Prognostic role of the advanced lung cancer inflammation index in cancer patients: a meta-analysis

Advanced Lung Cancer Inflammation Index: A Novel Comprehensive Biomarker of Host Status for Patients with Metastatic Colorectal Cancer

Numerous biomarkers that reflect host status have been identified for patients with metastatic colorectal cancer (mCRC). However, there has been a paucity of biomarker studies that comprehensively indicate body composition, nutritional assessment, and systemic inflammation status. The advanced lung cancer inflammation index (ALI), initially introduced as a screening tool for patients with non-small-cell lung cancer in 2013, emerges as a holistic marker encompassing all body composition, nutritional status, and systemic inflammation status. The index is calculated by the simple formula: body mass index × albumin value / neutrophil-to-lymphocyte ratio. Given its accessibility in routine clinical practice, the ALI has exhibited promising clinical utility in prognosticating outcomes for patients with multiple types of cancer. In this review, we focus on the significance of host status and the clinical applicability of the ALI in the treatment and management of patients with malignancies, including mCRC. We also suggest its potential in guiding the formulation of treatment strategies against mCRC and outline future perspectives.

A novel nutritional inflammation index for predicting mortality in acute ischemic stroke patients: insights into advanced lung cancer inflammation index from the Medical Information Mart for Intensive Care-IV database

Objective

This investigation aimed to delineate the association between the advanced lung cancer inflammation index (ALI) and all-cause mortality (ACM) in individuals experiencing acute ischemic stroke (AIS).

Methods

Drawing on information from the Medical Information Mart for Intensive Care (MIMIC)-IV database, release 2.2, covering the years 2012 to 2019, this research assessed the advanced lung cancer inflammation index (ALI) by factoring in body mass index (BMI), serum albumin levels (ALB), and the neutrophil-to-lymphocyte ratio (NLR). Patients with AIS were identified using codes from the International Classification of Diseases (ICD). To address potential confounding factors, a 1:1 propensity score matching (PSM) method was utilized. The investigation identified the pivotal ALI level impacting patient survival using maximally selected rank statistics. It then examined the effects on short- and long-term ACM through multivariate Cox proportional hazards regression models and Kaplan-Meier (K-M) survival analysis. Additionally, restricted cubic spline (RCS) methods were applied to delve into the linear or nonlinear nature of the relationship between ALI and ACM, with further insights gained from interaction and subgroup analyses.

Results

The cohort comprised 838 AIS patients. Post-PSM, analysis involved 199 matched patient pairs. Adjusted Cox proportional hazard models indicated a significant association of low ALI (<10.38) with increased in-hospital ACM, both before (HR: 1.98; 95% CI: 1.36-2.88; p < 0.001) and after PSM (HR: 2.16; 95% CI: 1.32-3.52; p = 0.002). Associations of low ALI with elevated risk were consistent across ICU, 30 days, 90 days, and 1 year ACM pre- and post-PSM. Subsequent RCS analysis post-PSM underscored a negative nonlinear relationship between ALI and ACM over both short and long terms, without significant interaction effects across different subgroups for ACM.

Conclusion

In this retrospective cohort study, by utilizing a nationally representative sample of United States patients with AIS, our analysis elucidates a negative correlation between the ALI and ACM in individuals with AIS, underscoring the utility of ALI as a novel, efficacious, and accessible inflammatory biomarker for prognosticating ACM. These results carry profound implications for public health policy and practice. A deeper comprehension of these associations can empower public health practitioners and researchers to devise more targeted interventions and policies, aimed specifically at catering to the distinct needs of the AIS patient population, thereby enhancing their health outcomes. The further research in other races/ethnicity is urgent, particularly before applying these findings in clinical practice.

Prognostic value of diffuse cancer inflammation index (ALI), serum neutrophil/lymphocyte (NLR) and platelet/lymphocyte (PLR) in advanced-stage lung cancer

INTRODUCTION

Lung cancer is the most common type of cancer that causes death worldwide. Systemic inflammation has been shown to play a role in cancer etiopathogenesis and can be activated from oncogenic changes in cancer cells. In our study, the prognostic effects of inflammatory parameters calculated from serum were investigated in lung cancer.

METHOD

One hundred fifteen patients with locally advanced and advanced lung cancer who were diagnosed in our chest diseases clinic between 2013 and 2015 were retrospectively analyzed. The relationship between advanced lung cancer inflammation index (ALI index), serum neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR) levels at the time of diagnosis were calculated, and their relationship with overall survival (OS), disease-free survival, and the treatment response and their effect on predicting prognosis were investigated.

FINDINGS

When the ALI value was examined in the group with non-small cell lung, the OS was found to be 9.018 months in the group over 18 years of age and it was 3.78 months in the group below. Low ALI index was significantly associated with short survival ( P <.05). When the NLR values were examined in the entire patient group, OS more than 5 was 5.95 months and less than 9.63 months. A high NLR value was significantly associated with short survival ( P <.05). No significant relationships were detected between PLR and OS. When the determined cut-off values were used, no significant correlation was found between NLR, ALI, and PLR levels and progression-free survival ( P >.05).

CONCLUSION

In our study, it was concluded that elevated NLR levels and low ALI values at the time of diagnosis of advanced-stage lung cancer were associated with poor survival, and those values may be useful in predicting survival and prognosis when the cut-off values were used. These parameters can be useful in routine use because they can be easily calculated without additional costs.

Effects of nutritional indices and inflammatory parameters on patients received immunotherapy for non-small cell lung cancer

OBJECTIVE

This research explored the relationship between a patient's nutritional state and inflammatory markers and the prognosis of their non-small cell lung cancer (NSCLC) treatment while receiving a combination of chemotherapy and immunotherapy.

METHOD

This retrospective and single-center analysis included NSCLC patients who received a combination of chemotherapy and immunotherapy at the Department of Oncology at Shanghai Lung Hospital. Patients were categorized based on malnutrition, sarcopenia, sarcopenic obesity, and advanced-lung-cancer-inflammation-index (ALI) scores after collecting nutritional and inflammatory indices. Kaplan-Meier and the Cox models were utilized to analyze survival.

RESULTS

There was a significant correlation between malnutrition, sarcopenia, sarcopenic obesity, and low ALI scores with lower overall survival (OS) and progression-free survival (PFS) (p < 0.05). Low ALI score and malnutrition were independent factors influencing patient survival in terms of both OS and PFS (p < 0.01).

