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

Iron, Ferroptosis, and Head and Neck Cancer

Ferroptosis is an iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species and lipid peroxidation. It plays a critical role not only in promoting drug resistance in tumors, but also in shaping therapeutic approaches for various malignancies. This review aims to elucidate the relationship between ferroptosis and head and neck cancer treatment by discussing its conceptual framework, mechanism of action, functional aspects, and implications for tumor therapy. In addition, this review consolidates strategies aimed at improving the efficacy of head and neck cancer treatment through modulation of ferroptosis, herein serving as a valuable reference for advancing the treatment landscape for this patient population.

Clustering and machine learning-based integration identify cancer associated fibroblasts genes’ signature in head and neck squamous cell carcinoma

Background: A hallmark signature of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) is abundantly infiltration of cancer-associated fibroblasts (CAFs), which facilitate HNSCC progression. However, some clinical trials showed targeted CAFs ended in failure, even accelerated cancer progression. Therefore, comprehensive exploration of CAFs should solve the shortcoming and facilitate the CAFs targeted therapies for HNSCC.

Methods: In this study, we identified two CAFs gene expression patterns and performed the single‐sample gene set enrichment analysis (ssGSEA) to quantify the expression and construct score system. We used multi-methods to reveal the potential mechanisms of CAFs carcinogenesis progression. Finally, we integrated 10 machine learning algorithms and 107 algorithm combinations to construct most accurate and stable risk model. The machine learning algorithms contained random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalised boosted regression modelling (GBM), and survival support vector machine (survival-SVM).

Results: There are two clusters present with distinct CAFs genes pattern. Compared to the low CafS group, the high CafS group was associated with significant immunosuppression, poor prognosis, and increased prospect of HPV negative. Patients with high CafS also underwent the abundant enrichment of carcinogenic signaling pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation. The MDK and NAMPT ligand–receptor cellular crosstalk between the cancer associated fibroblasts and other cell clusters may mechanistically cause immune escape. Moreover, the random survival forest prognostic model that was developed from 107 machine learning algorithm combinations could most accurately classify HNSCC patients.

Conclusion: We revealed that CAFs would cause the activation of some carcinogenesis pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation and revealed unique possibilities to target glycolysis pathways to enhance CAFs targeted therapy. We developed an unprecedentedly stable and powerful risk score for assessing the prognosis. Our study contributes to the understanding of the CAFs microenvironment complexity in patients with head and neck squamous cell carcinoma and serves as a basis for future in-depth CAFs gene clinical exploration.

A ferroptosis-related prognostic model with excellent clinical performance based on the exploration of the mechanism of oral squamous cell carcinoma progression

As a hot topic today, ferroptosis is closely involved in the progression and treatment of cancer. Accordingly, we built a prognostic model around ferroptosis to predict the overall survival of OSCC patients. We used up to 6 datasets from 3 different databases to ensure the credibility of the model. Then, through differentially expressed, Univariate Cox, and Lasso regression analyses, a model composed of nine prognostic-related differently expressed ferroptosis-related genes (CISD2, DDIT4, CA9, ALOX15, ATG5, BECN1, BNIP3, PRDX5 and MAP1LC3A) were constructed. Moreover, Kaplan-Meier curves, Receiver Operating Characteristic curves and principal component analysis used to verify the model's predictive ability showed the model's superiority. To deeply understand the mechanism of ferroptosis affecting the occurrence, development and prognosis of OSCC, we performed enrichment analysis in different risk groups identified by the model. The results showed that numerous TP53-related, immune-related and ferroptosis-related functions and pathways were enriched. Further immune microenvironment analysis and mutation analysis have once again revealed the correlation between risk score and immunity and TP53 mutation. Finally, the correlation between risk score and OSCC clinical treatment, as well as Nomogram show the brilliant clinical application prospects of the prognostic model.

Identifying a novel ferroptosis-related prognostic score for predicting prognosis in chronic lymphocytic leukemia

Background

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the western world. Although the treatment landscape for CLL is rapidly evolving, there are still some patients who develop drug resistance or disease refractory. Ferroptosis is a type of lipid peroxidation–induced cell death and has been suggested to have prognostic value in several cancers. Our research aims to build a prognostic model to improve risk stratification in CLL patients and facilitate more accurate assessment for clinical management.

Methods

The differentially expressed ferroptosis-related genes (FRGs) in CLL were filtered through univariate Cox regression analysis based on public databases. Least absolute shrinkage and selection operator (LASSO) Cox algorithms were performed to construct a prognostic risk model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were performed to estimate the immune infiltration score and immune-related pathways. A total of 36 CLL patients in our center were enrolled in this study as a validation cohort. Moreover, a nomogram model was established to predict the prognosis.

