A novel inflammatory nutrient index for predicting survival outcomes in patients with non-small cell lung cancer
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PROVIDER: S-EPMC11231790 | biostudies-literature | 2024 Jun
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe systemic immune-inflammation index (SII) is correlated with patient survival in various types of solid tumors. However, only a few studies have focused on the prognostic value of the SII in patients with surgically resected non-small cell lung cancer (NSCLC).MethodsThis study was a single center retrospective analysis of 569 NSCLC patients who underwent curative lobectomy at the Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College between 2006 and 2012. A receiver operating characteristic curve was plotted to compare the discriminatory ability of the SII for overall survival (OS). A Cox proportional hazards regression model was used to perform univariate and multivariate analyses.ResultsThe SII, neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR) all correlated with OS in NSCLC patients, and the SII was an independent prognostic factor for OS (hazard ratio 1.256, 95% confidence interval 1.018-1.551; P = 0.034). The area under the receiver operating characteristic curve of the SII (0.547) was larger than the NLR (0.541) and PLR (0.531). Furthermore, the SII retained prognostic significance in the lung adenocarcinoma subgroup.ConclusionThe SII is a promising prognostic predictor for patients with surgically resected NSCLC and retained prognostic significance in the lung adenocarcinoma subgroup. The prognostic value of the SII is superior to the NLR and PLR.
| S-EPMC6449249 | biostudies-literature
Project description:Immune checkpoint inhibitors (ICI) for early-stage non-small cell lung cancer (NSCLC) have been approved to improve outcomes and reduce recurrence. Biomarkers for patient selection are needed. In this paper, we proposed an inflammasome-based risk score (IRS) system for prognosis and prediction of ICI response for early-stage NSCLC. Cox regression analysis was used to identify significant genes (from 141 core inflammasome genes) for overall survival (OS) in a microarray discovery cohort (n = 467). IRS was established and independently validated by other datasets (n = 1320). We evaluated the inflammasome signaling steps based on five gene sets, which were IL1B-, CASP-1-, IL18-, GSDMD-, and inflammasome-regulated genes. Gene set enrichment analysis, the Kaplan-Meier curve, receiver operator characteristic with area under curve (AUC) analysis, and advanced bioinformatic tools were used to confirm the ability of IRS in prognosis and classification of patients into ICI responders and non-responders. A 30-gene IRS was developed, and it indicated good risk stratification at 10-year OS (AUC = 0.726). Patients were stratified into high- and low-risk groups based on optimal cutoff points, and high-risk IRS had significantly poorer OS and relapse-free survival. In addition, the high-risk group was characterized by an inflamed immunophenotype and higher proportion of ICI responders. Furthermore, expression of SLAMF8 was the key gene in IRS and indicated good correlation with biomarkers associated with immunotherapy. It could serve as a therapeutic target in the clinical setting of immunotherapy.
| S-EPMC9313462 | biostudies-literature
Project description:BACKGROUND The role of immune parameters in the prognosis of lung cancer has attracted more and more attention. However, studies of the association between immune scores and prognosis of lung cancer are scarce. The goal of our research was to investigate the correlation between immune scores and overall survival (OS) of early-stage non-small cell lung cancer (NSCLC). MATERIAL AND METHODS All data regarding patient immune and stromal scores, clinicopathological features, and survival was obtained from the TCGA datasets. Univariable and multivariable Cox regression analyses were utilized to recognize risk factors associated with OS. Afterward, a prognostic nomogram was constructed for predicting 3- and 5-year OS of stage I and II NSCLC patients. Calibration curves and receiver operating characteristic (ROC) were performed to assess the predictive accuracy of the nomogram. Kaplan-Meier methodology was also applied for the survival analysis. RESULTS In total, 764 NSCLC (stage I-II) patients were analyzed, and all patients were classified into 3 groups based on immune scores. Results showed that patients with medium-immune scores had significantly worse OS (hazard ratio=1.73, 95% confidence interval: 1.22-2.46) compared with those with low- and high immune scores. Area under the ROC curves (AUC) values for 3- and 5-year OS were 0.65 and 0.64, respectively. Calibration plots demonstrated good consistency in the probability of OS between nomogram predictions and actual observations. CONCLUSIONS Medium-immune scores are correlated with unsatisfactory prognosis in NSCLC (stage I-II) patients. In addition, the prognostic nomogram may be helpful in predicting OS for stage I and II NSCLC patients.