CONCLUSION

The nutritional and inflammatory indices of immunotherapy-treated NSCLC patients substantially affect their prognosis. Assessing these variables could aid in optimizing treatment strategies and improving patient outcomes. Additional research is required to comprehend the intricate relationship between nutrition, inflammation, and cancer progression and to develop individualized therapies.

The progresses of relevant factors on the efficacy of immune checkpoint inhibitors in the non-small cell lung cancer patients

Lung cancer has the highest mortality rate of all cancers worldwide. Although immune checkpoint inhibitor (ICI)-based therapy can improve the survival of patients with lung cancer, its efficacy is affected by many factors. Therefore, it is necessary to identify factors that affect the efficacy of ICI-based treatment and establish a model for predicting drug response and resistance before and during treatment for individualized and accurate treatment of patients. This review summarizes the clinical and biological factors related to ICI-based treatment of non-small cell lung cancer (NSCLC) and the recent research progress of predictive models for assessing ICI efficacy.

Clinical significance of the advanced lung cancer inflammation index in gastrointestinal cancer patients: a systematic review and meta-analysis

Background

The advanced lung cancer inflammation index (ALI) has been identified as a scientific and clinical priority in multiple malignancies. The aim of this study is to investigate the value of the ALI before treatment in evaluating postoperative complications (POCs) and survival outcomes in patients with gastrointestinal (GI) cancer.

Methods

Electronic databases including PubMed, Embase and Web of Science were comprehensively reviewed up to June 2022. The endpoints were POCs and survival outcomes. Subgroup analyses and sensitivity analyses were also performed.

Results

Eleven studies including 4417 participants were included. A significant heterogeneity in the ALI cut-off value among studies was observed. Patients in the low ALI group showed increased incidence of POCs (OR=2.02; 95%CI:1.60-2.57; P<0.001; I² = 0%). In addition, a low ALI was also significantly associated with worse overall survival (HR=1.96; 95%CI: 1.58-2.43; P<0.001; I² = 64%), which remained consistent in all subgroups based on country, sample size, tumor site, tumor stage, selection method and Newcastle Ottawa Scale score. Moreover, patients in the low ALI group had an obviously decreased disease-free survival compared to these in the high ALI group (HR=1.47; 95%CI: 1.28-1.68; P<0.001; I² = 0%).

Conclusion

Based on existing evidence, the ALI could act as a valuable predictor of POCs and long-term outcomes in patients with GI cancer. However, the heterogeneity in the ALI cut-off value among studies should be considered when interpreting these findings.

Lymphocyte-C-reactive protein ratio with calf circumference could better predict survival of patients with non-metastatic cancer

Systemic inflammatory responses caused by tumor cells play an important role in the occurrence and development of tumors. The aim of this study was to identify biomarkers that most accurately predict prognoses in patients with non-metastatic cancer and to evaluate their clinical significance when combined with muscle markers. This study retrospectively evaluated 2,797 cancer patients diagnosed with cancer at TNM stages I, II, and III. Lymphocyte-C-reactive protein ratio (LCR) in conjunction with calf circumference (CC) were used (or chosed) after evaluating the predictive value of 13 inflammatory marker combinations and five anthropometric indicators for patient outcomes using the C-index. The Kaplan-Meier method and Cox's proportional hazards regression modeling were used to analyze the individual and combined effects of these two potential biomarkers on overall survival. This study enrolled 1,604 men (57.3%) and 1,193 women (42.7%) with a mean age of 58.75 years. Among the 13 inflammatory nutritional indicators, the LCR was the most accurate predictor of prognoses in patients with non-metastatic cancer. After multifactorial adjustment, we found that low LCR had an adverse effect on overall survival (hazard ratio [HR]: 2.50; 95% confidence interval [CI]: 2.17, 2.88; P < 0.001). Low LCR combined with low CC was also shown to be an independent risk factor for poor overall survival (HR: 2.26; 95% CI: 1.80, 2.83; P < 0.001). Compared with LCR or CC alone, the combination of the two had greater prognostic value for patients with non-metastatic cancer. The LCR can be implemented as a useful biomarker to predict prognoses in patients with non-metastatic cancer. CC is the best anthropometric indicator of muscle loss in patients with non-metastatic cancer. The combination of LCR and CC can better predict the prognosis of patients with non-metastatic cancer, and can provide important information for clinicians to formulate diagnosis and treatment plans.

Systemic Inflammation/Nutritional Status Scores Are Prognostic but Not Predictive in Metastatic Non-Small-Cell Lung Cancer Treated with First-Line Immune Checkpoint Inhibitors

Biomarkers of systemic inflammation/nutritional status have been associated with outcomes in advanced-stage non-small-cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). However, most of them were not tested in cohorts of patients treated with ICIs in combination with chemotherapy (CT) (ICI + CT) or with CT alone, making it impossible to discriminate a predictive from a prognostic effect. We conducted a single-center retrospective study to search for associations between various baseline biomarkers/scores that reflected the systemic inflammation/nutritional status (Lung Immune Prognostic Index, Modified Lung Immune Prognostic Index, Scottish Inflammatory Prognostic Score, Advanced Lung Cancer Inflammation Index, EPSILoN, Prognostic Nutritional Index, Systemic Immune-Inflammation Index, Gustave Roussy Immune Score, Royal Marsden Hospital Prognostic Score, Lung Immuno-oncology Prognostic Score 3, Lung Immuno-oncology Prognostic Score 4, score published by Holtzman et al., and Glasgow Prognostic Score) and outcomes in metastatic NSCLC treated in a first-line setting either with ICI in monotherapy (cohort 1; n = 75), ICI + CT (cohort 2; n = 56), or CT alone (cohort 3; n = 221). In the three cohorts, the biomarkers/scores were moderately associated with overall survival (OS) and progression-free survival (PFS). Their prognostic performance was relatively poor, with a maximum c-index of 0.66. None of them was specific to ICIs and could help to choose the best treatment modality. The systemic inflammation/nutritional status, associated with outcomes independently of the treatment, is therefore prognostic but not predictive in metastatic NSCLC.

