The prognostic role of inflammation-scores on overall survival in lung cancer patients

Nomograms Predict PFS and OS for SCLC Patients After Standardized Treatment: A Real-World Study

Purpose

This study aims to investigate the process of small cell lung cancer (SCLC) patients from achieving optimal efficacy to experiencing disease progression until death. It examines the predictive value of the treatment response on progression free survival (PFS) and overall survival (OS) of SCLC patients.

Patients and Methods

We conducted a retrospective analysis on 136 SCLC patients diagnosed from 1992 to 2018. Important prognostic factors were identified to construct nomogram models. The predictive performance of the models was evaluated using the receiver operating characteristic curves and calibration curves. Survival differences between groups were compared using Kaplan-Meier survival curves. Subsequently, an independent cohort consisting of 106 SCLC patients diagnosed from 2014 to 2021 was used for validation.

Results

We constructed two nomograms to predict first-line PFS (PFS1) and OS of SCLC. The area under the receiver operating characteristic curves for the PFS1 nomogram predicting PFS at 3-, 6-, and 12-months were 0.919 (95% CI: 0.867-0.970), 0.908 (95% CI: 0.860-0.956) and 0.878 (95% CI: 0.798-0.958), and for the OS nomogram predicting OS at 6-, 12-, and 24-months were 0.814 (95% CI: 0.736-0.892), 0.819 (95% CI: 0.749-0.889) and 0.809 (95% CI: 0.678-0.941), indicating those two models with a high discriminative ability. The calibration curves demonstrated the models had a high degree of consistency between predicted and observed values. According to the risk scores, patients were divided into high-risk and low-risk groups, showing a significant difference in survival rate. And these findings were validated in another independent validation cohort.

Conclusion

Based on the patients' treatment response after standardized treatment, we developed and validated two nomogram models to predict PFS1 and OS of SCLC. The models demonstrated good accuracy, reliability and clinical applicability by validating in an independent cohort.

Effect of Microsatellite Status and Pan-Immune-Inflammation Score on Pathological Response in Patients with Clinical Stage III Stomach Cancer Treated with Perioperative Chemotherapy

Background and Objective: This study evaluated the relationship between microsatellite status (MSI) and pan-immune-inflammation score (PIV) in tumor response to neoadjuvant chemotherapy (NAC) in patients with clinical stage III gastric cancer (cStage III GC). Materials and Methods: Microsatellite instability (MSI) status was evaluated based on pathology preparations. Pan-immune-inflammation score (PIV) was obtained from pre-treatment blood tests. The relationship of both parameters with pathological complete response (pCR) was evaluated. Results: A total of 104 patients were included in this study. All the patients were stage III GC patients receiving perioperative treatment. There were 13 patients in total who achieved a pCR response. While CNS was detected in 11 of the patients who achieved a pCR, the MSI status of the other two patients was unknown. No pCR was observed in any patient with MSI-H. According to the cut-off value for PIV, 25 (24%) patients were in the PIV-low (≤53.9) group, while 79 (76%) were in the PIV-high (>53.9) group. Based on univariate analysis, a higher PIV was associated with worse outcomes for pathological response, disease recurrence, and survival (p < 0.05). Conclusions: In patients with clinically stage III GC, the presence of MSI-H may predict no benefit from perioperative treatment. Conversely, a pre-treatment PIV score using specific cut-off values may provide a positive prediction of pathological response and survival.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Pretreatment Platelet Count is a Prognostic Marker in Lung Cancer: A Danish Registry-based Cohort Study

BACKGROUND

Thrombocytosis has been associated with a poor prognosis in a wide range of malignancies. However, the results have been conflicting for lung cancer. Therefore, we evaluated the prognostic value of platelet count in a large cohort of lung cancer patients.

PATIENTS AND METHODS

All lung cancer patients diagnosed in The Central Denmark Region from 2009 to 2018 were included in the study. Data from the Danish Lung Cancer Registry were combined with data from the clinical laboratory information system on pretreatment platelet count. Platelet count was defined as low, normal, or high based on being below, within, or above the reference intervals. The prognostic value of platelet count was assessed by the Cox proportional hazard model. C-statistics were conducted to investigate if the platelet count added additional prognostic value to existing prognostic markers.