| S-EPMC7288828 | biostudies-literature
Project description:Lung cancer is the most common cause of cancer-related deaths worldwide with non-small cell lung cancer (NSCLC) making up most of these cases. Males have poorer overall survival compared to women following a lung cancer diagnosis. Many studies have focused on the effects of estrogen to explain higher survival rates among women, but few have looked at the effects of androgens. We describe the expression of the androgen receptor (AR) and Ki67 in lung cancer specimens in the Manitoba Tumor Bank (MTB) and correlate these factors with patient outcome. Using the MTB, we performed immunohistochemistry on lung cancer tissue to determine expression of the AR and Ki67. These were then correlated with patient outcome. Of the 136 cases, 55% were female and 55% were adenocarcinoma. AR expression was not independently associated with outcome. Ki67 was associated with a significantly higher hazard ratio for death and recurrence (HR 2.19, 95% CI 1.30-3.70; HR 1.92, 95% CI 1.07-3.46, respectively). AR expression modified the effect of Ki67 on outcome, such that when both were expressed, there was no association with recurrence or survival (HR 2.39, 95% CI 1.31-4.36 for AR- Ki67+ vs HR 1.54, 95% CI 0.44-5.37 for AR+ Ki67+). Ki67 was associated with poorer outcomes alone. AR status alone was not associated with outcome. Although the mechanism remains unclear, AR status seems to negate the association of a high Ki67 and poor outcome.
| S-EPMC10355975 | biostudies-literature
Project description:Long non-coding RNAs (lncRNAs) can influence the proliferation, autophagy, and apoptosis of non-small cell lung cancer (NSCLC). LncRNAs also emerge as valuable prognostic factors for NSCLC patients. Consequently, we set out to discover more autophagy-associated lncRNAs. We acquired autophagy-associated genes and information on lncRNAs from The Cancer Genome Atlas database (TCGA), and the Human Autophagy Database (HADb). Then, the prognostic prediction signature was constructed through using co-expression and Cox regression analysis. The signature was constructed including 7 autophagy-associated lncRNAs (ABALON, NKILA, LINC00941, AL161431.1, AL691432.2, AC020765.2, MMP2-AS1). After that, we used univariate and multivariate Cox regression analysis to calculate the risk score. The survival analysis and ROC curve analysis confirmed good performances of the signature. GSEA indicated that the high-risk group was principally enriched in the adherens junction pathway. In addition, biological experiments showed that ABALON promoted the proliferation, metastasis and autophagy levels of NSCLC cells. These findings demonstrate that the risk signature consisting of 7 autophagy-associated lncRNAs accurately predicts the prognosis of NSCLC patients and should be investigated for potential therapeutic targets in clinic.
| S-EPMC9479339 | biostudies-literature
Project description:Backgroundthe objective of this study is to evaluate the predictive power of the survival model using deep learning of diffusion-weighted images (DWI) in patients with non-small-cell lung cancer (NSCLC).MethodsDWI at b-values of 0, 100, and 700 sec/mm2 (DWI0, DWI100, DWI700) were preoperatively obtained for 100 NSCLC patients who underwent curative surgery (57 men, 43 women; mean age, 62 years). The ADC0-100 (perfusion-sensitive ADC), ADC100-700 (perfusion-insensitive ADC), ADC0-100-700, and demographic features were collected as input data and 5-year survival was collected as output data. Our survival model adopted transfer learning from a pre-trained VGG-16 network, whereby the softmax layer was replaced with the binary classification layer for the prediction of 5-year survival. Three channels of input data were selected in combination out of DWIs and ADC images and their accuracies and AUCs were compared for the best performance during 10-fold cross validation.Results66 patients survived, and 34 patients died. The predictive performance was the best in the following combination: DWI0-ADC0-100-ADC0-100-700 (accuracy: 92%; AUC: 0.904). This was followed by DWI0-DWI700-ADC0-100-700, DWI0-DWI100-DWI700, and DWI0-DWI0-DWI0 (accuracy: 91%, 81%, 76%; AUC: 0.889, 0.763, 0.711, respectively). Survival prediction models trained with ADC performed significantly better than the one trained with DWI only (p-values < 0.05). The survival prediction was improved when demographic features were added to the model with only DWIs, but the benefit of clinical information was not prominent when added to the best performing model using both DWI and ADC.ConclusionsDeep learning may play a role in the survival prediction of lung cancer. The performance of learning can be enhanced by inputting precedented, proven functional parameters of the ADC instead of the original data of DWIs only.
| S-EPMC10417371 | biostudies-literature
Project description:Background: The prognostic nutritional index (PNI) is related to the prognosis of multiple malignancies. This study investigated whether the PNI has prognostic value in advanced non-small cell lung cancer (NSCLC) patients treated with programmed death 1 (PD-1) inhibitors. Methods: We retrospectively analyzed advanced NSCLC patients treated with PD-1 inhibitors from July 2018 to December 2019. Pretreatment PNI was calculated by peripheral lymphocyte count and serum albumin level, and the cut-off value was determined. Subsequently, we investigated the relationship between PNI and early progression, and evaluated its prognostic role on survival outcomes. Ultimately, based on the results of survival analysis, a nomogram was established. Results: A total of 123 patients were included. Of these, 24 (19.5%) patients had experienced early progression. Multivariate logistic analysis indicated that low PNI (odds ratio, 3.709, 95% confidence interval [CI], 1.354-10.161; P = 0.011) was closely correlated with early progression. Moreover, multivariate Cox regression analysis confirmed that low PNI was an independent risk factor for progression-free survival (hazard ratio [HR], 2.698, 95% CI, 1.752-4.153; P < 0.001) and overall survival (HR, 7.222, 95% CI, 4.081-12.781; P < 0.001), respectively. The prediction accuracy of nomogram based on PNI is moderate. Conclusion: PNI was an independent predictor of early progression and survival outcomes in advanced NSCLC patients treated with PD-1 inhibitors.
