Predictor Of Survival Research Articles

Prognostic Significance of Plasma Neutrophil Extracellular Trap Levels in Patients with Non-Small Cell Lung Cancer Treated with Immune Checkpoint Inhibitors.

Neutrophil extracellular traps (NETs) released from neutrophils are related to cancer progression. However, the relationship between the therapeutic effects of immune checkpoint inhibitors (ICIs) such as anti-PD-1 and anti-PD-L1 antibodies and plasma NET concentration in patients with non-small cell lung cancer (NSCLC) is poorly understood. In this study, concentrations of citrullinated histone H3 (CitH3), a surrogate marker of NETs, in plasma before/after treatment were examined in patients with advanced or recurrent NSCLC undergoing ICI treatment (n = 185). The clinical significances of NET levels before/after treatment and posttreatment changes were statistically evaluated. As a result, multivariate Cox analysis showed that high NET levels before treatment were statistically significant predictors of unfavorable overall survival (OS; p < 0.001, HR 1.702, 95% CI 1.356-2.137) and progression-free survival (PFS; p < 0.001, HR 1.566, 95% CI 1.323-1.855). The Kaplan-Meier curves showed significant separation between the high- and low-NET groups in OS (p = 0.002) and PFS (p < 0.001). Additionally, high NET levels after treatment were also significantly associated with worse OS (p < 0.001) and PFS (p < 0.001) by multivariate Cox analysis. Notably, the pretreatment NET levels were significantly correlated with the plasma levels of NET-related inflammatory cytokines, such as IL-6 and IL-8, and with NET-related gene expression and immune-suppressive profile in peripheral blood mononuclear cells. Our findings suggest that NETs released from activated neutrophils might reduce the clinical efficacy of ICIs in patients with NSCLC.

Can contralateral prophylactic mastectomy and oophorectomy increase survival in BRCA-related breast cancer? Results from the Italian MUTina study

IntroductionIn the Emilia-Romagna region of Italy, a unique Hub and Spoke model was adopted to recognize BRCA-related breast cancer (BC) patients. Characteristics and outcomes of tumors identified by this model will be presented. MethodsThis multicenter retrospective cohort study involved patients diagnosed with BRCA-related BC identified in the Emilia-Romagna region between January 2000 and December 2013. Seven provinces collected data on patient and tumor characteristics; clinical and gene testing information were also registered. Comparisons between BRCA1 and BRCA2 BC were performed. To balance different variants to identify significant predictors of survival, an inverse probability of treatment weighting (IPTW) analysis on Cox regression was conducted. ResultsFrom 2000 to 2013, 284 BRCA-related BC were registered (171 BRCA1, 110 BRCA2, and 3 BRCA1 and BRCA2). BRCA1 were diagnosed at an earlier stage compared to BRCA2 (50.1 % vs 30 %, respectively, in stage I, P = 0.0015). BRCA2 patients underwent more up-front surgery (85 % vs. 74.9 %, P = 0.049) and less chemotherapy (69.1 % vs 88.9 %, P = 0.004) than BRCA1 patients. At 11.8 years median follow-up, BRCA1 patients developed more second contralateral BC (P = 0.09), while BRCA2 had more visceral relapses (P = 0.013). No differences in overall survival (OS) between BRCA1 and BRCA2 patients (P = 0.07) were found. An advantage in OS was independently seen for patients who underwent contralateral prophylactic mastectomy (P = 0.0001) and oophorectomy (P < 0.0001). ConclusionsIn conclusion, adopting a homogeneous regional framework provides important information about prevention and treatment strategies of BRCA-related BC and suggests using maximal surgery to improve OS.

The Clinical Features and Outcomes of Pseudocirrhosis in Breast Cancer.

