mechanism of IPF. Leveraging three IPF datasets, we identified 817 upregulated and 560 downregulated differentially expressed genes (DEGs). Of these, 14 DEGs associated with copper metabolism were identified, shedding light on the potential involvement of disrupted copper metabolism in IPF progression. Immune infiltration analysis revealed dysregulated immune cell infiltration in IPF, with a notable correlation between copper metabolism-related genes and immune cells. Weighted gene co-expression network analysis (WGCNA) identified a central module correlated with IPF-associated genes, among which STEAP2 emerged as a key hub gene. Subsequent in vivo and in vitro studies confirmed the upregulation of STEAP2 in IPF model. Knockdown of STEAP2 using siRNA alleviated fibrosis in vitro, suggesting potential pathway related to copper metabolism in the pathophysiological progression of IPF. Our study established a novel link between immune cell infiltration and dysregulated copper metabolism. The revelation of intracellular copper overload and upregulated STEAP2 unravelled a potential therapeutic option. These findings offer valuable insights for future research and therapeutic interventions targeting STEAP2 and associated pathways in IPF.

Copper homeostasis dysregulation in respiratory diseases: a review of current knowledge

Cu is an essential micronutrient for various physiological processes in almost all human cell types. Given the critical role of Cu in a wide range of cellular processes, the local concentrations of Cu and the cellular distribution of Cu transporter proteins in the lung are essential for maintaining a steady-state internal environment. Dysfunctional Cu metabolism or regulatory pathways can lead to an imbalance in Cu homeostasis in the lungs, affecting both acute and chronic pathological processes. Recent studies have identified a new form of Cu-dependent cell death called cuproptosis, which has generated renewed interest in the role of Cu homeostasis in diseases. Cuproptosis differs from other known cell death pathways. This occurs through the direct binding of Cu ions to lipoylated components of the tricarboxylic acid cycle during mitochondrial respiration, leading to the aggregation of lipoylated proteins and the subsequent downregulation of Fe-S cluster proteins, which causes toxic stress to the proteins and ultimately leads to cell death. Here, we discuss the impact of dysregulated Cu homeostasis on the pathogenesis of various respiratory diseases, including asthma, chronic obstructive pulmonary disease, idiopathic interstitial fibrosis, and lung cancer. We also discuss the therapeutic potential of targeting Cu. This study highlights the intricate interplay between copper, cellular processes, and respiratory health. Copper, while essential, must be carefully regulated to maintain the delicate balance between necessity and toxicity in living organisms. This review highlights the need to further investigate the precise mechanisms of copper interactions with infections and immune inflammation in the context of respiratory diseases and explore the potential of therapeutic strategies for copper, cuproptosis, and other related effects.

Construction and significance of a breast cancer prognostic model based on cuproptosis-related genotyping and lncRNAs

BACKGROUND

/Purpose: Cuproptosis may play a significant role in breast cancer (BC). We aimed to investigate the prognostic impact of cuproptosis-related lncRNAs in BC.

METHODS

Consensus clustering analysis categorized TCGA-BRCA samples into 3 clusters, followed by survival and immune analyses of the 3 clusters. LASSO-COX analysis was performed on cuproptosis-related lncRNAs differentially expressed in BC to construct a BC prognostic model. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment, immune, and drug prediction analyses were performed on the high-risk and low-risk groups. Cell experiments were conducted to analyze the results of drug prediction and two cuproptosis-related lncRNAs (AC104211.1 and LINC01863).

RESULTS

Significant differences were observed in survival outcomes and immune infiltration levels among the three clusters (p < 0.05). The validation of the model showed significant differences in survival outcomes between the high-risk and low-risk groups in both the training and validation sets (p < 0.05). Differential mRNAs between the two groups were significantly enriched in the Neuroactive ligand-receptor interaction and cAMP signaling pathway. Additionally, significant differences were found in immune infiltration levels, human leukocyte antigen (HLA) expression, Immunophenoscore (IPS) scores, and Tumor Immune Dysfunction and Exclusion (TIDE) scores between the two groups (p < 0.05). Drug prediction and corresponding cell experimental results showed that Trametinib, 5-fluorouracil, and AICAR significantly inhibited the viability of MCF-7 cells (p < 0.05). AC104211.1 and LINC01863 were found to impact the proliferation of BC cells.

CONCLUSION

The risk-scoring model obtained in this study may serve as a robust prognostic biomarker, potentially aiding in clinical decision-making for BC patients.

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.

Disulfidptosis-related lncRNA signature reveals immune microenvironment and novel molecular subtyping of stomach adenocarcinoma

The main challenge in treating stomach adenocarcinoma (STAD) is chemotherapy resistance, which is characterized by changes in the immune microenvironment. Disulfidptosis, a novel form of programmed cell death, is involved in STAD but its mechanism is not fully understood. Long non-coding RNAs (LncRNAs) may play a role in regulating disulfidptosis and influencing the immune microenvironment and chemotherapy resistance in STAD. This study aims to establish disulfidptosis-related lncRNA (DRL) features and explore their significance in the immune microenvironment and chemotherapy resistance in STAD patients. By analyzing RNA sequencing and clinical data from STAD patients and extracting disulfidptosis-related genes, we identified DRLs through co-expression, single-factor and multi-factor Cox regression, and Lasso regression analyses. We also investigated differences in the immune microenvironment, immune function, immune checkpoint gene expression, and chemotherapy resistance between different risk groups using various algorithms. A prognostic risk model consisting of 2 DRLs was constructed, with a strong predictive value for patient survival, outperforming other clinical-pathological factors in predicting 3-year and 5-year survival. Immune-related analysis revealed a strong positive correlation between T cell CD4+ cells and risk score across all algorithms, and higher expression of immune checkpoint genes in the high-risk group. In addition, high-risk patients showed better sensitivity to Erlotinib, Oxaliplatin, and Gefitinib. Furthermore, three novel molecular subtypes of STAD were identified based on the 2-DRLs features, with evaluation of the immune microenvironment and chemotherapy drug sensitivity for each subgroup, which holds significant implications for achieving precise treatment in STAD. Overall, our 2-DRLs prognostic model demonstrates high predictive value for patient survival in STAD, potentially providing new targets for individualized immune and chemical therapy.

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.

Identification of cuproptosis-related miRNAs in triple-negative breast cancer and analysis of the miRNA-mRNA regulatory network

Introduction

The close association between cuproptosis and tumor immunity in triple-negative breast cancer (TNBC) allows its monitoring for predicting the prognosis of patients with TNBC. Nevertheless, the biological function and prognostic value of cuproptosis-related miRNAs and their target genes have not been reported.

