Ferroptosis-related gene signature predicts the prognosis in Oral squamous cell carcinoma patients

Current knowledge of ferroptosis in the pathogenesis and prognosis of oral squamous cell carcinoma

Therapeutic strategies are the hot-spot issues in treating patients with advanced oral squamous cell carcinoma (OSCC). Mounting studies have proved that triggering ferroptosis is one of the promising targets for OSCC management. In this study, we performed a first attempt to collect the current evidence on the proposed roles of ferroptosis in OSCC through a comprehensive review. Based on clinical data from the relevant studies within this topic, we found that ferroptosis-associated tumor microenvironment, ferroptosis-related genes (FRGs), and ferroptosis-related lncRNAs exhibited a potent prognostic value for OSCC patients. Mechanistically, experimental data revealed that the proliferation and tumorigenesis of OSCC might be associated with the inhibition of cellular ferroptosis through the activation of glutathione peroxidase 4 (GPX4) and adipocyte enhancer-binding protein 1 (AEBP1), suppression of glutathione (GSH) and Period 1 (PER1) expression, and modulation of specific non-coding RNAs (i.e., miR-520d-5p, miR-34c-3p, and miR-125b-5p) and their targeted proteins. Several specific interventions (i.e., Quisinostat, Carnosic acid, hyperbaric oxygen, melatonin, aqueous-soluble sporoderm-removed G. lucidum spore powder, and disulfiram/copper complex) were found to dramatically induce ferroptosis cell death of OSCC via multiple mechanisms. This review highlighted the pivotal role of ferroptosis in the pathogenesis and prognosis of OSCC. Future anticancer therapeutic strategies targeting ferroptosis and its associated molecules might provide a new insight for OSCC treatment.

Novel ferroptosis signature for improving prediction of prognosis and indicating gene targets from single-cell level in oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) is one of the most prevalent kinds of cancers. Therefore, there is a pressing need to create a new risk scoring model to personalize the prognosis of OSCC patients and screen for patient-specific therapeutic agents and molecular targets.

Methods

Firstly, A series of bioinformatics was performed to construct a novel ferroptosis-related prognostic model; Further, drug sensitivity analysis was used to screen for specific therapeutic agents for OSCC; Single-cell analysis was employed to investigate the enrichment of FRDEGs (ferroptosis-related differentially expressed genes) in the OSCC microenvironment; Finally, various experiments were conducted to screen and validate molecular therapeutic targets for OSCC.

Results

In this study, we constructed a novel 10-FRDEGs risk scoring model. Base on the risk scoring model, we founded three potential chemotherapeutic agents for OSCC: 5Z)-7-Oxozeaenol, AT-7519, KIN001-266; In addition, FRDEGs were enriched in the epithelial cells of OSCC. Finally, we found that CA9 and CAV1 could regulate OSCC proliferation, migration and ferroptosis in vitro.

Conclusion

A novel 10-FRDEGs risk scoring model can predict the prognosis of patients with OSCC.Further,5Z)-7-Oxozeaenol, AT-7519, KIN001-266 are potential chemotherapeutic agents for OSCC.Moreover, we identified CA9、CAV1 as potential molecular target for the treatment of OSCC.Our findings provide new directions for prognostic assessment and precise treatment of oral cell squamous carcinoma.

Ferroptosis-Regulated Natural Products and miRNAs and Their Potential Targeting to Ferroptosis and Exosome Biogenesis

