Diversity and heterogeneity of immune states in non-small cell lung cancer and small cell lung cancer

Prognostic impact of Interleukin-8 levels in lung cancer: A meta-analysis and a bioinformatic validation

BACKGROUND

High interleukin-8 (IL-8) levels have been linked to poor prognosis in lung cancer, but conclusive data are lacking.

MATERIALS AND METHODS

A comprehensive search was conducted on April 1st, 2023, from electronic databases, focusing on studies with IL-8 expression evaluations and the availability of hazard ratio (HR) and 95% confidence intervals (CI) for overall survival (OS), progression-free survival (PFS) and disease-free survival (DFS) or adequate data for their estimation. Then, we examined IL-8 and CXCR1 RNA-seq data from The Cancer Genome Atlas (TCGA) dataset, and we correlated these data with OS.

RESULTS

Among 2655 produced records, 10 manuscripts involving both non-small cell lung cancer and small cell lung cancer, were included in the analysis. Two manuscripts and one study included two and three different cohorts, respectively, for a total of 14 cohorts of patients. Overall, 4 cohorts evaluated IL-8 levels in patients treated with chemotherapy, 3 cohorts immunotherapy, 2 cohorts surgical patients and 4 cohorts other treatments; 1 cohort was removed, as the type of treatments was lacking. The 12 cohorts included in the OS analysis revealed that patients with high IL-8 levels have a lower OS probability, as compared to patients with low IL-8 levels (HR=1.75, 95 % CI 1.36-2.26). No significant difference between patients with high and low IL-8 levels was observed in the 8 cohorts available for PFS analysis. Sensitivity analysis according to treatment revealed significant PFS and OS differences for patients treated with chemotherapy or immunotherapy. Analysis of RNA-seq data from TCGA, confirmed the correlation between high IL-8 and CXCR1 expression and worse OS in patients with resected lung cancer.

CONCLUSION

To the best of our knowledge, this study represents the first meta-analysis demonstrating a negative prognostic impact of high IL-8 level in lung cancer, particularly in patients treated with chemotherapy and/or immunotherapy.

Preoperative systemic immune-inflammation index-based nomogram for lung carcinoma following microwave ablation -a real world single center study

Background

The preoperative inflammatory condition significantly influences the prognosis of malignancies. We aimed to investigate the potential significance of preoperative inflammatory biomarkers in forecasting the long-term results of lung carcinoma after microwave ablation (MWA).

Method

This study included patients who received MWA treatment for lung carcinoma from Jan. 2012 to Dec. 2020. We collected demographic, clinical, laboratory, and outcome information. To assess the predictive capacity of inflammatory biomarkers, we utilized the area under the receiver operating characteristic curve (AUC-ROC) and assessed the predictive potential of inflammatory biomarkers in forecasting outcomes through both univariate and multivariate Cox proportional hazard analyses.

Results

A total of 354 individuals underwent MWA treatment, of which 265 cases were included in this study, whose average age was 69.1 ± 9.7 years. The AUC values for the Systemic Inflammatory Response Index (SIRI) to overall survival (OS) and disease-free survival (DFS) were 0.796 and 0.716, respectively. The Cox proportional hazards model demonstrated a significant independent association between a high SIRI and a decreased overall survival (hazard ratio [HR]=2.583, P<0.001). Furthermore, a high SIRI independently correlated with a lower DFS (HR=2.391, P<0.001). We developed nomograms utilizing various independent factors to forecast the extended prognosis of patients. These nomograms exhibited AUC of 0.900, 0.849, and 0.862 for predicting 1-year, 3-year, and 5-year OS, respectively. Additionally, the AUC values for predicting 1-year, 3-year, and 5-year DFS were 0.851, 0.873, and 0.883, respectively.

Conclusion

SIRI has shown promise as a valuable long-term prognostic indicator for forecasting the outcomes of lung carcinoma patients following MWA.

