Data mining on identifying diagnosis and prognosis biomarkers in head and neck squamous carcinoma

Mapping the intricacies of GLI1 in hedgehog signaling: A combined bioinformatics and clinical analysis in Head & Neck cancer in Western India

BACKGROUND

Activation of various cancer stem cell pathways are thought to be responsible for treatment failure and loco-regional recurrence in Head and Neck cancer. Hedgehog signaling, a major cancer stem signaling pathway plays a major role in relapse of disease. GLI1, a transcription activator, plays an important role in canonical/non-canonical activation of Hedgehog signaling.

METHODS

Data for H&N cancer patients were collected from The Cancer Genome Atlas- H&N Cancer (TCGA-HNSC). GLI1 co-expressed genes in TCGA-HNSC were then identified using cBioPortal and subjected to KEGG pathway analysis by DAVID tool. Network Analyzer and GeneMania plugins from CytoScape were used to identify hub genes and predict a probable pathway from the identified hub genes respectively. To confirm the hypothesis, real-time gene expression was carried out in 75 patients of head and neck cancer.

RESULTS

Significantly higher GLI1 expression was observed in tumor tissues of H&N cancer and it also showed worst overall survival. Using cBioPortal tool, 2345 genes were identified that were significantly co-expressed with GLI1. From which, 15 hub genes were identified through the Network Analyzer plugin in CytoScape. A probable pathway prediction based on hub genes showed the interconnected molecular mechanism and its role in non-canonical activation of Hedgehog pathway by altering the GLI1 activity. The expressions of SHH, GLI1 and AKT1 were significant with each other and were found to be significantly associated with Age, Lymph-Node status and Keratin.

CONCLUSION

The study emphasizes the critical role of the Hh pathway's activation modes in H&N cancer, particularly highlighting the non-canonical activation through GLI1 and AKT1. The identification of SHH, GLI1 and AKT1 as potential diagnostic biomarkers and their association with clinic-pathological parameters underscores their relevance in prognostication and treatment planning. Hh pathway activation through GLI1 and its cross-talk with various pathways opens up the possibility of newer treatment strategies and developing a panel of therapeutic targets in H&N cancer patients.

Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model

The dysregulation of protein-coding genes involved in various biological functions is closely associated with the progression of thyroid cancer. This study aimed to investigate the effects of dysregulated gene expressions on the prognosis of classical papillary thyroid carcinoma (cPTC). Using expression profiling datasets from the Cancer Genome Atlas (TCGA) database, we performed differential expression analysis to identify differentially expressed genes (DEGs). Cox regression and Kaplan-Meier analysis were used to identify DEGs, which were used to construct a risk model to predict the prognosis of cPTC patients. Functional enrichment analysis unveiled the potential significance of co-expressed protein-encoding genes in tumors. We identified 4 DEGs (SALL3, PPBP, MYH1, and SYNDIG1), which were used to construct a risk model to predict the prognosis of cPTC patients. These 4 genes were independent of clinical parameters and could be functional in cPTC carcinogenesis. Furthermore, PPBP exhibited a strong correlation with poorer overall survival (OS) in the advanced stage of the disease. This study suggests that the 4-gene signature could be an independent prognostic biomarker to improve prognosis prediction in cPTC patients older than 46.

Identification of Prognostic and Immune Characteristics of Two Lung Adenocarcinoma Subtypes Based on TRPV Channel Family Genes

Lung adenocarcinoma (LUAD) is one of the deadliest malignant tumors worldwide. Transient receptor potential vanilloid (TRPV) channels take pivotal parts in many cancers, but their impact on LUAD remains unexplored. In this study, LUAD samples were classified into two subtypes according to the expression characteristics of TRPV1-6 genes, with LUAD subtype cluster2 exhibiting significantly higher survival rates than cluster1. Subsequently, analysis of differentially expressed genes (DEGs) was performed between cluster1 and cluster2, revealing enrichment of DEGs in channel activity and Ca2+ signaling pathways. We established a protein-protein interaction network based on DEGs and constructed a LUAD prognostic model by using Cox regression analysis based on genes corresponding to 170 protein nodes. The prognostic model demonstrated good predictive ability for patient prognosis, with higher survival rates observed in the low-risk (LR) group. The risk score was validated as an independent prognostic indicator, according to Cox regression analysis. A clinically applicable nomogram was plotted. Immunological analysis indicated that the LR and high-risk (HR) groups had varied proportions of immune cell infiltration. The immunotherapy prediction indicated that LUAD patients in LR group had a greater likelihood to benefit from immune checkpoint blockade therapy. Furthermore, we hypothesized that the expression patterns of feature genes in the LUAD model were related to the sensitivity to lung cancer therapeutic drugs TAS-6417 and Erlotinib. To sum up, our LUAD prognostic model possessed clinical applicability for prognosis and immunotherapy response prediction.

B-cell infiltration is associated with survival outcomes following programmed cell death protein 1 inhibition in head and neck squamous cell carcinoma

BACKGROUND

Programmed cell death protein 1 (PD-1) axis blockade has become the mainstay in the treatment of recurrent and/or metastatic (R/M) head and neck squamous cell cancer (HNSCC). Programmed death-ligand 1 (PD-L1) is the only approved biomarker for patient selection; however, response rate is limited even among high expressors. Our primary objective was to investigate the association of immune cell-related biomarkers in the tumor and tumor microenvironment with PD-1 checkpoint inhibitors' outcomes in patients with R/M HNSCC.