Pseudocirrhosis is a diffuse nodularity of the liver that radiologically mimics cirrhosis but is a distinct pathological process. It is seen almost exclusively in patients with liver metastases and may represent a response to systemic treatment. Data on the risk factors for pseudocirrhosis and outcomes are limited. In total, 170 patients with a diagnosis of breast cancer and pseudocirrhosis in a 10-year period were identified and retrospectively analysed. Data were collected on baseline patient characteristics, treatments received, and outcomes. Median time between diagnosis of liver metastases and diagnosis of pseudocirrhosis was 17.1 months (range, 0-149 months). In total, 89.4% of patients received chemotherapy between their diagnosis of breast cancer liver metastases and their diagnosis of pseudocirrhosis, most commonly a taxane (74.7%) or capecitabine (67.1%), and the median treatment lines received was 3. Median OS from first diagnosis of pseudocirrhosis was 7.6 months (95% CI: 6.1-9.6 months) and was longer in patients with HER2+ disease at 16.7 months (95% CI: 6.4-32.9 months), which was statistically significant. In our study, pseudocirrhosis occurred in the presence of liver metastases and was associated with a poor prognosis. HER2+ patients with pseudocirrhosis had a better prognosis than other subtypes, but we did not identify other significant predictors of survival. Chemotherapy was not a prerequisite for pseudocirrhosis development, although the majority of patients had received at least one line of chemotherapy before pseudocirrhosis was diagnosed.

BAP1-mediated MAFF deubiquitylation regulates tumor growth and is associated with adverse outcomes in colorectal cancer

BackgroundDespite improvements in colorectal cancer (CRC) treatment, the prognosis for advanced CRC patients remains poor. Disruption of protein stability is one of the important factors in cancer development and progression. In this study, we aim to identify and analyze novel dysregulated proteins in CRC, assessing their significance and the mechanisms. MethodsUsing quantitative proteomics, expression pattern analysis, and gain-of-function/loss-of-function experiments, we identify novel functional protein dysregulated by ubiquitin-proteasome axis in CRC. Prognostic significance was evaluated in a training cohort of 546 patients and externally validated in 794 patients. Mechanistic insights are gained through molecular biology experiments, deubiquitinating enzymes (DUBs) expression library screening, and RNA sequencing. ResultsMAFF protein emerged as the top novel candidate substrate regulated by ubiquitin-proteasome in CRC. MAFF protein was preferentially downregulated in CRC compared to adjacent normal tissues. More importantly, multicenter cohort study identified reduced MAFF protein expression as an independent predictor of overall and disease-free survival in CRC patients. The in vitro and vivo assays showed that MAFF overexpression inhibited CRC growth, while its knockdown had the opposite effect. Intriguingly, we found the abnormal expression of MAFF protein was predominantly regulated via ubiquitination of MAFF, with K48-ubiquitin being dominant. BAP1 as a nuclear deubiquitinating enzyme (DUB), bound to and deubiquitinated MAFF, thereby stabilizing it. Such stabilization upregulated DUSP5 expression, resulting in the inhibition of ERK phosphorylation. ConclusionsThis study describes a novel BAP1-MAFF signaling axis which is crucial for CRC growth, potentially serving as a therapeutic target and a promising prognostic biomarker for CRC.

Sex hormone trajectories and association to outcomes after out-of-hospital cardiac arrest

BackgroundOutcomes and susceptibility to out-of-hospital cardiac arrest (OHCA) are known to differ by sex, yet little is known about changes in sex hormones after OHCA. We sought to determine the trajectory of sex hormones after OHCA and their association to survival and neurological outcome. MethodsPlasma samples were collected from those that survived to hospital admission at four time points (1, 6, 24, and 48 h) and estrone, estradiol, progesterone, and testosterone concentrations were quantified via liquid chromatography-mass spectrometry. Trends in hormones were plotted over time by sex and outcomes. The association between sex, hormone levels with survival and neurological outcome (cerebral performance category 1–2 indicating good outcome and 3–5 for poor outcome) were determined using generalized estimating equation models. ResultsOf the 94 OHCA patients, 50 were males and 44 females, with a mean age of 61.3 (+15.7) years. Despite older age and lower BCPR in females compared to males, females had higher proportion of good neurological outcome compared to males. Over the 48 h, estrone increased, testosterone decreased, and estradiol and progesterone remained flat. Survivors had lower levels of estrone at all time points but only at early time points for estradiol, progesterone and testosterone. Lower estrone level predicted survival at discharge, even after adjusting for time, sex, age, and hormones independently (β = −3.38, 95% CI = −5.71, −0.85). Females had better neurological scores compared to males after adjusting for estrone (β = 1.27, 95% CI = 0.01, 2.53) and estradiol (β = 2.92, 95% CI = 1.13, 4.70). ConclusionsSurvivors and those with favorable neurological outcome had lower trend in estrone. The sex hormone estrone, present in both males and females, may be a predictor of survival. When adjusted for estrogens, female sex had better neurological recovery compared to males. The difference in neurological outcome by sex is not explained by estrogens. However, these finding open the door for exploration of other sex-specific pathways in resuscitation after OHCA.