The prognostic value of advanced lung cancer inflammation index (ALI) in elderly patients with heart failure

Purpose

The advanced lung cancer inflammation index (ALI) is a novel inflammatory and nutritional index that exerts prognostic value in various types of cancer. A previous study demonstrated that ALI at discharge could predict the prognosis in patients with acute decompensated heart failure (ADHF). However, the long-term prognostic value of ALI on admission in elderly heart failure (HF) inpatients remains unclear.

Materials and methods

We retrospectively collected HF inpatients over 65-year-old who were hospitalized in our cardiology center during the whole year of 2015. ALI was calculated as body mass index (BMI) × serum albumin (Alb)/neutrophil-to-lymphocyte ratio (NLR). Patients were divided into two groups by the optimal cutoff value of ALI for predicting all-cause mortality using time-dependent receiver operating characteristic (ROC) curves. The Spearman rank correlation coefficient was computed to evaluate the correlation between ALI and the geriatric nutritional risk index (GNRI). Kaplan–Meier curves, Cox survival analyses, time-dependent ROC analyses, and net reclassification improvement (NRI) analyses were used to assess the prognostic effect of ALI on all-cause mortality and cardiovascular mortality.

Results

Over a 28-month median follow-up, all-cause and cardiovascular mortality occurred in 192 (35.4%) and 132 (24.3%) out of 543 patients, respectively. The optimal cutoff value of ALI for predicting all-cause mortality at 2 years was 25.8. Spearman’s correlation coefficient showed a moderate positive linear correlation between ALI and GNRI (r = 0.44, p < 0.001). The Kaplan–Meier analysis revealed that the cumulative incidences of both all-cause and cardiovascular mortalities were significantly higher in patients with lower ALI (log-rank test, all-cause mortality: p < 0.0001; cardiovascular mortality: p < 0.0001). The multivariate Cox proportional hazard analyses indicated that ALI was an independent predictor for both all-cause mortality (HR 0.550, 95% CI 0.349–0.867, p = 0.01) and cardiovascular mortality (HR 0.536, 95% CI 0.302–0.953, p = 0.034). Time-dependent ROC analyses showed that ALI was comparable to GNRI in predicting long-term all-cause mortality (AUC: ALI 0.625, GNRI 0.641, p = 0.976) and cardiovascular mortality (AUC: ALI 0.632, GNRI 0.626, p = 0.999) at 2 years. However, the estimated NRI indicated that the addition of ALI could not significantly improve risk stratification of base models for all-cause mortality (categorical NRI 4.9%, p = 0.433, continuous NRI 25%, p = 0.022) or cardiovascular mortality (categorical NRI 6.5%, p = 0.223, continuous NRI 27.5%, p = 0.029).

Conclusion

Higher ALI was significantly associated with lower all-cause and cardiovascular mortalities in elderly HF patients. ALI on admission could be a competent nutrition-inflammation marker with independent predictive value for evaluating the long-term mortality of HF in elder population.

Advances and applications of monoolein as a novel nanomaterial in mitigating chronic lung diseases

Chronic lung diseases such as asthma, chronic obstructive pulmonary disease, lung cancer, and the recently emerged COVID-19, are a huge threat to human health, and among the leading causes of global morbidity and mortality every year. Despite availability of various conventional therapeutics, many patients remain poorly controlled and have a poor quality of life. Furthermore, the treatment and diagnosis of these diseases are becoming increasingly challenging. In the recent years, the application of nanomedicine has become increasingly popular as a novel strategy for diagnosis, treatment, prevention, as well as follow-up of chronic lung diseases. This is attributed to the ability of nanoscale drug carriers to achieve targeted delivery of therapeutic moieties with specificity to diseased site within the lung, thereby enhancing therapeutic outcomes of conventional therapies whilst minimizing the risks of adverse reactions. For this instance, monoolein is a polar lipid nanomaterial best known for its versatility, thermodynamic stability, biocompatibility, and biodegradability. As such, it is commonly employed in liquid crystalline systems for various drug delivery applications. In this review, we present the applications of monoolein as a novel nanomaterial-based strategy for targeted drug delivery with the potential to revolutionize therapeutic approaches in chronic lung diseases.

Advanced lung cancer inflammation index (ALI) predicts prognosis of patients with gastric cancer after surgical resection

Introduction

Advanced lung cancer inflammation index (ALI) has been implicated in the prognosis of many types of tumors. But few studies elucidate its role in gastric cancer (GC).

Materials and methods

We consecutively recruited 615 GC patients who underwent radical gastrectomy. Patients were grouped according to ALI status. Risk factors for overall survival (OS) and disease-free survival (DFS) in overall and sex-stratified cohorts were determined using multivariate cox regression analysis. We also compared survival differences between the two groups after one-to-one propensity score matching (PSM).

Results

Patients with low ALI showed larger tumor size, more advanced TNM staging, shorter OS (median: 37 vs 42 months) and DFS (median: 37 vs 42 months) (all P < 0.001). Multivariate analysis showed that elevated ALI was independently associated with longer OS and DFS. After stratification by sex, low ALI was an independent risk factor for OS and DFS in male patients but not in female patients. But our further PSM analysis showed prognostic value of ALI in both male and female subgroups.

Conclusion

Preoperative ALI is an independent prognostic factor for GC patients undergoing curative gastrectomy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09774-z.

The prognostic value of the advanced lung cancer inflammation index (ALI) for patients with neuroblastoma

Objective

The advanced lung cancer inflammation index (ALI) can predict the survival of patients with lung cancer and other malignancies. However, the prognostic significance of ALI in neuroblastoma has not been reported. This study aimed to evaluate the correlation between ALI and neuroblastoma patient prognosis.

Methods

We retrospectively analyzed the data of 72 neuroblastoma patients treated between January 2014 and August 2020. ALI calculation: Body mass index (BMI) × serum albumin (ALB)/neutrophil-to-lymphocyte ratio (NLR). The optimal cutoff points of prognostic biomarkers were determined by generating receiver operating characteristic (ROC) curves. According to the cutoff value, the patients were categorized into low or high ALI groups. The chi-square test was used to compare clinical parameters between the two groups. Potential prognostic factors associated with overall survival (OS) were assessed using Kaplan–Meier and Cox regression analyses.

Results

The optimal cutoff value of ALI was 49.17. The low ALI group showed more severe clinical characteristics and poorer survival rates. Univariate and multivariate Cox analyses suggested that ALI and the International Neuroblastoma Staging System (INSS) stage were independent prognostic factors for neuroblastoma patients.

