Five-year survival and prognostic factors according to histology in 6101 non-small-cell lung cancer patients

The clinical significance of endoplasmic reticulum stress related genes in non-small cell lung cancer and analysis of single nucleotide polymorphism for CAV1

In recent years, protein homeostasis imbalance caused by endoplasmic reticulum stress has become a major hallmark of cancer. Studies have shown that endoplasmic reticulum stress is closely related to the occurrence, development, and drug resistance of non-small cell lung cancer, however, the role of various endoplasmic reticulum stress-related genes in non-small cell lung cancer is still unclear. In this study, we established an endoplasmic reticulum stress scores based on the Cancer Genome Atlas for non-small cell lung cancer to reflect patient features and predict prognosis. Survival analysis showed significant differences in overall survival among non-small cell lung cancer patients with different endoplasmic reticulum stress scores. In addition, endoplasmic reticulum stress scores was significantly correlated with the clinical features of non-small cell lung cancer patients, and can be served as an independent prognostic indicator. A nomogram based on endoplasmic reticulum stress scores indicated a certain clinical net benefit, while ssGSEA analysis demonstrated that there was a certain immunosuppressive microenvironment in high endoplasmic reticulum stress scores. Gene Set Enrichment Analysis showed that scores was associated with cancer pathways and metabolism. Finally, weighted gene co-expression network analysis displayed that CAV1 was closely related to the occurrence of non-small cell lung cancer. Therefore, in order to further analyze the role of this gene, Chinese non-smoking females were selected as the research subjects to investigate the relationship between CAV1 rs3779514 and susceptibility and prognosis of non-small cell lung cancer. The results showed that the mutation of rs3779514 significantly reduced the risk of non-small cell lung cancer in Chinese non-smoking females, but no prognostic effect was found. In summary, we proposed an endoplasmic reticulum stress scores, which was an independent prognostic factor and indicated immune characteristics in the microenvironment of non-small cell lung cancer. We also validated the relationship between single nucleotide polymorphism locus of core genes and susceptibility to non-small cell lung cancer.

The Construction of a Nomogram Using the Pan-Immune-Inflammation Value Combined with a PILE Score for Immunotherapy Prediction Prognosis in Advanced NSCLC

Purpose

The purpose of this study was to investigate the predictive value of Pan-Immune-Inflammation Value (PIV) combined with the PILE score for immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) and to construct a nomogram prediction model to provide reference for clinical work.

Patients and Methods

Patients with advanced NSCLC who received ICIs treatment in Qingdao Municipal Hospital from January 2019 to December 2021 were selected as the study subjects. The chi-square test, Kaplan-Meier survival analysis, and Cox proportional risk regression analysis were used to evaluate the prognosis. The results were visualized by a nomogram, and the performance of the model was judged by indicators such as the area under the subject operating characteristic curve (AUC) and C-index. The patients were divided into high- and low-risk groups by PILE score, and the prognosis of patients in different risk groups was evaluated.

Results

Multivariate Cox regression analysis showed that immune-related adverse events (irAEs) were prognostic factors for overall survival (OS) improvement, and ECOG PS score ≥2, bone metastases before treatment, and high PIV expression were independent risk factors for OS. The C index of OS predicted by the nomogram model is 0.750 (95% CI: 0.677-0.823), and the Calibration and ROC curves show that the model has good prediction performance. Compared with the low-risk group, patients in the high-risk group of PILE were associated with a higher inflammatory state and poorer physical condition, which often resulted in a poorer prognosis.

Conclusion

PIV can be used as a prognostic indicator for patients with advanced NSCLC treated with ICIs, and a nomogram prediction model can be constructed to evaluate the survival prediction of patients, thus contributing to better clinical decision-making and prognosis assessment.

Fibrinogen-to-albumin ratio (FAR) is the best biomarker for the overall survival of patients with non-small-cell lung cancer

Objective

The inflammatory response and the nutritional status are associated with overall survival (OS) in patients with non-small cell lung cancer (NSCLC), but it is unclear which biomarkers are better suited to predict prognosis. This study sought to determine which of the commonly existing inflammatory and nutritional indicators best predicted the OS.

