Prognostic Correlation of an Autophagy-Related Gene Signature in Patients with Head and Neck Squamous Cell Carcinoma

Identification of a disulfidptosis-related lncRNA signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma

PURPOSE

Disulfidptosis, a newly identified form of cell death, is triggered by disulfide stress. Herein, a unique signature was developed based on disulfidptosis-related lncRNAs (DRlncRNAs) for the prognostic and immune landscape prediction of head and neck squamous cell carcinoma (HNSCC).

METHODS

Transcriptome, somatic mutation, and clinical data were acquired at The Cancer Genome Atlas database. Individuals were partitioned into training and test cohorts at a 1:1 ratio to facilitate the development of a DRlncRNA signature using the least absolute shrinkage and selection operation method. Based on the median risk score, all HNSCC individuals were stratified into the high-risk group (HRG) and low-risk group (LRG). Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) analyses were used to estimate the prognostic value, and a nomogram was generated for survival prediction. To provide a more comprehensive assessment, the tumor microenvironment, functional enrichment, immune cell infiltration, and immunotherapeutic sensitivity were explored between LRG and HRG.

RESULTS

A DRlncRNA signature was established with 10 DRlncRNAs. The corresponding values of areas under the ROC curves for 1-, 3-, and 5-year overall survival were 0.710, 0.692, and 0.640. A more favorable prognosis was noted in the patients with lower risk, along with higher immune scores, increased immune-related functions, and immune cell infiltration, as well as improved response to the immunotherapeutic intervention in comparison with individuals at higher risk.

CONCLUSION

These findings demonstrate that the developed DRlncRNA signature holds promise as a reliable prognostic marker and predictor of immunotherapy response in HNSCC patients.

The dual role of autophagy in HPV-positive head and neck squamous cell carcinoma: a systematic review

PURPOSE

Human papilloma virus (HPV)-positive head and neck squamous cell carcinoma (HNSCC) displays distinct epidemiological, clinical, and molecular characteristics compared to the negative counterpart. Alterations in autophagy play an important role in cancer, and emerging evidence indicates an interplay of autophagy in HNSCC carcinogenesis and tumor promotion. However, the influence of HPV infection on autophagy in HNSCC has received less attention and has not been previously reviewed. Therefore, we here aimed to systematically review the role of autophagy explicitly in HPV+ HNSCC.

METHODS

Studies accessible in PubMed, Embase, Scopus, and Web of Science investigating HNSCC, highlighting the molecular biological differences between HPV- and HPV+ HNSCC and its influences on autophagy in HNSCC were analyzed according to the PRISMA statement. A total of 10 articles were identified, included, and summarized.

RESULTS

The HPV16 E7 oncoprotein was reported to be involved in the degradation of AMBRA1 and STING, and to enhance chemotherapy-induced cell death via lethal mitophagy in HNSCC cells. Autophagy-associated gene signatures correlated with HPV-subtype and overall survival. Additionally, immunohistochemical (IHC) analyses indicate that high LC3B expression correlates with poor overall survival in oropharyngeal HNSCC patients.

CONCLUSION

HPV may dampen general bulk autophagic flux via degradation of AMBRA1 but may promote selective autophagic degradation of STING and mitochondria. Interpretations of correlations between autophagy-associated gene expressions or IHC analyses of autophagy-related (ATG) proteins in paraffin embedded tissue with clinicopathological features without biological validation need to be taken with caution.

Identification of a new anoikis-related gene signature for prognostic significance in head and neck squamous carcinomas

Anoikis, a mode of programmed cell death, is essential for normal development and homeostasis in the organism and plays an important role in the onset and progression of cancers. The authors of this research sought to establish a gene signature associated with anoikis to predict therapy outcomes and patient prognosis for individuals with head and neck squamous cell carcinoma (HNSCC). Transcriptome data of anoikis-related genes (ARGs) in individuals with HNSCC were retrieved from public databases to aid in the formulation of the gene signature. A novel ARG signature was then created using a combination of the Least Absolute Shrinkage and Selection Operator regression and Cox regression analysis. The relationship between ARGs and tumor immune microenvironment in HNSCC was explored using single-cell analysis. HNSCC individuals were classified into high-risk and low-risk groups as per the median value of risk score. The study also investigated the variations in the infiltration status of immune cells, tumor microenvironment, sensitivity to immunotherapy and chemotherapeutics, as well as functional enrichment between the low-risk and high-risk categories. A total of 18 ARGs were incorporated in the formulation of the signature. Our signature's validity as a standalone predictive predictor was validated by multivariate Cox regression analysis and Kaplan-Meier survival analysis. Generally, the prognosis was worse for high-risk individuals. Subjects in the low-risk groups had a better prognosis and responded in a better way to combination immunotherapy, had higher immunological ratings and activity levels, and had more immune cell infiltration. In addition, gene set enrichment analysis findings showed that the low-risk subjects exhibited heightened activity in several immune-related pathways. However, the high-risk patients responded better to chemotherapy. The aim of this research was to develop a new ARG signature to predict the prognosis and sensitivity to immunotherapeutic and chemotherapeutic schemes for HNSCC patient. As a result, this could help spur the creation of new chemotherapeutics and immunotherapeutic approaches for patients with HNSCC.