Conclusions

Low ALI is associated with poor prognosis in neuroblastoma patients. ALI may be an independent prognostic biomarker for neuroblastoma.

Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with lung cancer inflammation index, computed tomography and chest radiograph

The purpose of the present study was to evaluate the feasibility of applying the advanced lung cancer inflammation index (ALI) in patients with coronavirus disease 2019 (COVID-19) and to establish a combined ALI and radiologic risk prediction model for disease exacerbation. The present study included patients diagnosed with COVID-19 infection in our single institution from March to October 2020. Patients without clinical information and/or chest computed tomography (CT) upon admission were excluded. A radiologist assessed the CT severity score and abnormality on chest radiograph. The combined ALI and radiologic risk prediction model was developed via random forest classification. Among 79 patients (age, 43±19 years; male/female, 45:34), 72 experienced improvement and seven patients experienced exacerbation after admission. Significant differences were observed between the improved and exacerbated groups in the ALI (median, 47.6 vs. 13.2; P=0.011), frequency of chest radiograph abnormality (24.7 vs. 83.3%; P<0.001), and chest CT score (CCTS; median, 1 vs. 9; P<0.001). For the accuracy of predicting exacerbation, the receiver-operating characteristic curve analysis demonstrated an area under the curve of 0.79 and 0.92 for the ALI and CCTS, respectively. The combined ALI and radiologic risk prediction model had a sensitivity of 1.00 and a specificity of 0.81. Overall, ALI alone and CCTS alone modestly predicted the exacerbation of COVID-19, and the combined ALI and radiologic risk prediction model exhibited decent sensitivity and specificity.

An integrated analysis of prognostic and immune infiltrates for hub genes as potential survival indicators in patients with lung adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) is one of the major subtypes of lung cancer that is associated with poor prognosis. The aim of this study was to identify useful biomarkers to enhance the treatment and diagnosis of LUAD.

Methods

GEO2R was used to identify common up-regulated differentially expressed genes (DEGs) in the GSE32863, GSE40791, and GSE75037 datasets. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis as well as construction of the protein-protein interaction (PPI) network, while the molecular complex detection (MCODE) plug-in was employed to filter important subnetworks. The expression levels of the hub genes and their prognostic values were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The timer algorithm was utilized to determine the correlation between immune cell infiltration and the expression levels of hub genes in LUAD tissues. In addition, the hub gene mutation landscape and the correlation analysis with tumor mutational burden (TMB) score were evaluated using maftools package and ggstatsplot package in R software, respectively.

Results

We identified 156 common up-regulated DEGs, with gene ontology and pathway enrichment analysis indicating that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF, and KNTC1. The seven hub genes were highly expressed in LUAD tissues and were associated with poor prognosis. These hub genes were negatively correlated with most immune cells. The somatic mutation landscape showed that AURKB, CDC20, CENPF, and KNTC1 had mutations and were positively correlated with TMB scores.

Conclusions

Our findings demonstrate that increased expression of seven hub genes is associated with poor prognosis for LUAD patients. Additionally, the TMB score indicates that the high expression of hub gene increases immune cell infiltration in patients with lung adenocarcinoma which may significantly improve response to immunotherapy.

In Silico Bioinformatics Followed by Molecular Validation Using Archival FFPE Tissue Biopsies Identifies a Panel of Transcripts Associated with Severe Asthma and Lung Cancer

Simple Summary

The present study identified a panel of transcripts involved in the pathogenesis of both severe asthma and lung cancer. The genes identified using publicly available transcriptomics data were validated on cell lines, plasma samples, and archival tissue biopsies from asthmatic and lung cancer patients. The functional roles of the identified markers in both the diseases were ascertained from the literature. These molecular markers might be useful for diagnosing lung cancer at early stages.

Abstract

Severe asthma and lung cancer are both heterogeneous pathological diseases affecting the lung tissue. Whilst there are a few studies that suggest an association between asthma and lung cancer, to the best of our knowledge, this is the first study to identify common genes involved in both severe asthma and lung cancer. Publicly available transcriptomic data for 23 epithelial brushings from severe asthmatics and 55 samples of formalin-fixed paraffin-embedded (FFPE) lung cancer tissue at relatively early stages were analyzed by absolute gene set enrichment analysis (GSEA) in comparison to 37 healthy bronchial tissue samples. The key pathways enriched in asthmatic patients included adhesion, extracellular matrix, and epithelial cell proliferation, which contribute to tissue remodeling. In the lung cancer dataset, the main pathways identified were receptor tyrosine kinase signaling, wound healing, and growth factor response, representing the early cancer pathways. Analysis of the enriched genes derived from the pathway analysis identified seven genes expressed in both the asthma and lung cancer sets: BCL3, POSTN, PPARD, STAT1, MYC, CD44, and FOSB. The differential expression of these genes was validated in vitro in the cell lines retrieved from different lung cancer and severe asthma patients using real-time PCR. The effect of the expression of the seven genes identified in the study on the overall survival of lung cancer patients (n = 1925) was assessed using a Kaplan–Meier plot. In vivo validation performed in the archival biopsies obtained from patients diagnosed with both the disease conditions provided interesting insights into the pathogenesis of severe asthma and lung cancer, as indicated by the differential expression pattern of the seven transcripts in the mixed group as compared to the asthmatics and lung cancer samples alone.

Immune Infiltration Analysis with the CIBERSORT Method in Lung Cancer

Background

Immune infiltration of lung cancer (LC) is tightly related to clinical results. Nevertheless, past researches have not elucidated the diversities of functionally different cellular types making up the immunoresponse.

Methods

In the present research, on the foundation of a deconvolution algorithm (CIBERSORT) and clinically annotated expression profiles, our team studied the tumor-infiltrating immune cells (TIICs) presenting in 502 LC samples and 49 normal samples in a comprehensive way. The fraction of 22 immunocyte subgroups was assessed to identify the relationship among every cellular type and survival and reaction to chemical therapies.

