A robust five-gene signature was established and validated for prognostic stratification of locally advanced NPC and outperformed a published metastasis-related model.
Abstract
Purpose
Nasopharyngeal carcinoma (NPC) is a multifactorial malignancy often diagnosed at an advanced stage due to nonspecific early symptoms. Accurate prognostic stratification is essential for individualized therapy but remains challenging because of biological and clinical heterogeneity. This study aimed to develop and validate a gene expression-based prognostic signature for locally advanced NPC.
Methods
This retrospective biomarker investigation combined transcriptomic profiling and survival analyses. A prognostic model was constructed using LASSO-Cox regression in the discovery cohort (N = 99) from the multicenter, randomized phase III trial NPC-0501, and validated in independent cohorts (N = 133) from Queen Elizabeth Hospital (QEH), Hong Kong, and Sun Yat-sen University Cancer Center (SYS), Guangzhou, using different profiling methods. Mechanisms underlying the signature were explored using single-cell RNA-seq (scRNA-seq) data. The primary outcome was 3-year progression-free survival (PFS), with 5-year overall survival (OS) as a secondary end point. Performance was evaluated using hazard ratios (HRs) and survival probabilities (SP).
Results
A 5-gene signature (SPP1, IL18BP, PALMD, WDR35, and SOCS6) predicted 3-year PFS in the NPC-0501 cohort (HR, 0.046 [95% CI, 0.011 to 0.190]; P < .001), separating high-risk from low-risk patients (SP, 42.9% v 96.0%; P < .0001). It predicted survival in both validation cohorts, with associations with 5-year OS in QEH (SP, 72.7% v 100%; P < .001) and 3-year PFS in SYS (SP, 70.5% v 93.2%; P = .0059). It outperformed a published metastasis-related model. Single-cell analyses showed SPP1 enrichment in M2 macrophages, linking high risk with an immunosuppressive tumor microenvironment.
Conclusion
A robust five-gene signature was established and validated for prognostic stratification of locally advanced NPC. Reproducibility across transcriptomic platforms and biological relevance support clinical application to guide personalized treatment.
BACKGROUND
Breast cancer is still a leading cause of tumor mortality in women. Indeed, despite advancements in early diagnosis, molecular profiling and novel therapeutic approaches, the disease outcome is still not always predictable. This evidence underlines the need for validated biomarkers to predict recurrences, to personalize both disease monitoring and tailored therapies. And to this aim, miRNAs have shown promising applications as circulating biomarkers. Starting from plasma samples collected from women with early-stage breast cancer at the time of diagnosis, we explored the expression of ct-miRNAs to define a molecular signature predictive of recurrence.
METHODS
Two independent cohorts of plasma samples were retrospectively and prospectively collected at Fondazione IRCCS Istituto Nazionale dei Tumori di Milano (INT) for a total of 203 patients. Ct-miRNAs were previously profiled by using the OpenArray Human microRNA panel (OA) (Thermo Fisher Scientific). Relapse-free survival (RFS) was analyzed using Cox regression models adjusted for cohort to assess associations with clinicopathological variables and circulating miRNA levels. Models performance was evaluated using c-statistics (95% Confidence interval) and an internal validation was performed with bootstrap resamples. Clinicopathological variables were added to the signature in multivariate models to evaluate their independent prognostic value.
RESULTS
We identified a three ct-miRNA (miR-125b, miR-26b-3p and miR-532-5p) signature associated with disease outcome, with a hazard ratio of 2.803 (95% CI, 1.721-4.565). The c-statistic was 0.72 (95%, CI 0.62-0.83) and was confirmed by the resampling procedure, with a c-median bootstrap statistic of 0.73 (IQR, 0.65-0.81). The 3-circulating miRNA signature retained its significant prognostic performance with respect to RFS even after the inclusion of clinicopathological variables in multivariate models. Considering both ct-miRNA signature and Nottingham Prognostic Index (NPI), the c-statistic of the bivariate model was equal to 0.80 (95% CI, 0.70; 0.90).