Ferroptosis, which comprises iron-dependent cell death, is crucial in cancer and non-cancer treatments. Exosomes, the extracellular vesicles, may deliver biomolecules to regulate disease progression. The interplay between ferroptosis and exosomes may modulate cancer development but is rarely investigated in natural product treatments and their modulating miRNAs. This review focuses on the ferroptosis-modulating effects of natural products and miRNAs concerning their participation in ferroptosis and exosome biogenesis (secretion and assembly)-related targets in cancer and non-cancer cells. Natural products and miRNAs with ferroptosis-modulating effects were retrieved and organized. Next, a literature search established the connection of a panel of ferroptosis-modulating genes to these ferroptosis-associated natural products. Moreover, ferroptosis-associated miRNAs were inputted into the miRNA database (miRDB) to bioinformatically search the potential targets for the modulation of ferroptosis and exosome biogenesis. Finally, the literature search provided a connection between ferroptosis-modulating miRNAs and natural products. Consequently, the connections from ferroptosis-miRNA-exosome biogenesis to natural product-based anticancer treatments are well-organized. This review sheds light on the research directions for integrating miRNAs and exosome biogenesis into the ferroptosis-modulating therapeutic effects of natural products on cancer and non-cancer diseases.

Astaxanthin Synergizes with Ionizing Radiation (IR) in Oral Squamous Cell Carcinoma (OSCC)

Astaxanthin (ATX) is known for its antioxidant and anti-inflammation functions yet its role in cancers requires more research. This study is aimed to reveal the potential synergetic effect of ATX with ionizing radiation (IR) in OSCC. Cell survival was measured after human OSCC cells including CAL27 and SCC9, and normal human oral keratinocytes (NHOKs) were treated with different concentrations of ATX for 24 h. Colony formation assays were performed after OSCC cells were treated with IR, ATX (20 μ M), or combined and survival fraction was analyzed. Malondialdehyde (MDA), glutathione (GSH), and intercellular iron levels were measured. Western blot method was used to measure the ferroptosis-related proteins, GPX4, SLC7A11, and ACSL4. In xenograft mice model, we evaluated the tumor volumes, tumor growth, and examined the GPX4/ACSL4 proteins in tumor tissues using Immunohistochemistry (IHC). ATX inhibited viability of OSCC cells but not NHOK. In OSCC cells, ATX further enhanced the cell death induced by IR. In addition, ATX promoted the MDA content, Iron levels but inhibited the GSH regulated by IR in cells. ATX could synergize with IR, further inhibiting GPX4, SLC7A11 and promoting ACSL4 in OSCC cells. In vivo, ATX and IR treatment inhibited OSCC tumor growth and the group with combined treatment showed the most inhibitory effect. GPX4 was inhibited by IR and further inhibited in the combined group while ACSL4 was promoted by IR and enhanced more significantly in the combined group. ATX might synergize with IR treatment in OSCC partly via ferroptosis.

Exploring beyond Common Cell Death Pathways in Oral Cancer: A Systematic Review

Oral squamous cell carcinoma (OSCC) is the most common and lethal type of head and neck cancer in the world. Variable response and acquisition of resistance to traditional therapies show that it is essential to develop novel strategies that can provide better outcomes for the patient. Understanding of cellular and molecular mechanisms of cell death control has increased rapidly in recent years. Activation of cell death pathways, such as the emerging forms of non-apoptotic programmed cell death, including ferroptosis, pyroptosis, necroptosis, NETosis, parthanatos, mitoptosis and paraptosis, may represent clinically relevant novel therapeutic opportunities. This systematic review summarizes the recently described forms of cell death in OSCC, highlighting their potential for informing diagnosis, prognosis and treatment. Original studies that explored any of the selected cell deaths in OSCC were included. Electronic search, study selection, data collection and risk of bias assessment tools were realized. The literature search was carried out in four databases, and the extracted data from 79 articles were categorized and grouped by type of cell death. Ferroptosis, pyroptosis, and necroptosis represented the main forms of cell death in the selected studies, with links to cancer immunity and inflammatory responses, progression and prognosis of OSCC. Harnessing the potential of these pathways may be useful in patient-specific prognosis and individualized therapy. We provide perspectives on how these different cell death types can be integrated to develop decision tools for diagnosis, prognosis, and treatment of OSCC.