Selective Gastrectomy with Knowledge of Gastric Anatomy and Blood Supply are Key to Complete Tumour Removal in Advanced Pseudomyxoma: Single-Centre Experience with 120 Gastrectomies Over 20 Years.

The role of gastrectomy to achieve complete cytoreduction (CCR) for pseudomyxoma peritonei (PMP) is controversial due to uncertain risk/benefit ratio. The outcomes of patients who gastrectomy over a twenty-year period in a high-volume unit are reported. All patients requiring gastrectomy to achieve CCR for appendiceal PMP between 2000 and 2020 were reviewed. Demographics, disease, operative, complication, and survival data were analysed. The first and second decades were compared. A total of 2148 patients underwent CRS and HIPEC, of which 78% had CCR. Gastrectomy was performed in 7.1%. Median age was 55 years, and 52% were female. Among gastrectomy patients, 94.2% had ≥1 elevated tumour marker, and 18% had high-grade disease. Median PCI was 30, and 30% required subtotal colectomy. Clavien-Dindo III-IV complications occurred in 32%, and 90-day mortality was 1.75%. Median survival was 104 months, and 10-year OS was 47%. Comparing the two decades, total CRS cases almost tripled, with a greater proportion achieving CCR (82.2% vs. 67.8%) but fewer requiring gastrectomy (5.3% vs. 13.5%). In those who had gastrectomy, disease was more advanced (higher PCI, more high-grade disease, more colectomies) in the later period. However, on multivariable analysis, there was no difference in survival between decades. High-grade histology was the only predictor of survival. Gastrectomy can achieve good long-term survival with low mortality and acceptable morbidity and should not deter surgeons from achieving CCR. However, increasing experience shows CCR can be achieved, preserving the stomach in the majority of cases through careful consideration of the anatomy and gastric blood supply.

A study on HDL-Cholesterol and Apolipoprotein A-I as predictors of survival in patients with decompensated chronic liver disease

A study on HDL-Cholesterol and Apolipoprotein A-I as predictors of survival in patients with decompensated chronic liver disease

Prediction of median survival in decompensated chronic liver disease patients using standard MELD and CTP scores

Prediction of median survival in decompensated chronic liver disease patients using standard MELD and CTP scores

Predictors of survival for severe alcoholic hepatitis patients treated with low volume centrifugal plasma exchange and low dose steroid

Predictors of survival for severe alcoholic hepatitis patients treated with low volume centrifugal plasma exchange and low dose steroid

Predicting prognostic factors in kidney transplantation using a machine learning approach to enhance outcome predictions: a retrospective cohort study.

Accurate forecasting of clinical outcomes after kidney transplantation is essential for improving patient care and increasing the success rates of transplants. Our study employs advanced machine learning (ML) algorithms to identify crucial prognostic indicators for kidney transplantation. By analyzing complex datasets with ML models, we aim to enhance prediction accuracy and provide valuable insights to support clinical decision-making. Analyzing data from 4077 KT patients (June 1990 - May 2015) at a single center, this research included 27 features encompassing recipient/donor traits and peri-transplant data. The dataset was divided into training (80%) and testing (20%) sets. Four ML models-eXtreme Gradient Boosting (XGBoost), Feedforward Neural Network, Logistic Regression, and Support Vector Machine-were trained on carefully selected features to predict the success of graft survival. Performance was assessed by precision, sensitivity, F1 score, Area Under the Receiver Operating Characteristic (AUROC), and Area Under the Precision-Recall Curve. XGBoost emerged as the best model, with an AUROC of 0.828, identifying key survival predictors like T-cell flow crossmatch positivity, creatinine levels two years post-transplant and human leukocyte antigen mismatch. The study also examined the prognostic importance of histological features identified by the Banff criteria for renal biopsy, emphasizing the significance of intimal arteritis, interstitial inflammation, and chronic glomerulopathy. The study developed ML models that pinpoint clinical factors crucial for KT graft survival, aiding clinicians in making informed post-transplant care decisions. Incorporating these findings with the Banff classification could improve renal pathology diagnosis and treatment, offering a data-driven approach to prioritizing pathology scores.