RESULTS

Totally, 6,758 patients with non-small-cell lung cancer (NSCLC) and 1150 patients with small-cell lung cancer (SCLC) were included. Low and high platelet count were significantly associated with decreased overall survival (OS) in NSCLC patients (low: adjusted hazard ratio (HR)=1.75 (95% confidence interval [CI]: 1.49-2.06); high: adjusted HR=1.24 (95% CI: 1.16-1.33)). In SCLC patients, only low platelet count was significantly associated with decreased OS (adjusted HR = 2.71 [95% CI: 2.02-3.65]). C-statistics showed that the prognostic models were significantly improved by the addition of platelet count for both NSCLC and SCLC patients (P < .0001).

CONCLUSION

Low and high platelet count were adverse prognostic factors in NSCLC patients, while only low platelet count was a prognostic marker in SCLC patients.

Inflammation-based different association between anatomical severity of coronary artery disease and lung cancer

BACKGROUND

Coronary artery disease (CAD) is associated with cancer. The role of inflammation in the association of CAD with cancer remains unclear. The study investigated whether inflammation could impact the relationship between CAD and lung cancer.

METHODS

The study involved 96 newly diagnosed lung cancer patients without receiving anti-cancer therapy and 288 matched non-cancer patients. All the patients underwent coronary angiography and were free from previous percutaneous coronary intervention or coronary artery bypass grafting. SYNTAX score (SXscore) were used to assess severity of CAD. High SXscore (SXhigh) grade was defined as SXscore > 16 (highest quartile). Neutrophil-to-lymphocyte ratio (NLR) served as an inflammatory biomarker. NLR-high grade referred to NLR > 2.221 (median).

RESULTS

Among 384 study patients, 380 patients (98.96%) had NLR value (median: 2.221, interquartile range: 1.637-3.040). Compared to non-cancer patients, lung cancer patients had higher rate of SXhigh among total study patients (P = 0.014) and among patients with NLR-high (P = 0.006), but had not significantly higher rate of SXhigh among patients with NLR-low (P = 0.839). Multivariate logistic regression analysis showed that SXhigh was associated with lung cancer [odds ratio (OR) = 1.834, 95% CI: 1.063-3.162, P = 0.029]. Subgroup analysis showed that SXhigh was associated with lung cancer among patients with NLR-high (OR = 2.801, 95% CI: 1.355-5.794, P = 0.005), however, the association between SXhigh and lung cancer was not significant among patients with NLR-low (OR = 0.897, 95% CI: 0.346-2.232, P = 0.823).

CONCLUSIONS

Inflammation could lead different association between anatomical severity of CAD and lung cancer. Severity of CAD was significantly associated with increased risk of lung cancer among patients with high inflammation rather than among patients with low inflammation.

Prognosis value of IL-6, IL-8, and IL-1β in serum of patients with lung cancer: A fresh look at interleukins as a biomarker

Interleukins are assumed to be closely related to the occurrence and development of human malignant tumors, while a few of them were commonly used as diagnostic markers in clinical cancer, including lung cancer. This study aimed to explore the value of serum interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-8 (IL-8) combined with carcinoembryonic antigen (CEA) as biomarker panel for the diagnosis and metastasis prediction of lung cancer. IL-1β, IL-6, IL-8, and CEA in serum were determined using electrochemiluminescence immunoassay (ECLIA) and flow cytometry, and the diagnostic value of each marker was analyzed using receiver operating characteristic (ROC) curves and logistic fitting regression. We found that the levels of serum IL-1β, IL-6, and IL-8 showed no significant difference among squamous cell carcinoma, adenocarcinoma, and small cell carcinoma, while they were significantly higher in the lung cancer group or benign group than those in the healthy group. The levels of IL-8 and CEA were positively correlated with clinical stages respectively. Importantly, the panel of CEA + IL-6 + IL-8 has the highest efficacy for the diagnosis of lung cancer (AUC = 0.883) among all the detected panels, while the panel of IL-8 + CEA showed the most promising predictive value for the lymph node metastasis (AUC = 0.686) and distant metastasis of lung cancer (AUC = 0.793). In conclusion, IL-6 and IL-8 could be used as promising molecular biomarkers to diagnose and predict the metastasis of lung cancer independent of pathological types, improving the specificity and sensitivity of diagnosis for lung cancer when they were combined with CEA.