Correlation analysis between immune-related genes and cell infiltration revealed prostate cancer immunotherapy biomarkers linked to T cells gamma delta

Prostate cancer (PCa) is a urological malignancy with poor prognosis. Immune-related genes are associated with immune infiltration in prostate cancer, but their role in immunogenic PCa is less well understood. We assessed the infiltration patterns of 22 immune cells in PCa and the relationship of immune-related differentially expressed genes (IDEGs) with them. The 87 IDEGs are involved in the interaction between the extracellular matrix and the tumor microenvironment. The model, including seven IDEGs (SLPI, DES, IAPP, NPY, ISG15, PLA2G2A, and HLA-DMB), showed a good predictive power. The SLPI expression is positively correlated with the infiltration level of T cells gamma delta. In addition, PCa has high infiltration levels in Macrophages M1 (18.07%) and Dendritic cells activated (17.64%). The correlation analysis between IDEGs and immune cell infiltration suggested that PCa immunotherapy biomarkers may be closely related to T cells gamma delta.

Identification and validation of a prognostic signature of autophagy, apoptosis and pyroptosis-related genes for head and neck squamous cell carcinoma: to imply therapeutic choices of HPV negative patients

Introduction

An effective tool is needed to predict the prognosis of head and neck squamous cell carcinoma (HNSCC). Human papillomavirus (HPV) positive HNSCC patients generally have a favorable survival and a promising responsiveness to radiotherapy, chemoradiotherapy and checkpoint blockades. However, HPV negative patients, the majority of HNSCC patients, have been largely overlooked. Cell death has been involved in the therapeutic resistance of cancers. To this end, we aimed to identify the association of autophagy, apoptosis and pyroptosis-related genes with the prognosis of HNSCC, and construct a prognostic signature to predict the prognosis for HNSCC, especially for HPV negative HNSCC.

Methods

Autophagy and apoptosis-related genes were obtained from Gene Set Enrichment Analysis (GSEA) website, and pyroptosis-related genes were obtained from GSEA and Gene Ontology (GO) database. We established the cell death index (CDI) based on RNA sequencing (RNA-seq) data and clinicopathological information from The Cancer Genome Atlas (TCGA) dataset. The prognostic value of CDI was verified by Kaplan-Meier, receiver operating characteristic (ROC) and univariate and multivariate Cox regression analyses in TCGA dataset, and validated with the datasets from Gene Expression Omnibus (GEO) and Qilu Hospital of Shandong University. We further assessed the immune microenvironment of patients with high and low CDI scores. Moreover, the expression of the signature genes in HNSCC cell lines were explored.

Results

We found that CDI was an independent prognostic indicator for overall survival (hazard ratio 3.80, 95% confidential interval: 2.70-5.40, P < 0.001). Furthermore, HNSCC patients with high CDI scores obtained increased overall survival post radiation indicating benefits from radiotherapy of this subgroup. On the other hand, HPV negative HNSCC patients with low CDI exhibited increased checkpoint gene expressions, an inflamed tumor microenvironment and an enriched immune response-related functions, suggesting the potential benefits from checkpoint immunotherapies of this subgroup. Moreover, we validated the baseline and induced expressions of above 16 genes in two HPV negative HNSCC cell lines, CAL27 and SCC-15.

Discussion

We established a prognostic signature and emphasized its implements in the therapeutic choices of HPV negative HNSCC patients, the majority and the poor outcome population of HNSCC.