Results

Consequently, profiles of immunity infiltration change remarkably between paired tumor and precancerous tissues, and the change can describe the diversity of individuals. Of the cellular subgroups studied, cancers without dendritic resting cells or with a decreased quantity of follicular helper T (Tfh) cells were related to the poor prognosis. Correlation analysis between different stages of LC and 22 immune cell subpopulations revealed that the amount of 14 immune cells in LC was remarkably related to tumor stage. The high expression of resting dendritic cells and follicular helper T cells predicted better prognostic value, and univariate analyses proved that two TIICs were significantly associated with patients' prognosis.

Conclusions

To sum up, the data herein reveal that there may be subtle differences in the cell constituents of the immune infiltrate in LC, and those diversities may be vital determinating factors of prognostic results and reactions to therapies.

Prognostic value of pretreatment advanced lung cancer inflammation index in non-small cell lung cancer: a meta-analysis

Purpose To identify the prognostic value of pretreatment advanced lung cancer inflammation index (ALI) in non-small cell lung cancer (NSCLC) including surgical patients who were diagnosed with early stage. Methods The PubMed, EMBASE and Web of Science electronic databases were searched up to January 12, 2022 for relevant studies. The hazard ratios (HRs) with 95% confidence intervals (CIs) were combined to assess the association between pretreatment ALI and overall survival (OS) or progression-free survival (PFS) of NSCLC patients. All statistical analyses were conducted by STATA 12.0 software. Results A total of 14 studies involving 3607 participates were included. The pooled results indicated that lower pretreatment ALI was significantly related with poorer OS (HR =2.20, 95% CI: 1.46-3.33, P<0.001) and PFS (HR =1.78, 95% CI: 1.49-2.13, P<0.001). Besides, subgroup analysis also demonstrated that lower pretreatment ALI was associated with worse OS in surgical (P<0.001) and non-metastatic (P<0.001) patients and worse PFS of surgical (P<0.001) NSCLC patients. Conclusion Pretreatment ALI was a novel and reliable prognostic indicator in NSCLC and lower pretreatment ALI predicted worse survival including patients diagnosed with early stage. However, more prospective high-quality studies are still needed to verify above findings.

Association Between Systemic Inflammation and Malnutrition With Survival in Patients With Cancer Sarcopenia-A Prospective Multicenter Study

Objective

Systemic inflammation and malnutrition are correlated with cancer sarcopenia and have deleterious effects on oncological outcomes. However, the combined effect of inflammation and malnutrition in patients with cancer sarcopenia remains unclear.

Methods

We prospectively collected information on 1,204 patients diagnosed with cancer sarcopenia. the mean (SD) age was 64.5 (11.4%) years, and 705 (58.60%) of the patients were male. The patients were categorized into the high advanced lung cancer inflammation index (ALI) group (≥18.39) and the low ALI group (<18.39) according to the optimal survival cut-off curve. We selected the optimal inflammation marker using the C-index, decision curve analysis (DCA), and a prognostic receiver operating characteristic curve. Univariate and multivariate survival analyses were performed to determine the prognostic value of the optimal inflammation indicator. We also analyzed the association between inflammation and malnutrition in patients with cancer.

Results

The C-index, DCA, and prognostic area under the curve of ALI in patients with cancer sarcopenia were higher or better than those of neutrophil-lymphocyte ratio (NLR), prognostic nutritional index (PNI), systemic immune-inflammation index (SII), and platelet-lymphocyte ratio (PLR). The prognosis for patients in the low ALI group was worse than that of patients in the high ALI group [HR (95%CI) = 1.584 (1.280–1.959), P < 0.001]. When the ALI was divided into quartiles, we observed that decreased ALI scores strongly correlated with decreased overall survival (OS). Patients with both a low ALI and severe malnutrition (vs. patients with high ALI and well-nourished) had a 2.262-fold death risk (P < 0.001). Subgroup analysis showed a significant interactive association between the ALI and death risk in terms of TNM stage (P for interaction = 0.030).

Conclusions

The inflammation indicator of the ALI was better than those of the NLR, PNI, SII, and PLR in patients with cancer sarcopenia. Inflammation combined with severe malnutrition has a nearly 3-fold death risk in patients with cancer sarcopenia, suggesting that reducing systemic inflammation, strengthening nutritional intervention, and improving skeletal muscle mass are necessary.

The Role of Mitochondrial miRNAs in the Development of Radon-Induced Lung Cancer

MicroRNAs are short, non-coding RNA molecules regulating gene expression by inhibiting the translation of messenger RNA (mRNA) or leading to degradation. The miRNAs are encoded in the nuclear genome and exported to the cytosol. However, miRNAs have been found in mitochondria and are probably derived from mitochondrial DNA. These miRNAs are able to directly regulate mitochondrial genes and mitochondrial activity. Mitochondrial dysfunction is the cause of many diseases, including cancer. In this review, we consider the role of mitochondrial miRNAs in the pathogenesis of lung cancer with particular reference to radon exposure.

Prognostic Significance of Modified Advanced Lung Cancer Inflammation Index in Patients With Renal Cell Carcinoma Undergoing Laparoscopic Nephrectomy: A Multi-Institutional, Propensity Score Matching Cohort Study

Background

We conducted a multi-institutional clinical study to assess the prognostic value of the advanced lung cancer inflammatory index (ALI) and modified ALI (mALI) in patients with renal cell carcinoma (RCC).

Methods

We collected 440 patients who underwent laparoscopic nephrectomy at three centers from 2014 to 2019. ALI was defined as body mass index (BMI) × serum albumin (ALB)/neutrophil-to-lymphocyte ratio (NLR) and mALI as L3 muscle index × ALB/NLR. Kaplan-Meier curves, receiver operating characteristic (ROC) curves and Cox survival analysis were used to assess the effect of ALI and mALI on overall survival (OS). In addition, we performed 1:1 propensity score matching (PSM) for the high mALI and low mALI groups to further explore the impact of mALI on survival in RCC patients.

Results

The optimal cut-off values for ALI and mALI were 40.6 and 83.0, respectively. Based on the cut-off values, we divided the patients into high ALI and low ALI groups, high mALI and low mALI groups. ALI and mALI were significantly associated with the AJCC stage, Fuhrman grade, T stage, and M stage. Low ALI (p = 0.002) or low mALI (p < 0.001) was associated with poorer prognosis. ROC curves showed that mALI was a better predictor of OS than ALI. Multivariate Cox regression analysis showed that low mALI (aHR = 2.22; 95% CI 1.19–4.13, p = 0.012) was an independent risk factor for OS in RCC patients who underwent nephrectomy, while ALI (aHR = 1.40; 95% CI 0.73–2.66, p = 0.309) was not significantly associated. Furthermore, after PSM analysis, we found that mALI remained an independent risk factor for OS (aHR = 2.88; 95% CI 1.33–6.26, p = 0.007) in patients with RCC.