| S-EPMC8040881 | biostudies-literature
Project description:IntroductionNon-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables.MethodsProspective registry and study data were analysed using Cox proportional hazards regression to derive a prognostic model (hospital 1, n=695), which was subsequently tested (Harrell's c-statistic for discrimination and Cox-Snell residuals for calibration) in two independent validation cohorts (hospital 2, n=479 and hospital 3, n=284).ResultsThe derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score⩽9); 61, 61, and 42% (LCPI 2: score 10-13); 33, 32, and 14% (LCPI 3: score 14-16); 7, 16, and 5% (LCPI 4: score ⩾15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts.ConclusionsThe LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses.
| S-EPMC5572183 | biostudies-literature
Project description:Background Brain metastasis (BM) is one of the most common metastatic sites in patients with small cell lung cancer (SCLC), and the prognosis remains very poor. This study aimed to establish a novel nomogram for predicting the cancer-specific survival (CSS) in SCLC patients with BM. Methods SCLC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 were retrospectively collected. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors, which were further used to construct the prognostic nomogram. The discrimination and calibration of nomogram were evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, the area under ROC curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness. Kaplan-Meier survival curve was applied to analyze the survival outcome. Results A total of 2,462 patients were enrolled in this study, and randomly assigned into training cohort (n=1,723) and validation cohort (n=739). Age, N stage, surgery, radiation, chemotherapy, bone metastasis, liver metastasis and lung metastasis were identified as independent prognostic factors of CSS. The C-indexes of nomogram was 0.683 [95% confidence interval (CI): 0.667–0.699] in the training cohort, and 0.659 (95% CI: 0.634–0.684) in the validation cohort. The AUC values of 6-, 9- and 12-month CSS were 0.723, 0.742 and 0.737 respectively in the training cohort, while 0.715, 0.737 and 0.739 in the validation cohort. The ROC, calibration and DCA curves showed good discrimination, calibration and clinical applicability of this nomogram in predicting prognosis. Moreover, patients in high-risk group had a worse survival outcome than patients in medium-risk and low-risk groups. Conclusions A novel nomogram was constructed and validated for predicting individual prognosis in SCLC patients with BM. This nomogram could help clinicians make effective treatment strategies for patients.
| S-EPMC9834596 | biostudies-literature
Project description:BACKGROUND: Improved methods are needed for predicting prognosis and the benefit of delivering adjuvant chemotherapy (ACT) in patients with non-small-cell lung cancer (NSCLC). METHODS: A novel prognostic algorithm was identified using genomic profiles from 332 stage I-III adenocarcinomas and independently validated on a separate series of 264 patients with stage I-II tumors, compiled from five previous studies. The prognostic algorithm was used to interrogate genomic data from a series of patients treated with adjuvant chemotherapy. Those genes associated with outcome in the adjuvant treatment setting, independent to prognosis were used to train an algorithm able to classify a patient as either a responder or non-responder to ACT. The performance of this signature was independently validated on a separate series of genomic profiles from patients enrolled in a randomized controlled trial of cisplatin/vinorelbine vs. observation alone (JBR.10). RESULTS: NSCLC patients exhibiting the high-risk, poor-prognosis form of the 160-gene prognosis signature experienced a 2.80-times higher rate of 5-year disease specific death (log rank P?<?0.0001) compared to those with the low-risk, good prognosis profile, adjusted for covariates. The prognosis signature was found to especially accurate at identifying early stage patients at risk of disease specific death within 24?months of diagnosis when compared to traditional methods of outcome prediction.Separately, NSCLC patients with the 37-gene ACT-response signature (n?=?70, 64?%), benefited significantly from cisplatin/vinorelbine (adjusted HR: 0.23, P?=?0.0032). For those patients predicted to be responders, receiving this form of ACT conferred a 25?% improvement in the probability of 5-year-survival, compared to observation alone and adjusted for covariates. Conversely, in those patients predicted to be non-responders, ACT was observed to offer no significant survival benefit (adjusted HR: 0.55, P?=?0.32).The two gene signatures overlap by one gene only SPSB3, which interacts with the oncogene MET. In this study, higher levels of SPSB3 which were associated with favorable prognosis and benefit from ACT. CONCLUSIONS: These complimentary prognostic and predictive gene signatures may assist physicians in their management and treatment of patients with early stage lung cancer.
| S-EPMC3407714 | biostudies-literature