Purpose

To construct the miRNA and mRNA-based risk models associated with cuproptosis for patients with TNBC.

Methods

Comparison of expression levels for genes associated with cuproptosis was executed between patients in the normal individuals and the TCGA-TNBC cohort. Conducting differential analysis resulted in the identification of differentially expressed miRNA (DE-miRNAs) and differentially expressed genes (DEGs) between the TNBC and Control samples. Screening for prognostic miRNAs and biomarkers involved employing univariate Cox analysis and least absolute shrinkage and selection operator regression analyses. These methods were utilized to construct risk models aimed at predicting the survival of patients with TNBC. Based on the median value of risk scores, patients were then stratified into low- and high-risk groups. Functional enrichment analysis was employed to explore the potential function and pathways of prognostic genes. Additionally, independent prognostic analysis was performed through univariate and multivariate Cox regression. Immune infiltration analysis was performed to examine disparities in the infiltration of immune cells between the two risk groups. Finally, the prognostic gene expression was mined in key cell types of TNBC.

Results

We obtained 5213 DEGs and 204 DE-miRNAs related to cuproptosis between TNBC and Control samples. Five prognostic miRNAs (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were closely associated with TNBC. Significant differences in the functions of prognostic genes between the two risk groups were observed, encompassing adipogenesis, inflammatory response, and hormone metabolic process. The prognostic gene regulatory network revealed that miR200C-3p regulated ZFPM2 and CFL2, and miR-1277-3p regulated BMP2 and RORA. A nomogram was created based on riskScore, cancer status, and pathologic stage to predict 1/3/5-year survival of patients with TNBC. Immune infiltration analysis indicated that the immune microenvironment may be associated with the progression of TNBC. Interestingly, prognostic genes exhibited higher expression levels in T cells, fibroblasts, endothelial cells, and monocytes compared to other cells.

Conclusions

Five prognostic miRNA (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were significantly associated with TNBC, it provides new therapeutic targets for the treatment and prognosis of TNBC.

Identification of cuproptosis-related lncRNAs with the significance in prognosis and immunotherapy of oral squamous cell carcinoma

Cuproptosis, a recently characterized programmed cell death mechanism, has emerged as a potential contributor to tumorigenesis, metastasis, and immune modulation. Long non-coding RNAs (lncRNAs) have demonstrated diverse regulatory roles in cancer and hold promise as biomarkers. However, the involvement and prognostic significance of cuproptosis-related lncRNAs (CRLs) in oral squamous cell carcinoma (OSCC) remain poorly understood. Based on TCGA-OSCC data, we integrated single-sample gene set enrichment analysis (ssGSEA), the LASSO algorithm, and the tumor immune dysfunction and exclusion (TIDE) algorithm. We identified 11 CRLs through differential expression, Spearman correlation, and univariate Cox regression analyses. Two distinct CRL-related subtypes were unveiled, delineating divergent survival patterns, tumor microenvironments (TME), and mutation profiles. A robust CRL-based signature (including AC107027.3, AC008011.2, MYOSLID, AC005785.1, AC019080.5, AC020558.2, AC025265.1, FAM27E3, and LINC02367) prognosticated OSCC outcomes, immunotherapy responses, and anti-tumor strategies. Superior predictive power compared to other lncRNA models was demonstrated. Functional assessments confirmed the influence of FAM27E3, LINC02367, and MYOSLID knockdown on OSCC cell behaviors. Remarkably, the CRLs-based signature maintained stability across OSCC patient subgroups, underscoring its clinical potential for survival prediction. This study elucidates CRLs' roles in TME of OSCC and establishes a potential signature for precision therapy.

The Prognosis and Immunotherapy Prediction Model of Ovarian Serous Cystadenocarcinoma Patient was Constructed Based on Cuproptosis-Related LncRNA

Cuproptosis can serve as potential prognostic predictors in patients with cancer. However, the role of this relationship in ovarian serous cystadenocarcinoma (OV) remains unclear. 376 OV tumor samples were obtained from the Cancer Genome Atlas (TCGA) database, and long non-coding RNAs (lncRNAs) related to cuproptosis were obtained through correlation analysis. The risk assessment model was further constructed by univariate Cox regression analysis and LASSO Cox regression. Bioinformatics was used to analyze the regulatory effect of relevant risk assessment models on tumor mutational burden (TMB) and immune microenvironment. We obtained 5 lncRNAs (AC025287.2, AC092718.4, AC112721.2, LINC00996, and LINC01639) and incorporated them into the Cox proportional hazards model. Kaplan-Meier (KM) curve analysis of the prognosis found that the high-risk group was associated with a poorer prognosis. The receiver operating characteristic (ROC) curve showed stronger predictive power compared to other clinicopathological features. Immune infiltration analysis showed that high-risk scores were inversely correlated with CD8+ T cells, CD4+ T cells, macrophages, NK cells, and B cells. Functional enrichment analysis found that they may act via the extracellular matrix (ECM)-interacting proteins and other pathways. We successfully constructed a reliable cuproptosis-related lncRNA model for the prognosis of OV.

Long noncoding RNA LINC00921 serves as a predictive biomarker for lung adenocarcinoma: An observational study

Lung adenocarcinoma (LUAD) is usually diagnosed at advanced stages. Hence, there is an urgent need to seek an effective biomarker to predict LUAD status. Long noncoding RNAs (lncRNAs) play key roles in the development of tumors. However, the relationship between LINC00921 and LUAD remains unclear. The gene expression data of LUAD were downloaded from the Cancer Genome Atlas database to investigate the expression level of LINC00921 in LUAD. Diagnostic ability analysis, survival analysis, tumor mutational burden analysis, and immune cell infiltration analysis of LINC00921 in LUAD patients were performed simultaneously. According to the median expression value of LINC00921, patients were divided into LINC00921 high- and low-expression groups. The function of LINC00921 in LUAD was identified through difference analysis and enrichment analysis. Moreover, drugs that may be relevant to LUAD treatment were screened. Finally, blood samples were collected for real-time polymerase chain reaction. LINC00921 was significantly lower in LUAD tumor tissues. Notably, patients with low expression of LINC00921 had a shorter median survival time. Decreased immune cell infiltration in the tumor microenvironment in the low LINC00921 expression group may contribute to poorer patient outcomes. Tumor mutational burden was significantly different in survival between the LINC00921 high- and low-expression groups. In addition, LINC00921 may exert an influence on cancer development through its regulation of target genes transcription. Glyceraldehyde-3-phosphate dehydrogenase-related drugs may be more likely to be therapeutically effective in LUAD. LINC00921 was able to be used as the potential diagnostic indicator for LUAD.