Methods

This study included 15 compound indicators based on inflammation or nutrition, with cutoff points obtained through the receiver operating characteristic (ROC) curve. Univariate and multivariate Cox proportional risk models were used to evaluate the relationship between these predictors and OS. Kaplan-Meier curves were used for survival analysis, and log-rank tests were used to compare differences between groups. The C-index was calculated to evaluate the predictive ability of the different indicators.

Results

The study included 899 patients with NSCLC. In the univariate analysis, all 15 measures were significantly associated with the OS of patients (all p < 0.05). The results of the C-index analysis showed that the fibrinogen-to-albumin ratio (FAR), the systemic immune-inflammation index (SII), and the albumin-to-alkaline phosphatase ratio (AAPR) were the three indices with the best predictive performance. Among them, FAR (C-index = 0.639) had the best predictive power for OS in patients with NSCLC. In the different subgroups, FAR had the highest C-index in male, non-smoking, adenocarcinoma, and stage II patients. The C-index of the platelet-to-lymphocyte ratio (PLR) in female patients was the highest. SII was the highest in smokers, in those aged <65 and ≥65 years, and in stage III patients. The C-index of AAPR was the highest in non-adenocarcinomas. The C-index of the pan-immune-inflammation value (PIV) was the highest in stage I patients. In the multivariate Cox regression analysis, among FAR, SII, and AAPR, only FAR was an independent predictor of OS in patients with NSCLC. A high FAR was associated with a higher risk of death in patients with NSCLC (HR = 1.601, 95% CI = 1.028-2.495). In order to further evaluate the potential prognostic value of FAR, SII, and AAPR in patients with different stages, Cox regression analysis was performed for those with stage I-II and stage III NSCLC. The results showed that FAR was an independent prognostic factor for OS in patients with stage I-II NSCLC.

Conclusion

For all patients with NSCLC, the prognostic power of FAR was superior to that of other inflammatory and nutritional indicators.

The advanced lung cancer inflammation index (ALI) predicted the postoperative survival rate of patients with non-small cell lung cancer and the construction of a nomogram model

OBJECTIVE

To investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with operable non-small-cell lung carcinoma (NSCLC). By constructing the nomogram model, it can provide a reference for clinical work.

METHODS

A total of 899 patients with non-small cell lung cancer who underwent surgery in our hospital between January 2017 and June 2021 were retrospectively included. ALI was calculated by body mass index (BMI) × serum albumin/neutrophil to lymphocyte ratio (NLR). The optimal truncation value of ALI was obtained using the receiver operating characteristic (ROC) curve and divided into two groups. Survival analysis was represented by the Kaplan-Meier curve. The predictors of Overall survival (OS) were evaluated by the Cox proportional risk model using single factor and stepwise regression multifactor analysis. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling 1 000 times) was used for internal verification of the nomogram model. The concordance index (C-index) was used to represent the prediction performance of the nomogram model, and the calibration graph method was used to visually represent its prediction conformity. The application value of the model was evaluated by decision curve analysis (DCA).

RESULTS

The optimal cut-off value of ALI was 70.06, and the low ALI group (ALI < 70.06) showed a poor survival prognosis. In multivariate analyses, tumor location, pathological stage, neuroaggression, and ALI were independently associated with operable NSCLC-specific survival. The C index of OS predicted by the nomogram model was 0.928 (95% CI: 0.904-0.952). The bootstrap self-sampling method (B = 1000) was used for internal validation of the prediction model, and the calibration curve showed good agreement between the prediction and observation results of 1-year, 2-year, and 3-year OS. The ROC curves for 1-year, 2-year, and 3-year survival were plotted according to independent factors, and the AUC was 0.952 (95% CI: 0.925-0.979), 0.951 (95% CI: 0.916-0.985), and 0.939 (95% CI: 0.913-0.965), respectively. DCA shows that this model has good clinical application value.

CONCLUSION

ALI can be used as a reliable indicator to evaluate the prognosis of patients with operable NSCLC, and through the construction of a nomogram model, it can facilitate better individualized treatment and prognosis assessment.

Prognostic factors and predictive model construction in patients with non-small cell lung cancer: a retrospective study

Objective

The purpose of this study was to construct a nomogram model based on the general characteristics, histological features, pathological and immunohistochemical results, and inflammatory and nutritional indicators of patients so as to effectively predict the overall survival (OS) and progression-free survival (PFS) of patients with non-small cell lung cancer (NSCLC) after surgery.

