Project description:A retrospective, longitudinal study of 1,616 patients with primary laryngeal squamous cell carcinoma (LSCC) at a single center in Norway during 1983-2010 was undertaken to investigate overall survival, disease specific survival, disease-free survival, prognostic factors for overall survival, and impact of recurrence among all-stage laryngeal cancer patients over 15 years' follow-up. The prognostic impact of gender, age, smoking/alcohol, subsite, tumour, node and metastasis staging, period and modality of treatment were evaluated using Kaplan-Meier and Cox proportional hazard analyses. The importance of recurrence on survival was assessed based on case fatality rates. Five-year overall survival was 56.8%, 64.0% and 38.8%, and disease-specific survival was 80.2%, 87% and 61.6%, respectively, for the entire cohort and for glottic and supraglottic LSCC. Old age, advanced-stage LSCC and supraglottic cancer were associated with lower overall survival. The risk of disease-specific death plateaued after five years and varied significantly by subsite. Multivariate analysis of glottic LSCC revealed that surgical treatment improved overall survival, whereas old age, alcohol, T3-T4 status, positive N-status and no treatment were associated with worse survival. In supraglottic LSCC, age, alcohol, and positive N-status had a significant impact on overall survival by multivariate analysis. Five-year overall survival and disease-specific survival among patients with recurrent disease were 34% and 52%, respectively. In conclusion, marked difference in overall survival between glottic and supraglottic LSCC underline the importance of subsite-specific survival analysis. T-status and primary surgical management is essential only for glottic LSCC, emphasizing the importance of correct disease classification. Inferior outcomes in supraglottic LSCC are associated with old age, positive N-status, and improved follow-up routines are necessary. Primary tumor control is essential since recurrence impairs survival considerably in all subsites. The potential benefit of a primary surgical approach towards T3 LSCC awaits further investigation.

Project description:PurposeLocal recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. This study attempts to identify and independently validate gene expression models, prognostic of disease-free survival (DFS) in operable laryngeal cancer.Materials and methodsUsing Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 66 patients with laryngeal cancer treated locally with surgery. We applied Cox regression proportional hazards modeling to identify multigene predictors of recurrence. Gene models were then validated in two independent cohorts of 54 and 187 patients (fresh-frozen and formalin-fixed tissue validation sets, respectively).ResultsWe focused on genes univariately associated with DFS (p<0.01) in the training set. Among several models comprising different numbers of genes, a 30-probe set model demonstrated optimal performance in both the training (log-rank, p<0.001) and 1(st) validation (p=0.010) sets. Specifically, in the 1(st) validation set, median DFS as predicted by the 30-probe set model, was 34 and 80 months for high- and low-risk patients, respectively. Hazard ratio (HR) for recurrence in the high-risk group was 3.87 (95% CI 1.28-11.73, Wald's p=0.017). Testing the expression of selected genes from the above model in the 2(nd) validation set, with qPCR, revealed significant associations of single markers, such as ACE2, FLOT1 and PRKD1, with patient DFS. High PRKD1 remained an unfavorable prognostic marker upon multivariate analysis (HR=2.00, 95% CI 1.28-3.14, p=0.002) along with positive nodal status.ConclusionsWe have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. It seems worthy to prospectively validate PRKD1 expression as a laryngeal cancer prognostic marker, for routine clinical applications.