Cuproptosis-related ceRNA axis triggers cell proliferation and cell cycle through CBX2 in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) has high morbidity and mortality. Despite substantial advances in treatment, the prognosis of patients with LUAD remains unfavorable. The ceRNA axis has been reported to play an important role in the pathogenesis of LUAD. In addition, cuproptosis is considered an important factor in tumorigenesis. The expression of CBX2 has been associated with the development of multiple tumors, including LUAD. However, the precise molecular mechanisms through which the cuproptosis-related ceRNA network regulates CBX2 remain unclear.

METHODS

The DEGs between tumor and normal samples of LUAD were identified in TCGA database. The "ConsensusClusterPlus" R package was used to perform consensus clustering based on the mRNA expression matrix and cuproptosis-related gene expression profile. Then, LASSO-COX regression analysis was performed to identify potential prognostic biomarkers associated with cuproptosis, and the ceRNA network was constructed. Finally, the mechanisms of ceRNA in LUAD was studied by cell experiments.

RESULTS

In this study, the AC144450.1/miR-424-5p axis was found to promote the progression of LUAD by acting on CBX2. The expression of AC144450.1 and miR-424-5p can be altered to regulate CBX2 and is correlated with cell proliferation and cell cycle of LUAD. Mechanistically, AC144450.1 affects the expression of CBX2 by acting as the ceRNA of miR-424-5p. In addition, a cuproptosis-related model were constructed in this study to predict the prognosis of LUAD.

CONCLUSIONS

This study is the first to demonstrate that the AC144450.1/miR-424-5p/CBX2 axis is involved in LUAD progression and may serve as a novel target for its diagnosis and treatment.

An Analysis Regarding the Association Between DAZ Interacting Zinc Finger Protein 1 (DZIP1) and Colorectal Cancer (CRC)

Colorectal cancer (CRC) is the third most common malignant disease worldwide, and its incidence is increasing, but the molecular mechanisms of this disease are highly heterogeneous and still far from being fully understood. Increasing evidence suggests that fibrosis mediated by abnormal activation of fibroblasts based in the microenvironment is associated with a poor prognosis. However, the function and pathogenic mechanisms of fibroblasts in CRC remain unclear. Here, combining scrna-seq and clinical specimen data, DAZ Interacting Protein 1 (DZIP1) was found to be expressed on fibroblasts and cancer cells and positively correlated with stromal deposition. Importantly, pseudotime-series analysis showed that DZIP1 levels were up-regulated in malignant transformation of fibroblasts and experimentally confirmed that DZIP1 modulates activation of fibroblasts and promotes epithelial-mesenchymal transition (EMT) in tumor cells. Further studies showed that DZIP1 expressed by tumor cells also has a driving effect on EMT and contributes to the recruitment of more fibroblasts. A similar phenomenon was observed in xenografted nude mice. And it was confirmed in xenograft mice that downregulation of DZIP1 expression significantly delayed tumor formation and reduced tumor size in CRC cells. Taken together, our findings suggested that DZIP1 was a regulator of the CRC mesenchymal phenotype. The revelation of targeting DZIP1 provides a new avenue for CRC therapy.

Novel cuproptosis-related prognostic gene profiles in preeclampsia

BACKGROUND

Preeclampsia (PE) is a pregnancy-specific disorder with complex pathogenesis. Cuproptosis is a novel identified form of programmed cell death, however, the link between cuproptosis and clinical outcomes in PE is still not fully understood. In this study, we searched for cuproptosis-related genes (CRGs) in the placental tissues of normal and PE patients to clarify the importance of cuproptosis in the development of PE and provide potential predictive indicators for the occurrence of PE.

METHODS

Using RNA sequencing data in the GEO database, we conducted functional enrichment analysis of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), supported by linear regression model and operating characteristic curve (ROC) curve analysis, and summarized the role of CRGs in preeclampsia.

RESULTS

A total of 2831 differentially expressed genes related to PE were screened through multiple database analyses. After further intersection with 19 reported CRGs, 5 CRGs have been closely associated with the pathogenesis of PE, including NFE2L2, PDHA1, PDHB, DLD and GLS. NFE2L2 was identified as a key central gene. Pearson correlation analysis showed that CRGs could be related to several maternal and fetal outcome factors, including the highest pregnancy blood pressure, placenta weight, umbilical blood flow pulsatility index (PI), and neonatal weight. Linear regression equation revealed that the expression of NFE2L2 is negatively correlated with the highest pregnancy blood pressure and umbilical blood flow PI but positively correlated with placental weight and neonatal weight. QRT-PCR showed that the expression of these CRGs was significantly lower in placental tissues.

CONCLUSIONS

This cuproptosis pattern may be a potential prognostic factor in patients with PE and could provide new insights into disease progression.

A novel disulfidptosis-related lncRNAs signature for predicting survival and immune response in hepatocellular carcinoma

The accumulation of intracellular disulfides induces a novel and unique form of metabolic-related cell death known as disulfidptosis. A previous study revealed the prognostic value of a risk model of disulfidptosis-related genes in hepatocellular carcinoma (HCC). However, to date, no studies have investigated the relationship between disulfidptosis-related long non-coding RNAs (DRLs) and HCC. In this study, we collected and analyzed RNA sequencing data from 370 HCC samples to explore the DRLs in the tumorigenesis and development of HCC. By employing Lasso Cox regression and multivariate Cox regression analyses, we identified five prognostic DRLs, which were used to construct a prognostic signature. The signature was subsequently validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, Cox regression analyses, nomograms, and calibration curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed, revealing that the DRLs signature was associated with HCC and several cancer-related pathways. Furthermore, the DRLs signature showed correlations with the infiltration of M0 and M1 macrophages, immune-related functions, and multiple immune checkpoints, including PDCD1, LAG3, CTLA4, TIGIT, CD47, and others. Analysis using the tumor immune dysfunction and exclusion (TIDE) approach demonstrated that the DRLs signature could predict the response to immunotherapy. Finally, we screened potential chemotherapy drugs that could sensitize HCC. In conclusion, our novel DRLs signature provides valuable insights into predicting patient survival and immunotherapy responses.

