Nomogram for predicting prognosis of patients with metastatic melanoma after immunotherapy: A Chinese population–based analysis

Abstract

BackgroundPrevious studies indicated the evidence that baseline levels of thyroid antibodies, thyroid status, and serum lactate dehydrogenase (LDH) and M stage may influence the prognosis of patients with advanced or metastatic melanoma treated with immune checkpoint inhibitors that targets programmed cell death-1 (PD-1) or programmed death ligand 1, which reported that dramatic improvements in survival rates were observed; however, the presence of controversy has prevented consensus from being reached. Study objectives were to develop a nomogram to identify several prognostic factors in Chinese patients with metastatic melanoma receiving immunotherapy.MethodsThis retrospective study included 231 patients from Sun Yat-sen University Cancer Center, and patients were split into internal cohort (n = 165) and external validation cohort (n = 66). We developed a nomogram for the prediction of response and prognosis on the basis of the levels of serum thyroid peroxidase antibody (A-TPO), free T3 (FT3), and LDH and M stage that were measured at the baseline of anti–PD-1 infusion. In addition, the follow-up lasted at least until 5 years after the treatment or mortality. RECIST v1.1 was used to classify treatment responses.ResultsChi-square test showed that PD-1 antibody was more effective in patients with melanoma with high level baseline FT4 or earlier M stage. A multivariate Cox analysis showed that baseline FT3 (P = 0.009), baseline A-TPO (P = 0.016), and LDH (P = 0.013) levels and M stage (P < 0.001) independently predicted overall survival (OS) in patients with melanoma. The above factors are integrated, and a prediction model is established, i.e., nomogram. Survival probability area-under-the-curve values of 1, 2, and 3 years in the training, internal validation, and external validation cohorts showed the prognostic accuracy and clinical applicability of nomogram (training: 0.714, 0.757, and 0.764; internal validation: 0.7171963, 0.756549, and 0.7651486; external validation: 0.748, 0.710, and 0.856). In addition, the OS of low-risk (total score ≤ 142.65) versus high-risk (total score > 142.65) patients varied significantly in both training group (P < 0.0001) and external validation cohort (P = 0.0012).ConclusionsAccording to this study, baseline biomarkers are associated with response to immunotherapy and prognosis among patients with metastatic melanoma. Treatment regimens can be tailor-made on the basis of these biomarkers.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single‐cell transcriptomics analysis via 101 machine learning algorithms

“…Cutaneous melanoma is a highly malignant form of skin cancer that continues to pose a significant public health concern due to its rising incidence worldwide. [27][28][29] While traditional research in melanoma has predominantly focused on genetic mutations, oncogenic signalling pathways and immune responses, this study sought to shed light on the underappreciated role of low-density lipoprotein (LDL) metabolism in the context of cutaneous melanoma. 30 Our comprehensive analysis utilized single-cell sequencing, machine learning and various other bioinformatics approaches Cell communication analysis further demonstrated that high-LDL metabolism cells had more extensive intercellular interactions, particularly between fibroblasts and endothelial cells.…”

“…Cutaneous melanoma is a highly malignant form of skin cancer that continues to pose a significant public health concern due to its rising incidence worldwide 27–29 . While traditional research in melanoma has predominantly focused on genetic mutations, oncogenic signalling pathways and immune responses, this study sought to shed light on the underappreciated role of low‐density lipoprotein (LDL) metabolism in the context of cutaneous melanoma 30 .…”

Deciphering the molecular landscape: integrating single-cell transcriptomics to unravel myofibroblast dynamics and therapeutic targets in clear cell renal cell carcinomas

“…Coefficient values of prognostic genes were computed, and the distribution of these genes in the high and low-risk groups was visualized through a heatmap. Kaplan-Meier curves illustrated the survival at different time points in the high- and low-risk groups ( 39 , 40 ), while the specificity and sensitivity of prognostic genes were further validated using time-dependent receiver operating characteristic (ROC) curves ( 41 , 42 ).…”

Integrating single-cell analysis and machine learning to create glycosylation-based gene signature for prognostic prediction of uveal melanoma

“…A nomogram combining GCNS, age, gender, and the pathological stage was developed to estimate the 1-, 2-, and 3-year OS probability ( 24 ). The accuracy of the nomogram was assessed through ROC curves and calibration curves.…”