A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Early-Stage Breast Cancer

Hua, Xin; Duan, Fangfang; Zhai, Wenyu; Song, Cailu; Jiang, Chang; Wang, Li; Huang, Jiajia; Lin, Huan‐Xin; Yuan, Zhongyu

doi:10.2147/jir.s338421

“…They also conducted a nomogram based on INPS for survival prediction in resected stage III gastric cancer with adjuvant chemotherapy. Hua et al ( 34 ) also validated the prognostic value of INPS by LASSO regression analysis in 1,259 patients with early stage breast cancer. In another study including 334 patients with limited-stage small cell lung cancer, the authors also indicated that several hematological nutritional and inflammatory indexes were associated with OS ( 35 ).…”

Section: Discussionmentioning

confidence: 99%

“…Nomogram, as a prognostic statistical model, can not only visually display the relevant indicators affecting the outcome in Cox PH regression analyses but also predict the survival probability through a simple graphical representation, which makes the prediction more simple and convenient ( 44 ). Wang et al ( 33 ) and Hua et al ( 34 ) both established a nomogram based on INPS to predict prognosis. They also revealed that the INPS-based prognostic nomogram showed a good prognostic stratification.…”

Section: Discussionmentioning

confidence: 99%

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

Feng

¹

,

Wang²,

Yang³

et al. 2022

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BackgroundThis study aimed to establish and validate a novel predictive model named integrative inflammatory and nutritional score (IINS) for prognostic prediction in esophageal squamous cell carcinoma (ESCC).Materials and methodsWe retrospectively recruited 494 pathologically confirmed ESCC patients with surgery and randomized them into training (n = 346) or validation group (n = 148). The least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) regression analysis was initially used to construct a novel predictive model of IINS. The clinical features and prognostic factors with hazard ratio (HRs) and 95% confidence intervals (CIs) grouped by IINS were analyzed. Nomogram was also established to verify the prognostic value of IINS.ResultsAccording to the LASSO Cox PH regression analysis, a novel score of IINS was initially constructed based on 10 inflammatory and nutritional indicators with the optimal cut-off level of 2.35. The areas under the curve (AUCs) of IINS regarding prognostic ability in 1-year, 3-years, and 5-years prediction were 0.814 (95% CI: 0.769–0.854), 0.748 (95% CI: 0.698–0.793), and 0.792 (95% CI: 0.745–0.833) in the training cohort and 0.802 (95% CI: 0.733–0.866), 0.702 (95% CI: 0.621–0.774), and 0.748 (95% CI: 0.670–0.816) in the validation cohort, respectively. IINS had the largest AUCs in the two cohorts compared with other prognostic indicators, indicating a higher predictive ability. A better 5-years cancer-specific survival (CSS) was found in patients with IINS ≤ 2.35 compared with those with IINS > 2.35 in both training cohort (54.3% vs. 11.1%, P < 0.001) and validation cohort (53.7% vs. 18.2%, P < 0.001). The IINS was then confirmed as a useful independent factor (training cohort: HR: 3.000, 95% CI: 2.254–3.992, P < 0.001; validation cohort: HR: 2.609, 95% CI: 1.693–4.020, P < 0.001). Finally, an IINS-based predictive nomogram model was established and validated the CSS prediction (training set: C-index = 0.71 and validation set: C-index = 0.69, respectively).ConclusionPreoperative IINS is an independent predictor of CSS in ESCC. The nomogram based on IINS may be used as a potential risk stratification to predict individual CSS and guide treatment in ESCC with radical resection.

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“…Wang et al found that preoperative INPS is an independent predictor of outcomes for stage III GC patients [31]. Hua et al demonstrated that patients with high INPS had significantly worse survival than those with low INPS [32]. In that research, the authors chose the LASSO regression model to establish the INPS.…”

Section: Discussionmentioning

confidence: 99%

A Novel Inflammatory and Nutritional Prognostic Scoring System for Nonpathological Complete Response Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