Conclusions

For RCC patients undergoing laparoscopic nephrectomy, low ALI and low mALI were associated with poor prognosis, and preoperative mALI can be used as a potential independent prognostic indicator for RCC patients.

Systemic Inflammation Index and Tumor Glycolytic Heterogeneity Help Risk Stratify Patients with Advanced Epidermal Growth Factor Receptor-Mutated Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitor Therapy

Simple Summary

Patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma have been known to respond to first-line tyrosine kinase inhibitor (TKI) treatment. However, a subgroup of patients are non-responsive to the treatment, with poor survival outcomes, and those who are initially responsive may still experience resistance. A reliable prognostic tool may provide a valuable direction for tailoring individual treatment strategies in this clinical setting. With this aim, the present study explores the prognostic power of the combination of the systemic inflammation index (portrayed by hematological markers) and tumor glycolytic heterogeneity (characterized by ¹⁸F-fluorodeoxyglucose positron emission tomography images). The model integrating these two biomarkers could be used to improve risk stratification, and the subsequent personalized management strategy in patients with advanced EGFR-mutated lung adenocarcinoma.

Abstract

Tyrosine kinase inhibitors (TKIs) are the first-line treatment for patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma. Over half of patients failed to achieve prolonged survival benefits from TKI therapy. Awareness of a reliable prognostic tool may provide a valuable direction for tailoring individual treatments. We explored the prognostic power of the combination of systemic inflammation markers and tumor glycolytic heterogeneity to stratify patients in this clinical setting. One hundred and five patients with advanced EGFR-mutated lung adenocarcinoma treated with TKIs were retrospectively analyzed. Hematological variables as inflammation-induced biomarkers were collected, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII). First-order entropy, as a marker of heterogeneity within the primary lung tumor, was obtained by analyzing ¹⁸F-fluorodeoxyglucose positron emission tomography images. In a univariate Cox regression analysis, sex, smoking status, NLR, LMR, PLR, SII, and entropy were associated with progression-free survival (PFS) and overall survival (OS). After adjusting for confounders in the multivariate analysis, smoking status, SII, and entropy, remained independent prognostic factors for PFS and OS. Integrating SII and entropy with smoking status represented a valuable prognostic scoring tool for improving the risk stratification of patients. The integrative model achieved a Harrell’s C-index of 0.687 and 0.721 in predicting PFS and OS, respectively, outperforming the traditional TNM staging system (0.527 for PFS and 0.539 for OS, both p < 0.001). This risk-scoring model may be clinically helpful in tailoring treatment strategies for patients with advanced EGFR-mutated lung adenocarcinoma.

Nomograms Based on the Advanced Lung Cancer Inflammation Index for the Prediction of Coronary Artery Disease and Calcification

Aim

To develop and validate 3 nomograms incorporating the advanced lung cancer inflammation index (ALI) that can aid in predicting the risk of coronary artery disease (CAD) and coronary artery calcification (CAC).

Methods

The study enrolled 562 consecutive patients with suspected CAD who underwent coronary computed tomographic angiography between September 2015 and June 2017. Independent risk factors for CAD, CAC, and CAD with CAC were identified via univariate and multivariate analysis, and nomograms were established based on the independent predictors identified. The area under the curve (AUC), calibration curve, and decision curve analysis were used to evaluate the nomograms. Correlations between ALI and other clinical indicators were examined via Spearman correlation analysis.

Results

In total, 549 patients with suspected CAD who underwent coronary computed tomographic angiography were included. Male sex, hypertension, diabetes, dyslipidemia, ischemic stroke, and ALI were independent predictors of both CAD and CAC. Male sex, hypertension, diabetes, dyslipidemia, and ALI were also identified as independent predictors of CAD with CAC. The AUC values for the nomograms developed using these risk factors were 0.739 (95% confidence interval [CI], 0.693-0.785), 0.728 (95% CI, 0.684-0.772), and 0.717 (95% CI 0.673-0.761), respectively. ALI was negatively correlated with neutrophil-to-lymphocyte ratio and CAC score and positively correlated with serum albumin levels and body mass index (all P < .05).

Conclusions

ALI is an independent predictor of CAD, CAC, and CAD with CAC. Our ALI-based nomograms can provide accurate and individualized risk predictions for patients with suspected CAD.

Association of the advanced lung cancer inflammation index (ALI) with immune checkpoint inhibitor efficacy in patients with advanced non-small-cell lung cancer

Background

The advanced lung cancer inflammation index [ALI: body mass index × serum albumin/neutrophil-to-lymphocyte ratio (NLR)] reflects systemic host inflammation, and is easily reproducible. We hypothesized that ALI could assist guidance of non-small-cell lung cancer (NSCLC) treatment with immune checkpoint inhibitors (ICIs).

Patients and methods

This retrospective study included 672 stage IV NSCLC patients treated with programmed death-ligand 1 (PD-L1) inhibitors alone or in combination with chemotherapy in 25 centers in Greece and Germany, and a control cohort of 444 stage IV NSCLC patients treated with platinum-based chemotherapy without subsequent targeted or immunotherapy drugs. The association of clinical outcomes with biomarkers was analyzed with Cox regression models, including cross-validation by calculation of the Harrell's C-index.

Results

High ALI values (>18) were significantly associated with longer overall survival (OS) for patients receiving ICI monotherapy [hazard ratio (HR) = 0.402, P < 0.0001, n = 460], but not chemo-immunotherapy (HR = 0.624, P = 0.111, n = 212). Similar positive correlations for ALI were observed for objective response rate (36% versus 24%, P = 0.008) and time-on-treatment (HR = 0.52, P < 0.001), in case of ICI monotherapy only. In the control cohort of chemotherapy, the association between ALI and OS was weaker (HR = 0.694, P = 0.0002), and showed a significant interaction with the type of treatment (ICI monotherapy versus chemotherapy, P < 0.0001) upon combined analysis of the two cohorts. In multivariate analysis, ALI had a stronger predictive effect than NLR, PD-L1 tumor proportion score, lung immune prognostic index, and EPSILoN scores. Among patients with PD-L1 tumor proportion score ≥50% receiving first-line ICI monotherapy, a high ALI score >18 identified a subset with longer OS and time-on-treatment (median 35 and 16 months, respectively), similar to these under chemo-immunotherapy.