Methods

Patients with NSCLC who received surgical treatment in our hospital from January 2017 to June 2021 were selected as the study subjects. The predictors of OS and PFS were evaluated by univariate and multivariable Cox regression analysis using the Cox proportional risk model. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling for 1 000 times) was used to internally verify the nomogram model, and C-index was used to represent the prediction performance of the nomogram model. The calibration graph method was used to visually represent its prediction compliance, and decision curve analysis (DCA) was used to evaluate the application value of the model.

Results

Univariate and multivariate analyses were used to identify independent prognostic factors and to construct a nomogram of postoperative survival and disease progression in operable NSCLC patients, with C-index values of 0.927 (907-0.947) and 0.944 (0.922-0.966), respectively. The results showed that the model had high predictive performance. Calibration curves for 1-year, 2-year, and 3-year OS and PFS show a high degree of agreement between the predicted probability and the actual observed probability. In addition, the results of the DCA curve show that the model has good clinical application value.

Conclusion

We established a predictive model of survival prognosis and disease progression in patients with non-small cell lung cancer after surgery, which has good predictive performance and can guide clinicians to make the best clinical decision.

Association between immune-related adverse events and prognosis in patients with advanced non-small cell lung cancer: a systematic review and meta-analysis

Background

The emergence of immune checkpoint inhibitors (ICIs) provides a variety of options for patients with advanced non-small-cell lung cancer (NSCLC). After the application of ICIs, the immune system of patients was highly activated, and immune-related adverse events (irAEs) could occur in some organ systems, and irAEs seemed to be associated with the survival prognosis of patients. Therefore, we evaluated the association between survival outcomes and irAEs in NSCLC patients and conducted a systematic review and meta-analysis.

Methods

We conducted systematic reviews of PubMed, Embase, Cochrane, and Web of Science databases until December 2021. The forest map was constructed by combining the hazard ratio (HR) and 95% confidence interval (CI). I2 estimated the heterogeneity between studies. A meta-analysis was performed using R 4.2.1 software.

Results

Eighteen studies included 4808 patients with advanced NSCLC. In pooled analysis, the occurrence of irAEs was found to be a favorable factor for improved prognosis (PFS: HR: 0.48, 95% CI: 0.41-0.55, P <0.01; OS: HR: 0.46, 95% CI: 0.42-0.52, P <0.01). In subgroup analyses, cutaneous irAE, gastrointestinal irAE, endocrine irAE and grade ≥3 irAEs were associated with improvements in PFS and OS, but pulmonary and hepatic irAEs were not.

Conclusion

Existing evidence suggests that the occurrence of irAEs may be a prognostic biomarker for advanced NSCLC. However, further research is needed to explore the prospect of irAEs as a prognostic biomarker in patients undergoing immunotherapy.

Systematic review registration

https://www.crd.york.ac.uk/PROSPEROFILES/405333_STRATEGY_20240502.pdf, identifier CRD42023405333.

The current landscape of using direct inhibitors to target KRASG12C-mutated NSCLC

Mutation in KRAS protooncogene represents one of the most common genetic alterations in NSCLC and has posed a great therapeutic challenge over the past ~ 40 years since its discovery. However, the pioneer work from Shokat's lab in 2013 has led to a recent wave of direct KRASG12C inhibitors that utilize the switch II pocket identified. Notably, two of the inhibitors have recently received US FDA approval for their use in the treatment of KRASG12C mutant NSCLC. Despite this success, there remains the challenge of combating the resistance that cell lines, xenografts, and patients have exhibited while treated with KRASG12C inhibitors. This review discusses the varying mechanisms of resistance that limit long-lasting effective treatment of those direct inhibitors and highlights several novel therapeutic approaches including a new class of KRASG12C (ON) inhibitors, combinational therapies across the same and different pathways, and combination with immunotherapy/chemotherapy as possible solutions to the pressing question of adaptive resistance.