Unravelling the roles of Autophagy in OSCC: A renewed perspective from mechanisms to potential applications

Oral squamous cell carcinoma (OSCC) is associated with a low survival rate and a high disability rate, making it a serious health burden, particularly in Southeast Asian countries. Therefore, improvements in the diagnosis, treatment, and prognosis prediction of OSCC are highly warranted. Autophagy has a significant impact on cancer development. Studies on autophagy in various human cancers have made outstanding contributions; however, the relationship between autophagy and OSCC remains to be explored. This review highlights the roles of autophagy in OSCC and discusses the relationship between autophagy and Epithelial–mesenchymal transition. Considering the lack of OSCC biomarkers, we focus on the studies involving OSCC-related bioinformatics analysis and molecular targets. Based on some classical targets, we summarize several key autophagy-related biomarkers with a considerable potential for clinical application, which may become the hotspot of OSCC research. In conclusion, we elaborate on the interrelationship between autophagy and OSCC and highlight the shortcomings of current studies to provide insights into the potential clinical strategies.

Inflammation-Related Gene Signature for Predicting the Prognosis of Head and Neck Squamous Cell Carcinoma

Purpose

The inflammatory response was associated with the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to perform a novel prognostic signature based on inflammation-related genes (IRGs) for a better understanding of the prognosis of HNSCC.

Patients and Methods

IRGs were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed to explore potential pathways. Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation. The nomogram model was constructed based on independent prognostic factors after univariate and multivariate Cox regression. The immune cell infiltration level was analyzed via the Tumor Immune Estimation Resource (TIMER) database.

Results

In this study, we confirmed that 60% IRGs were abnormally expressed in HNSCC samples, and these were associated with important oncobiology. Then, a prognostic signature comprising 7 hub genes was generated based on TCGA database. The results were validated in 97 patients from GSE41613. A nomogram comprising risk score, age, M stage and N stage was generated to improve the accuracy of prognosis evaluation. The immune cell infiltration analysis suggested that 5 hub genes (ADGRE1, OLR1, TIMP1, GPR132 and CCR7) were negatively correlated with tumor purity and positively correlated with the infiltration of immune cells.

Conclusion

Our study established a novel signature consisting of 7 hub genes for the prognostic prediction in patients with HNSCC.

Identification of an autophagy-related gene signature for predicting prognosis and immune activity in pancreatic adenocarcinoma

Adenocarcinoma of the pancreas (PAAD) is a cancerous growth that deteriorates rapidly and has a poor prognosis. Researchers are investigating autophagy in PAAD to identify a new biomarker and treatment target. An autophagy-related gene (ARG) model for overall survival (OS) was constructed using multivariate Cox regression analyses. A cohort of the Cancer Genome Atlas (TCGA)-PAAD was used as the training group as a basis for model construction. This prediction model was validated with several external datasets. To evaluate model performance, the analysis with receiver operating characteristic curves (ROC) was performed. The Human Protein Atlas (HPA) and Cancer Cell Line Encyclopedia (CCLE) were investigated to validate the effects of ARGs expression on cancer cells. Comparing the levels of immune infiltration between high-risk and low-risk groups was finished through the use of CIBERSORT. The differentially expressed genes (DEGs) between the low-/high-risk groups were analyzed further via Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, which were used to identify potential small-molecule compounds in Connectivity Map (CMap), followed by half-maximal inhibitory concentration (IC50) examination with PANC-1 cells. The risk score was finally calculated as follows: BAK1 × 0.34 + ITGA3 × 0.38 + BAG3 × 0.35 + APOL1 × 0.26-RAB24 × 0.67519. ITGA3 and RAB24 both emerged as independent prognostic factors in multivariate Cox regression. Each PAAD cohort had a significantly shorter OS in the high-risk group than in the low-risk group. The high-risk group exhibited infiltration of several immune cell types, including naive B cells (p = 0.003), plasma cells (p = 0.044), and CD8 T cells (nearly significant, p = 0.080). Higher infiltration levels of NK cells (p = 0.025), resting macrophages (p = 0.020), and mast cells (p = 0.007) were found in the high-risk group than the low-risk group. The in vitro and in vivo expression of signature ARGs was consistent in the CCLE and HPA databases. The top 3 enriched Gene Ontology biological processes (GO-BPs) were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were MAPK, cAMP, and cell adhesion molecules. Four potential small-molecule compounds (piperacetazine, vinburnine, withaferin A and hecogenin) that target ARGs were also identified. Taking the results together, our research shows that the ARG signature may serve as a useful prognostic indicator and reveal potential therapeutic targets in patients with PAAD.

Identification and Validation of Autophagy-Related Genes in Necrotizing Enterocolitis

BACKGROUND

Autophagy plays an essential role in the occurrence and progression of necrotizing enterocolitis (NEC). We intend to carry out the identification and validation of the probable autophagy-related genes of NEC via bioinformatics methods and experiment trials.