Ferroptosis in head and neck squamous cell carcinoma: from pathogenesis to treatment

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignant tumor worldwide, with high morbidity and mortality. Surgery and postoperative chemoradiotherapy have largely reduced the recurrence and fatality rates for most HNSCCs. Nonetheless, these therapeutic approaches result in poor prognoses owing to severe adverse reactions and the development of drug resistance. Ferroptosis is a kind of programmed cell death which is non-apoptotic. Ferroptosis of tumor cells can inhibit tumor development. Ferroptosis involves various biomolecules and signaling pathways, whose expressions can be adjusted to modulate the sensitivity of cells to ferroptosis. As a tool in the fight against cancer, the activation of ferroptosis is a treatment that has received much attention in recent years. Therefore, understanding the molecular mechanism of ferroptosis in HNSCC is an essential strategy with therapeutic potential. The most important thing to treat HNSCC is to choose the appropriate treatment method. In this review, we discuss the molecular and defense mechanisms of ferroptosis, analyze the role and mechanism of ferroptosis in the inhibition and immunity against HNSCC, and explore the therapeutic strategy for inducing ferroptosis in HNSCC including drug therapy, radiation therapy, immunotherapy, nanotherapy and comprehensive treatment. We find ferroptosis provides a new target for HNSCC treatment.

Integrated Single-cell and Bulk RNA Sequencing Analysis Cross Talk between Ferroptosis-related Genes and Prognosis in Oral Cavity Squamous Cell Carcinoma

BACKGROUND

Ferroptosis is a new type of programmed apoptosis and plays an important role in tumour inhibition and immunotherapy.

OBJECTIVE

In this study, we aimed to explore the potential role of ferroptosis-related genes (FRGs) and the potential therapeutic targets in oral cavity squamous cell carcinoma (OCSCC).

METHODS

The transcription data of OCSCC samples were obtained from the Cancer Genome Atlas (TCGA) database as a training dataset. The prognostic FRGs were extracted by univariate Cox regression analysis. Then, we constructed a prognostic model using the least absolute shrinkage and selection operator (LASSO) and Cox analysis to determine the independent prognosis FRGs. Based on this model, risk scores were calculated for the OCSCC samples. The model's capability was further evaluated by the receiver operating characteristic curve (ROC). Then, we used the GSE41613 dataset as an external validation cohort to confirm the model's predictive capability. Next, the immune infiltration and somatic mutation analysis were applied. Lastly, single-cell transcriptomic analysis was used to identify the key cells.

RESULTS

A total of 12 prognostic FRGs were identified. Eventually, 6 FRGs were screened as independent predictors and a prognostic model was constructed in the training dataset, which significantly stratified OCSCC samples into high-risk and low-risk groups based on overall survival. The external validation of the model using the GSE41613 dataset demonstrated a satisfactory predictive capability for the prognosis of OCSCC. Further analysis revealed that patients in the highrisk group had distinct immune infiltration and somatic mutation patterns from low-risk patients. Mast cell infiltrations were identified as prognostic immune cells and played a role in OCSCC partly through ferroptosis.

CONCLUSION

We successfully constructed a novel 6 FRGs model and identified a prognostic immune cell, which can serve to predict clinical prognoses for OCSCC. Ferroptosis may be a new direction for immunotherapy of OCSCC.

The Evaluation of Prognostic Value and Immune Characteristics of Ferroptosis-Related Genes in Lung Squamous Cell Carcinoma

Background  The purpose of our study was to construct a prognostic model based on ferroptosis-related gene signature to improve the prognosis prediction of lung squamous carcinoma (LUSC). Methods  The mRNA expression profiles and clinical data of LUSC patients were downloaded. LUSC-related essential differentially expressed genes were integrated for further analysis. Prognostic gene signatures were identified through random forest regression and univariate Cox regression analyses for constructing a prognostic model. Finally, in a preliminary experiment, we used the reverse transcription-quantitative polymerase chain reaction assay to verify the relationship between the expression of three prognostic gene features and ferroptosis. Results  Fifty-six ferroptosis-related essential genes were identified by using integrated analysis. Among these, three prognostic gene signatures (HELLS, POLR2H, and POLE2) were identified, which were positively affected by LUSC prognosis but negatively affected by immune cell infiltration. Significant overexpression of immune checkpoint genes occurred in the high-risk group. In preliminary experiments, we confirmed that the occurrence of ferroptosis can reduce three prognostic gene signature expression. Conclusions  The three ferroptosis-related genes could predict the LUSC prognostic risk of antitumor immunity.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

