Systemic immune-inflammation index predicts prognosis in patients with different EGFR-mutant lung adenocarcinoma

Prognostic significance of systemic immune inflammatory index in NSCLC: a meta-analysis

Aim: The aim of this meta-analysis was to investigate the relationship between the baseline systemic immune inflammatory index (SII) and prognosis in patients with NSCLC. Materials & methods: The relation between pretreatment SII and overall survival, disease-free survival, cancer-specific survival, progression-free survival and recurrence-free survival in NSCLC patients was analyzed combined with hazard ratio and 95% CI. Results: The results showed that high SII was significantly correlated with overall survival and progression-free survival of NSCLC patients, but not with disease-free survival, cancer-specific survival and recurrence-free survival. Conclusion: The study suggests that a higher SII has association with worse prognosis in NSCLC patients. PROSPERO registration number: CRD42022336270.

Inflammatory parameters in NSCLC with driver mutation

Aim: The tumor microenvironment of NSCLC with driver mutations, such as EGFR, ALK and ROS, is less inflammatory. Materials & methods: This retrospective study included 38 patients with NSCLC driver mutations. The relationship between clinical and inflammatory markers concerning progression-free survival and overall survival was analyzed based on Kaplan-Meier curves. Results: The mean age of the patients was 59.8 ± 11.9. Progression-free survival and overall survival were significantly longer in patients under 65 years of age and with low neutrophil-lymphocyte ratio, low systemic immune-inflammation index and high lymphocyte count (p < 0.05). Conclusion: Unlike tumor biology, peripheral inflammatory parameters, such as neutrophil-lymphocyte ratio, systemic immune-inflammation index and lymphocyte count may be associated with survival in NSCLC patients with driver mutations.

Opportunistic lung cancer screening with low-dose computed tomography in National Cancer Center of China: The first 14 years' experience

BACKGROUND

In China, over 50% of lung cancer cases occur in nonsmokers. Thus, identifying high-risk individuals for targeted lung cancer screening is crucial. Beyond age and smoking, determining other risk factors for lung cancer in the Asian population has become a focal point of research. Using 30,000 participants in the prospectively enrolled cohort at China's National Cancer Center (NCC) over the past 14 years, we categorized participants by risk, with an emphasis on nonsmoking females.

MATERIALS AND METHODS

Between November 2005 and December 2019, 31,431 individuals voluntarily underwent low-dose computed tomography (LDCT) scans for lung cancer screening at the NCC. We recorded details like smoking history, exposure to hazards, and family history of malignant tumors. Using the 2019 NCCN criteria, participants were categorized into high-, moderate-, and low-risk groups. Additionally, we separated non-high-risk groups into female never smokers (aged over 40) exposed to second-hand smoke (SHS) and others. Any positive results from initial scans were monitored per the I-ELCAP protocol (2006), and suspected malignancies were addressed through collaborative decisions between patients and physicians. We analyzed and compared the detection rates of positive results, confirmed lung cancers, and cancer stages across risk, age, and gender groups.

RESULTS

Out of 31,431 participants (55.9% male, 44.1% female), 3695 (11.8%) showed positive baseline LDCT scans with 197 (0.6%; 106 females, 91 males) confirmed as lung cancer cases pathologically. Malignancy rate by age was 0.1% among those aged under 40 years, 0.4% among those aged 40-49 years, 0.8% among those aged 50-59 years, and 1.2% among those aged 60 years and older. From the 25,763 participants (56.9% male, 43.1% female) who completed questionnaires, 1877 (7.3%) were categorized as high risk, 6500 (25.2%) as moderate risk, and 17,386 (67.5%) as low risk. Of the 23,886 in the non-high-risk category, 8041 (33.7%) were females over 40 years old exposed to SHS. The high-risk group showed the highest lung cancer detection rate at 1.4%. However, females exposed to SHS had a notably higher detection rate than the rest of the non-high-risk group (1.1% vs. 0.5%; p < 0.0001). In this cohort, 84.8% of the detected lung cancers were at an early stage.

CONCLUSIONS

In our study, using LDCT for lung cancer screening proved significant for high-risk individuals. For non-high-risk populations, LDCT screening could be considered for nonsmoking women with exposure to SHS.

Potential application of peripheral blood biomarkers in intracranial aneurysms

Intracranial aneurysm (IA) counts are increasing yearly, with a high mortality and disability after rupture. Current diagnosis and treatment rely on costly equipment, lacking effective indicators for progression prediction and specific drugs for treatment. Recently, peripheral blood biomarkers, as common clinical test samples, reflecting the immune and inflammatory state of the body in real-time, have shown promise in providing additional information for risk stratification and treatment in IA patients, which may improve their outcomes after aneurysm rupture through anti-inflammatory therapy. Therefore, this paper reviewed the progress of potential biomarkers of IAs, including inflammatory blood indicators, cytokines, and blood lipids, aiming to aid individual management and therapy of aneurysms in clinical practices.