MALAT1: A key regulator in lung cancer pathogenesis and therapeutic targeting

Lung cancer remains a formidable global health burden, necessitating a comprehensive understanding of the underlying molecular mechanisms driving its progression. Recently, lncRNAs have become necessary controllers of various biological functions, including cancer development. MALAT1 has garnered significant attention due to its multifaceted role in lung cancer progression. Lung cancer, among other malignancies, upregulates MALAT1. Its overexpression has been associated with aggressive tumor behavior and poor patient prognosis. MALAT1 promotes cellular proliferation, epithelial-mesenchymal transition (EMT), and angiogenesis in lung cancer, collectively facilitating tumor growth and metastasis. Additionally, MALAT1 enhances cancer cell invasion by interacting with numerous signaling pathways. Furthermore, MALAT1 has been implicated in mediating drug resistance in lung cancer, contributing to the limited efficacy of conventional therapies. Recent advancements in molecular biology and high-throughput sequencing technologies have offered fresh perspectives into the regulatory networks of MALAT1 in lung cancer. It exerts its oncogenic effects by acting as a ceRNA to sponge microRNAs, thereby relieving their inhibitory effects on target genes. Moreover, MALAT1 also influences chromatin remodeling and post-translational modifications to modulate gene expression, further expanding its regulatory capabilities. This review sheds light on the multifaceted roles of MALAT1 in lung cancer progression, underscoring its potential as an innovative therapeutic target and diagnostic biomarker. Targeting MALAT1 alone or combined with existing therapies holds promise to mitigate lung cancer progression and improve patient outcomes.

Rucaparib inhibits lung adenocarcinoma cell proliferation and migration via the SHCBP1/CDK1 pathway

Src homolog and collagen homolog binding protein 1 (SHCBP1) binds to the SH2 domain of SHC-transforming protein 1 (SHC1) and is involved in midbody organization and cytokinesis completion. SHCBP1 has been reported to be a cancer driver gene, promoting cancer progression. However, the functional role and underlying mechanism of SHCBP1 in regulating lung adenocarcinoma (LUAD) cell proliferation and migration are incompletely understood. Here, we discovered that SHCBP1 is overexpressed in LUAD tissues and is associated with a poor prognosis. SHCBP1 knockdown inhibited LUAD cell proliferation and migration by arresting the cell cycle and preventing epithelial-mesenchymal transition (EMT) via decreasing cyclin-dependent kinase 1 (CDK1) expression. Mechanistically, CDK1 overexpression reversed SHCBP1 knockdown-induced inhibition of proliferation and migration, confirming CDK1 as a key downstream target of SHCBP1. In addition, we proposed that rucaparib may be a small-molecule inhibitor of SHCBP1 and validated both in vitro and in vivo that rucaparib inhibits cell proliferation and migration via suppression of the SHCBP1/CDK1 pathway in LUAD. Our study elucidates a newly identified role of SHCBP1 in promoting cell proliferation and migration in LUAD, and suggests rucaparib as a potential inhibitor for LUAD treatment.

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.

Crosstalk between copper homeostasis and cuproptosis reveals a lncRNA signature to prognosis prediction, immunotherapy personalization, and agent selection for patients with lung adenocarcinoma

BACKGROUND

Copper homeostasis and cuproptosis play critical roles in various biological processes of cancer; however, whether they can impact the prognosis of lung adenocarcinoma (LUAD) remain to be fully elucidated. We aimed to adopt these concepts to create and validate a lncRNA signature for LUAD prognostic prediction.

METHODS

For this study, the TCGA-LUAD dataset was used as the training cohort, and multiple datasets from the GEO database were pooled as the validation cohort. Copper homeostasis and cuproptosis regulated genes were obtained from published studies, and various statistical methods, including Kaplan-Meier (KM), Cox, and LASSO, were used to train our gene signature CoCuLncSig. We utilized KM analysis, COX analysis, receiver operating characteristic analysis, time-dependent AUC analysis, principal component analysis, and nomogram predictor analysis in our validation process. We also compared CoCuLncSig with previous studies. We performed analyses using R software to evaluate CoCuLncSig's immunotherapeutic ability, focusing on eight immune algorithms, TMB, and TIDE. Additionally, we investigated potential drugs that could be effective in treating patients with high-risk scores. Additionally qRT-PCR examined the expression patterns of CoCuLncSig lncRNAs, and the ability of CoCuLncSig in pan-cancer was also assessed.

RESULTS

CoCuLncSig containing eight lncRNAs was trained and showed strong predictive ability in the validation cohort. Compared with previous similar studies, CoCuLncSig had more prognostic ability advantages. CoCuLncSig was closely related to the immune status of LUAD, and its tight relationship with checkpoints IL10, IL2, CD40LG, SELP, BTLA, and CD28 may be the key to its potential immunotherapeutic ability. For the high CoCuLncSig score population, we found 16 drug candidates, among which epothilone-b and gemcitabine may have the most potential. The pan-cancer analysis found that CoCuLncSig was a risk factor in multiple cancers. Additionally, we discovered that some of the CoCuLncSig lncRNAs could play crucial roles in specific cancer types.

CONCLUSION

The current study established a powerful prognostic CoCuLncSig signature for LUAD that was also valid for most pan-cancers. This signature could serve as a potential target for immunotherapy and might help the more efficient application of drugs to specific populations.

Identification of cuproptosis-related asthma diagnostic genes by WGCNA analysis and machine learning

OBJECTIVE

Cuproptosis is the latest novel form of cell death. However, the relationship between asthma and cuproptosis is not fully understood.

METHODS

In this study, we screened differentially expressed cuproptosis-related genes from the Gene Expression Omnibus (GEO) database and performed immune infiltration analysis. Subsequently, patients with asthma were typed and analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was performed to calculate the module-trait correlations, and the hub genes of the intersection were taken to construct machine learning (XGB, SVM, RF, GLM). Finally, we used TGF-β to establish a BEAS-2B asthma model to observe the expression levels of hub genes.

RESULTS

Six cuproptosis-related genes were obtained. Immune-infiltration analysis shows that cuproptosis-related genes are associated with a variety of biological functions. We classified asthma patients into two subtypes based on the expression of cuproptosis-related genes and found significant Gene Ontology (GO) and immune function differences between the different subtypes. WGCNA selected 2 significant modules associated with disease features and typing. Finally, we identified TRIM25, DYSF, NCF4, ABTB1, CXCR1 as asthma biomarkers by taking the intersection of the hub genes of the 2 modules and constructing a 5-genes signature, which nomograph, decision curve analysis (DCA) and calibration curves, receiver operating characteristic curve (ROC) showed high efficiency in diagnosing the probability of survival of asthma patients. Finally, in vitro experiments have shown that DYSF and CXCR1 expression is up expressed in asthma.

CONCLUSIONS

Our study provides further directions for studying the molecular mechanism of asthma.

