Construction of a necroptosis-related lncRNA signature to predict the prognosis and immune microenvironment of head and neck squamous cell carcinoma

The fate and function of non-coding RNAs during necroptosis

Necroptosis is a novel form of cell death which is activated when apoptotic cell death signals are disrupted. Accumulating body of observations suggests that noncoding RNAs, which are the lately discovered mystery of the human genome, are significantly associated with necroptotic signaling circuitry. The fate and function of miRNAs have been well documented in human disease, especially cancer. Recently, lncRNAs have gained much attention due to their diverse regulatory functions. Although available studies are currently based on bioinformatic analysis, predicted interactions desires further attention, as these hold significant promise and should not be overlooked. In the light of these, here we comprehensively review and discuss noncoding RNA molecules that play significant roles during execution of necroptotic cell death.

Identification of two distinct head and neck squamous cell carcinoma subtypes based on fatty acid metabolism-related signatures: Implications for immunotherapy and chemotherapy

The dysregulation of lipid metabolism is a critical factor in the initiation and progression of tumors. In this investigation, we aim to characterize the molecular subtypes of head and neck squamous cell carcinoma (HNSCC) based on their association with fatty acid metabolism and develop a prognostic risk model. The transcriptomic and clinical data about HNSCC were obtained from public databases. Clustering analysis was conducted on fatty acid metabolism genes (FAMG) associated with prognosis, utilizing the non-negative matrix factorization algorithm. The immune infiltration, response to immune therapy, and drug sensitivity between molecular subtypes were evaluated. Differential expression genes were identified between subtypes, and a prognostic model was constructed using Cox regression analyses. A nomogram for HNSCC was constructed and evaluated. Thirty FAMGs have been found to exhibit differential expression in HNSCC, out of which three are associated with HNSCC prognosis. By performing clustering analysis on these 3 genes, 2 distinct molecular subtypes of HNSCC were identified that exhibit significant heterogeneity in prognosis, immune landscape, and treatment response. Using a set of 7778 genes that displayed differential expression between the 2 molecular subtypes, a prognostic risk model for HNSCC was constructed comprising 11 genes. This model has the ability to stratify HNSCC patients into high-risk and low-risk groups, which exhibit significant differences in prognosis, immune infiltration, and immune therapy response. Moreover, our data suggest that this risk model is negatively correlated with B cells and most T cells, but positively correlated with macrophages, mast cells, and dendritic cells. Ultimately, we constructed a nomogram incorporating both the risk signature and radiotherapy, which has demonstrated exceptional performance in predicting prognosis for HNSCC patients. A molecular classification system and prognostic risk models were developed for HNSCC based on FAMGs. This study revealed the potential involvement of FAMGs in modulating tumor immune microenvironment and response to treatment.

Pancancer analysis reveals the role of disulfidptosis in predicting prognosis, immune infiltration and immunotherapy response in tumors

Disulfidptosis has been reported as a novel cell death process, suggesting a therapeutic strategy for cancer treatment. Herein, we constructed a multiomics data analysis to reveal the effects of disulfidptosis in tumors. Data for 33 kinds of tumors were downloaded from UCSC Xene, and disulfidptosis-related genes (DRGs) were selected from a previous study. After finishing processing data by the R packages, the expression and coexpression of DRGs in different tumors were assessed as well as copy number variations. The interaction network was drawn by STRING, and the activity of disulfidptosis was compared to the single-sample gene set enrichment analysis algorithm. Subsequently, the differences in DRGs for prognosis and clinicopathological features were evaluated, and the tumor immune microenvironment was assessed by the TIMER and TISCH databases. Tumor mutation burden, stem cell features and microsatellite instability were applied to predict drug resistance, and the expression of checkpoints was identified for the prediction of immunotherapy. Moreover, the TCIA, CellMiner and Enrichr databases were also utilized for selecting potential agents. Ten DRGs were differentially expressed in tumors, and the plots of coexpression and interaction revealed their correlation. Survival analysis suggested SLC7A11 as the most prognosis-related DRG with the most significant results. Additionally, the comparison also reflected the differences in DRGs in the status of pathologic lymph node metastasis for 5 types of tumors. The tumor immune microenvironment showed commonality among tumors based on immune infiltration and single-cell sequencing, and the analysis of tumor mutation burden, stemness and microsatellite instability showed a mostly positive correlation with DRGs. Moreover, referring to the prediction about clinical treatment, most DRGs can enhance sensitivity to chemotherapeutic agents but decrease the response to immune inhibitors with increasing expression. In this study, a primarily synthetic landscape of disulfidptosis in tumors was established and provided guidance for further exploration and investigation.