MMP14 expression levels accurately predict the presence of extranodal extensions in oral squamous cell carcinoma: a retrospective cohort study

BACKGROUND

Extranodal extension (ENE) is an adverse prognostic factor for oral squamous cell carcinoma (OSCC), and patients with OSCC along with ENE require neck dissection. In this study, we developed a novel ENE histology-based pathological predictor using MMP14 expression patterns in small biopsy specimens.

METHODS

A total of 71 surgically resected tissue, 64 dissected lymph node (LN), and 46 biopsy specimens were collected from 71 patients with OSCC. Immunohistochemical analyses of total MMP14 expression in the tumour nest and cancer-associated fibroblasts (CAFs) were performed using the MMP14 co-scoring system (high- or low-risk). The association analysis of MMP14 expression in metastatic LNs was performed with respect to the presence and absence of ENE. Clinicopathological analyses and multivariate examinations were performed to assess the risks of metastasis and ENE presence. The predictive value of ENE and the impact of ENE and MMP14 expression on 5-year overall survival were examined.

RESULTS

High-risk MMP14 expression was detected in metastatic LN specimens with ENE. MMP14 expression in tumour nests and CAFs and its overexpression at the tumour-stromal interface significantly correlated with the presence of ENE. The MMP14 co-scoring system was an independent risk predictor for ENE, with sensitivity, specificity, and accuracy of over 80% in biopsy samples; patients with a high risk in the MMP14 co-scoring system had significantly worse prognoses in both resections and biopsies.

CONCLUSION

The MMP14 co-scoring system accurately predicted ENE presence and poor prognosis via immunohistochemical evaluation of small biopsies. This system is a simple, accurate, and inexpensive immunohistochemical approach that can be used in routine pathological diagnosis for effective treatment planning.

Identification and validation of a novel ferroptosis-related gene signature for prognosis and potential therapeutic target prediction in cholangiocarcinoma

Cholangiocarcinoma (CCA) is a highly heterogeneous and aggressive malignancy of the bile ducts with a poor prognosis and high mortality rate. Effective targeted therapy and accurate prognostic biomarkers are still lacking. Ferroptosis is a form of regulated cell death implicated in cancer progression and has emerged as a potential therapeutic target in various cancers. However, a comprehensive analysis of ferroptosis-related genes (FRGs) for predicting CCA prognosis and therapeutic targets and determining the role of ferroptosis in CCA remain to be performed. Here, we developed a prognostic FRG signature using a least absolute shrinkage and selection operator Cox regression analysis in a training cohort. We then validated it using four independent public datasets. The six-FRG signature was developed to predict CCA patient survival, stratifying them into low-risk and high-risk groups based on survival time. Significantly, the high-risk CCA patients had shorter overall survival. A receiver operating characteristic curve analysis further confirmed the prognostic FRG signature's strong predictive ability, indicating that it was an independent prognostic indicator for CCA patients. Furthermore, the high-risk group was associated with fluke infection and high clinical stages. Cancer-associated fibroblast (CAF) score and CAF markers were significantly higher in the high-risk group than the low-risk group. Moreover, our FRG signature could predict immune checkpoint markers for immunotherapy and drug sensitivity. The mRNA expression levels of the six-FRG signature was validated in 10 CCA cell lines and dividing them into low-risk and high-risk groups using the FRG signature. We further showed that high-risk CCA cell lines were more resistant to ferroptosis inducers, including erastin and RSL3, than the low-risk CCA cell lines. Our study constructed a novel FRG signature model to predict CCA prognoses which might provide prognostic biomarkers and potential therapeutic targets for CCA patients. Ferroptosis sensitivity in high-risk and low-risk CCA cell lines suggests that ferroptosis resistance is associated with high-risk group CCA. Therefore, ferroptosis could be a promising therapeutic target for precision therapy in CCA patients.