CONCLUSION
We identified a three ct-miRNA prognostic signature in early breast cancer women. Considering the accessibility and stability of ct-miRNAs, this signature might improve the recurrence risk prediction identifying who, despite the early diagnosis, might need a more intensive screening or secondary prevention strategies.
G. Cosentino, Mara Lecchi, M. Giussani et al.· Breast Cancer Research· 0 citations
The 36-gene signature developed in this study demonstrates favorable predictive performance and stability across both the training and independent Chinese cohorts, and underscores the critical roles of energy metabolism and ECM remodeling in NMIBC recurrence.
Liyuan Dong, Yun Peng, Yuxuan Song et al.· Beijing da xue xue bao. Yi x...· 0 citations
Background: Recurrence rates following curative resection for hepatocellular carcinoma (HCC) remain persistently high, benefit from adjuvant immunotherapy varies substantially across patients, and the field currently lacks a standardized framework to characterize the postoperative host immune contexture. Purpose: To propose and validate a Multi-stage Precision Stratification (MPS) framework and evaluate its value in prognostic stratification and prediction of immunotherapy response. Methods: The Immune Health Index (IHI = S + R - E) integrating immune surveillance (S), immune exhaustion (E), and immune reserve (R) was constructed to define four immune phenotypes. Prognostic value was assessed in four public HCC cohorts (n=931) with single-cell transcriptomic validation (GSE140228, 61,690 cells); a blood-count-based clinical version cIHI_v8 was constructed in the Qinghai QPHCC cohort (n=490 survival analysis). Results: IHI was an independent protective prognostic factor in TCGA-LIHC (multivariate HR=0.795, P=0.034); four-cohort random-effects meta-analysis yielded HR=0.818 (95% CI: 0.696-0.961), I-squared=31.4%. QPHCC cIHI_v8 multivariate HR=0.452, HR=0.715 after ALBI adjustment; Bayesian evidence synthesis yielded BF_10=1280 for cIHI_v8 (>100 constitutes Decisive evidence), whereas the 4-cohort meta BF_10=2.19 (Anecdotal). Following NLP-based reverse stage derivation (n=490, achieving full AJCC/BCLC stage coverage from 0%), IHI remained significant after AJCC adjustment (HR=0.8642, P=0.000079), IHI provided positive incremental C-index across all stage-adjusted models; stratified analysis showed the strongest effect in early-stage (AJCC I-II: HR=0.8109, P<0.0001) and MVI-negative patients (HR=0.8538, P=0.0020). Bootstrap 1000x resampling: median HR=0.8646 (95% CI: 0.7985-0.9443), all iterations yielded HR<1. Conclusions: The MPS framework provides a mechanism-driven biological stratification tool for adjuvant immunotherapy in post-resection HCC, moving from "fixed-protocol extrapolation" to "immune contexture navigation."
Z. Dang, J. Dan, W. Su et al.· medRxiv· 0 citations
This study establishes a novel CSC–associated gene signature for diagnosis and prognosis in HCC and nominates belinostat as a repurposing candidate for targeting stemness‐related pathways, offering a promising strategy for personalized therapy.
Yang Zi, Ying Zhang, Jun Wu et al.· Stem Cells International· 0 citations
Findings indicate that prognostic gene expression signatures derived from NanoString data showed limited reproducibility across independent cohorts and provided little additional predictive value beyond established clinical factors, and suggest that robust clinical variables remain the most reliable predictors of recurrence risk in this setting.
P. Quarles van Ufford, R. Bojesen, L. R. Olsen et al.· medRxiv· 0 citations
A six-gene-fibrosis-based prognostic model based on six genes stratifies survival risk and correlates with immune features and drug sensitivity, but provides a preliminary framework requiring prospective clinical validation.
Yanyan Qiu, Cui Lv, Shubo Ding· Clinical and Translational O...· 0 citations