Coxsackievirus A11 is an immunostimulatory oncolytic virus that induces complete tumor regression in a human non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Innovative treatment is required to improve overall survival rates for advanced NSCLC. Oncolytic virotherapy using enteroviruses has emerged as a promising anticancer strategy. To identify a novel, potent virotherapy with an improved safety profile, we assessed the oncolytic activity of 28 enteroviral strains and focused on coxsackievirus A11 (CVA11). CVA11 infection caused extensive oncolytic activity in all three of the examined human NSCLC cell lines, with high intercellular adhesion molecule-1 (ICAM-1) expression associated with greater CVA11-induced cytotoxicity. In vitro inhibition analysis using a pan-caspase inhibitor and western blot detection of cleaved poly (ADP-ribose) polymerase (PARP) indicated that apoptosis partly contributed to CVA11-driven cytotoxicity. CVA11 infection-induced immunogenic cell death in vitro was strongly suggested by substantial calreticulin expression and release of high mobility group box-1 protein (HMGB1). Moreover, in vivo treatment of human NSCLC xenografts with intratumoral CVA11 injection caused complete tumor regression in all treated mice, without significant weight loss. Our findings indicate that novel oncolytic virotherapy utilizing CVA11 may be less toxic and more effective than current treatments for human NSCLC, thus warranting further investigation in clinical trial settings, especially in combination with immunotherapy.

Genetic differences between smokers and never-smokers with lung cancer

Smoking is a major risk factor for lung cancer, therefore lung cancer epidemiological trends reflect the past trends of cigarette smoking to a great extent. The geographic patterns in mortality closely follow those in incidence. Although lung cancer is strongly associated with cigarette smoking, only about 15% of smokers get lung cancer, and also some never-smokers develop this malignancy. Although less frequent, lung cancer in never smokers is the seventh leading cause of cancer deaths in both sexes worldwide. Lung cancer in smokers and never-smokers differs in many aspects: in histological types, environmental factors representing a risk, and in genes associated with this disease. In this review, we will focus on the genetic differences between lung cancer in smokers versus never-smokers: gene expression, germ-line polymorphisms, gene mutations, as well as ethnic and gender differences. Finally, treatment options for smokers and never-smokers will be briefly reviewed.

The role of autoantibody detection in the diagnosis and staging of lung cancer

Background

Previously, the clinical value of seven autoantibodies (p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1, and CAGE) has been surveyed in our pilot observation and other published studies. Herein, we aimed to further investigate the role of these autoantibodies in the diagnosis and staging of LC.

Methods

We included a total of 135 individuals, who were divided into a Lung cancer (LC) group and a control group according to the final diagnosis. Seven autoantibody detection kits were used (ELISA method) for the expression measurement. The patients’ demographics information (e.g., age, gender, and smoking history) were also documented.

Results

Among the seven types of autoantibodies, only P53 and GBU4-5 were significantly increased in the LC group compared to the controls. Also, the P53 autoantibody was markedly different among the various subtype groups. Meanwhile, the GBU4-5 level was significantly higher in the small cell lung cancer (SCLC) patients compared to patients with adenocarcinoma (ADC). Autoantibodies against PGP9.5, SOX2, GBU4-5, and CAGE were found to be associated with stages. Their expressions were notably higher in the advanced stage (IV) versus early stages (I–II). Using logistic regression, the outcomes of LC prediction and stage prediction showed that the area under curve (AUCs) of the receiver operating characteristic (ROC) curves were 0.743 and 0.798, respectively.

Conclusions

In summary, our study confirmed the diagnostic value of tumor-associated autoantibodies, which may be useful as latent tumor markers to facilitate the detection of early LC. Single autoantibody testing is not yet sufficient in LC cancer screening, and the combined detection of autoantibodies can improve the sensitivity of detection compared with single antibody detection, especially for P53, PGP9.5, SOX2, GBU4-5, and CAGE autoantibodies.

Novel Insights Into MALAT1 Function as a MicroRNA Sponge in NSCLC

The long non-coding RNA metastasis-associated lung adenocarcinoma transcript-1 (MALAT1) was initially found to be overexpressed in early non-small cell lung cancer (NSCLC). Accumulating studies have shown that MALAT1 is overexpressed in the tissue or serum of NSCLC and plays a key role in its occurrence and development. In addition, the expression level of MALAT1 is significantly related to the tumor size, stage, metastasis, and distant invasion of NSCLC. Therefore, MALAT1 could be used as a biomarker for the early diagnosis, severity assessment, or prognosis evaluation of NSCLC patients. This review describes the basic properties and biological functions of MALAT1, focuses on the specific molecular mechanism of MALAT1 as a microRNA sponge in the occurrence and development of NSCLC in recent years, and emphasizes the application and potential prospect of MALAT1 in molecular biological markers and targeted therapy of NSCLC.