Project description:Background: Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. Methods: Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 59 patients with operable laryngeal cancer. All patients were treated locally with surgery, with or without radiation therapy. We performed Cox regression proportional hazards modeling to identify multigene predictors of recurrence. The end-point of our analysis was disease-free survival (DFS). Gene models were directly validated in a separate, similarly treated cohort of 50 patients using Affymetrix chips. In an attempt to further validate our results, we profiled 12 selected genes of our model in formalin-fixed tumor tissues from an independent cohort of 75 patients, using quantitative real time-polymerase chain reaction (qRT-PCR). Results: We focused on genes univariately associated with DFS (p<0.05) in the training set. Among several gene models comprising different numbers of genes, a 30-gene model demonstrated optimal performance (log-rank, p<0.001). We directly applied these gene models to the validation set, after adjusting for non-biological experimental variability, and observed similar results. Specifically, median DFS, as predicted by the 30-gene model, was 34 and 80 months for high- and low-risk patients, respectively (p=0.01). Hazard Ratio (HR) for recurrence for the high-risk group was 3.87 (95% CI 1.28-11.73, p=0.017). Furthermore, unsupervised hierarchical clustering of the 75 patients, based on the qRT-PCR 12-gene profile, yielded two groups, which differed significantly in DFS (log-rank, p=0.027). HR= for recurrence was 2.26, (95% CI 1.08-4.76, p=0.031). Conclusion: We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. Thus, patients with unfavorable prognosis, when accurately identified, could be ideal candidates for the application of more aggressive treatment modalities. Training set comprises 59 samples and validation set 50 samples

Project description:Background: Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. Methods: Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 59 patients with operable laryngeal cancer. All patients were treated locally with surgery, with or without radiation therapy. We performed Cox regression proportional hazards modeling to identify multigene predictors of recurrence. The end-point of our analysis was disease-free survival (DFS). Gene models were directly validated in a separate, similarly treated cohort of 50 patients using Affymetrix chips. In an attempt to further validate our results, we profiled 12 selected genes of our model in formalin-fixed tumor tissues from an independent cohort of 75 patients, using quantitative real time-polymerase chain reaction (qRT-PCR). Results: We focused on genes univariately associated with DFS (p<0.05) in the training set. Among several gene models comprising different numbers of genes, a 30-gene model demonstrated optimal performance (log-rank, p<0.001). We directly applied these gene models to the validation set, after adjusting for non-biological experimental variability, and observed similar results. Specifically, median DFS, as predicted by the 30-gene model, was 34 and 80 months for high- and low-risk patients, respectively (p=0.01). Hazard Ratio (HR) for recurrence for the high-risk group was 3.87 (95% CI 1.28-11.73, p=0.017). Furthermore, unsupervised hierarchical clustering of the 75 patients, based on the qRT-PCR 12-gene profile, yielded two groups, which differed significantly in DFS (log-rank, p=0.027). HR= for recurrence was 2.26, (95% CI 1.08-4.76, p=0.031). Conclusion: We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. Thus, patients with unfavorable prognosis, when accurately identified, could be ideal candidates for the application of more aggressive treatment modalities.

Project description:BackgroundLaryngeal cancer (LC), a prevalent malignant tumor of the head and neck, is characterized by a high rate of postoperative recurrence and significant treatment challenges upon recurrence, severely impacting patients' quality of life. There is a pressing need for effective biomarkers in clinical practice to predict the risk of LC recurrence and guide the development of personalized treatment plans. This study uses bioinformatics methods to explore potential biomarkers for LC recurrence, focusing on key genes and exploring their functions and mechanisms of action in LC recurrence. The aim is to provide new perspectives and evidence for clinical diagnosis, prognostic evaluation, and targeted treatment of LC.MethodsGene expression profiles from the GSE25727 data set in the Gene Expression Omnibus database were analyzed to detect the differentially expressed genes (DEGs) between the tumor tissues of postoperative recurrent and non-recurrent early stage LC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were also conducted. A protein-protein interaction (PPI) network and transcription factor (TF)-DEG-microRNA (miRNA) network were developed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with key genes selected using the Molecular Complex Detection (MCODE) plugin. A Gene Set Enrichment Analysis (GSEA) was carried out to investigate the possible mechanisms of the key genes. A retrospective analysis was conducted using the clinical data of 83 LC patients. Immunohistochemical staining was used to examine the transcription level of the key genes in the LC tumor tissues and the factors affecting postoperative recurrence.ResultsA total of 248 upregulated and 34 downregulated DEGs were identified in the GSE25727 data set. The PPI network analysis identified a significant module and five candidate genes (i.e., RRAGA, SLC38A9, WDR24, ATP6V1B1, and LAMTOR3). The construction of the TF-DEG-miRNA network indicated that ATP6V1B1 might be regulated by one TF and interact with 17 miRNAs. The KEGG and GSEA analyses suggested that ATP6V1B1 may influence LC recurrence through the involvement of pro-inflammatory and pro-fibrotic mediators, glutathione metabolism, matrix metalloproteinases, immune regulation, and lymphocyte interactions. The recurrence rate of the 83 LC patients included in the study was 19.3% (16/83). The immunohistochemistry results indicated that ATP6V1B1 was highly expressed in patients with recurrent LC. The univariate and multivariate logistic regression analyses revealed that tumor stage T3 (P=0.04), tumor stage T4 (P=0.01), and a high expression of ATP6V1B1 (P=0.02) were risk factors for recurrence after surgical treatment in LC patients.ConclusionsThe key genes and signaling pathways identified through the bioinformatics screening provide insights into the potential mechanisms of the pathogenesis of LC. ATP6V1B1 may promote the recurrence of LC by weakening the immune phenotype. Our findings provide a theoretical basis for further research into clinical diagnostics and treatment strategies for LC.