MiRNA expression as outcome predictor in pediatric AML: systematic evaluation of a new model

Accurately predicting patient outcomes is essential for optimizing treatment and improving outcomes in pediatric acute myeloid leukemia (AML). In recent years, microRNAs have emerged as a promising prognostic marker, with a growing body of evidence supporting their potential predictive value. We systematically reviewed all previous studies that have analyzed the expression of microRNAs as predictors of survival in pediatric AML and found 16 microRNAs and 4 microRNA signatures previously proposed as predictors of survival. We then used a public access cohort of 1414 pediatric AML patients from the TARGET project to develop a new predictive model using penalized lasso Cox regression based on microRNA expression. Here we propose a new score based on a 37-microRNA signature that is associated with AML and is able to predict survival more accurately than previous microRNA-based methods.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period

BackgroundSevere heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period.MethodsWe extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values.ResultsA total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality.ConclusionsThe survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Survival status and predictors of mortality among severely acute malnourished under-5 children admitted to stabilisation centers in selected government hospitals in Addis Ababa, Ethiopia, 2022: a retrospective cohort study

ObjectiveThis study aims to assess the survival status and predictors of mortality among under-5 children with severe acute malnutrition in Addis Ababa, Ethiopia.DesignA retrospective cohort study was employed on randomly...

Unveiling an anoikis-related risk model and the role of RAD9A in colon cancer

ObjectiveColorectal cancer (CRC), specifically colon adenocarcinoma, is the third most prevalent and the second most lethal form of cancer. Anoikis is found to be specialized form of programmed cell death (PCD), which plays a pivotal role in tumor progression. This study aimed to investigate the role of the anoikis related genes (ARGs) in colon cancer. MethodsConsensus unsupervised clustering, differential expression analysis, tumor mutational burden analysis, and analysis of immune cell infiltration were utilized in the study. For the analysis of RNA sequences and clinical data of COAD patients, data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were obtained. A prognostic scoring system for overall survival (OS) prediction was developed using Cox regression and LASSO regression analysis. Furthermore, loss-of-function assay was utilized to explore the role of RAD9A played in the progression of colon cancer. ResultsThe prognostic value of a risk score composed of NTRK2, EPHA2, RAD9A, CDC25C, and SNAI1 genes was significant. Furthermore, these findings suggested potential mechanisms that may influence prognosis, supporting the development of individualized treatment plans and management of patient outcomes. Further experiments confirmed that RAD9A could promote proliferation and metastasis of colon cancer cells. These effects may be achieved by affecting the phosphorylation of AKT. ConclusionDifferences in survival time and the tumor immune microenvironment (TIME) were observed between two gene clusters associated with ARGs. In addition, a prognostic risk model was established and confirmed as an independent risk factor. Furthermore, our data indicated that RAD9A promoted tumorigenicityby activating AKT in colon cancer.

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non–small-cell lung cancer

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical aspects of clinical study design and biomarker discovery but also ensures that the ‘right patient’ receives the ‘right treatment’. However, survival prediction is a highly complex task, given the large number of ‘omics; and clinical features, as well as the high degree of freedom that drive patient survival. Prior knowledge could play a critical role in uncovering the complexity of a disease and understanding the driving factors affecting a patient’s survival. We introduce a methodology for incorporating prior knowledge into machine learning–based models for prediction of patient survival through Knowledge Graphs, demonstrating the advantage of such an approach for NSCLC patients. Using data from patients treated with immuno-oncologic therapies in the POPLAR (NCT01903993) and OAK (NCT02008227) clinical trials, we found that the use of knowledge graphs yielded significantly improved hazard ratios, including in the POPLAR cohort, for models based on biomarker tumor mutation burden compared with those based on knowledge graphs. Use of a model-defined mutational 10-gene signature led to significant overall survival differentiation for both trials. We provide parameterized code for incorporating knowledge graphs into survival analyses for use by the wider scientific community.