Novel Signatures Based on the Lymphocyte-to-C-Reactive Protein Ratio Predict the Prognosis of Patients with Early Breast Cancer: A Retrospective Study

Background

The value of the lymphocyte-to-C-reactive protein (CRP) ratio (LCR) in early breast cancer (BC) is unclear. We explored the correlation between the LCR and survival of patients with early BC and established effective LCR-based prognostic signatures for predicting prognosis.

Methods

In this retrospective study, we randomized 623 patients with early-stage BC diagnosed in December 2010 to October 2012 at the Sun Yat-sen University Cancer Center to training and verification datasets. The median follow-up of all patients was 109 months. The survival differences were calculated by Kaplan–Meier method using the Log rank test. For overall survival (OS) and disease-free survival (DFS), the independent factors in the training dataset were identified using univariate and multivariate Cox analyses, in which two-tailed P-values < 0.05 were considered statistically significant. Based on this, we respectively constructed novel signatures for survival prediction and validated the efficiency of signatures through the concordance index (C-index), calibration and receiver operating characteristic (ROC) curves in both datasets.

Results

The LCR, lymphatic vessel invasion (LVI), progesterone receptor (PR) status, and Ki67 index were independent prognostic factors of OS. And the LCR and LVI are associated to DFS too. High LCR was associated with better OS and DFS. We constructed the prediction signatures based on those independent prognostic factors and calculated the risk scores. Patients in the training dataset with higher risk scores had significantly worse prognosis (P < 0.001). The signature had excellent discrimination capacity, with an OS C-index of 0.785 [95% confidence interval (CI): 0.713–0.857] and 0.750 (95% CI: 0.669–0.832) in the training and verification datasets, respectively. The time–ROC curves also suggest accurate prediction by the signature.

Conclusion

The LCR was a significant prognostic predictor of OS and DFS in early BC. The LCR-based prognostic signatures could be a useful tool for individualized therapeutic guidance.

The Association between the Pan-Immune-Inflammation Value and Cancer Prognosis: A Systematic Review and Meta-Analysis

Background: Prognostic scores derived from the blood count have garnered significant interest as an indirect measure of the inflammatory pressure in cancer. The recently developed pan-immune-inflammation value (PIV), an equation including the neutrophil, platelet, monocyte, and lymphocyte levels, has been evaluated in several cohorts, although with variations in the tumor types, disease stages, cut-offs, and treatments. Therefore, we evaluated the association between survival and PIV in cancer, performing a systematic review and meta-analysis. Methods: We conducted a systematic review from the Pubmed, Medline, and Embase databases to filter the published studies until 17 May 2022. The meta-analyses were performed with the generic inverse-variance method with a random-effects model. Results: Fifteen studies encompassing 4942 patients were included. In the pooled analysis of fifteen studies, the patients with higher PIV levels had significantly increased risk of death than those with lower PIV levels (HR: 2.00, 95% CI: 1.51−2.64, p < 0.001) and increased risk of progression or death (HR: 1.80, 95% CI: 1.39−2.32, p < 0.001). Analyses were consistent across several clinical scenarios, including non-metastatic or metastatic disease, different cut-offs (500, 400, and 300), and treatment with targeted therapy or immunotherapy (p < 0.001 for each). Conclusion: The available evidence demonstrates that PIV could be a prognostic biomarker in cancer. However, further research is needed to explore the promise of PIV as a prognostic biomarker in patients with non-metastatic disease or patients treated without immunotherapy or targeted therapy.

Highly elevated systemic inflammation is a strong independent predictor of early mortality in advanced non-small cell lung cancer

BACKGROUND

Ample evidence support inflammation as a marker of outcome in non-small cell lung cancer (NSCLC). Here we explore the outcome for a subgroup of patients with advanced disease and substantially elevated systemic inflammatory activity.

METHODS

The source cohort included consecutive patients diagnosed with NSCLC between January 2016 - May 2017 (n = 155). Patients with active infection were excluded. Blood parameters were examined individually, and cut-offs (ESR > 60 mm, CRP > 20 mg/L, WBC > 10 × 10⁹, PLT > 400 × 10⁹) were set to define the group of hyperinflamed patients. A score was developed by assigning one point for each parameter above cut-off (0-4 points).