A cuproptosis-related lncRNA signature predicts the prognosis and immune cell status in head and neck squamous cell carcinoma

Introduction

The incidence of head and neck squamous cell carcinoma (HNSCC), one of the most prevalent tumors, is increasing rapidly worldwide. Cuproptosis, as a new copper-dependent cell death form, was proposed recently. However, the prognosis value and immune effects of cuproptosis-related lncRNAs (CRLs) have not yet been elucidated in HNSCC.

Methods

In the current study, the expression pattern, differential profile, clinical correlation, DNA methylation, functional enrichment, univariate prognosis factor, and the immune effects of CRLs were analyzed. A four-CRL signature was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm.

Results

Results showed that 20 CRLs had significant effects on the stage progression of HNSCC. Sixteen CRLs were tightly correlated with the overall survival (OS) of HNSCC patients. Particularly, lnc-FGF3-4 as a single risk factor was upregulated in HNSCC tissues and negatively impacted the prognosis of HNSCC. DNA methylation probes of cg02278768 (MIR9-3HG), cg07312099 (ASAH1-AS1), and cg16867777 (TIAM1-AS1) were also correlated with the prognosis of HNSCC. The four-CRL signature that included MAP4K3-DT, lnc-TCEA3-1, MIR9-3HG, and CDKN2A-DT had a significantly negative effect on the activation of T cells follicular helper and OS probability of HNSCC. Functional analysis revealed that cell cycle, DNA replication, and p53 signal pathways were enriched.

Discussion

A novel CRL-related signature has the potential of prognosis prediction in HNSCC. Targeting CRLs may be a promising therapeutic strategy for HNSCC.

Bioinformatics analysis of necroptosis‑related lncRNAs and immune infiltration, and prediction of the prognosis of patients with esophageal carcinoma

Esophageal carcinoma (ESCA) is one of the most common malignancies in the world, and has high morbidity and mortality rates. Necrosis and long noncoding RNAs (lncRNAs) are involved in the progression of ESCA; however, the specific mechanism has not been clarified. The aim of the present study was to investigate the role of necrosis-related lncRNAs (nrlncRNAs) in patients with ESCA by bioinformatics analysis, and to establish a nrlncRNA model to predict ESCA immune infiltration and prognosis. To form synthetic matrices, ESCA transcriptome data and related information were obtained from The Cancer Genome Atlas. A nrlncRNA model was established by coexpression, univariate Cox (Uni-Cox), and least absolute shrinkage and selection operator analyses. The predictive ability of this model was evaluated by Kaplan-Meier, receiver operating characteristic (ROC) curve, Uni-Cox, multivariate Cox regression, nomogram and calibration curve analyses. A model containing eight nrlncRNAs was generated. The areas under the ROC curves for 1-, 3- and 5-year overall survival were 0.746, 0.671 and 0.812, respectively. A high-risk score according to this model could be used as an indicator for systemic therapy use, since the half-maximum inhibitory concentration values varied significantly between the high-risk and low-risk groups. Based on the expression of eight prognosis-related nrlncRNAs, the patients with ESCA were regrouped using the 'ConsensusClusterPlus' package to explore potential molecular subgroups responding to immunotherapy. The patients with ESCA were divided into three clusters based on the eight nrlncRNAs that constituted the risk model: The most low-risk group patients were classified into cluster 1, and the high-risk group patients were mainly concentrated in clusters 2 and 3. Survival analysis showed that Cluster 1 had a better survival than the other groups (P=0.016). This classification system could contribute to precision treatment. Furthermore, two nrlncRNAs (LINC02811 and LINC00299) were assessed in the esophageal epithelial cell line HET-1A, and in the human esophageal cancer cell lines KYSE150 and TE1. There were significant differences in the expression levels of these lncRNAs between tumor and normal cells. In conclusion, the present study suggested that nrlncRNA models may predict the prognosis of patients with ESCA, and provide guidance for immunotherapy and chemotherapy decision making. Furthermore, the present study provided strategies to promote the development of individualized and precise treatment for patients with ESCA.

