Prognostic factors for laryngeal sarcoma and nomogram development for prediction: a retrospective study based on SEER database

The role of mitochondria-related lncRNAs in characterizing the immune landscape and supervising the prognosis of osteosarcoma

Mitochondrial damage is related to the functional properties of immune cells as well as to tumorigenesis and progression. Nevertheless, there is an absence concerning the systematic evaluation of mitochondria-associated lncRNAs (MALs) in the immune profile and tumor microenvironment of osteosarcoma patients. Based on transcriptomic and clinicopathological data from the TARGET database, MAL-related patterns were ascertained by consistent clustering, and gene set variation analysis of the different patterns was completed. Next, a MAL-derived scoring system was created using Cox and LASSO regression analyses and validated by Kaplan-Meier and ROC curves. The GSEA, ESTIMATE, and CIBERSORT algorithms were utilized to characterize the immune status and underlying biological functions in the different MAL score groups. MAL-derived risk scores were well stabilized and outperformed traditional clinicopathological features to reliably predict 5-year survival in osteosarcoma cohorts. Moreover, patients with increased MAL scores were observed to suffer from poorer prognosis, higher tumor purity, and an immunosuppressive microenvironment. Based on estimated half-maximal inhibitory concentrations, the low-MAL score group benefited more from gemcitabine and docetaxel, and less from thapsigargin and sunitinib compared to the high-MAL score group. Pan-cancer analysis demonstrated that six hub MALs were strongly correlated with clinical outcomes, immune subtypes, and tumor stemness indices in various common cancers. Finally, we verified the expression patterns of hub MALs in osteosarcoma with qRT-PCR. In summary, we identified the crosstalk between prognostic MALs and tumor-infiltrating immune cells in osteosarcoma, providing a potential strategy to ameliorate clinical stratification management.

Non-squamous Laryngeal Cancer

The pathology of non-squamous carcinoma of the larynx is broad and there is a wide differential diagnosis. The most common presenting symptoms for laryngeal malignancies, both squamous and non-squamous, are hoarseness and dyspnea. Presentation with persistent or worsening symptoms and a submucosal lesion should raise suspicion for a non-squamous malignancy of the larynx. Accurate histology determines the most appropriate treatment and has an impact on prognosis.

A deep learning-based model predicts survival for patients with laryngeal squamous cell carcinoma: a large population-based study

OBJECTIVES

To assess the performance of DeepSurv, a deep learning-based model in the survival prediction of laryngeal squamous cell carcinoma (LSCC) using the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

In this large population-based study, we developed and validated a deep learning survival neural network using pathologically diagnosed patients with LSCC from the SEER database between January 2010 and December 2018. Totally 13 variables were included in this network, including patients baseline characteristics, stage, grade, site, tumor extension and treatment details. Based on the total risk score derived from this algorithm, a three-knot restricted cubic spline was plotted to exhibit the difference of survival benefits from two treatment modalities.

RESULTS

Totally 6316 patients with LSCC were included in the study, of which 4237 cases diagnosed between 2010 and 2015 were selected as the development cohort, and the rest (2079 cases diagnosed from 2016 to 2018) were the validation cohort. A state-of-the-art deep learning-based model based on 23 features (i.e., 13 variables) was generated, which showed more superior performance in the prediction of overall survival (OS) than the tumor, node, and metastasis (TNM) staging system (C-index for DeepSurv vs TNM staging = 0.71; 95% CI 0.69-0.74 vs 0.61; 95% CI 0.60-0.63). Interestingly, a significantly nonlinear association between total risk score and treatment effectiveness was observed. When the total risk score ranges 0.1-1.5, surgical treatment brought more survival benefits than nonsurgical one for LSCC patients, especially in 70.5% of patients staged III-IV.

CONCLUSIONS

The deep learning-based model shows more potential benefits in survival estimation for patients with LSCC, which may potentially serve as an auxiliary approach to provide reliable treatment recommendations.

Laryngeal soft tissue sarcoma: Systematic review and individual patient data analysis of 300 cases

BACKGROUND

Laryngeal sarcoma is rare. We performed a systematic review and individual patient analysis to evaluate the patterns of care, prognostic factors, and role of radiotherapy in laryngeal soft tissue sarcoma.

METHODS

A systematic search on PubMed and Google scholar was done. An individual patient data analysis was done.

RESULTS

Of the 300 cases of laryngeal sarcoma, 80% underwent surgery. 44% underwent larynx preservation surgery and 25% received radiotherapy with surgery. Median progression free survival (PFS) was 48 months and overall survival (OS) of 224 months for the entire cohort. Patients with large primary, cartilage invasion, and positive margins had numerically worse PFS. Cartilage invasion and primary tumor size >3 cm were the most common risk factors for adjuvant radiation therapy. Patients receiving radiotherapy were not associated with better survival.

CONCLUSION

Laryngeal sarcoma associated with a good survival. Larynx preservation surgery is feasible in nearly half patients. Adjuvant radiotherapy may be warranted in patients poor prognostic factors.