RESULTS

High systemic inflammation was associated with advanced stage and was seldom present in limited NSCLC. However, the one year survival of patients in stage IIIB-IV (n = 93) with an inflammation score of ≥2 was 0% compared to 33% and 50% among patients with a score of 1 and 0 respectively. The effect of a high inflammation score on overall survival remained significant in multi-variate analysis adjusted for confounding factors. The independent hazard ratio of an inflammation score ≥ 2 in multi-variate analysis (HR 3.43, CI 1.76-6.71) was comparable to a change in ECOG PS from 0 to 2 (HR 2.42, CI 1.13-5.18).

CONCLUSION

Our results show that high level systemic inflammation is a strong independent predictor of poor survival in advanced stage NSCLC. This observation may indicate a need to use hyperinflammation as an additional clinical parameter for stratification of patients in clinical studies and warrants further research on underlying mechanisms linked to tumor progression.

A Low Advanced Lung Cancer Inflammation Index Predicts a Poor Prognosis in Patients With Metastatic Non–Small Cell Lung Cancer

Introduction: Inflammation plays a crucial role in cancers, and the advanced lung cancer inflammation index (ALI) is considered to be a potential factor reflecting systemic inflammation.

Objectives: This work aimed to explore the prognostic value of the ALI in metastatic non–small cell lung cancer (NSCLC) and classify patients according to risk and prognosis.

Methods: We screened 318 patients who were diagnosed with stage IV NSCLC in Hubei Cancer Hospital from July 2012 to December 2013. The formula for ALI is body mass index (BMI, kg/m²) × serum albumin (Alb, g/dl)/neutrophil–lymphocyte ratio (NLR). Categorical variables were analyzed by the chi-square test or Fisher’s exact test. The overall survival (OS) rates were analyzed by the Kaplan–Meier method and plotted with the R language. A multivariate Cox proportional hazard model was used to analyze the relationship between ALI and OS.

Results: According to the optimal cut-off value determined by X-tile software, patients were divided into two groups (the ALI <32.6 and ALI ≥32.6 groups), and the median OS times were 19.23 and 39.97 months, respectively (p < 0.01). A multivariable Cox regression model confirmed that ALI and chemotherapy were independent prognostic factors for OS in patients with NSCLC. OS in the high ALI group was better than that in the low ALI group (HR: 1.39; 95% CI: 1.03–1.89; p = 0.03).

Conclusions: Patients with a low ALI tend to have lower OS among those with metastatic NSCLC, and the ALI can serve as an effective prognostic factor for NSCLC patients.

Inflammation-scores as prognostic markers of overall survival in lung cancer: a register-based study of 6,210 Danish lung cancer patients

Background

Inflammation-scores based on general inflammation markers are suggested as prognostic markers of overall survival (OS) in lung cancer. However, whether these inflammation-scores improves the prognostication performed by well-established prognostic markers is unsettled. In a large register-based lung cancer patient cohort, nine different inflammation-scores were compared, and their ability to optimize the prognostication of OS was evaluated.

Methods

Lung cancer patients diagnosed from 2009–2018 in The Central Denmark Region were identified in the Danish Lung Cancer Registry. Pre-treatment inflammation markers were extracted from the clinical laboratory information system. Prognostication of OS was evaluated by Cox proportional hazard models. Comparison of the inflammation-scores and their added value to established prognostic markers were assessed by Akaike's information criteria and Harrel's C-index.

Results

In total, 5,320 patients with non-small cell lung cancer (NSCLC) and 890 patients with small cell lung cancer (SCLC) were identified. In NSCLC, the Aarhus composite biomarker score (ACBS), including albumin, C-reactive protein, neutrophil count, lymphocyte count and haemoglobin, and the neutrophil-lymphocyte-ratio (NLR) were superior. Furthermore, they improved the prognostication of OS significantly (p <0.0001) (ACBS: HR: 2.24 (95%CI: 1.97–2.54); NLR: HR: 1.58 (95%CI: 1.47 – 1.69)). In SCLC, three scores were equally superior and improved the prognostication of OS p < 0.0001): neutrophil–lymphocyte-ratio (HR:1.62 (95%CI: 1.38–1.90)), modified Glasgow Prognostic Score (mGPS) (HR:1.70 (95%CI: 1.55–1.86) and the Combined NLR and GPS (CNG) (HR:2.10 (95%CI: 1.77–2.49).