Employing machine learning using ferroptosis-related genes to construct a prognosis model for patients with osteosarcoma

Identifying effective biomarkers in osteosarcoma (OS) is important for predicting prognosis. We investigated the prognostic value of ferroptosis-related genes (FRGs) in OS. Transcriptome and clinical data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. FRGs were obtained from the ferroptosis database. Univariate COX regression and LASSO regression screening were performed and an FRG-based prognostic model was constructed, which was validated using the Gene Expression Omnibus cohort. The predictive power of the model was assessed via a subgroup analysis. A nomogram was constructed using clinical markers with independent prognostic significance and risk score results. The CIBERSORT algorithm was used to detect the correlation between prognostic genes and 22 tumor-infiltrating lymphocytes. The expression of prognostic genes in erastin-treated OS cell lines was verified via real-time PCR. Six prognostic FRGs (ACSL5, ATF4, CBS, CDO1, SCD, and SLC3A2) were obtained and used to construct the risk prognosis model. Subjects were divided into high- and low-risk groups. Prognosis was worse in the high-risk group, and the model had satisfactory prediction performance for patients younger than 18 years, males, females, and those with non-metastatic disease. Univariate COX regression analysis showed that metastasis and risk score were independent risk factors for patients with OS. Nomogram was built on independent prognostic factors with superior predictive power and patient benefit. There was a significant correlation between prognostic genes and tumor immunity. Six prognostic genes were differentially expressed in ferroptosis inducer-treated OS cell lines. The identified prognostic genes can regulate tumor growth and progression by affecting the tumor microenvironment.

NCBP2 and TFRC are novel prognostic biomarkers in oral squamous cell carcinoma

There are few prognostic biomarkers and targeted therapeutics currently in use for the clinical management of oral squamous cell carcinoma (OSCC) and patient outcomes remain poor in this disease. A majority of mutations in OSCC are loss-of-function events in tumour suppressor genes that are refractory to conventional modes of targeting. Interestingly, the chromosomal segment 3q22-3q29 is amplified in many epithelial cancers, including OSCC. We hypothesized that some of the 468 genes located on 3q22-3q29 might be drivers of oral carcinogenesis and could be exploited as potential prognostic biomarkers and therapeutic targets. Our integrative analysis of copy number variation (CNV), gene expression and clinical data from The Cancer Genome Atlas (TCGA), identified two candidate genes: NCBP2, TFRC, whose expression positively correlates with worse overall survival (OS) in HPV-negative OSCC patients. Expression of NCBP2 and TFRC is significantly higher in tumour cells compared to most normal human tissues. High NCBP2 and TFRC protein abundance is associated with worse overall, disease-specific survival, and progression-free interval in an in-house cohort of HPV-negative OSCC patients. Finally, due to a lack of evidence for the role of NCBP2 in carcinogenesis, we tested if modulating NCBP2 levels in human OSCC cell lines affected their carcinogenic behaviour. We found that NCBP2 depletion reduced OSCC cell proliferation, migration, and invasion. Differential expression analysis revealed the upregulation of several tumour-promoting genes in patients with high NCBP2 expression. We thus propose both NCBP2 and TFRC as novel prognostic and potentially therapeutic biomarkers for HPV-negative OSCC.

Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics

BACKGROUND

Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome.

METHODS

This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database.

RESULTS

The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan-Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making.

CONCLUSION

In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.

A five ferroptosis-related genes risk score for prognostic prediction of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most common bone cancer in adolescents, and has a high propensity to metastasize. Ferroptosis is a unique modality of cell death, driving the metastasis of cancer cells. Identifying ferroptosis-related genes (FRGs) as prognostic factors will be critical to predict the outcomes of OS. This study aimed to explore the prognostic value of FRGs in OS and build a prognostic model to indirectly improve OS patients' outcomes.