Conclusions

The ALI score is a powerful prognostic and predictive biomarker for patients with advanced NSCLC treated with PD-L1 inhibitors alone, but not in combination with chemotherapy. Its association with outcomes appears to be stronger than that of other widely used parameters. For PD-L1-high patients, an ALI score >18 could assist the selection of cases that do not need addition of chemotherapy.

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ALI is prognostic and predictive for patients with advanced NSCLC treated with immunotherapy monotherapy, but not chemo-immunotherapy.

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Its association with outcomes is stronger than that of other parameters (PD-L1 TPS, NLR, lung immune prognostic index, EPSILoN).

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For PD-L1-high patients, an ALI score >18 could assist the selection of cases that do not need addition of chemotherapy.

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

This study aims to develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. We assessed the model's incremental value to the TNM staging system, clinical treatment, and Epstein‐Barr virus (EBV) DNA copy number for individual OS estimation. We retrospectively analyzed 519 consecutive patients with NPC. A prognostic model was generated using the Lasso regression model in the training cohort. Then we compared the predictive accuracy of the novel prognostic model with TNM staging, clinical treatment, and EBV DNA copy number using concordance index (C‐index), time‐dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, we built a nomogram for OS incorporating the prognostic model, TNM staging, and clinical treatment. Finally, we stratified patients into high‐risk and low‐risk groups according to the model risk score, and we analyzed the survival time of these two groups using Kaplan–Meier survival plots. All results were validated in the independent validation cohort. Using the Lasso regression, we established a prognostic model consisting of 13 variables with respect to patient prognosis. The C‐index, tdROC, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort than did TNM staging, clinical treatment, and EBV DNA copy number. Nomogram consisting of the prognostic model, TNM staging, clinical treatment, and EBV DNA copy number showed some superior net benefit. Based on the model risk score, we split the patients into two subgroups: low‐risk (risk score ≤ −1.423) and high‐risk (risk score > −1.423). There were significant differences in OS between the two subgroups of patients. Similar results were observed in the validation cohort. The proposed novel prognostic model based on clinical features and serological markers may represent a promising tool for estimating OS in NPC patients.

Prognostic Significance of the L3 Skeletal Muscle Index and Advanced Lung Cancer Inflammation Index in Elderly Patients with Esophageal Cancer

Objective

To evaluate the correlation between the advanced lung cancer inflammation index (ALI) and the L3 skeletal muscle index (L3SMI) and their prognostic value in elderly patients with esophageal cancer (EC).

Materials and Methods

The clinical data of 158 elderly patients with EC were collected retrospectively. The L3SMI measures the area of skeletal muscle at the level of the third lumbar (L3) vertebra using computed tomography (CT). A high L3SMI and low L3SMI group were created using sex-based quartiles. The ALI, prognostic nutrition index (PNI), and geriatric nutrition risk index (GNRI) were calculated according to standard laboratory protocols.

Results

The CT diagnostic criteria for senile sarcopenia in South China are height ≤32.96 cm²/m² for females and height ≤35.4 cm²/m² for males. The logistic regression analysis showed that a low L3SMI was significantly associated with a low ALI. Survival analysis revealed EC patients with a low L3SMI and a low ALI had poorer overall survival (OS) than patients with a high L3SMI and a high ALI. Univariate and multivariate Cox analyses showed that the L3SMI and ALI were independent predictors of EC prognosis in elderly individuals.

Conclusion

There was a significant correlation between the PNI, GNRI, ALI, and L3SMI. Overall, our findings show the L3SMI and ALI are clinical indicators that can potentially be used to independently predict the prognosis of elderly EC patients and display good predictive value.

Prognostic Impact of the Advanced Lung Cancer Inflammation Index (ALI) in Metastatic Non-Small Cell Lung Cancer Treated with First Line Chemotherapy

Background:

The advanced lung cancer inflammation index (ALI) has been reported to predict the overall survival in patients with advanced non-small cell lung cancer (NSCLC). However, no previous studies have examined the prognostic significance of ALI in metastatic NSCLC treated with first line chemotherapy. The objective of this study was to explore the relationship between ALI and the prognosis of metastatic NSCLC treated with first line chemotherapy.

Materials and Methods:

Data of 109 metastatic NSCLC patients who had completed first line treatment with chemotherapy was collected. A multivariate flexible parametric proportional-hazards model with restricted cubic splines (RCS) was used to explore and identify the independent prognostic factors, including clinical potential factors and ALI for the overall survival. Multivariate regression analysis was used to evaluate the potential prognostic factors associated with short survival less than 6 months. The analysis of the restricted mean survival time (RMST) method was used to estimate the event-free time from zero to 18 months.

Results:

The median OS was 10.9 months (95%CI 9.57-13.18) and median PFS was 7.5 months (95%CI 6.85-8.00).The multivariate survival analyses revealed two prognostic factors for worse survival: Poor ECOG PS (HR46.90; 95%CI 2.90-758.73; p=0.007) and progressive disease after completing the first line chemotherapy treatment (HR 2.85; 95%CI1.18-6.88; p=0.02),whereas a low ALI <11 referred to a non-significant prognostic factor (HR 1.42; 95%CI 0.67-3.01; p=0.364).The results of the multivariate regression analysis revealed that the low ALI and progressive disease status were significantly associated with the short survival outcome (OR 5.12; 95%CI 1.11-23.65; p=0.037; OR 12.57; 95%CI 3.00-52.73; p=0.001).

Conclusions:

A low ALI was associated with the short survival in metastatic NSCLC treated with chemotherapy. However, using ALI as a prognostic factor only was still too limited. Other considerable clinical prognostic factors should also be used simultaneously, which would have strong significant prognostic impacts.

Advanced lung cancer inflammation index and its prognostic value in HPV-negative head and neck squamous cell carcinoma: a multicentre study

Purpose

The aim of this study is to evaluate the prognostic value of pre-treatment advanced lung cancer inflammation index (ALI) in patients with HPV-negative HNSCC undergoing up-front surgical treatment.