METHODS

The autophagy-related differentially expressed genes (arDEGs) of NEC were identified by analyzing the RNA sequencing data of the experiment neonatal mouse model and dataset GSE46619. Protein-protein interactions (PPIs), Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used for the arDEGs. Then, co-expressed autophagy-related genes in two datasets were identified by Venn analysis and verified by qRT-PCR in experimental NEC.

RESULTS

Autophagy increased in experimental NEC and 47 arDEGs were identified in experimental NEC by RNA-sequencing. The PPI results proclaimed those genes interplayed with each other. The GO and KEGG enrichment results of arDEGs reported certain enriched pathways related to autophagy and macroautophagy. Furthermore, 22 arDEGs were identified in human NEC from dataset GSE46619. The GO and KEGG enrichment analysis of these genes showed similar enriched terms with the results of experimental NEC. Finally, HIF-1a, VEGFA, ITGA3, ITGA6, ITGB4, and NAMPT were identified as co-expressed autophagy-related genes by Venn analysis in human NEC from dataset GSE46619 and experimental NEC. The result of quantified real-time PCR (qRT-PCR) revealed that the expression levels of HIF-1a and ITGA3 were upregulated, while VEGFA and ITGB4 were downregulated in experimental NEC.

CONCLUSION

We identified 47 arDEGs in experimental NEC and 22 arDEGs in human NEC via bioinformatics analysis. HIF-1a, ITGA3, VEGFA, and ITGB4 may have effects on the progression of NEC through modulating autophagy.

FKBP1A upregulation correlates with poor prognosis and increased metastatic potential of HNSCC

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy globally. The etiology of HNSCC is multifactorial, including cellular stress induced by a tobacco smoking, tobacco chewing excess alcohol consumption, and human papillomavirus infection. The induction of stress includes autophagy as one of the response pathways in maintaining homeostatic equilibrium. We evaluated the expression of autophagy-related genes in HNSCC tissues from RNA sequencing datasets and identified 19 genes correlated with poor prognosis and 18 genes correlated with improved prognosis of HNSCC patients. Further analysis of independent gene expression datasets revealed that ATG12, HSP90AB1, and FKBP1A are overexpressed in HNSCC and correlate with poor prognosis, whereas the overexpression of ANXA1, FOS, and ULK3 correlates with improved prognosis. Using independent datasets, we also found that ATG12, HSP90AB1, and FKBP1A expression increased with an increase in the T-stage of HNSCC. Among all the datasets analyzed, FKBP1A was overexpressed in HNSCC and was strongly associated with lymph node metastasis in multiple in silico datasets. In conclusion, our analysis indicates dynamic alterations in autophagy genes during HNSCC and warrants further investigation, specifically on FKBP1A and its role in tumor progression and metastasis.

A Novel Ferroptosis-Related Gene Signature to Predict Prognosis in Patients with Head and Neck Squamous Cell Carcinoma

The clinical TNM staging system is currently used to evaluate the prognosis of head and neck squamous cell carcinoma (HNSCC). The 5-year survival rate for patients with HNSCC is less than 50%, which is attributed to the lack of reliable prognostic biomarkers. Ferroptosis-related genes (FRGs) regulate cancer initiation and progression. Therefore, we analyzed the correlation between FRGs and the clinical outcomes of patients with HNSCC. A typical prognostic model of FRGs for HNSCC was constructed using bioinformatics tools and data from public databases, including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and GeneCards. The model was generated based on the following six FRGs: ATG5, PRDX6, OTUB1, FTH1, SOCS1, and MAP3K5. The accuracy of model prediction was analyzed systematically. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. The AUC for 1-year, 3-year, and 5-year survival were 0.645, 0.721, and 0.737, respectively, in the training set (TCGA cohort) and 0.726, 0.620, and 0.584, respectively, in the validation set (GSE65858). The multivariate Cox regression analysis revealed that the risk score was an independent prognostic factor for HNSCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that six FRGs were enriched in the ferroptosis pathway. A novel FRG prognostic signature model was established for HNSCC. The findings of this study reveal that FRGs are potential biomarkers for HNSCC.