METHODS

OS data were downloaded from the TARGET database and 2 Gene Expression Omnibus datasets. Univariate Cox regression was conducted to assess FRGs. A risk score model basing on 5 FRGs was constructed via LASSO-Cox regression. Multivariate Cox regression analysis was used to determine the independent prognostic factors. The Nomogram model was built using independent prognostic factors. The relationship between the risk score and the immune cell infiltration was estimated by CIBERSORT, and the correlation between the risk score and immune checkpoints was also analyzed.

RESULTS

Based on the prognosis-related FRGs, we built a regression model: Risk score = (-0.01382853 × ACSL4) - (0.05371778 × HMOX1) - (0.02434655 × GPX4) - (0.16432810 × PRNP) - (0.15567120 × ATG7). OS patients with high risk score tended to suffer from poor prognosis, validated in 2 Gene Expression Omnibus datasets. The Nomogram model showed the combination of the risk score and the tumour-node-metastasis stage improved predictive effectiveness. The risk score was also related to immune cell infiltration and immune checkpoint expression.

CONCLUSION

The risk score model based on 5 FRGs was a reliable prognostic predictive indicator for OS patients.

Identification of Immune-Related Subtypes and Construction of a Novel Prognostic Model for Bladder Urothelial Cancer

The purpose of this study was to explore the relationship between bladder urothelial cancer (BLCA) and immunity, to screen prognosis-related immune genes (PIGs), and to construct an immune-related prognosis model (IRPM). We processed the relevant data of The Cancer Genome Atlas (TCGA-BLCA) and GSE13507 using R software and Perl. We divided BLCA into high-immunity and low-immunity subtypes. There were significant differences in the two subtypes. In addition, we identified 13 PIGs of BLCA by jointly analyzing the gene expression data and survival information of GSE13507 and TCGA-BLCA, and constructed IRPM through nine of them. The low-risk group had better survival outcome than the high-risk group. We also constructed a nomogram based on clinicopathological information and risk scores of the patients. Moreover, the prognosis of BLCA patients was significantly impacted by the expression of almost every gene used to calculate the risk score. The result of real-time fluorescence quantitative polymerase chain reaction revealed that all the genes used to calculate the risk score were differentially expressed between BLCA and adjacent normal tissues, except PDGFRA. Our research provided potential targets for the treatment of BLCA and a reference for judging the prognosis of BLCA.

Association between cancer stem cell gene expression signatures and prognosis in head and neck squamous cell carcinoma

Background

Various cancer stem cell (CSC) biomarkers and the genes encoding them in head and neck squamous cell carcinoma (HNSCC) have been identified and evaluated. However, the validity of these factors in the prognosis of HNSCC has been questioned and remains unclear. In this study, we examined the clinical significance of CSC biomarker genes in HNSCC, using five publicly available HNSCC cohorts.

Methods

To predict the prognosis of patients with HNSCC, we developed and validated the expression signatures of CSC biomarker genes whose mRNA expression levels correlated with at least one of the four CSC genes (CD44, MET, ALDH1A1, and BMI1).

Results

Patients in The Cancer Genome Atlas (TCGA) HNSCC cohort were classified into CSC gene expression-associated high-risk (CSC-HR; n = 285) and CSC gene expression-associated low-risk (CSC-LR; n = 281) subgroups. The 5-year overall survival and recurrence-free survival rates were significantly lower in the CSC-HR subgroup than in the CSC-LR subgroup (p = 0.04 and 0.02, respectively). The clinical significance of the CSC gene expression signature was validated using four independent cohorts. Analysis using Cox proportional hazards models showed that the CSC gene expression signature was an independent prognostic factor of non-oropharyngeal HNSCC which mostly indicates HPV (–) status. Furthermore, the CSC gene expression signature was associated with the prognosis of HNSCC patients who received radiotherapy.