Multiomics data analyses to identify SLC25A17 as a novel biomarker to predict the prognosis and immune microenvironment in head and neck squamous cell carcinoma

OBJECTIVE

This study aims to explore the predictive value of SLC25A17 in the prognosis and tumor microenvironment (TME) of patients with head and neck squamous cell carcinoma (HNSCC) and to provide ideas for individual clinical treatment.

METHODS

A pancancer analysis of the differential expression of SLC25A17 among different tumors was first conducted via the TIMER 2.0 database. Subsequently, the expression of SLC25A17 and related clinical information of HNSCC patients were obtained from the TCGA database, and patients were divided into two groups according to the median value of SLC25A17 expression. K‒M survival analysis was conducted to compare the overall survival (OS) and progression-free survival (PFS) between the groups. The Wilcoxon test was used to compare the distribution of SLC25A17 in different clinical characteristics, and univariate Cox and multivariate Cox analyses were performed to analyze independent prognostic factors to establish a predictive nomogram. Calibration curves were generated to verify the reliability of predicting 1-year, 3-year and 5-year survival rates and another cohort (GSE65858) was used for external validation. Gene set enrichment analysis was conducted to compare the enriched pathways, and the immune microenvironment was assessed using the CIBERSORT and estimate packages. Furthermore, the expression levels of SLC25A17 in immune cells were also analyzed with single-cell RNA-seq via the TISCH. Moreover, the immunotherapeutic response and chemotherapy drug sensitivity were compared between the two groups to guide precise treatment. The TIDE database was applied to predict the possibility of immune escape in the TCGA-HNSC cohort.

RESULTS

Compared with normal samples, the expression of SLC25A17 was much higher in HNSCC tumor samples. For patients with high SLC25A17 expression, the OS and PFS were shorter than those with low SLC25A17 expression, indicating a worse prognosis. The expression of SLC25A17 varied in different clinical features. Univariate Cox and multivariate COX analyses showed that SLC25A17, age, and lymph node metastasis are independent prognostic risk factors for HNSCC, and the survival prediction model based on these factors had reliable predictive value. Patients in the low-expression group exhibited more immune cell infiltration, higher TME scores, higher IPS scores and lower TIDE scores than those in the high-expression groups, suggesting better immunotherapeutic response with lower SLC25A17 expression. Moreover, patients in the high-expression group were more sensitive to chemotherapy.

CONCLUSIONS

SLC25A17 can effectively predict the prognosis of HNSCC patients and could be a precise individual-targeted indicator for the treatment of HNSCC patients.

5-Methylcytosine-related lncRNAs: predicting prognosis and identifying hot and cold tumor subtypes in head and neck squamous cell carcinoma

BACKGROUND

5-Methylcytosine (m⁵C) methylation is recognized as an mRNA modification that participates in biological progression by regulating related lncRNAs. In this research, we explored the relationship between m⁵C-related lncRNAs (mrlncRNAs) and head and neck squamous cell carcinoma (HNSCC) to establish a predictive model.

METHODS

RNA sequencing and related information were obtained from the TCGA database, and patients were divided into two sets to establish and verify the risk model while identifying prognostic mrlncRNAs. Areas under the ROC curves were assessed to evaluate the predictive effectiveness, and a predictive nomogram was constructed for further prediction. Subsequently, the tumor mutation burden (TMB), stemness, functional enrichment analysis, tumor microenvironment, and immunotherapeutic and chemotherapeutic responses were also assessed based on this novel risk model. Moreover, patients were regrouped into subtypes according to the expression of model mrlncRNAs.