The Glasgow Prognostic Score as a Predictor of Survival after Chemoradiotherapy for Limited-Disease Small Cell Lung Cancer.

Established biomarkers for predicting chemoradiotherapy efficacy for limited-disease small cell lung cancer (LD-SCLC) are lacking. The inflammation-based Glasgow Prognostic Score (GPS), comprising serum C-reactive protein (CRP) and albumin levels, can predict survival in advanced cancer. This study investigated whether metabolic and inflammatory markers, including the GPS, can predict the efficacy of chemoradiotherapy in patients with LD-SCLC. We retrospectively analyzed 124 patients who underwent chemoradiotherapy for LD-SCLC at two institutions between April 2007 and June 2021, and assessed the prognostic significance of various metabolic and inflammatory markers. The GPS was calculated using the CRP and albumin concentrations, and categorized as follows: 0, CRP &lt;1.0 mg/dL and albumin ≥3.5 mg/dL; 1, elevated CRP or decreased albumin; and 2, CRP ≥1.0 mg/dL and albumin&lt;3.5 mg/dL. Differences in progression-free survival (PFS) and overall survival (OS) were examined using Kaplan-Meier curves and Cox proportional-hazard models. The overall response rate was 95.1% (95% confidence interval [CI]: 89.6-97.9%). The median PFS and OS from chemoradiotherapy initiation were 12.6 (95% CI: 9.9-15.4) and 29.0 (95% CI: 24.8-45.5) months, respectively. The GPS demonstrated independent predictive ability for the effectiveness of chemoradiotherapy, wherein favorable scores (GPS 0-1) were significantly correlated with superior PFS and OS compared to unfavorable scores (GPS 2: PFS: 14.8 vs. 6.7 months, p = 0.0001; OS: 35.4 vs. 11.0 months, p &lt; 0.0001). This preliminary examination revealed that the GPS was significantly associated with PFS and OS in patients undergoing chemoradiotherapy for LD-SCLC, indicating its potential utility in assessing the therapeutic outcomes in LD-SCLC.

Assessment of survival prediction after surgery in spinal metastases patients using the Global Spine Study Tumor Group (GSTSG) risk calculator; an external validation from a tertiary cancer hospital.

We aim to validate the Global Spine Tumor Study Group (GSTSG) score compared to previous prognostic scoring systems in spinal metastasis. We conducted a retrospective study from January 2013 to December 2022. The survival prediction was compared between the GSTSG, Tomita Score, Revised Tokuhashi Score, and Skeletal Oncology Research Group (SORG) Nomogram. Single-variable factors associated with survival rate were analyzed using univariate Cox regression and multivariable Cox proportional hazard model. Receiver operating characteristic was used for external validity analysis at 3, 6, 12, and 24months. The overall survival rate was reported using the Kaplan-Meier survival curve. 248 spinal metastasis patients were included. The mean age was 59.23 ± 12.55years. The mean duration of follow-up time was 470.29 ± 441.98days. The external validity of GSTSG was the highest at all follow-up times (sufficiently accurate AUC > 0.7), which was about the same as SORG at 3months (both AUC of GSTSG and SORG = 0.76) and higher than modified Tokuhashi and Tomita score at 12months (AUC of GSTSG = 0.78, SORG = 0.71, Tomita = 0.64, and modified Tokuhashi = 0.61, respectively). From our study, the Multivariate Cox regression analysis indicates that the significant factors related to survival rate are regular analgesic use of weak opioids, lung metastasis, and previous chemotherapy. Compared to other traditional spinal metastases prognostic scoring systems, GSTSG shows the highest AUC for external validity in all follow-up times up to 24months.

A Kernel Attention-based Transformer Model for Survival Prediction of Heart Disease Patients.

