Nasopharyngeal necrosis contributes to overall survival in nasopharyngeal carcinoma without distant metastasis: a comprehensive nomogram model

Peripheral hemoglobin to albumin ratio predicts prognosis in patients with nasopharyngeal carcinoma underwent concurrent chemoradiotherapy

BACKGROUND

Recently, the hemoglobin to albumin ratio (HAR) has been shown to be closely associated with the survival of certain malignancies. However, its prognostic value in nasopharyngeal carcinoma (NPC) remained to be elucidated. Herein, we aimed to explore the correlation between HAR and overall survival (OS) in NPC patients treated with concurrent chemoradiotherapy (CCRT).

METHODS

This retrospective study included a total of 858 patients with NPC receiving CCRT between January 2010 and December 2014 in Sun Yat-sen University Cancer Center. We randomly divided them into the training cohort (N = 602) and the validation cohort (N = 206). We performed univariate and multivariate Cox regression analyses to identify variables associated with OS, based on which, a predictive nomogram was constructed and assessed.

RESULTS

In both the training and validation cohorts, patients were classified into low- and high-HAR groups according to the cutoff value determined by the maximally selected rank statistics. This HAR cutoff value effectively divided patients into two distinct prognostic groups with significant differences. Multivariable Cox analysis revealed that higher T-stage, N-stage, and HAR values were significantly related to poorer prognosis in NPC patients and served as independent prognostic factors for NPC. Based on these, a predictive model was constructed and graphically presented as a nomogram, whose predictive performance is satisfactory with a C-index of 0.744 [95%CI: 0.679-0.809] and superior to traditional TNM staging system [C-index = 0.609, 95%CI: 0.448-0.770].

CONCLUSION

The HAR value was an independent predictor for NPC patients treated with CCRT, the predictive model based on HAR with superior predictive performance than traditional TNM staging system might improve individualized survival predictions.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

Serum ferritin predicted prognosis in patients with nasopharyngeal carcinoma

Elevated serum ferritin (SF) levels have been associated with poor prognosis in various cancer types, but its impact on nasopharyngeal carcinoma (NPC) remains unclear. This retrospective study analyzed clinical data from 252 non-metastatic NPC patients admitted to Hainan General Hospital between January 2014 and May 2016. SF levels were measured using the chemiluminescence method. Patients were categorized into low, medium, and high-level SF groups based on tertile median SF levels. Survival outcomes were assessed using Kaplan-Meier analysis and Cox regression models. The overall survival rates of the entire patient cohort at 1, 3, 5, and 8 years were 95.2%, 85.7%, 76.2%, and 68.9% respectively. The high-level SF group (SF > 164.00 ng/mL) had significantly worse overall survival (83.1 vs 96.3 months, P = 0.023) and progression-free survival (77.8 vs 93.3 months, P = 0.019) compared to the low-level SF group. Univariate and multivariate analyses confirmed that high SF levels, along with T3/T4 staging and N3 staging, were independent risk factors for poor prognosis. In conclusion, high SF levels are associated with shorter overall survival and progression-free survival in NPC patients.