CDKN2A was a cuproptosis-related gene in regulating chemotherapy resistance by the MAGE-A family in breast cancer: based on artificial intelligence (AI)-constructed pan-cancer risk model

BACKGROUND

Before the discovery of cuproptosis, copper-loaded nanoparticle is a wildly applied strategy for enhancing the tumor-cell-killing effect of chemotherapy. Although copper(ii)-related researches are wide, details of cuproptosis-related bioprocess in pan-cancer are not clear yet now, especially for prognosis and drug sensitivity prediction yet now.

METHODS

In this study, VOSviewer is used for the literature review, and R4.2.0 is used for data analysis. Public data are collected from TCGA and GEO, local breast cancer cohort is collected to verify the expression level of CDKN2A.

RESULTS

7036 published articles exhibited a time-dependent linear relationship (R=0.9781, p<0.0001), and breast cancer (33.4%) is the most researched topic. Cuproptosis-related-genes (CRGs)-based unsupervised clustering divides pan-cancer subgroups into four groups (CRG subgroup) with differences in prognosis and tumor immunity. 44 tumor-driver-genes (TDGs)-based prediction model of drug sensitivity and prognosis is constructed by artificial intelligence (AI). Based on TDGs and clinical features, a nomogram is (C- index: 0.7, p= 6.958e- 12) constructed to predict the prognosis of breast cancer. Importance analysis identifies CDKN2A has a pivotal role in AI modeling, whose higher expression indicates worse prognosis in breast cancer. Furthermore, inhibition of CDKN2A down-regulates decreases Snail1, Twist1, Zeb1, vimentin and MMP9, while E-cadherin is increased. Besides, inhibition of CDKN2A also decreases the expression of MEGEA4, phosphorylated STAT3, PD-L1, and caspase3, while cleaved-caspase3 is increased. Finally, we find down-regulation of CDKN2A or MAGEA inhibits cell migration and wound healing, respectively.

CONCLUSIONS

AI identified CRG subgroups in pan-cancer based on CRGs-related TDGs, and 44-gene-based AI modeling is a novel tool to identify chemotherapy sensitivity in breast cancer, in which CDKN2A/MAGEA4 pathway played the most important role.

Identification of a novel intermittent hypoxia-related prognostic lncRNA signature and the ceRNA of lncRNA GSEC/miR-873-3p/EGLN3 regulatory axis in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is still the most prevalent type of respiratory cancer. Intermittent hypoxia can increase the mortality and morbidity associated with lung cancer. Long non-coding RNAs (lncRNAs) are crucial in lung adenocarcinoma. However, the effects of intermittent hypoxia-related long non-coding RNAs (IHRLs) on lung adenocarcinoma are still unknown.

Method

In the current research, eight IHRLs were selected to create a prognostic model. The risk score of the prognostic model was evaluated using multivariate and univariate analyses, and its accuracy and reliability were validated using a nomogram and ROC. Additionally, we investigated the relationships between IHRLs and the immune microenvironment.

Result

Our analysis identified GSEC, AC099850.3, and AL391001.1 as risk lncRNAs, while AC010615.2, AC010654.1, AL513550.1, LINC00996, and LINC01150 were categorized as protective lncRNAs. We observed variances in the expression of seven immune cells and 15 immune-correlated pathways between the two risk groups. Furthermore, our results confirmed the ceRNA network associated with the intermittent hypoxia-related lncRNA GSEC/miR-873-3p/EGLN3 regulatory pathway. GSEC showed pronounced expression in lung adenocarcinoma tissues and specific cell lines, and its inhibition resulted in reduced proliferation and migration in A549 and PC9 cells. Intriguingly, GSEC manifested oncogenic properties by sponging miR-873-3p and demonstrated a tendency to modulate EGLN3 expression favorably.

Conclusion

GSEC acts as an oncogenic lncRNA by interacting with miR-873-3p, modulating EGLN3 expression. This observation underscores the potential of GSEC as a diagnostic and therapeutic target for LUAD.

Comprehensive analysis of cuproptosis and copper homeostasis genotyping and related immune land scape in lung adenocarcinoma

Cuproptosis is a manner of cell death which is related to the homeostasis of copper ions in the cellular environment and is expected to open a new direction of anti-tumor therapy. However, the studies on cuproptosis and copper homeostasis in lung adenocarcinoma (LUAD) are still limited. In this study, we identified new cuproptosis and copper homeostasis related genes (CHRGs) which were effective in stratifying genotyping clusters with survival differences based on transcriptomic data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Weighted Gene Co-expression Network Analysis (WGCNA) further expands the screening boundary of CHRGs, and finally we established a 10-CHRGs-based prognostic signature using lasso-penalized cox regression method, which were validated in GSE30219. Comprehensive bioinformatics analysis revealed these genes are potential regulators of modulating immunotherapy efficacy, drug resistance, tumor microenvironment infiltration, and tumor mutation patterns. Lastly, the scRNA-seq datasets GSE183219 and GSE203360 offers the evidences that CHRGs signature are mainly distributed in cancer epithelial cells, real time quantitative polymerase chain reaction (RT-qPCR) also confirmed the differential expression of these genes between normal lung cell line and lung adenocarcinoma cell lines. Collectively, our findings revealed new cuproptosis and copper homeostasis related genotyping clusters and genes which may play important roles in predicting prognosis, influencing tumor microenvironment and drug efficacy in LUAD patients.

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.

Verification of cuproptosis-related diagnostic model associated with immune infiltration in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic autoimmune disease closely related to inflammation. Cuproptosis is a newly discovered unique type of cell death, and it has been found that it may play an essential role in the occurrence and development of RA. Therefore, we intend to explore the potential association between cuproptosis-related genes (CRGs) and RA to provide a new biomarker for the treatment and prognosis of RA.

Methods

Download GSE93777 datasets from the GEO database. Variance analysis was performed on the CRGs that had been reported. Then, the random forest (RF) model and nomogram of differentially expressed CRGs were constructed, and the ROC curve was used to evaluate the accuracy of the diagnostic model. Next, RA patients were subtyped by consensus clustering, and immune infiltration was analyzed in each subgroup to confirm the correlation between CRGs and abundance of immune cells. The expression levels of CRGs were verified by qRT-PCR.

Results

Eight differentially expressed CRGs (DLST, DLD, PDHB, PDHA1, ATP7A, CDKN2A, LIAS, DLAT) were screened out by differential analysis to construct an RF model. The ROC curve proved that this model had good diagnostic accuracy. Based on the above eight significant CRGs, a nomogram was built to predict effective and high-precision results. The consensus clustering method identified two CRG patterns. Most of the immune cells were enriched in cluster A, indicating that cluster A may be related to the development of RA. Finally, qRT-PCR verified the expression of eight key genes, further confirming our findings.

