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BackgroundSoft tissue sarcoma (STS) are heterogeneous and rare tumors, and few studies have explored predicting the prognosis of patients with STS. The Lung Immune Prognostic Index (LIPI), calculated based on baseline serum lactate dehydrogenase (LDH) and the derived neutrophils/(leukocytes minus neutrophils) ratio (dNLR), was considered effective in predicting the prognosis of patients with pulmonary cancer and other malignancies. However, the efficacy of the LIPI in predicting the prognosis of patients with STS remains unclear.MethodsThis study retrospectively reviewed patients with STS admitted to our center from January 2016 to January 2021. Their hematological and clinical characteristics were collected and analyzed to construct the LIPI specific to STS. The correlations between various predictive factors and overall survival (OS) were examined using Kaplan–Meier and Cox regression analyses. Independent risk factors for OS were identified using univariate and multivariate analyses. Finally, a LIPI nomogram model for STS was established.ResultsThis study enrolled 302 patients with STS, of which 87 (28.9%), 162 (53.6%), and 53 (17.5%) were classified into three LIPI-based categories: good, moderate, and poor, respectively (P < 0.0001). The time-dependent operator curve showed that the LIPI had better prognostic predictive ability than other hematological and clinical characteristics. Univariate and multivariate analyses identified the Fédération Nationale des Centres de Lutte Contre le Cancer grade (FNCLCC/G), tumor size, and LIPI as independent risk factors. Finally, a nomogram was constructed by integrating the significant prognostic factors. Its C-index was 0.72, and the calibration curve indicated that it could accurately predict the three- and five-year OS of patients with STS. The decision and clinical impact curves also indicated that implementing this LIPI-nomogram could significantly benefit patients with STS.ConclusionThis study explored the efficacy of the LIPI in predicting the prognosis of 302 patients with STS, classifying them into three categories to evaluate the prognosis. It also reconstructed a LIPI-based nomogram to assist clinicians in predicting the three- and five-year OS of patients with STS, potentially enabling timely intervention and customized management.

BackgroundOsteosarcoma is a primary malignant bone tumor with a high metastatic potential that accounts for a significant proportion of all bone tumors. The prognosis for patients with metastatic or recurrence disease remains poor. The neutrophil-to-lymphocyte ratio (NLR) has become a potential prognostic biomarker for cancer. Recent evidence suggests that the dynamic changes in neutrophil-to-lymphocyte ratio (NLR) during treatment may be more informative in predicting patient prognosis, but the value of dynamic NLR in osteosarcoma has not yet been determined.MethodsThis retrospective study retrospectively analyzed the clinical information of 251 osteosarcoma patients diagnosed and treated in West China Hospital of Sichuan University, explored the impact of baseline NLR and changes in NLR during treatment on the prognosis of osteosarcoma patients, and further combined baseline NLR with Delta NLR to build an NLR staging system.ResultsThe results showed that both baseline NLR and delta NLR had some predictive ability for the prognosis of osteosarcoma patients (P = 6.90e-4, P = 0.022). Patients with high baseline NLR were more likely to have a decrease in delta NLR (P = 1.24e-10). The NLR stage had a better predictive ability than baseline NLR and delta NLR, and was an independent prognostic factor for overall survival in osteosarcoma patients HR: 2.456 (1.625-3.710) (P = 1.97e-05).ConclusionNLR has value in continuous monitoring, and continuous monitoring of NLR can better predict the survival of osteosarcoma patients compared to baseline NLR.

Osteosarcoma is a primary malignant bone tumor with high metastatic potential. To date, achieving long-term survival of osteosarcoma patients remains a difficult task. Metabolic reprogramming has emerged as a new hallmark of cancer. However, studies on the prognostic value of hematological markers related to nutritional and metabolism in cancer patients are limited and contradictory. In this retrospective study, we extensively collected 16 hematological markers related to nutritional and metabolism in 223 osteosarcoma patients. A nutritional metabolism related prognostic scoring system (NMRS) in patients with osteosarcoma was constructed by least absolute contraction and selection operator (LASSO) cox regression analysis. Compared with individual hematological indicators, NMRS has stronger predictive power (training set: 0.811 vs. 0.362–2.638; validation set: 0.767 vs. 0.333–0.595). It is an independent prognostic factor for the survival of patients with osteosarcoma [HR: 1.957 (1.375–2.786) training set; HR: 3.146 (1.574–6.266) validation set]. NMRS-based nomograms have good and stable predictive power. NMRS facilitates further risk stratification of patients with the same clinical characteristics.