Jiang

¹

,

Xiu

²

,

Zhang

³

et al. 2022

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Background. It has been demonstrated that inflammatory and nutritional variables are associated with poor breast cancer survival. However, some studies do not include these variables due to missing data. To investigate the predictive potential of the INPS, we constructed a novel inflammatory-nutritional prognostic scoring (INPS) system with machine learning. Methods. This retrospective analysis included 249 patients with malignant breast tumors undergoing neoadjuvant chemotherapy (NAC). After comparing seven potent machine learning models, the best model, Xgboost, was applied to construct an INPS system. K-M survival curves and the log-rank test were employed to determine OS and DFS. Univariate and multivariate analyses were carried out with the Cox regression model. Additionally, we compared the predictive power of INPS, inflammatory, and standard nutritional variables using the Z test. Results. After comparing seven machine learning models, it was determined that the XGBoost model had the best OS and DFS performance ( AUC = 0.865 and 0.771 , respectively). For overall survival (OS, cutoff value = 0.3917 ) and disease-free survival ( cutoff value = 0.4896 ), all patients were divided into two groups by the INPS. Those with low INPS had higher 5-year OS and DFS rates (77.2% vs. 50.0%, P < 0.0001 ; and 59.6% vs. 32.1%, P < 0.0001 , respectively) than patients with high INPS. For OS and DFS, the INPS exhibited the highest AUC compared to the other inflammatory and nutritional variables ( AUC = 0.615 , P = 0.0003 ; AUC = 0.596 , P = 0.0003 , respectively). Conclusion. The INPS was an independent predictor of OS and DFS and exhibited better predictive ability than BMI, PNI, and MLR. For patients undergoing NAC for nonpCR breast cancer, INPS was a crucial and comprehensive biomarker. It could also forecast individual survival in breast cancer patients with low HER-2 expression.

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“…Furthermore, the diagnostic efficiency of the dysregulated metabolites was evaluated. As a good method in machine learning, LASSO regression has been widely used in cancer marker studies (Hua et al, 2022;Koplewitz et al, 2022). In this study, with LASSO regression analysis, we constructed two risk models with the seven key metabolites for NIHL status predication.…”

Section: Central Carbon Metabolismmentioning

confidence: 99%

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers

Zhang

¹

,

Li

²

,

Cui

³

et al. 2022

Front. Mol. Biosci.

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Noise exposure can lead to various kinds of disorders. Noise-induced hearing loss (NIHL) is one of the leading disorders confusing the noise-exposed workers. It is essential to identify NIHL markers for its early diagnosis and new therapeutic targets for its treatment. In this study, a total of 90 plasma samples from 60 noise-exposed steel factory male workers (the noise group) with (NIHL group, n = 30) and without NIHL (non-NIHL group, n = 30) and 30 male controls without noise exposure (control group) were collected. Untargeted human plasma metabolomic profiles were determined with HPLC-MS/MS. The levels of the metabolites in the samples were normalized to total peak intensity, and the processed data were subjected to multivariate data analysis. The Wilcoxon test and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed. With the threshold of p < 0.05 and the variable importance of projection (VIP) value >1, 469 differential plasma metabolites associated with noise exposure (DMs-NE) were identified, and their associated 58 KEGG pathways were indicated. In total, 33 differential metabolites associated with NIHL (DMs-NIHL) and their associated 12 KEGG pathways were identified. There were six common pathways associated with both noise exposure and NIHL. Through multiple comparisons, seven metabolites were shown to be dysregulated in the NIHL group compared with the other two groups. Through LASSO regression analysis, two risk models were constructed for NIHL status predication which could discriminate NIHL from non-NIHL workers with the area under the curve (AUC) values of 0.840 and 0.872, respectively, indicating their efficiency in NIHL diagnosis. To validate the results of the metabolomics, cochlear gene expression comparisons between susceptible and resistant mice in the GSE8342 dataset from Gene Expression Omnibus (GEO) were performed. The immune response and cell death-related processes were highlighted for their close relations with noise exposure, indicating their critical roles in noise-induced disorders. We concluded that there was a significant difference between the metabolite’s profiles between NIHL cases and non-NIHL individuals. Noise exposure could lead to dysregulations of a variety of biological pathways, especially immune response and cell death-related processes. Our results might provide new clues for noise exposure studies and NIHL diagnosis.

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A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Early-Stage Breast Cancer

Cited by 18 publications

References 56 publications

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

A Novel Inflammatory and Nutritional Prognostic Scoring System for Nonpathological Complete Response Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers

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