Methods

The present multi-centre, retrospective study was performed in a consecutive cohort of patients who underwent upfront surgery with or without adjuvant (chemo)-radiotherapy for head and neck squamous cell carcinoma (HNSCC). Patients were stratified by ALI, and survival outcomes were compared between groups. In addition, the prognostic value of ALI was compared with two other indices, the prognostic nutritional index (PNI) and systemic inflammatory index (SIM).

Results

Two hundred twenty-three patients met the inclusion criteria (151 male and 72 female). Overall and progression-free survival were significantly predicted by ALI < 20.4 (HR 3.23, CI 1.51–6.90 for PFS and HR 3.41, CI 1.47–7.91 for OS). Similarly, PNI < 40.5 (HR = 2.43, 95% CI: 1.31–4.51 for PFS and HR = 2.40, 95% CI: 1.19–4.82 for OS) and SIM > 2.5 (HR = 2.51, 95% CI: 1.23–5.10 for PFS and HR = 2.60, 95% CI: 1.19–5.67 for OS) were found to be significant predictors. Among the three indices, ALI < 20.4 identified the patients with the worst 5-year outcomes. Moreover, patients with a combination of low PNI and low ALI resulted to be a better predictor of progression (HR = 5.26, 95% CI: 2.01–13.73) and death (HR = 5.68, 95% CI: 1.92–16.79) than low ALI and low PNI considered alone.

Conclusions

Our results support the use of pre-treatment ALI, an easily measurable inflammatory/nutritional index, in daily clinical practice to improve prognostic stratification in surgically treated HPV-negative HNSCC.

The Predictive Significance of the Advanced Lung Cancer Inflammation Index (ALI) in Patients with Melanoma Treated with Immunotherapy as Second-Line Therapy

PURPOSE

The advanced lung cancer inflammation index (ALI) is a useful tool to predict the clinical outcome in several malignancies. The ALI not only contains indices related to inflammation but also the body mass index (BMI), which was reported to correlate with the sarcopenic status. However, to date, its predictive significance in metastatic melanoma patients treated with second-line immunotherapy has not been evaluated.

METHODS

We retrospectively analyzed data from patients who were diagnosed with metastatic melanoma and treated with immunotherapy as second-line therapy between 2016 and 2019. Weight, height, neutrophil, lymphocyte and serum albumin were collected at baseline prior to receiving immunotherapy. The BMI was calculated by dividing the weight by height squared. The neutrophil-to-lymphocyte ratio (NLR) was calculated by dividing the absolute neutrophil count by the absolute lymphocyte count. The ALI was defined as follows: ALI=BMI×serum albumin/NLR. The receiver operator curve (ROC) was used to determine the best cutoff value for ALI in predicting disease control (consisting of complete response, partial response and stable disease). The aim of this study was to investigate whether the ALI is a predictive indicator for progression-free survival in melanoma patients.

RESULTS

Forty-three patients were included in this retrospective cohort study. By ROC, ALI>50.98 before immunotherapy was predictive of disease control. Baseline continuous variables, such as BMI, NLR, C-reactive protein and C-reactive protein-to-albumin ratio, had significantly worse scores in patients of the low-ALI group (n=24) than high-ALI group (n=19). The median progression-free survival was significantly worse in the patients with ALI<50.98 than the patients with ALI>50.98 (2.60 months vs 11.17 months, P = 0.023, hazard ratio: 2.241, 95% confidence interval: 1.167-5.097).

CONCLUSION

The advanced lung cancer inflammation index (ALI) >50.98 before immunotherapy is a strong predictor for disease control. The ALI also provides great predictive value for metastatic melanoma patients treated with immunotherapy as second-line therapy.

The advanced lung cancer inflammation index predicts outcomes in patients with Crohn's disease after surgical resection

AIM

Precise biomarkers for predicting prognosis could help to identify high-risk Crohn's disease (CD) patients to facilitate better follow-up during the postoperative course. In this study, the primary aim is the identification of the most reliable nutrition marker that predicts surgical relapse in CD patients.

METHOD

We first evaluated the predictive value of various nutrition markers for postoperative surgical relapse in CD patients and identified the advanced lung cancer inflammation index (ALI) as a promising biomarker. Then, we assessed the clinical significance of preoperative ALI in CD patients using two cohorts.

RESULTS

Preoperative ALI showed the highest correlation with reoperation rate compared with other nutritional parameters in CD patients receiving surgical resection (sensitivity 53%, specificity 86%, area under the curve 0.71). Lower levels of preoperative ALI were significantly correlated with the presence of perianal disease. A lower level of preoperative ALI was an independent prognostic factor for reoperation rate after an intestinal resection (hazard ratio 3.37, 95% CI 1.38-10.12, P = 0.006), and the prognostic impact of preoperative ALI was successfully validated in an independent cohort using the same cut-off value.

CONCLUSION

Preoperative ALI might be useful for postoperative management of CD patients.

Prognostic significance of Rab27 expression in solid cancer: a systematic review and meta-analysis

Rab27 is an essential molecule of vesicle fusion and trafficking in exosome secretion process, which plays important roles in cancer progression and metastasis. Recent studies reported that Rab27 expression is also associated with cancer prognosis. Therefore, we performed a meta-analysis to reveal the prognostic significance of Rab27 expression in solid cancer. Data were extracted by searching on PubMed, Embase and Cochrane library until February 15 2020. Pooled hazard ratio (HR) with confidence interval (CI) was calculated to evaluate the association between Rab27 expression and survival in solid cancer. Ten studies with 1434 cancer patients were including for this meta-analysis. High expression of Rab27 was associated with poor survival (HR 2.67, 95% CI 1.52-4.69, p = 0.001). High expression of Rab27A was significantly associated with lymph node metastasis (HR 1.53, 95% CI 1.00-2.34, p = 0.048). High expression of Rab27B was significantly correlated with lymph node and distant metastasis (HR 2.15, 95% CI 1.56-2.95, p < 0.001; HR 6.80, 95% CI 3.12-14.85, p < 0.001), and higher TNM stage (HR 2.55, 95% CI 1.78-3.65, p < 0.001). This meta-analysis revealed that Rab27 expression could be a potential prognostic marker in solid cancer.