PATIENTS AND METHODS

NCT03652142 was a prospective study in nivolumab-treated platinum-refractory R/M HNSCC, aiming to evaluate biomarkers of response to treatment. Tumor biopsies and blood samples were collected from 60 patients at baseline, post-treatment, and at progression. Immune cells in the tumor and stromal compartments were quantified by immunofluorescence using a five-protein panel (CD3, CD8, CD20, FoxP3, cytokeratin). Tertiary lymphoid structures (TLSs), PD-L1 expression, and peripheral blood immune cell composition were also evaluated for associations with outcome. Our findings were validated by gene set enrichment analysis (GSEA) messenger RNA in situ expression data from the same patients, for B-cell- and TLS-associated genes.

RESULTS

High pre-treatment density of stromal B cells was associated with prolonged progression-free survival (PFS) (P = 0.011). This result was validated by GSEA, as stromal enrichment with B-cell-associated genes showed association with response to nivolumab. PD-L1 positivity combined with high B-cell counts in stroma defined a subgroup with significantly longer PFS and overall survival (P = 0.013 and P = 0.0028, respectively).

CONCLUSIONS

Increased B cells in pre-treatment HNSCC biopsy samples correlate with prolonged benefit from PD-1-based immunotherapy and could further enhance the predictive value of PD-L1 expression.

Disulfidptosis features and prognosis in head and neck squamous cell carcinoma patients: unveiling and validating the prognostic signature across cohorts

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a significant health concern with a variable global incidence and is linked to regional lifestyle factors and HPV infections. Despite treatment advances, patient prognosis remains variable, necessitating an understanding of its molecular mechanisms and the identification of reliable prognostic biomarkers.

METHODS

We analyzed 959 HNSCC samples and employed batch correction to obtain consistent transcriptomic data across cohorts. We examined 79 disulfidptosis-related genes to determine consensus clusters and utilized high-throughput sequencing to identify genetic heterogeneity within tumors. We established a disulfidptosis prognostic signature (DSPS) using least absolute shrinkage and selection operator (LASSO) regression and developed a prognostic nomogram integrating the DSPS with clinical factors. Personalized chemotherapy prediction was performed using the "pRRophetic" R package.

RESULTS

Batch corrections were used to harmonize gene expression data, revealing two distinct disulfidptosis subtypes, C1 and C2, with differential gene expression and survival outcomes. Subtype C1, characterized by increased expression of the MYH family genes ACTB, ACTN2, and FLNC, had a mortality rate of 48.4%, while subtype C2 had a mortality rate of 38.7% (HR = 0.77, 95% CI: 0.633-0.934, P = 0.008). LASSO regression identified 15 genes that composed the DSPS prognostic model, which independently predicted survival (HR = 2.055, 95% CI: 1.420-2.975, P < 0.001). The prognostic nomogram, which included the DSPS, age, and tumor stage, predicted survival with AUC values of 0.686, 0.704, and 0.789 at 3, 5, and 8 years, respectively, indicating strong predictive capability. In the external validation cohort (cohort B), the DSPS successfully identified patients at greater risk, with worse overall survival outcomes in the high-DSPS subgroup (HR = 1.54, 95% CI: 1.17-2.023, P = 0.002) and AUC values of 0.601, 0.644, 0.636, and 0.748 at 3, 5, 8, and 10 years, respectively, confirming the model's robustness.

CONCLUSION

The DSPS provides a robust prognostic tool for HNSCC, underscoring the complexity of this disease and the potential for tailored treatment strategies. This study highlights the importance of molecular signatures in oncology, offering a step toward personalized medicine and improved patient outcomes in HNSCC management.

Ferroptosis-Related Genes Are Associated with Radioresistance and Immune Suppression in Head and Neck Cancer

Background: Ferroptosis is associated with tumor development; however, its contribution to radioresistant head and neck cancer (HNC) remains unclear. In this study, we used bioinformatics analysis and in vitro testing to explore ferroptosis-related genes associated with HNCs radiosensitivity. Materials and Methods: GSE9714, GSE90761, and The Cancer Genome Atlas (TCGA) datasets were searched to identify ferroptosis-related differentially expressed genes between radioresistant and radiosensitive HNCs or radiation-treated and nonradiation-treated HNCs. A protein-protein interaction analysis on identified hub genes was then performed. Receiver operating characteristic curves and Kaplan-Meier survival analysis were used to assess the diagnostic and prognostic potential of the hub genes. Cell counting kit-8, transwell assay, and flow cytometry were applied to examine the role of hub gene collagen type IV, alpha1 chain (COL4A1) on the proliferation, migration, invasion, and apoptosis of TU686 cells. Results: Hub genes MMP10, MMP1, COL4A1, IFI27, and INHBA showed diagnostic potential for HNC and were negatively correlated with overall survival and disease-free survival in the TCGA dataset. Also, IL-1B, IFI27, INHBA, and COL4A1 mRNA levels were significantly increased in TCGA patients with advanced clinical stages or receiving radiotherapy, whereas COL4A1, MMP10, and INHBA expressions were negatively correlated with immune infiltration. Furthermore, the knockdown of COL4A1 inhibited cell proliferation, migration, and invasion while promoting apoptosis in TU686 cells. Conclusion: Ferroptosis-related hub genes, such as COL4A1, are potential diagnostic and prognostic indicators as well as therapeutic targets for HNC.

Inference of differential gene regulatory networks using boosted differential trees

Summary

Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses.

Availability and implementation

BoostDiff is available at https://github.com/scibiome/boostdiff_inference.