Project description:The aim of the present study was to identify novel microRNA (miRNA) or long noncoding RNA (lncRNA) signatures of laryngeal cancer recurrence and to investigate the regulatory mechanisms associated with this malignancy. Datasets of recurrent and nonrecurrent laryngeal cancer samples were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database (GSE27020 and GSE25727) to examine differentially expressed miRNAs (DE‑miRs), lncRNAs (DE‑lncRs) and mRNAs (DEGs). miRNA‑mRNA and lncRNA‑miRNA networks were constructed by investigating the associations among these RNAs in various databases. Subsequently, the interactions identified were combined into a competing endogenous RNA (ceRNA) regulatory network. Feature genes in the miRNA‑mRNA network were identified via topological analysis and a recursive feature elimination algorithm. A support vector machine (SVM) classifier was established using the betweenness centrality values in the miRNA‑mRNA network, consisting of 32 optimal feature‑coding genes. The classification effect was tested using two validation datasets. Furthermore, coding genes in the ceRNA network were examined via pathway enrichment analyses. In total, 21 DE‑lncRs, 507 DEGs and 55 DE‑miRs were selected. The SVM classifier exhibited an accuracy of 94.05% (79/84) for sample classification prediction in the TCGA dataset, and 92.66 and 91.07% in the two validation datasets. The ceRNA regulatory network comprised 203 nodes, corresponding to mRNAs, miRNAs and lncRNAs, and 346 lines, corresponding to the interactions among RNAs. In particular, the interactions with the highest scores were HLA complex group 4 (HCG4)‑miR‑33b, HOX transcript antisense RNA (HOTAIR)‑miR‑1‑MAGE family member A2 (MAGEA2), EMX2 opposite strand/antisense RNA (EMX2OS)‑miR‑124‑calcitonin related polypeptide α (CALCA) and EMX2OS‑miR‑124‑γ‑aminobutyric acid type A receptor γ2 subunit (GABRG2). Gene enrichment analysis of the genes in the ceRNA network identified that 11 pathway terms and 16 molecular function terms were significantly enriched. The SVM classifier based on 32 feature coding genes exhibited high accuracy in the classification of laryngeal cancer samples. miR‑1, miR‑33b, miR‑124, HOTAIR, HCG4 and EMX2OS may be novel biomarkers of recurrent laryngeal cancer, and HCG4‑miR‑33b, HOTAIR‑miR‑1‑MAGEA2 and EMX2OS‑miR‑124‑CALCA/GABRG2 may be associated with the molecular mechanisms regulating recurrent laryngeal cancer.

Dataset Information

Risk of Recurrence in Laryngeal Cancer.

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Risk of Recurrence in Laryngeal Cancer.

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