Conclusion

The diagnosis model of RA based on the above eight CRGs has excellent diagnostic potential. Based on these, patients can be divided into two different molecular subtypes; it is expected to develop a new treatment strategy for RA.

The role of TOP2A in immunotherapy and vasculogenic mimicry in non-small cell lung cancer and its potential mechanism

Type IIA topoisomerase (TOP2A) is significantly associated with malignant tumor development, invasion, treatment and its prognosis, and has been shown to be a therapeutic target against cancer. In contrast, the role of TOP2A in the immunotherapy of non-small cell lung cancer as well as in Vasculogenic mimicry (VM) formation and its potential mechanisms are unclear. The aim of this study was to investigate the role of TOP2A in proliferation, skeleton regulation, motility and VM production in non-small cell lung cancer and its mechanisms by using bioinformatics tools and molecular biology experiments. Subgroup analysis showed that the low-risk group had a better prognosis, while the high-risk group was positively correlated with high tumor mutational load, M1-type macrophage infiltration, immune checkpoint molecule expression, and immunotherapy efficacy. As confirmed by further clinical specimens, the presence of TOP2A and VM was significantly and positively correlated with poor prognosis. Our study established a model based on significant co-expression of TOP2A genes, which significantly correlated with mutational load and immunotherapy outcomes in patients with non-small cell lung cancer. Further mechanistic exploration suggests that TOP2A plays an important role in immunotherapy and VM formation in NSCLC through upregulation of Wnt3a and PD-L1 expression.

Identification of a cuproptosis-related lncRNA prognostic signature in lung adenocarcinoma

PURPOSE

Cuproptosis-related long non-coding RNA (lncRNA) diseases are associated with the occurrence and development of tumors. This study aimed to investigate whether cuproptosis-related lncRNA can predict the prognosis of patients with lung adenocarcinoma (LUAD).

METHODS

Cuproptosis-related lncRNA prognosis (CLPS) model was successfully constructed through cox regression and lasso regression analyses. Then, the prognostic value of CLPS model was tested through the survival analysis, the ROC curve and the nomogram. Finally, the correlation of CLPS model with tumor immunity and tumor mutation burden was analyzed, and the potential susceptibility of drugs for LUAD were predicted.

RESULTS

CLPS model for LUAD (AC090948.1, CRIM1-DT, AC026356.2, AC004832.5, AL161431.1) was successfully constructed, which has an independent prognostic value. Furthermore, the risk score of CLPS model was correlated with tumor immune characteristics and immune escape, which can predict the sensitivity of drugs including Cisplatin, Etoposide, Gemcitabine, and Erlotinib.

CONCLUSIONS

In conclusion, it was found that CLPS model was associated with tumor immunity and tumor mutation load, which also predicted four potentially sensitive drugs for LUAD patients at different risks.

Correlation between chromatin epigenetic-related lncRNA signature (CELncSig) and prognosis, immune microenvironment, and immunotherapy in non-small cell lung cancer

Chromatin regulators drive cancer epigenetic changes, and lncRNA can play an important role in epigenetic changes as chromatin regulators. We used univariate Cox, LASSO, and multivariate Cox regression analysis to select epigenetic-associated lncRNA signatures. Twenty-five epigenetic-associated lncRNA signatures (CELncSig) were identified to establish the immune prognostic model. According to Kaplan-Meier analysis, the overall survival of the high-risk group was significantly lower than the low-risk group. Receiver operating characteristic (ROC) curves, C-index, survival curve, nomogram, and principal component analysis (PCA) were performed to validate the risk model. In GO/KEGG analysis, differentially expressed lncRNAs were correlated with the PI3K-Akt pathway, suggesting that they were highly associated with the metastasis of LUAD. Interestingly, in the immune escape analysis, the TIDE score was lower, and the possibility of immune dysfunction is also slighter in the high-risk group, which means they still have the potential to receive immunotherapy. And CELncsig is highly correlated with immune pathways T_cell_co-inhibition and Check-point. Also, the IMvigor210 cohort analysis indicated that our risk-scoring model has significant potential clinical application value in lung cancer immunotherapy. And we also screened out ten potential chemotherapy agents using the 'pRRophetic' package.

Identification and validation of a novel cuproptosis-related stemness signature to predict prognosis and immune landscape in lung adenocarcinoma by integrating single-cell and bulk RNA-sequencing

Background

Cancer stem cells (CSCs) play vital roles in lung adenocarcinoma (LUAD) recurrence, metastasis, and drug resistance. Cuproptosis has provided a novel insight into the treatment of lung CSCs. However, there is a lack of knowledge regarding the cuproptosis-related genes combined with the stemness signature and their roles in the prognosis and immune landscape of LUAD.

Methods

Cuproptosis-related stemness genes (CRSGs) were identified by integrating single-cell and bulk RNA-sequencing data in LUAD patients. Subsequently, cuproptosis-related stemness subtypes were classified using consensus clustering analysis, and a prognostic signature was constructed by univariate and least absolute shrinkage operator (LASSO) Cox regression. The association between signature with immune infiltration, immunotherapy, and stemness features was also investigated. Finally, the expression of CRSGs and the functional roles of target gene were validated in vitro.

Results

We identified six CRSGs that were mainly expressed in epithelial and myeloid cells. Three distinct cuproptosis-related stemness subtypes were identified and associated with the immune infiltration and immunotherapy response. Furthermore, a prognostic signature was constructed to predict the overall survival (OS) of LUAD patients based on eight differently expressed genes (DEGs) with cuproptosis-related stemness signature (KLF4, SCGB3A1, COL1A1, SPP1, C4BPA, TSPAN7, CAV2, and CTHRC1) and confirmed in validation cohorts. We also developed an accurate nomogram to improve clinical applicability. Patients in the high-risk group showed worse OS with lower levels of immune cell infiltration and higher stemness features. Ultimately, further cellular experiments were performed to verify the expression of CRSGs and prognostic DEGs and demonstrate that SPP1 could affect the proliferation, migration, and stemness of LUAD cells.

Conclusion

This study developed a novel cuproptosis-related stemness signature that can be used to predict the prognosis and immune landscape of LUAD patients, and provided potential therapeutic targets for lung CSCs in the future.

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.

Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease

Background

Idiopathic pulmonary fibrosis (IPF) has attracted considerable attention worldwide and is challenging to diagnose. Cuproptosis is a new form of cell death that seems to be associated with various diseases. However, whether cuproptosis-related genes (CRGs) play a role in regulating IPF disease is unknown. This study aims to analyze the effect of CRGs on the progression of IPF and identify possible biomarkers.