Monitoring Blood Immune Cells in Patients with Advanced Small Cell Lung Cancer Undergoing a Combined Immune Checkpoint Inhibitor/Chemotherapy

In this exploratory prospective observational study on 40 small cell lung cancer (SCLC) patients treated with a combination of chemotherapy and immune checkpoint inhibitors, blood immune cells were characterized by multi-color flow cytometry at the baseline and at the third therapy cycle. The numbers of neutrophils and of T-, B-, and NK cells, as well as the frequency of HLA-DR^low monocytes, 6-SulfoLacNAc (slan)+ non-classical monocytes and circulating dendritic cell (DC) subtypes were determined. The prognostic value of the parameters was evaluated by the patient's survival analysis with overall survival (OS) as the primary endpoint. In addition, blood cell parameters from SCLC patients were compared to those from non-SCLC (NSCLC). The global median OS of patients was 10.4 ± 1.1 months. Disease progression (15% of patients) correlated with a higher baseline neutrophil/lymphocyte ratio (NLR), more HLA-DR^low monocytes, and lower NK cell and DC numbers. The risk factors for poor OS were the presence of brain/liver metastases, a baseline NLR ≥ 6.1, HLA-DR^low monocytes ≥ 21% of monocytes, slan+ non-classical monocytes < 0.12%, and/or CD1c+ myeloid DC < 0.05% of leukocytes. Lymphocytic subpopulations did not correlate with OS. When comparing biomarkers in SCLC versus NSCLC, SCLC had a higher frequency of brain/liver metastases, a higher NLR, the lowest DC frequencies, and lower NK cell numbers. Brain/liver metastases had a substantial impact on the survival of SCLC patients. At the baseline, 45% of SCLC patients, but only 24% of NSCLC patients, had between three and five risk factors. A high basal NLR, a high frequency of HLA-DR^low monocytes, and low levels of slan+ non-classical monocytes were associated with poor survival in all lung cancer histotypes. Thus, the blood immune cell signature might contribute to a better prediction of SCLC patient outcomes and may uncover the pathophysiological peculiarities of this tumor entity.

Platelet-lymphocyte ratio is a prognostic marker in small cell lung cancer-A systemic review and meta-analysis

Aim

The aim of this study was to evaluate the relationship between platelet-lymphocyte ratio (PLR) and prognosis in small cell lung cancer (SCLC) patients.

Method

A comprehensive search was carried out to collect related studies. Two independent investigators extracted the data of hazard ratio (HR) and 95% confidence interval (CI) for overall survival (OS) or progression-free survival (PFS). A random-effect model was applied to analyze the effect of different PLR levels on OS and PFS in SCLC patients. Moreover, subgroup analysis was conducted to seek out the source of heterogeneity.

Results

A total of 26 articles containing 5,592 SCLC patients were included for this meta-analysis. SCLC patients with a high PLR level had a shorter OS compared with patients with a low PLR level, in both univariate (HR = 1.56, 95% CI 1.28-1.90, p < 0.0001) and multivariate (HR = 1.31, 95% CI 1.08-1.59, p = 0.007) models. SCLC patients with a high PLR level had a shorter PFS compared with patients with a low PLR level, in the univariate model (HR = 1.71, 95% CI 1.35-2.16, p < 0.0001), but not in the multivariate model (HR = 1.17, 95% CI 0.95-1.45, p = 0.14). Subgroup analysis showed that a high level of PLR shortened OS in some subgroups, including the Asian subgroup, the younger subgroup, the mixed-stage subgroup, the chemotherapy-dominant subgroup, the high-cutoff-point subgroup, and the retrospective subgroup. PLR level did not affect OS in other subgroups.

Conclusion

PLR was a good predictor for prognosis of SCLC patients, especially in patients received chemotherapy dominant treatments and predicting OS.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022383069.

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Objective

The purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm.

Methods

Patients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models.

Results

A total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC.

Conclusion

This study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making.

Value of plasma vitamin D level and nomogram model for predicting the prognosis of patients with small cell lung cancer treated with platinum plus etoposide as first-line chemotherapy

BACKGROUND

To assess the value of plasma vitamin D level and nomogram model in predicting the prognosis of patients with small cell lung cancer (SCLC) treated with platinum plus etoposide (PPE) as first-line chemotherapy.

METHODS

In this retrospective study, we included 178 patients with SCLC. The data of 25(OH)D level, basic clinical information, overall survival (OS) and progression-free survival (PFS) were collected. Moreover, a nomogram was constructed to predict the prognosis of the patients.

RESULTS

The median OS value for patients with 25(OH)D < 10 ng/mL was 12.5 months. The median PFS value was 6.6 months. Sex, smoking status, clinical stage, and plasma vitamin D were independent prognostic predictors. Besides, the decision curve analysis and receiver operating characteristic curve indicated that the nomogram prediction models showed positive clinical benefit.

CONCLUSIONS

The plasma vitamin D level is of great significance in prognosis of patients with SCLC. The construction of nomograms is beneficial in predicting the prognosis of patients with SCLC treated with PPE.