Conclusion

The CSC gene expression signature is associated with the prognosis of HNSCC and may help in personalized treatments for patients with HNSCC, especially in cases with HPV (–) status who were classified in more detail.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10184-4.

A prognostic model for oral squamous cell carcinoma using 7 genes related to tumor mutational burden

Background

Oral squamous cell carcinoma (OSCC) is a rising problem in global public health. The traditional physical and imageological examinations are invasive and radioactive. There is a need for less harmful new biomarkers. Tumor mutational burden (TMB) is a novel prognostic biomarker for various cancers. We intended to explore the relationship between TMB-related genes and the prognosis of OSCC and to construct a prognostic model.

Methods

TMB-related differential expressed genes (DEGs) were screened by differential analysis and optimized via the univariate Cox and LASSO Cox analyses. Risk Score model was constructed by expression values of screened genes multiplying coefficient of LASSO Cox.

Results

Seven TMB-related DEGs (CTSG, COL6A5, GRIA3, CCL21, ZNF662, TDRD5 and GSDMB) were screened. Patients in high-risk group (Risk Score >  − 0.684511507) had worse prognosis compared to the low-risk group (Risk Score <  − 0.684511507). Survival rates of patients in the high-risk group were lower in the gender, age and degrees of differentiation subgroups compared to the low-risk group.

Conclusions

The Risk Score model constructed by 7 TMB-related genes may be a reliable biomarker for predicting the prognosis of OSCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02193-3.

Emerging Role of Autophagy in the Development and Progression of Oral Squamous Cell Carcinoma

Autophagy is a cellular catabolic process, which is characterized by degradation of damaged proteins and organelles needed to supply the cell with essential nutrients. At basal levels, autophagy is important to maintain cellular homeostasis and development. It is also a stress responsive process that allows the cells to survive when subjected to stressful conditions such as nutrient deprivation. Autophagy has been implicated in many pathologies including cancer. It is well established that autophagy plays a dual role in different cancer types. There is emerging role of autophagy in oral squamous cell carcinoma (OSCC) development and progression. This review will focus on the role played by autophagy in relation to different aspects of cancer progression and discuss recent studies exploring the role of autophagy in OSCC. It will further discuss potential therapeutic approaches to target autophagy in OSCC.

Development and validation of a ferroptosis-related prognostic model for the prediction of progression-free survival and immune microenvironment in patients with papillary thyroid carcinoma

BACKGROUND

Ferroptosis is an iron-dependent and regulated cell death that has been widely reported in a variety of malignancies. The overall survival of papillary thyroid cancer (PTC) is excellent, but the identification of patients with poor prognosis still faces challenges. Nevertheless, whether ferroptosis-related genes (FRGs) can be used to screen high-risk patients is not clear.

METHODS

We obtained the clinical data of patients with PTC and FRGs from the UCSC Xena platform and the FerrDb respectively. Differentially expressed genes (DEGs) of FRGs were obtained from the entire The Cancer Genome Atlas (TCGA). Subsequently, the entire TCGA dataset was randomly split into two subsets: training and test datasets. Based on DEGs, we constructed a predictive model which was tested in the test dataset and the entire TCGA dataset to predict progression-free survival (PFS). Patients were categorized into high- or low-risk groups based on their median risk score. We analyzed differences in some aspects, including pathway enrichment analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), tumor microenvironment (TME), human leukocyte antigen (HLA) genes, and tumor mutation burden (TMB) analyses, between high-risk and low-risk groups.

RESULTS

A predictive model with three FRGs (HSPA5, AURKA, and TSC22D3) was constructed. Patients in the high-risk group had worse PFS compared with patients in the low-risk group. Functional analysis results revealed that ssGSEA, immune cell infiltration, TME, HLA, and TMB were closely associated with ferroptosis.

CONCLUSION

The prognostic model constructed in this study can effectively predict PFS for patients with PTC.