Survival analysis is employed to scrutinize time-to-event data, with emphasis on comprehending the duration until the occurrence of a specific event. In this article, we introduce two novel survival prediction models: CosAttnSurv and CosAttnSurv DyACT. CosAttnSurv model leverages transformer-based architecture and a softmax-free kernel attention mechanism for survival prediction. Our second model, CosAttnSurv DyACT, enhances CosAttnSurv with Dynamic Adaptive Computation Time (DyACT) control, optimizing computation efficiency. The proposed models are validated using two public clinical datasets related to heart disease patients. When compared to other state-of-the-art models, our models demonstrated an enhanced discriminative and calibration performance. Furthermore, in comparison to other transformer architecture-based models, our proposed models demonstrate comparable performance while exhibiting significant reduction in both time and memory requirements. Overall, our models offer significant advancements in the field of survival analysis and emphasize the importance of computationally effective time-based predictions, with promising implications for medical decision-making and patient care.

Outcomes After Definitive Surgery for Spinal and Sacral Chordoma in 101 Patients Over 20 Years.

Spinal chordomas are primary bone tumors where surgery remains the primary treatment. However, their low incidence, lack of evidence, and late disease presentation make them challenging to manage. Here, we report the postoperative outcomes of a large cohort of patients after surgical resection, investigate predictors for overall survival (OS) and local recurrence-free survival (LRFS) times, and trend functional outcomes over multiple time periods. Retrospective review of all patients followed for spinal chordoma at a quaternary spinal oncology center from 2003 to 2023 was included. Data were collected regarding demographics, preoperative and perioperative management, and follow-up since initial definitive surgery. Primary outcomes were OS and LRFS, whereas secondary outcomes were functional deficits. One hundred one patients had an average follow-up of 6.0 ± 4.2 years. At the time of census, 25/101 (24.8%) had experienced a recurrence and 10/101 (9.9%) had died. After surgery, patients experienced a significant decrease in pain over time, but rates of sensory deficits, weakness, and bowel/bladder dysfunction remained static. Tumors ≥100 cm3 (hazard ratio (HR) = 5.89, 95% CI 1.72-20.18, P = .005) and mobile spine chordomas (HR = 7.73, 95% CI 2.09-28.59, P = .002) are related to worse LRFS, whereas having neoadjuvant radiotherapy is associated with improved LRFS (HR = 0.09, 95% CI 0.01-0.88, P = .038). On the other hand, being age ≥65 years was associated with decreased OS (HR = 16.70, 95% CI 1.54-181.28, P = .021). Surgeons must often weigh the pros and cons of en bloc resection and sacrificing important but affected native tissues. Our findings can provide a benchmark for counseling patients with spinal chordoma. Tumors ≥100 cm3 appear to have a 5.89-times higher risk of recurrence, mobile spine chordomas have a 7.73 times higher risk, and neoadjuvant radiotherapy confers an 11.1 times lower risk for local recurrence. Patients age ≥65 years at surgery have a 16.70 times higher risk of mortality than those <65 years.

Predictors of survival in immunotherapy-based treatments in advanced melanoma: a meta-analysis.

The introduction of immunotherapy-based strategies has significantly improved the prognosis for melanoma patients. Nevertheless, some patients still have dismal outcomes, emphasizing the significance of survival predictive indicators in immunotherapy-based approaches. We systematically searched randomized controlled clinical trials investigating dual immunotherapy or chemoimmunotherapy versus placebo or mono-immunotherapy or chemotherapy alone in advanced melanoma patients. R version 4.3.0. was employed to perform all analyses. A comprehensive analysis was conducted on a total of 13,809 patients with advanced melanoma from 19 randomized clinical trials. Immunotherapy-based strategies (alone or in combination) could significantly lengthen the overall survival(OS) and recurrence-free survival (RFS) compared with corresponding controls. Mono-immunotherapy improved RFS and OS in PD-L1 positive patients, in stage AJCC IIIC, and with 4 or morepositive lymph nodes, compared with chemotherapy. Combined immunotherapy statistically improved RFS and OS in those aged < 65, with an Eastern Cooperative Oncology Group (ECOG) status of 0, and LDH ≤ ULN at baseline compared with single treatment alone. Our findings indicated that certain clinicopathological and molecular features could assist in choosing appropriate melanoma patients for immune-based treatments.