Next-generation sequencing-based identification of EGFR and NOTCH2 complementary mutations in non-small cell lung cancer

Although targeted therapy has emerged as an effective treatment strategy for non-small cell lung cancer (NSCLC), some patients cannot benefit from such therapy due to the limited number of therapeutic targets. The present study aimed to identify mutated genes associated with clinicopathological characteristics and prognosis and to screen for mutations that are not concurrent with applicable drug target sites in patients with NSCLC. Tumor tissue and blood samples were obtained from 97 patients with NSCLC. A lung cancer-specific panel of 55 genes was established and analyzed using next-generation sequencing (NGS). The results obtained from the clinical cohort were compared with the NSCLC dataset from The Cancer Genome Atlas (TCGA). Subsequently, 25 driver genes were identified by taking the intersection of the 55 lung-cancer-specific genes with three databases, namely, the Catalog of Somatic Mutations in Cancer database, the Network of Cancer Genes database and Vogelstein's list. Functional annotation and protein-protein interaction analysis were conducted on these 25 driver genes. The χ² test and logistic regression were used to evaluate the association between mutations in the 25 driver genes and the clinicopathological characteristics of 97 patients, and phosphatase and tensin homolog (PTEN) and kirsten rat sarcoma viral oncogene homolog (KRAS) were associated with stage at diagnosis and sex, respectively, while epidermal growth factor receptor (EGFR) was associated with sex, stage at diagnosis, metastasis, CEA and CYFRA21-1. Moreover, the association between the 25 driver gene mutations and overall survival were examined using Cox regression analysis. Age and Notch homolog 2 (NOTCH2) mutations were independent prognostic factors in TCGA dataset. The correlations between statistically significant mutations in EGFR, KRAS, PTEN and NOTCH2 were further examined, both in the clinical data and TCGA dataset. There was a negative correlation between EGFR and NOTCH2 mutations (correlation coefficient, −0.078; P=0.027). Thus, the present study highlights the importance of NOTCH2 mutations and might provide novel therapeutic options for patients with NSCLC who do not harbor EGFR mutations.

A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BACKGROUND

Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC.

METHODS

The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset.

RESULTS

Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively.

CONCLUSION

A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

Survival Rates of Patients with Non-Small Cell Lung Cancer Depending on Lymph Node Metastasis: A Focus on Saliva

The aim of this study was to compare overall survival (OS) rates at different pN stages of NSCLC depending on tumor characteristics and to assess the applicability of saliva biochemical markers as prognostic signs. The study included 239 patients with NSCLC (pN₀-120, pN₁-51, pN₂-68). Saliva was analyzed for 34 biochemical indicators before the start of treatment. For pN₀, the tumor size does not have a prognostic effect, but the histological type should be taken into account. For pN₁ and pN₂, long-term results are significantly worse in squamous cell cancer with a large tumor size. A larger volume of surgical treatment reduces the differences between OS. The statistically significant factors of an unfavorable prognosis at pN₀ are the lactate dehydrogenase activity <1294 U/L and the level of diene conjugates >3.97 c.u. (HR = 3.48, 95% CI 1.21-9.85, p = 0.01541); at pN₁, the content of imidazole compounds >0.296 mmol/L (HR = 6.75, 95% CI 1.28-34.57, p = 0.00822); at pN₂ levels of protein <0.583 g/L and Schiff bases >0.602 c.u., as well as protein >0.583 g/L and Schiff bases <0.602 c.u. (HR = 2.07, 95% CI 1.47-8.93, p = 0.04351). Using salivary biochemical indicators, it is possible to carry out stratification into prognostic groups depending on the lymph node metastasis.