Methods

Based on the GSE38958 dataset, we systematically evaluated the differentially expressed CRGs and immune characteristics of IPF disease. We then explored the cuproptosis-related molecular clusters, the related immune cell infiltration, and the biological characteristics analysis. Subsequently, a weighted gene co-expression network analysis (WGCNA) was performed to identify cluster-specific differentially expressed genes. Lastly, the eXtreme Gradient Boosting (XGB) machine-learning model was chosen for the analysis of prediction and external datasets validated the predictive efficiency.

Results

Nine differentially expressed CRGs were identified between healthy and IPF patients. IPF patients showed higher monocytes and monophages M0 infiltration and lower naive B cells and memory resting T CD4 cells infiltration than healthy individuals. A positive relationship was found between activated dendritic cells and CRGs of LIPT1, LIAS, GLS, and DBT. We also identified cuproptosis subtypes in IPF patients. Go and KEGG pathways analysis demonstrated that cluster-specific differentially expressed genes in Cluster 2 were closely related to monocyte aggregation, ubiquitin ligase complex, and ubiquitin-mediated proteolysis, among others. We also constructed an XGB machine model to diagnose IPF, presenting the best performance with a relatively lower residual and higher area under the curve (AUC= 0.700) and validated by external validation datasets (GSE33566, AUC = 0.700). The analysis of the nomogram model demonstrated that XKR6, MLLT3, CD40LG, and HK3 might be used to diagnose IPF disease. Further analysis revealed that CD40LG was significantly associated with IPF.

Conclusion

Our study systematically illustrated the complicated relationship between cuproptosis and IPF disease, and constructed an effective model for the diagnosis of IPF disease patients.

Comprehensive analyses reveal the prognosis and biological function roles of chromatin regulators in lung adenocarcinoma

The present study explored the prognosis and biological function roles of chromatin regulators (CRs) in patients with lung adenocarcinoma (LUAD). Using transcriptome profile and clinical follow-up data of LUAD dataset, we explored the molecular classification, developed, and validated a CR prognostic model, built an individual risk scoring system in LUAD, and compared the clinical and molecular characteristics between different subtypes and risk stratifications. We investigated the chemotherapy sensitivity and predicted potential immunotherapy response. Lastly, we collected the clinical samples and validated the prognosis and potential function role of NAPS2. Our study indicated that LUAD patients could be classified into two subtypes that had obviously different clinical background and molecular features. We constructed a prognostic model with eight CR genes, which was well validated in several other population cohort. We built high- and low-risk stratifications for LUAD patients. Patients from high-risk group were totally different from low-risk groups in clinical, biological function, gene mutation, microenvironment, and immune infiltration levels. We idented several potential molecular compounds for high-risk group treatment. We predicted that high-risk group may have poor immunotherapy response. We finally found that Neuronal PAS Domain Protein 2 (NPAS2) involved in the progression of LUAD via regulating cell adhesion. Our study indicated that CR involved in the progression of LUAD and affect their prognosis. Different therapeutic strategies should be developed for different molecular subtypes and risk stratifications. Our comprehensive analyses uncover specific determinants of CRs in LUAD and provides implications for investigating disease-associated CRs.

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.

Construction and validation of a cuproptosis-related lncRNA signature for the prediction of the prognosis of LUAD and LUSC

Lung cancer is one of the most prevalent malignant tumors worldwide, with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) accounting for the majority of cases. Cuproptosis, tumor immune microenvironment (TIME) and long non-coding RNA (lncRNA) have been demonstrated to be associated with tumorigenesis. The objective of the present study was to develop a novel cuproptosis-related lncRNA signature to assess the association between cuproptosis and TIME in patients with LUAD or LUSC and to predict prognosis. Based on the outputs of the least absolute shrinkage and selection operator regression model, a cuproptosis-related lncRNA signature was developed. Kaplan-Meier survival curves were generated to confirm the predictive ability of the signature. Univariate and multivariate analysis was also performed to determine the association between overall survival and this signature and other clinical characteristics, and a nomogram was created. Additionally, the relationship between the signature, TIME, tumor mutation burden and m6A methylation was established. The results of the present study revealed that 8 cuproptosis-related lncRNAs were associated with the prognosis of patients with LUAD and LUSC. This novel cuproptosis-related lncRNA signature is associated with TIME and m6A methylation in LUAD and LUSC and can predict prognosis with accuracy.

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.

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.

Cuproptosis-Related LncRNA-Based Prediction of the Prognosis and Immunotherapy Response in Papillary Renal Cell Carcinoma

Cuproptosis, a new cell death pattern, is promising as an intervention target to treat tumors. Abnormal long non-coding RNA (lncRNA) expression is closely associated with the occurrence and development of papillary renal cell carcinoma (pRCC). However, cuproptosis-related lncRNAs (CRLs) remain largely unknown as prognostic markers for pRCC. We aimed to forecast the prognosis of pRCC patients by constructing models according to CRLs and to examine the correlation between the signatures and the inflammatory microenvironment. From the Cancer Genome Atlas (TCGA), RNA sequencing, genomic mutations and clinical data of TCGA-KIRP (Kidney renal papillary cell carcinoma) were analyzed. Randomly selected pRCC patients were allotted to the training and testing sets. To determine the independent prognostic impact of the training characteristic, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized, together with univariate and multivariate Cox regression models. Further validation was performed in the testing and whole cohorts. External datasets were utilized to verify the prognostic value of CRLs as well. The CRLs prognostic features in pRCC were established based on the five CRLs (AC244033.2, LINC00886, AP000866.1, MRPS9-AS1 and CKMT2-AS1). The utility of CRLs was evaluated and validated in training, testing and all sets on the basis of the Kaplan-Meier (KM) survival analysis. The risk score could be a robust prognostic factor to forecast clinical outcomes for pRCC patients by the LASSO algorithm and univariate and multivariate Cox regression. Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) data demonstrated that differentially expressed genes (DEGs) are primarily important for immune responses and the PI3K-Akt pathway. Arachidonic acid metabolism was enriched in the high-risk set by Gene Set Enrichment Analysis (GSEA). In addition, Tumor Immune Dysfunction and Exclusion (TIDE) analysis suggested that there was a high risk of immune escape in the high-risk cohort. The immune functions of the low- and high-risk sets differed significantly based on immune microenvironment analysis. Finally, four drugs were screened with a higher sensitivity to the high-risk set. Taken together, a novel model according to five CRLs was set up to forecast the prognosis of pRCC patients, which provides a potential strategy to treat pRCC by a combination of cuproptosis and immunotherapy.