RESULTS

Assessed by the predictive risk model, patients were distinguished into the low-MLRS and high-MLRS groups, showing satisfactory predictive effects with AUCs of 0.673, 0.712, and 0.681 for the ROCs, respectively. Patients in the low-MLRS groups exhibited better survival status, lower mutated frequency, and lower stemness but were more sensitive to immunotherapeutic response, whereas the high-MLRS group appeared to have higher sensitivity to chemotherapy. Subsequently, patients were regrouped into two clusters: cluster 1 displayed immunosuppressive status, but cluster 2 behaved as a hot tumor with a better immunotherapeutic response.

CONCLUSIONS

Referring to the above results, we established a m⁵C-related lncRNA model to evaluate the prognosis, TME, TMB, and clinical treatments for HNSCC patients. This novel assessment system is able to precisely predict the patients' prognosis and identify hot and cold tumor subtypes clearly for HNSCC patients, providing ideas for clinical treatment.

Novel insights into the interplay between m6A modification and programmed cell death in cancer

N6-methyladenosine (m6A) methylation, the most prevalent and abundant RNA modification in eukaryotes, has recently become a hot research topic. Several studies have indicated that m6A modification is dysregulated during the progression of multiple diseases, especially in cancer development. Programmed cell death (PCD) is an active and orderly method of cell death in the development of organisms, including apoptosis, autophagy, pyroptosis, ferroptosis, and necroptosis. As the study of PCD has become increasingly profound, accumulating evidence has revealed the mutual regulation of m6A modification and PCD, and their interaction can further influence the sensitivity of cancer treatment. In this review, we summarize the recent advances in m6A modification and PCD in terms of their interplay and potential mechanisms, as well as cancer therapeutic resistance. Our study provides promising insights and future directions for the examination and treatment of cancers.

Identification of a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape of head and neck squamous cell carcinoma

Background

Cuproptosis is considered a novel copper-induced cell death model regulated by targeting lipoylated TCA cycle proteins. In this study, we established a novel signature based on cuproptosis-related lncRNAs (crlncRNAs) to predict the prognosis and immune landscape of head and neck squamous cell carcinoma.

Methods

RNA-seq matrix, somatic mutation files, and clinical data were obtained from The Cancer Genome Atlas database. After dividing patients into two sets, a crlncRNA signature was established based on survival related crlncRNAs, which were selected by the univariate Cox analysis and least absolute shrinkage and selection operator Cox regression. To evaluate the model, Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) were utilized, and a nomogram was established for survival prediction. Immune landscape analysis, drug sensitivity, cluster analysis, tumor mutation burden (TMB) and ceRNA network analysis were conducted subsequently.

Results

A crlncRNA related prognosis signature was finally constructed with 12 crlncRNAs. The areas under the ROC curves (AUCs) were 0.719, 0.705 and 0.693 respectively for 1, 3, and 5-year's overall survival (OS). Patients in the low-risk group behaved a better prognosis, lower TMB, higher immune function activity and scores. In addition, patients from cluster 2 were more sensitive to chemotherapy and immunotherapy.

Conclusion

In this study, we constructed a novel crlncRNA risk model to predict the survival of HNSCC patients. This reliable and acceptable prognostic signature may guide and promote the progress of novel treatment strategies for HNSCC patients.

Exploration of a predictive model based on genes associated with fatty acid metabolism and clinical treatment for head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent malignant tumors of the head and neck and presents high risks of recurrence and poor prognosis postoperatively. The aim of this study was to establish a predictive model based on fatty acid metabolism (FAM) genes to forecast the prognosis of HNSCC patients and the subsequent treatment strategies.

METHODS

We accessed the TCGA and GEO databases for HNSCC genes and clinical data. The FAM risk score model was created and validated using a combination of univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Combining risk scores and clinical characteristics, a nomogram was established and assessed. Subsequently, the function, gene mutation, immune difference, and chemotherapeutic drug sensitivity of the groups with high- and low-risk scores were analyzed. Consequently, the mode's validity was evaluated comprehensively by combining single gene analysis.