Results

A total of 15 differentially expressed FRGs with prognostic significance were screened out. After minimizing the potential risk of overfitting, we constructed a novel ferroptosis-related prognostic score (FPS) model with nine FRGs (AKR1C3, BECN1, CAV1, CDKN2A, CXCL2, JDP2, SIRT1, SLC1A5, and SP1) and stratified patients into low- and high-risk groups. Kaplan–Meier analysis showed that patients with high FPS had worse overall survival (OS) (P<0.0001) and treatment-free survival (TFS) (P<0.0001). ROC curves evaluated the prognostic prediction ability of the FPS model. Additionally, the immune cell types and immune-related pathways were correlated with the risk scores in CLL patients. In the validation cohort, the results confirmed that the high-risk group was related to worse OS (P<0.0001), progress-free survival (PFS) (P=0.0140), and TFS (P=0.0072). In the multivariate analysis, only FPS (P=0.011) and CLL-IPI (P=0.010) were independent risk indicators for OS. Furthermore, we established a nomogram including FPS and CLL-IPI that could strongly and reliably predict individual prognosis.

Conclusion

A novel FPS model can be used in CLL for prognostic prediction. The model index may also facilitate the development of new clinical ferroptosis-targeted therapies in patients with CLL.

A Prognostic Ferroptosis-Related lncRNA Model Associated With Immune Infiltration in Colon Cancer

Colon cancer (CC) is a common malignant tumor worldwide, and ferroptosis plays a vital role in the pathology and progression of CC. Effective prognostic tools are required to guide clinical decision-making in CC. In our study, gene expression and clinical data of CC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed ferroptosis-related lncRNAs using the differential expression and gene co-expression analysis. Then, univariate and multivariate Cox regression analyses were used to identify the effective ferroptosis-related lncRNAs for constructing the prognostic model for CC. Gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment analysis. CIBERSORT and single-sample GSEA were performed to investigate the association between our model and the immune microenvironment. Finally, three ferroptosis-related lncRNAs (XXbac-B476C20.9, TP73-AS1, and SNHG15) were identified to construct the prognostic model. The results of the validation showed that our model was effective in predicting the prognosis of CC patients, which also was an independent prognostic factor for CC. The GSEA analysis showed that several ferroptosis-related pathways were significantly enriched in the low-risk group. Immune infiltration analysis suggested that the level of immune cell infiltration was significantly higher in the high-risk group than that in the low-risk group. In summary, we established a prognostic model based on the ferroptosis-related lncRNAs, which could provide clinical guidance for future laboratory and clinical research on CC.

Identification of Ferroptosis-Related lncRNA Pairs for Predicting the Prognosis of Head and Neck Squamous Cell Carcinoma

Background

Ferrogenesis was strongly associated with tumorigenesis and development, and activating the ferrogenic process was a novel regimen in treating cancer, especially conventional treatment-resistant cancers. The purpose of the article was to construct a ferroptosis-related long noncoding RNAs (FRlncRNAs) signature, regardless of expression levels to effectively predict prognosis and immunotherapeutic response for head and neck squamous cell carcinoma (HNSCC).

Methods

The RNA-seq data for HNSCC and corresponding clinical information were obtained in the TCGA database, and ferroptosis-related genes (FRGs) were extracted in the ferroptosis database. On this basis, differentially expressed FRlncRNAs (DEFRlncRNAs) pairs were identified through coexpression analysis, differential expression analysis, and a fresh pairing algorithm. Then, a risk assessment model was established with univariate Cox, LASSO, and multivariate Cox regression analysis. Finally, we evaluated the model from various aspects, including survival status, clinicopathological characteristics, infiltration status of immune cells, immune functions, chemotherapeutic sensitivity, immune checkpoint inhibitors (ICIs)-related molecules, and N6-methyladenosine (m⁶A) mRNA status.

Result

We established a signature of 11-DEFRlncRNA pairs related to the prognosis of HNSCC that had AUC values above 0.75 in the one-, three-, and five-year ROC curves, underscoring the high susceptibility and specifiability of predicting HNSCC prognosis. Survival rates were remarkably higher for the low-risk patients than for the high-risk patients, and the signature was significantly correlated with survival, clinical, T, and N stages. Finally, immune cell infiltration status, immune functions, chemotherapeutic sensitivity, and expression levels of ICIs-related and m⁶A-related molecules were statistically different among different groups.

Conclusion

Our study established a novel lncRNA signature, which is independent of specific expression levels, could predict patient prognosis, and might have promising clinical applications in HNCSS.