Prevention of Advanced Cancer by Vitamin D3 Supplementation: Interaction by Body Mass Index Revisited

Meta-analyses of randomized controlled trials (RCTs) have demonstrated a protective effect of vitamin D₃ (cholecalciferol) supplementation against cancer mortality. In the VITAL study, a RCT including 25,871 men ≥ 50 years and women ≥ 55 years, protective effects of vitamin D₃ supplementation (2000 IU/day over a median of 5.3 years) with respect to incidence of any cancer and of advanced cancer (metastatic cancer or cancer death) were seen for normal-weight participants but not for overweight or obese participants. We aimed to explore potential reasons for this apparent variation of vitamin D effects by body mass index. We conducted complementary analyses of published data from the VITAL study on the association of body weight with cancer outcomes, stratified by vitamin D₃ supplementation. Significantly increased risks of any cancer and of advanced cancer were seen among normal-weight participants compared to obese participants in the control group (relative risk (RR), 1.27; 95% confidence interval (CI), 1.07-1.52, and RR, 1.44; 95% CI, 1.04-1.97, respectively). No such patterns were seen in the intervention group. Among those with incident cancer, vitamin D₃ supplementation was associated with a significantly reduced risk of advanced cancer (RR, 0.86; 95% CI, 0.74-0.99). The observed patterns point to pre-diagnostic weight loss of cancer patients and preventive effects of vitamin D₃ supplementation from cancer progression as plausible explanations for the body mass index (BMI)-intervention interactions. Further research, including RCTs more comprehensively exploring the potential of adjuvant vitamin D therapy for cancer patients, should be pursued with priority.

Clinicopathological and prognostic value of S100A4 expression in non-small cell lung cancer: a meta-analysis

Background: Numerous published studies have shown that S100A4 is frequently overexpressed in various human cancers. However, the association between S100A4 expression and prognosis or clinicopathological parameters in non-small cell lung cancer (NSCLC) remains unclear. Therefore, a meta-analysis was performed to identify the significance of S100A4 in NSCLC.

Methods: Systematic literature search was conducted using PubMed, Embase, Web of Science, the Cochrane Library, the Chinese National Knowledge Infrastructure database (CNKI), and the Wanfang database to obtain relevant articles. A combined hazard ratio (HR) and its corresponding 95% confidence interval (CI) were used to evaluate the association between S100A4 expression and prognosis in NSCLC patients. Pooled odds ratio (OR) and 95% CI were calculated to assess the association between S100A4 expression and clinicopathological features in NSCLC.

Results: NSCLC patients with overexpression of S100A4 had a worse prognosis than patients with low expression of S100A4 (HR = 1.77, 95% CI: 1.55–2.02, P<0.001). Additionally, overexpression of S100A4 was significantly correlated to patients’ age (OR = 0.67, 95% CI: 0.49–0.91, P=0.010), tumor differentiation (OR = 2.20, 95% CI: 1.69–2.85, P<0.001), lymph node metastasis (LNM) (OR = 3.70, 95% CI: 2.25–6.06, P<0.001), Tumor-Node-Metastasis (TNM) stage (OR = 3.08, 95% CI: 2.10–4.53, P<0.001), and pathological subtype (OR = 1.77, 95% CI: 1.09–2.88, P=0.020). However, there was no association between S100A4 expression and other clinicopathological features in NSCLC, including gender, tumor size, and smoking.

Conclusion: S100A4 overexpression was associated with tumor progression and poor prognosis in NSCLC patients. Hence, S100A4 might serve as a potential prognostic biomarker in NSCLC.

A Nomogram Based on CT Deep Learning Signature: A Potential Tool for the Prediction of Overall Survival in Resected Non-Small Cell Lung Cancer Patients

PURPOSE

To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients.

PATIENTS AND METHODS

A total of 1792 deep learning features were extracted from non-enhanced and venous-phase CT images for each NSCLC patient in training cohort (n=231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness. In addition, in order to quantify the improvement in performance added by deep learning signature, the net reclassification improvement (NRI) was calculated. The results were validated in external validation cohort (n=77).

RESULTS

A deep learning signature with 9 selected features was significantly associated with OS in both training cohort (hazard ratio [HR]=5.455, 95% CI: 3.393-8.769, P<0.001) and external validation cohort (HR=3.029, 95% CI: 1.673-5.485, P=0.004). The nomogram combining deep learning signature with clinical risk factors of TNM stage, lymphatic vessel invasion and differentiation grade showed favorable discriminative ability with C-index of 0.800 as well as a good calibration, which was validated in external validation cohort (C-index=0.723). Additional value of deep learning signature to the nomogram was statistically significant (NRI=0.093, P=0.027 for training cohort; NRI=0.106, P=0.040 for validation cohort). Decision curve analysis confirmed the clinical usefulness of this nomogram in predicting OS.