Conclusions

The ACBS was the optimal score in NSCLC, whereas neutrophil–lymphocyte-ratio, mGPS and CNG were equally superior in SCLC. Additionally, these inflammation-scores all optimised the prognostication of OS and added value to well-established prognostic markers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09108-5.

A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

Objective

To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).

Background

Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.

Methods

PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.

Conclusions

The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.

An Inflammation-Related Nine-Gene Signature to Improve Prognosis Prediction of Lung Adenocarcinoma

Background

A novel predictive model was rarely reported based on inflammation-related genes to explore clinical outcomes of lung adenocarcinoma (LUAD) patients.

Methods

Using TCGA database, we screened nine inflammation-related genes with a prognostic value, and LASSO regression was applied for model construction. The predictive value of the prognostic signature developed from inflammation-related genes was assessed by survival assays and multivariate assays. PCA and t-SNE analysis were performed to demonstrate clustering abilities of risk scores.

Results

Thirteen inflammation-related genes (BTG2, CCL20, CD69, DCBLD2, GPC3, IL7R, LAMP3, MMP14, NMUR1, PCDH7, PIK3R5, RNF144B, and TPBG) with prognostic values were finally identified. LASSO regression further screened nine candidates (BTG2, CCL20, CD69, IL7R, MMP14, NMUR1, PCDH7, RNF144B, and TPBG). Then, a prognostic prediction model using the above nine genes was constructed. A reliable clustering ability of risk score was demonstrated by PCA and t-SNE assays in 500 LUAD patients. The survival assays revealed that the overall survivals of the high-risk group were distinctly poorer than those of the low-risk group with 1-, 3-, and 5-year AUC values of 0.695, 0.666, and 0.694, respectively. Finally, multivariate assays demonstrated the scoring system as an independent prognostic factor for overall survival.

Conclusions

Our study shows that the signature of nine inflammation-related genes can be used as a prognostic marker for LUAD.

Correlation of peripheral blood biomarkers with clinical outcomes in NSCLC patients with high PD-L1 expression treated with pembrolizumab

Background

Immune checkpoint inhibitors (ICIs) are currently the standard therapy in advanced non-small cell lung cancer (NSCLC); however, there is no well-established prognostic biomarker. We investigated the relationship between survival outcomes and three peripheral blood biomarkers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR), as well as a new score termed the risk blood biomarker (RBB), calculated from the combination of the neutrophil-monocyte-to-lymphocyte ratio (NMLR) and white blood cell count (WBC).

Methods

This study included patients with stage IV or recurrent NSCLC confirmed with programmed death ligand 1 (PD-L1) expression ≥50% who received pembrolizumab monotherapy as first-line treatment at the Virgen del Rocío University Hospital in Seville, Spain. To establish the relationship between baseline peripheral blood biomarkers and survival outcomes, progression free survival (PFS) and overall survival (OS), we used the Kaplan-Meier method and multivariable Cox regression models.

Results

A total of 51 patients were included in this study. In multivariate analysis, baseline NLR and PLR showed a strong association with PFS [NLR hazard ratio (HR): 0.19, 95% confidence interval (CI): 0.09–0.44, P<0.001; PLR HR: 0.46, 95% CI: 0.23–0.92, P=0.03] and OS (NLR HR: 0.07, 95% CI: 0.02–0.19, P<0.001; PLR HR: 0.29, 95% CI: 0.13–0.67, P=0.004), and the MLR was associated with OS (MLR HR: 0.34, 95% CI: 0.15–0.76, P=0.01). According to the RBB score, groups with lower scores were associated with superior PFS (group 0: HR: 0.16, 95% CI: 0.06–0.41, P<0.001 and group 1: HR: 0.29, 95% CI: 0.12–0.73, P=0.01) and OS (group 0: HR: 0.04, 95% CI: 0.01–0.17, P<0.001 and group 1: HR: 0.15, 95% CI: 0.05–0.42, P<0.001).

