Prognostic significance of modified lung immune prognostic index in osteosarcoma patients

Abstract

Purpose: Osteosarcoma is the most common primary malignancy of bone with a dismal prognosis for patients with pulmonary metastases. Evaluation of osteosarcoma prognosis would facilitate the prognosis consultation as well as the development of personalized treatment decisions. However, there is limited effective prognostic predictor at present. Lung Immune Prognostic Index (LIPI) is a novel prognostic factor in pulmonary cancers, whereas, the prognostic significance of LIPI in osteosarcoma has not yet been well clarified. In this study, we firstly explore the prognostic role of LIPI and further modify this predictive model in osteosarcoma. Patients and methods: A retrospectively study was conducted at Musculoskeletal Tumor Center of West China Hospital between January 2016 and January 2021. Hematological factors and clinical features of osteosarcoma patients were collected and analyzed. The area under curve (AUC) and optimal cuff-off of each single hematological factor was calculated. Results: In this study, lactate dehydrogenase (LDH), derived neurtrophil to lymphocyte ratio (dNLR), and Hydroxybutyrate dehydrogenase (HBDH) have higher AUC values. LIPI was composed of LDH and dNLR and was further modified by combing the HBDH, forming the osteosarcoma immune prognostic index (OIPI). OIPI divided 223 osteosarcoma patients divided into four groups, none, light, moderate, and severe (p < 0.0001). OIPI has a higher AUC value than LIPI and other hematological indexes in t-ROC curve. According to the univariate and multivariate analysis, pathological fracture, metastasis, NLR, platelet-lymphocyte ratio (PLR), and OIPI were associated with the prognosis; and metastasis and OIPI were independent prognostic factors of osteosarcoma patients. An OIPI-based nomogram was also established and could predict the 3-year and 5-year overall survival. In addition, OIPI was also revealed correlated with metastasis and pathological fracture in osteosarcoma. Conclusion: This study first explore the prognostic significance of LIPI in osteosarcoma patients. In addition, we developed a modified LIPI, the OIPI, for osteosarcoma patients. Both the LIPI and OIPI could predict the overall survival of osteosarcoma patients well, while OIPI may be more suitable for osteosarcoma patients. In particular, OIPI may have the ability to identify some high-risk patients from clinically low-risk patients.

Keywords: inflammation; lung immune prognostic index; osteosarcoma; prognostic predictive factors; tumor immune micro-environment.

FIGURE 1

ROC analysis of different hematological biomarkers. (A–F) The AUC and best cutoff values of dNLR, LDH, HBDH, LMR, NLR, and PLR were shown, respectively. The vertical axis represents the sensitivity and the horizontal axis represents the 1-specificity. dNLR, derived neurtrophil to lymphocyte ratio; LDH, lactate dehydrogenase; HBDH, Hydroxybutyrate dehydrogenase; LMR, lymphocyte-monocyte ratio; NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio.

FIGURE 2

Predictive ability of different hematological biomarkers on OS in 223 patients with osteosarcoma. (A–E) Prognostic predictive effect of different inflammatory biomarkers on OS. Cumulative hazard function was plotted by the Kaplan–Meier methodology and the p value was calculated with two-sided log-rank tests. According to the logistic regression analysis, the differences between four LIPI or OIPI groups in the survival probability were significant. OS, overall survival; LMR, lymphocyte-monocyte ratio; NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; LIPI, Lung Immune Prognostic Index; OIPI, osteosarcoma immune prognostic index.

FIGURE 3

Comparison of different hematological biomarkers in predicting the overall survival. (A) The difference of predictive ability was shown in time-dependent ROC curve, in which a larger AUC value meant a better prognostic predictive ability. (B) The Sankey showed the difference between LIPI and OIPI in distributing osteosarcoma patients. NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; LMR, lymphocyte-monocyte ratio; dNLR, derived neurtrophil to lymphocyte ratio; LDH, lactate dehydrogenase; HBDH, Hydroxybutyrate dehydrogenase; LIPI, Lung Immune Prognostic Index; OIPI, osteosarcoma immune prognostic index.

FIGURE 4

Independent risk factors of OS in 223 osteosarcoma patients. (A) Univariate analysis of clinical characters and inflammatory biomarkers. (B) Multivariate analysis of significant clinical characters and inflammatory biomarkers in univariate analysis to determinate independent prognostic factors. NLR; neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; LMR, lymphocyte-monocyte ratio; OIPI, osteosarcoma immune prognostic index.

FIGURE 5

Construction and validation of the osteosarcoma overall survival nomogram. (A) The nomogram was constructed by combing OIPI, PLR, NLR, metastasis and pathological fracture and the sum of the scores for each covariate was the nomogram total score. (B–D) This nomogram was validated by the calibration curve, decision curve analysis, and clinical impact curve. OIPI, osteosarcoma immune prognostic index; PLR, platelet–lymphocyte ratio; NLR; neutrophil–lymphocyte ratio.

FIGURE 6

Comparison of the predictive effect between OIPI and clinical characters on OS. A larger AUC in the t-ROC means a better predictive ability. OIPI, osteosarcoma immune prognostic index.

FIGURE 7

Association between OIPI and clinical characters including metastasis and pathological fracture. (A,B) The Spearman’s rank analysis showed that OIPI was related to the metastasis and pathological fracture. OIPI, osteosarcoma immune prognostic index.