RESULTS

The FAM risk score model for predicting HNSCC prognosis had certain validity. Patients in the high- and low-risk groups had genetic mutations, and the prognosis was the poorest for the high-risk groups with high genetic mutations. The patients with low-risk scores were suitable for immunotherapy since they had increased infiltration of immune cells. In contrast, the patients in the other groups were more suitable for chemotherapy.

CONCLUSION

The results of this study demonstrated that the FAM risk score model may predict the prognosis of HSNCC and has a certain therapeutic guidance value.

Identification of cuproptosis-related subtypes and characterization of the tumor microenvironment landscape in head and neck squamous cell carcinoma

BACKGROUND

Cuproptosis is considered a novel copper-dependent cell death model. In this study, we established a novel scoring system based on 10 cuproptosis-related genes (CRGs) to predict the prognosis and immune landscape of head and neck squamous cell carcinoma (HNSCC).

METHODS

The RNA-seq data of HNSCC patients were downloaded from the GEO and TCGA databases and were merged into a novel HNSCC cohort. Multiomics landscape analyses were conducted, including tumor mutation burden (TMB), copy number variations and the interaction network of CRGs. Patients were then divided into different cuproptosis subtypes based on the expression of 10 CRGs and subsequently regrouped into novel gene clusters referring to differentially expressed genes. A cuproptosis score (CS) system was established using principal component analysis. The CIBERSORT, ssGSEA and ESTIMATE algorithms were used to assess the tumor immune microenvironment. Moreover, the immunotherapeutic and chemotherapeutic responses were assessed.

RESULTS

Patients were divided into three cuproptosis subtypes and subsequently regrouped into three gene clusters, reflecting different immune infiltration. Assessed by the CS system, those with higher CSs exhibited worse prognosis and higher TMB frequency. Nevertheless, the immune-related analysis revealed patients in the low-CS group appeared immunosuppressive and easily suffered from immune escape. High CSs possibly show high expression of immune checkpoint genes and enhance chemotherapy sensitivity to cisplatin, docetaxel, and gemcitabine.

CONCLUSION

We established a novel scoring system to predict the prognosis and immune landscape of HNSCC patients. This signature exhibits satisfactory predictive effects and the potential to guide comprehensive treatment for patients.

A Novel Necroptosis-Related lncRNA Signature for Osteosarcoma

Backgrounds

Osteosarcoma (OS) is easy to metastasis. Necroptosis-related long noncoding RNA (lncRNA) (NRlncRNA) plays a vital role in the tumorigenesis of many malignant tumors. Nonetheless, there have been few studies investigating the relations between NRlncRNA and OS. During the investigation, NRlncRNAs in OS were confirmed and characterized and their relationships with prognoses were investigated.

Methods

NRlncRNAs were downloaded from The Cancer Genome Atlas (TCGA) OS expression data and clinical-pathological information. First, univariate Cox regression and LASSO regression analyses were used to screen for prognostic-related NRlncRNAs. Second, multivariate regression analyses were used to establish a prognostic nomogram for predicting individual survival probability. Survival analyses demonstrated that high-risk patients (HRPs) had a poor prognosis. In addition, gene set enrichment analyses (GSEA) were used to identify gene function in high- and low-risk groups based on the survival mode.

Results

The 7 NRlncRNAs (AC004812.2, AC022915.1, AC073073.2, AC090559.1, AL512330.1, DDN-AS1, and SENCR) were shown to have a distinct difference and were used to construct an NRlncRNA signature. Using the signature as a risk score was an independent factor for OS patients. The signature divided OS patients into the high- and low-risk groups. Furthermore, the seven lncRNAs were significantly enriched in cell migration and metabolism.

Conclusions

The 7 NRlncRNA survival models have the potential to serve as therapeutic targets and molecular biomarkers for patients with OS, as well as to precisely predict OS prognoses.