The systemic-level repercussions of cancer-associated inflammation mediators produced in the tumor microenvironment

The tumor microenvironment is a dynamic, complex, and redundant network of interactions between tumor, immune, and stromal cells. In this intricate environment, cells communicate through membrane-membrane, ligand-receptor, exosome, soluble factors, and transporter interactions that govern cell fate. These interactions activate the diverse and superfluous signaling pathways involved in tumor promotion and progression and induce subtle changes in the functional activity of infiltrating immune cells. The immune response participates as a selective pressure in tumor development. In the early stages of tumor development, the immune response exerts anti-tumor activity, whereas during the advanced stages, the tumor establishes mechanisms to evade the immune response, eliciting a chronic inflammation process that shows a pro-tumor effect. The deregulated inflammatory state, in addition to acting locally, also triggers systemic inflammation that has repercussions in various organs and tissues that are distant from the tumor site, causing the emergence of various symptoms designated as paraneoplastic syndromes, which compromise the response to treatment, quality of life, and survival of cancer patients. Considering the tumor-host relationship as an integral and dynamic biological system, the chronic inflammation generated by the tumor is a communication mechanism among tissues and organs that is primarily orchestrated through different signals, such as cytokines, chemokines, growth factors, and exosomes, to provide the tumor with energetic components that allow it to continue proliferating. In this review, we aim to provide a succinct overview of the involvement of cancer-related inflammation at the local and systemic level throughout tumor development and the emergence of some paraneoplastic syndromes and their main clinical manifestations. In addition, the involvement of these signals throughout tumor development will be discussed based on the physiological/biological activities of innate and adaptive immune cells. These cellular interactions require a metabolic reprogramming program for the full activation of the various cells; thus, these requirements and the by-products released into the microenvironment will be considered. In addition, the systemic impact of cancer-related proinflammatory cytokines on the liver-as a critical organ that produces the leading inflammatory markers described to date-will be summarized. Finally, the contribution of cancer-related inflammation to the development of two paraneoplastic syndromes, myelopoiesis and cachexia, will be discussed.

Optimizing data-independent acquisition (DIA) spectral library workflows for plasma proteomics studies

Traditional data-independent acquisition (DIA) workflows employ off-column fractionation with data-dependent acquisition (DDA) to generate spectral libraries for data extraction. Recent advances have led to the establishment of library-independent approaches for DIA analyses. The selection of a DIA workflow may affect the outcome of plasma proteomics studies. Here, we establish a gas-phase fractionation (GPF) workflow to create DIA libraries for DIA with parallel accumulation and serial fragmentation (diaPASEF). This workflow along with three other workflows, fractionated DDA libraries, fractionated DIA libraries, and predicted spectra libraries, were evaluated on 20 plasma samples from nonsmall cell lung cancer patients with low or high levels of IL-6. We sought to optimize protein identification and total experiment time. The novel GPF workflow for diaPASEF outperformed the traditional ddaPASEF workflow in the number of identified and quantified proteins. A library-independent workflow based on predicted spectra identified and quantified the most proteins in our experiment at the cost of computational power. Overall, the choice of DIA library workflow seemed to have a limited effect on the overall outcome of a plasma proteomics experiment, but it can affect the number of proteins identified and the total experiment time.

Effect of Eye Movement Training on Sleep Quality of Patients with Advanced Lung Cancer Based on Pittsburgh Sleep Quality Index

Objective

To explore the effect of eye movement training on sleep quality of patients with advanced lung cancer based on the Pittsburgh Sleep Quality Index (PSQI).

Methods

120 advanced lung cancer patients admitted to our hospital from January 2019 to January 2020 were selected as the research object and divided into group A (PSQI scores ≥ 10 points, n = 60) and group B (PSQI < 10 points, n = 60). Routine nursing was performed to both groups, and patients in group A received the eye movement training additionally, so as to compare their PSQI scores, negative emotion scores, adverse reaction rate (ARR), Cancer Coping Modes Questionnaire (CCMQ) scores, and pain scores.

Results

After training, group A obtained significantly better sleep quality (P < 0.05), lower negative emotion scores (P < 0.001), lower ARR (P < 0.05), better CCMQ scores (P < 0.05), and lower pain scores (P < 0.001) than group B.

Conclusion

Eye movement training should be promoted in practice because it can reduce negative emotions, alleviate pain sensation, improve sleep quality and body condition, and lower the ARR for advanced lung cancer patients.