Conclusions

Low baseline NLR, MLR and PLR are significantly associated with better PFS, and low baseline NLR and PLR are associated with better OS. Additionally, we identified three subgroups of patients using the RBB score, and low scores were associated with improved survival outcomes and response to therapy.

cGAS-STING pathway expression as a prognostic tool in NSCLC

Background

Disease recurrence in localized lung adenocarcinoma is a major obstacle for improving the overall outcome of lung cancer. Thus, better prognostic biomarkers are needed to identify patients at risk. In order to clear cancer, immune detection of tumor cells is of vital importance. DNA-leakage into the cytosol and tumor environment is one important tumor-associated danger signal and cGAS is a pivotal DNA-sensor that detects misplaced DNA and initiates an innate immune response. In this study, we investigate the cGAS-STING-pathway expression in tumor tissue and circulating immune cells from lung adenocarcinoma patients in relation to stage of disease and overall survival (OS).

Methods

Gene expression was measured using target specific droplet digital polymerase chain reaction (ddPCR) assays in a cohort of 80 patients with lung adenocarcinoma and 45 patients suspected of lung cancer, but determined to be cancer-free. The expression values were correlated to stage of disease. For further exploration of stage dependent expression, we used a publicly available gene expression data set to stratify patients by stage and correlate gene expression to OS.

Results

In both tumor tissue and peripheral blood mononuclear cells (PBMCs) from cancer patients, we observed differential expression of cGAS-STING pathway components compared to cancer-free individuals. Furthermore, cGAS-STING pathway expression was elevated in PBMCs from patients with localized disease (stage I and II) compared to patients with metastatic disease (stage III and IV). Survival analysis based on publicly available gene expression data sets demonstrated a superior OS for patients with localized disease and high levels of cGAS, STING and TBK1.

Conclusions

The expression of the cGAS-STING pathway is stage dependent and high expression is correlated with localized adenocarcinoma. For patients with localized disease, high cGAS, STING and TBK1 expression correlated with improved OS and may be a potential biomarker for this patient subgroup.

Clinical Characteristics and Changes of Clinical Features in Patients with Asthma-COPD Overlap in Korea according to Different Diagnostic Criteria

Background

Asthma–chronic obstructive pulmonary disease (COPD) overlap (ACO) is a condition characterized by the overlapping clinical features of asthma and COPD. To evaluate the appropriateness of different sets of ACO definition, we compared the clinical characteristics of the previously defined diagnostic criteria and the specialist opinion in this study.

Methods

Patients enrolled in the KOrea COpd Subgroup Study (KOCOSS) were evaluated. Based on the questionnaire data, the patients were categorized into the ACO and non-ACO COPD groups according to the four sets of the diagnostic criteria.

Results

In total 1,475 patients evaluated: 202 of 1,475 (13.6%), 32 of 1,475 (2.2%), 178 of 1,113 (16.0%), and 305 of 1,250 (24.4%) were categorized as ACO according to the modified Spanish Society of Pneumonology and Thoracic Surgery (SEPAR), American Thoracic Society (ATS) Roundtable, Global Initiative for Asthma (GINA)/Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria, and the specialists diagnosis, respectively. The ACO group defined according to the GINA/GOLD criteria showed significantly higher St. George's Respiratory Questionnaire and COPD Assessment Test scores than the non-ACO COPD group. When the modified SEPAR definition was applied, the ACO group showed a significantly larger decrease in the forced expiratory volume in 1 second (FEV₁, %). The ACO group defined by the ATS Roundtable showed significantly larger decrease in the forced vital capacity values compared to the non-ACO COPD group (–18.9% vs. –2.2%, p=0.007 and –412 mL vs. –17 mL, p=0.036). The ACO group diagnosed by the specialists showed a significantly larger decrease in the FEV₁ (%) compared to the non-ACO group (–5.4% vs. –0.2%, p=0.003).

Conclusion

In this study, the prevalence and clinical characteristics of ACO varied depending on the diagnostic criteria applied. With the criteria which are relatively easy to use, defining ACO by the specialists diagnosis may be more practical in clinical applications.