Development and validation of a nomogram based on stromal score to predict progression-free survival of patients with papillary thyroid carcinoma

Stromal score is a promising index in tumor patients' outcome determination

Background

Immune status is widely acknowledged as a valuable marker for predicting cancer prognosis and therapy response. However, there has been a limited understanding of the stromal landscape in cancer.

Methods

By employing ESTIMATE, stromal- and immune-scores were inferred for 6193 tumor samples spanning 12 cancer types sourced from The Cancer Genome Atlas (TCGA). Subsequently, the samples were categorized into seven groups based on their stromal and immune scores. A comparison of prognosis, lymphocyte and stromal cell infiltration, and the response to programmed death ligand 1 (PD-L1) therapy was conducted among these subtypes.

Results

It was unveiled by the analysis that, in the majority of cancer types, stromal score exhibited a more potent predictive capability for outcomes compared to the immune score. Furthermore, it was observed that in four cancer types, intermediate immune infiltration coupled with low stromal infiltration correlated with the most favorable overall survival, whereas an unfavorable outcome was predicted in colorectal cancer (CRC) and stomach adenocarcinoma (STAD) when high immune infiltration coexisted with intermediate or high stromal infiltration.

Conclusion

In summary, while high immune scores frequently correlate with a positive prognosis, such correlation is not universal. A potential strategy to address the current limitations of the immune score in specific circumstances could involve a focus on stromal scores or a subtle integration of stromal and immune status.

A new molecular subclassification and in silico predictions for diagnosis and prognosis of papillary thyroid cancer by alternative splicing profile

Introduction: Papillary thyroid cancer (PTC) is the most common endocrine malignancy. However, different PTC variants reveal high heterogeneity at histological, cytological, molecular and clinicopathological levels, which complicates the precise diagnosis and management of PTC. Alternative splicing (AS) has been reported to be potential cancer biomarkers and therapeutic targets. Method: Here, we aim to find a more sophisticated molecular subclassification and characterization for PTC by integrating AS profiling. Based on six differentially expressed alternative splicing (DEAS) events, a new molecular subclassification was proposed to reclassify PTC into three new groups named as Cluster0, Cluster1 and Cluster2 respectively. Results: An in silico prediction was performed for accurate recognition of new groups with the average accuracy of 91.2%. Moreover, series of analyses were implemented to explore the differences of clinicopathology, molecular and immune characteristics across them. It suggests that there are remarkable differences among them, but Cluster2 was characterized by poor prognosis, higher immune heterogeneity and more sensitive to anti-PD1 therapy. The splicing correlation networks proved the complicated regulation relationships between AS events and splicing factors (SFs). An independent prognostic indicator for PTC overall survival (OS) was established. Finally, three compounds (orantinib, tyrphostin-AG-1295 and AG-370) were discovered to be the potential therapeutic agents. Discussion: Overall, the six DEAS events are not only potential biomarkers for precise diagnosis of PTC, but also the probable prognostic predictors. This research would be expected to highlight the effect of AS events on PTC characterization and also provide new insights into refining precise subclassification and improving medical therapy for PTC patients.

A seven-autophagy-related gene signature for predicting the prognosis of differentiated thyroid carcinoma

Background

Numerous studies have implicated autophagy in the pathogenesis of thyroid carcinoma. This investigation aimed to establish an autophagy-related gene model and nomogram that can help predict the overall survival (OS) of patients with differentiated thyroid carcinoma (DTHCA).

Methods

Clinical characteristics and RNA-seq expression data from TCGA (The Cancer Genome Atlas) were used in the study. We also downloaded autophagy-related genes (ARGs) from the Gene Set Enrichment Analysis website and the Human Autophagy Database. First, we assigned patients into training and testing groups. R software was applied to identify differentially expressed ARGs for further construction of a protein-protein interaction (PPI) network for gene functional analyses. A risk score-based prognostic risk model was subsequently developed using univariate Cox regression and LASSO-penalized Cox regression analyses. The model’s performance was verified using Kaplan-Meier (KM) survival analysis and ROC curve. Finally, a nomogram was constructed for clinical application in evaluating the patients with DTHCA. Finally, a 7-gene prognostic risk model was developed based on gene set enrichment analysis.

Results

Overall, we identified 54 differentially expressed ARGs in patients with DTHCA. A new gene risk model based on 7-ARGs (CDKN2A, FGF7, CTSB, HAP1, DAPK2, DNAJB1, and ITPR1) was developed in the training group and validated in the testing group. The predictive accuracy of the model was reflected by the area under the ROC curve (AUC) values. Univariate and multivariate Cox regression analysis indicated that the model could independently predict the prognosis of patients with THCA. The constrained nomogram derived from the risk score and age also showed high prediction accuracy.

Conclusions

Here, we developed a 7-ARG prognostic risk model and nomogram for differentiated thyroid carcinoma patients that can guide clinical decisions and individualized therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02590-6.

Development and validation of a nomogram based on stromal score to predict progression-free survival of patients with papillary thyroid carcinoma

Background

Growing evidence has proved that stromal cells, as the critical component of tumor microenvironment (TME), are closely associated with tumor's progression. However, the model based on stromal score to predict progression‐free survival (PFS) in papillary thyroid carcinoma (PTC) has not been developed. The study aimed at exploring the relation between stromal score and prognosis, then establishing a nomogram to predict PFS of patients with PTC.

Method

We obtained the stromal score and clinicopathological characteristics of PTC patients from The Cancer Genome Atlas (TCGA) database. Cox regression analysis assisted in selecting prognosis‐related factors. A stromal score‐based nomogram was built and verified in the training and validation cohorts, respectively. The calibration curve, concordance index (C‐index), decision curve analysis (DCA) as well as receiver operating characteristic (ROC) curve assisted in measuring the performance exhibited by the nomogram.

Results

We divided 381 PTC patients into the training cohort (n = 269) and the validation cohort (n = 112) randomly. Compared with patients who had a low stromal score, patients with a high stromal score appeared with significantly better PFS [Hazard ratio (HR) and 95% confidence interval (CI): 0.294, 0.130–0.664]. The C‐index of the PFS nomogram was 0.764 (0.662–0.866) in the training cohort and 0.717 (0.603–0.831) in the validation cohort. The calibration curves for PFS prediction in the nomogram were remarkably consistent with the actual observation. DCA indicated superior performance of the nomogram to predict PFS than the American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system. The ROC curves showed the favorable sensitivity and specificity of the novel nomogram.

Conclusion

High stromal score was significantly associated with improved PFS in patients with PTC. The nomogram based on the stromal score and clinicopathological patterns yielded a reliable performance to predict the prognosis of PTC.