Soluble biomarkers to predict clinical outcomes in non-small cell lung cancer treated by immune checkpoints inhibitors

Lung cancer remains the first cause of cancer-related death despite many therapeutic innovations, including immune checkpoint inhibitors (ICI). ICI are now well used in daily practice at late metastatic stages and locally advanced stages after a chemo-radiation. ICI are also emerging in the peri-operative context. However, all patients do not benefit from ICI and even suffer from additional immune side effects. A current challenge remains to identify patients eligible for ICI and benefiting from these drugs. Currently, the prediction of ICI response is only supported by Programmed death-ligand 1 (PD-L1) tumor expression with perfectible results and limitations inherent to tumor-biopsy specimen analysis. Here, we reviewed alternative markers based on liquid biopsy and focused on the most promising biomarkers to modify clinical practice, including non-tumoral blood cell count such as absolute neutrophil counts, platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, and derived neutrophil to lymphocyte ratio. We also discussed soluble-derived immune checkpoint-related products such as sPD-L1, circulating tumor cells (detection, count, and marker expression), and circulating tumor DNA-related products. Finally, we explored perspectives for liquid biopsies in the immune landscape and discussed how they could be implemented into lung cancer management with a potential biological-driven decision.

Pre-radiotherapy systemic immune inflammation index associated with overall survival in patients with advanced EGFR mutant non-small cell lung cancer receiving thoracic radiotherapy

PURPOSE

This study aimed to investigate the prognostic potential of the pre-radiotherapy systemic immune-inflammation index (SII) for the survival of advanced lung adenocarcinoma patients with epidermal growth factor receptor (EGFR) mutations, which might provide a basis for optimizing the comprehensive treatment scheme.

METHODS

A total of 111 lung adenocarcinoma patients with EGFR mutations, who received thoracic radiotherapy, were included in this retrospective study. The primary endpoint of the study was based on the overall survival (OS) of patients. The receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off value of each immune inflammation index. Kaplan-Meier analysis was performed for the comparison of OS. The Cox proportional-hazard model was used for the multivariate and univariate regression analyses to determine the correlations of prognostic factors with the disease.

RESULTS

SII was divided into the high SII group (≥ 620.2; 45.95%) and the low SII group (SII < 620.2; 54.05%) based on the optimal cutoff values. The median OS rates were 53.3 and 33.3 months in the low and high SII groups, respectively, showing statistically significant differences ( hazard ratio (HR) = 0.459; 95% CI 0.286-0.736; P < 0.001). The multivariate analysis showed that, after adjusting for the significant covariates, the SII values were independently associated with the improved OS of the patients (adjusted HR = 0.444; 95% CI 0.279-0.709; P = 0.001). The low NLR values were associated with the better OS of patients (HR = 0.509; 95% CI 0.326-0.792; P = 0.005) and vice versa (HR = 0.422; 95% CI 0.213-0.836; P < 0.001). The patients in the low LMR group before radiotherapy exhibited longer OS as compared to those in the high LMR group (HR = 0.497; 95% CI 0.308-0.802; P = 0.001).

CONCLUSIONS

This study showed that these inflammatory indices might have an important prognostic potential for advanced lung adenocarcinoma patients with EGFR mutations, receiving thoracic radiotherapy and might provide a basis for the individualized treatment of these patients.

The Relationship Between Systemic Immune Inflammatory Index and Prognosis of Patients With Non-Small Cell Lung Cancer: A Meta-Analysis and Systematic Review

Background

The relationship between systemic immune inflammation index (SII) and the prognosis of cancer has always been a subject of intense interest. However, the prognostic value of SII in non-small cell lung cancer (NSCLC) patients remains a controversial topic.

Objective

To evaluate the effect of SII index on prognosis of NSCLC.

Methods

We conducted a comprehensive search of PubMed, EMBASE, and the Cochrane Library databases to determine correlation between SII index, clinicopathological features, overall survival (OS), and progression-free survival (PFS). Odds ratio (ORs) and 95% confidence interval (CIs) were used to assess the connection between SII and clinicopathological parameters, and HRs and 95% CIs were used to assess the connection between SII and survival.

Results

Seventeen studies with 8,877 cases were included in the analysis. Compared with NSCLC patients with low SII level, patients with NSCLC with high SII level had a poor OS (HR = 1.75, 95% CI, 1.50–2.00; P < 0.001) and had a poor PFS (HR = 1.61, 95% CI, 1.25–1.96; P < 0.001). In addition, patients with higher pathological stage (II–III) had higher SII levels (OR = 2.32, 95% CI, 2.06–2.62; P < 0.001).

Conclusions

The SII index is a promising prognostic biomarker for NSCLC and may help clinicians choose appropriate NSCLC treatments.

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.

Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis

Background

Tumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).

Methods

“GEO2R,” “limma” R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. “Survival,” “survminer,” “rms” R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.

Results

A total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = –0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By “survival ROC” R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.

Conclusion

In conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.