Prognostic Value of a Ferroptosis-Related Gene Signature in Patients With Head and Neck Squamous Cell Carcinoma

Background: Ferroptosis is an iron-dependent programmed cell death (PCD) form that plays a crucial role in tumorigenesis and might affect the antitumor effect of radiotherapy and immunotherapy. This study aimed to investigate distinct ferroptosis-related genes, their prognostic value and their relationship with immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The differentially expressed ferroptosis-related genes in HNSCC were filtered based on multiple public databases. To avoid overfitting and improve clinical practicability, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were performed to construct a prognostic risk model. Moreover, a nomogram was constructed to forecast individual prognosis. The differences in tumor mutational burden (TMB), immune infiltration and immune checkpoint genes in HNSCC patients with different prognoses were investigated. The correlation between drug sensitivity and the model was firstly analyzed by the Pearson method. Results: Ten genes related to ferroptosis were screened to construct the prognostic risk model. Kaplan-Meier (K-M) analysis showed that the prognosis of HNSCC patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001), and the area under the curve (AUC) of the 1-, 3- and 5-year receiver operating characteristic (ROC) curve increased year by year (0.665, 0.743, and 0.755). The internal and external validation further verified the accuracy of the model. Then, a nomogram was build based on the reliable model. The C-index of the nomogram was superior to a previous study (0.752 vs. 0.640), and the AUC (0.729 vs. 0.597 at 1 year, 0.828 vs. 0.706 at 3 years and 0.853 vs. 0.645 at 5 years), calibration plot and decision curve analysis (DCA) also shown the satisfactory predictive capacity. Furthermore, the TMB was revealed to be positively correlated with the risk score in HNSCC patients (R = 0.14; P < 0.01). The differences in immune infiltration and immune checkpoint genes were significant (P < 0.05). Pearson analysis showed that the relationship between the model and the sensitivity to antitumor drugs was significant (P < 0.05). Conclusion: Our findings identified potential novel therapeutic targets, providing further potential improvement in the individualized treatment of patients with HNSCC.

Identification of an autophagy-related prognostic signature in head and neck squamous cell carcinoma

BACKGROUND

Autophagy-related genes (ARGs) have been significantly implicated in tumorigenesis and served as promising prognostic biomarkers for human cancer. Hence, this study was aimed to develop an ARGs-based prognostic signature for Head and neck squamous cell carcinoma (HNSCC).

METHODS

Prognostic ARG candidates were identified by univariate and multivariate Cox regression analysis in the training dataset (TCGA-HNSC) and incorporated into a 3-ARGs (EGFR, FADD, and PARK2) prognostic signature which was further verified in two independent validation cohorts (GSE41613 and GSE42743). Kaplan-Meier plots, Cox regression analyses, and receiver operating characteristics curves (ROC) were employed to evaluate the prognostic prediction of 3-ARGs signature. Differential expression of these 3 ARG between cancer and normal counterparts as well as their associations with autophagy markers were assessed in 60 pairs of freshly collected HNSCC and adjacent non-tumor samples and datasets from Human Protein Atlas, respectively.

RESULTS

Patients with high-risk score had significantly inferior overall survival. Multivariate regression analyses revealed that 3-ARGs signature could be an independent prognostic factor after adjusting various clinicopathological parameters. ROC analyses revealed high predictive accuracy and sensitivity of the 3-ARGs signature. Increased mRNA and protein expression of EGFR, FADD, and PARK2 were found in HNSCC samples, and their expression significantly correlated with the abundances of ATG5, Beclin1, and LC3.

CONCLUSION

Our results reveal that 3-ARGs signature is a powerful prognostic biomarker for HNSCC, which could be integrated into the current prognostic regime to realize individualized outcome prediction. EGFR, FADD, and PARK2 likely contributed to autophagy during HNSCC tumorigenesis.

Tissue Probability Based Registration of Diffusion-Weighted Magnetic Resonance Imaging

BACKGROUND

Current registration methods for diffusion-MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types.

PURPOSE

To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs).

STUDY TYPE

Retrospective longitudinal study.

POPULATION

Thirty-two healthy participants were scanned twice (legacy data), divided into a training-set (n = 16) and a test-set (n = 16), and 35 randomly-selected participants from the Human Connectome Project.

FIELD STRENGTH/SEQUENCE

3.0T, diffusion-weighted spin-echo echo-planar sequence; T1-weighted spoiled gradient-recalled echo (SPGR) sequence.

ASSESSMENT

A joint segmentation-registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine-learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools.

STATISTICAL TESTS

Classifiers used for segmentation were evaluated using leave-one-out cross-validation and scored using Dice-index. Registration success was evaluated by voxel-wise variance, normalized cross-correlation of registered DTI maps, intra- and inter-subject similarity of the registered TPMs, and region-based intra-subject similarity using an anatomical atlas. One-way ANOVAs were performed to compare between our method and other registration tools.

RESULTS

The proposed method outperformed mainstream registration tools as indicated by lower voxel-wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1-0.2; P < 0.05).

DATA CONCLUSION

A joint segmentation-registration approach based on diffusion-driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a "translation" of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 1.