Role of cuproptosis-related gene in lung adenocarcinoma

Backgrounds

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, which is the leading cause of cancer death. Dysregulation of cell proliferation and death plays a crucial role in the development of LUAD. As of recently, the role of a new form of cell death, cuproptosis, and it has attracted more and more attention. As of yet, it is not clear whether cuproptosis is involved in the progression of LUAD.

Methods

An integrated set of bioinformatics tools was utilized to analyze the expression and prognostic significance of cuproptosis-related genes. Meanwhile, a robust risk signature was developed using machine learning based on prognostic cuproptosis-related genes and explored the value of prognostic cuproptosis-related signature for clinical applications, functional enrichment and immune landscape. Lastly, the dysregulation of the cuproptosis-related genes in LUAD was validated by in vitro experiment.

Results

In this study, first, cuproptosis-related genes were found to be differentially expressed in LUAD patients of public databases, and nine of them had prognostic value. Next, a cuproptosis-related model with five features (DLTA, MTF1, GLS, PDHB and PDHA1) was constructed to separate the patients into high- and low-risk groups based on median risk score. Internal validation set and external validation set were used for model validation and evaluation. What's more, Enrichment analysis of differential genes and the WGCNA identified that cuproptosis-related signatures affected tumor prognosis by influencing tumor immunity. Small molecule compounds were predicted based on differential expressed genes to improve poor prognosis in the high-risk group and a nomogram was constructed to further advance clinical applications. In closing, our data showed that FDX1 affected the prognosis of lung cancer by altering the expression of cuproptosis-related signature.

Conclusion

A new cuproptosis-related signature for survival prediction was constructed and validated by machine learning algorithm and in vitro experiments to reflect tumor immune infiltration in LUAD patients. The purpose of this article was to provide a potential diagnostic and therapeutic strategy for LUAD.

Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research

Many types of human cells self-destruct to maintain biological homeostasis and defend the body against pathogenic substances. This process, called regulated cell death (RCD), is important for various biological activities, including the clearance of aberrant cells. Thus, RCD pathways represented by apoptosis have increased in importance as a target for the development of cancer medications in recent years. However, because tumor cells show avoidance to apoptosis, which causes treatment resistance and recurrence, numerous studies have been devoted to alternative cancer cell mortality processes, namely necroptosis, pyroptosis, ferroptosis, and cuproptosis; these RCD modalities have been extensively studied and shown to be crucial to cancer therapy effectiveness. Furthermore, evidence suggests that tumor cells undergoing regulated death may alter the immunogenicity of the tumor microenvironment (TME) to some extent, rendering it more suitable for inhibiting cancer progression and metastasis. In addition, other types of cells and components in the TME undergo the abovementioned forms of death and induce immune attacks on tumor cells, resulting in enhanced antitumor responses. Hence, this review discusses the molecular processes and features of necroptosis, pyroptosis, ferroptosis, and cuproptosis and the effects of these novel RCD modalities on tumor cell proliferation and cancer metastasis. Importantly, it introduces the complex effects of novel forms of tumor cell death on the TME and the regulated death of other cells in the TME that affect tumor biology. It also summarizes the potential agents and nanoparticles that induce or inhibit novel RCD pathways and their therapeutic effects on cancer based on evidence from in vivo and in vitro studies and reports clinical trials in which RCD inducers have been evaluated as treatments for cancer patients. Lastly, we also summarized the impact of modulating the RCD processes on cancer drug resistance and the advantages of adding RCD modulators to cancer treatment over conventional treatments.

Identification and validation of a novel cuproptosis-related lncRNA gene signature to predict prognosis and immune response in bladder cancer

PURPOSE

Bladder cancer (BCa) is one of the most common malignant tumors in the urogenital system, characterized by the high recurrence rate, mortality rate and poor prognosis. Based on cuproptosis-related long noncoding RNAs (CRLs), this study set out to create a prediction signature to evaluate the prognosis of patients with BCa.

METHODS

RNA-seq data including CRLs and related clinicopathological data were gathered from The Cancer Genome Atlas (TCGA) database (n = 428). The predictive signature was constructed after correlation analysis. Subsequently, relying on the analyzed data from the TCGA database and our sample collection, we examined and verified the connections between CRLs model and important indexes included prognosis, route and functional enrichment, tumor immune evasion, tumor mutation, and treatment sensitivity.

RESULTS

Patients in the high-risk group had lower overall survival (OS) than that of low-risk group. Compared with clinicopathological variables, CRLs features have better predictive value according to receiver operating characteristic (ROC) curve. The expression level of CRLs was highly associated with the tumor progress, tumor microenvironment and tumor immune escape. Additionally, we identified that the mutation of TP53, TTN, KMT2D and MUC16 gene were founded in patients with BCa. Lapatinib, pazopanib, saracatinib, gemcitabine, paclitaxel and palenolactone had good antitumor effects for BCa patients in the high-risk group (all P < 0.001).

CONCLUSION

This study revealed the effects of CRLs on BCa and further established CRLs model, which can be used in clinic for predicting prognosis, immunological response and treatment sensitivity inpatient with BCa.

Upregulated Immunogenic Cell-Death-Associated Gene Signature Predicts Reduced Responsiveness to Immune-Checkpoint-Blockade Therapy and Poor Prognosis in High-Grade Gliomas

Background: Immunogenic cell death (ICD) has emerged as a potential mechanism mediating adaptive immune response and tumor immunity in anti-cancer treatment. However, the signature of ICD in high-grade gliomas (HGGs) remains largely unknown, and its relevance to immunotherapies is still undetermined. The purpose of this study is to identify ICD-associated genotypes in order to explore their relevance to tumor immunity, patient prognosis and therapeutic efficacy of immune checkpoint blockade (ICB) therapy in HGGs. Methods: Bulk RNA-seq data and clinical information on 169 and 297 patients were obtained from the Cancer Genome Atlas (TCGA) and China Glioma Genome Atlas (CGGA), respectively. The functional enrichment and characterization of ICD genotyping were detected, and the ICD prognostic signature prediction model was constructed using least absolute shrinkage and selection operator (LASSO) regression. The responsiveness to immunotherapy was predicted according to the scoring of the ICD prognostic signature. Results: The HGG patients with high ICD gene signature (C1) showed poor outcomes, increased activity of immune modulation and immune escape, high levels of immune-checkpoint markers, and HLA-related genes, which may explain their reduced response to ICB immunotherapy. A gene set of the ICD signature, composing FOXP3, IL6 LY96, MYD88 and PDIA3, showed an independent prognostic value in both the TCGA and the CGGA HGG cohort. Conclusions: Our in silico analyses identified the ICD gene signature in HGGs with potential implications for predicting the responsiveness to ICB immune therapy and patient outcomes.

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.