CONCLUSION

The deep learning signature-based nomogram is a robust tool for prognostic prediction in resected NSCLC patients.

Characteristics and prognosis of synchronous multiple primary lung cancer after surgical treatment: A systematic review and meta-analysis of current evidence

Background

This study aims to quantitatively summary the characteristics of synchronous multiple primary lung cancer (sMPLC), postoperative mortality, long‐term prognosis, and prognostic effects of potential clinical parameters in patients with sMPLC after surgery.

Methods

PubMed and Embase databases were systematically searched to identify studies that explored the prognosis of patients with sMPLC after surgery.

Results

Fifty‐two studies with 3486 participants were included, and clinical characteristics were quantitatively summarized. The pooled proportion of sMPLC in lung cancer was 2.0% (95%CI, 1.6%–2.5%) with an increasing trend over time, and postoperative mortality was 1.4% (95%CI, 0.5%–2.7%) with a decreasing trend over time. The 5‐year survival rate was 44.9% (95%CI, 37.4%–52.6%) and all long‐term survival rates showed increasing trends over time. Poor long‐term prognosis was observed in both limited resection (HR = 1.357, 95%CI, 1.047–1.759, p = 0.0210) and pneumonectomy (HR = 2.643, 95%CI, 1.539–4.541, p = 0.0004) by comparison of anatomical resection. Other clinical parameters of age, gender, smoking status, FEV1, and lymph node metastasis significantly impacted the long‐term prognosis (all p < 0.05).

Conclusions

The proportion of sMPLC in lung cancer and 5‐year survival rate are increasing, while postoperative mortality is decreasing trend over time. Lobectomy should be preferred, while pneumonectomy should be avoided for sMPLC. Age, gender, FEV1, smoking, tumor size, surgical methods, and lymph node status are prognostic factors for sMPLC. Considering the heterogeneity and publication bias, these findings should be treated with caution.

The benefits of smoking cessation on survival in cancer patients by integrative analysis of multi-omics data

Few studies have examined the association between smoking status (including former smokers) at diagnosis and overall survival among cancer patients. We aimed to assess the benefits of quitting smoking on cancer prognosis in cohorts of cancer patient smokers obtained from the Cancer Genome Atlas (TCGA) database. Hazard ratios (HR) were calculated to evaluate smoking behavior at cancer diagnosis (reformed smokers vs. current smokers) in association with overall survival using multivariate‐adjusted Cox regressions analysis. According to our analyses, quitting smoking was the independent protective factor for overall survival in lung squamous cell carcinoma (LUSC) (HR = 0.67, 95% CI = 0.48–0.94). Comprehensive analysis of multicomponent data across reformed and current smokers identified a total of 85 differential expressed genes (DEGs) affected by different modes of genetic and epigenetic regulation, potentially representing cancer drivers in smokers. Moreover, we provided a smoking‐associated gene expression signature, which could evaluate the true effect on prognosis with high power (HR = 1.70, 95% CI = 1.19–2.43, AUC = 0.65, 0.67, and 0.70 for 2‐, 3‐, and 5‐year survival, respectively). This signature was also applicable in other smoking‐related cancers, including bladder urothelial carcinoma (HR = 1.70, 95% CI = 1.01–2.88), cervical carcinoma (HR = 5.69, 95% CI = 1.37–23.69), head and neck squamous cell carcinoma (HR = 1.97, 95% CI = 1.41–2.76), lung adenocarcinoma (HR = 1.73, 95% CI = 1.16–2.57), and pancreatic adenocarcinoma (HR = 4.28, 95% CI = 1.47–12.47). In conclusion, this study demonstrates that quitting smoking at diagnosis decreases risk of death in cancer patients. We also provide a smoking‐associated gene expression signature to evaluate the effect of smoking on survival. Lastly, we suggest that smoking cessation could comprise a part of cancer treatment to improve survival rates of cancer patients.