The Prognostic Prediction Value of Systemic Inflammation Score and the Development of a Nomogram for Patients With Surgically Treated Breast Cancer

Prognostic significance of the novel immunonutritional marker of cholesterol-to-lymphocyte ratio in patients with non-metastatic breast cancer

BACKGROUND

Although there is a strong correlation between the novel cholesterol-to-lymphocyte ratio (CLR) and tumor survival, its prognostic significance in breast cancer (BC) is unknown. After analyzing the relationship between CLR and the overall survival (OS) of patients with BC, we created a predictive model.

METHODS

Following retrospective enrollment, 1316 patients with BC were randomized into two cohorts: validation (n = 392) and training (n = 924). Distinct factors within the training dataset were identified for OS by univariate and multivariate Cox analyses; two-tailed P-value < 0.05 were considered to indicate statistical significance. On this premise, we developed novel signals for survival prediction and utilized the calibration curve, receiver operating characteristic curves, and concordance index (C-index) to validate their efficacy across both datasets.

RESULTS

Patients with BC were categorized into two categories with differing prognoses based on the CLR score [hazard ratio = 0.492; 95% confidence interval (CI): 0.286-0.846, P = 0.009]. A prediction nomogram was created based on multivariate analysis, which showed that N stage, postoperative pathological categorization, and CLR score were all independently correlated with OS. In the training [C-index = 0.831 (95% CI: 0.788-0.874)] and validation [C-index = 0.775 (95% CI: 0.694-0.856)] cohorts, the nomogram demonstrated favorable performance in predicting OS. In both the training and validation cohorts, it outperformed the traditional staging system [C-index = 0.702 (95% CI: 0.623-0.782)] and [C-index = 0.709 (95% CI: 0.570-0.847)]. The accurate prediction by the signature was further demonstrated by the time-dependent receiver operating characteristic curves.

CONCLUSIONS

The novel immunonutritional marker CLR could function as a simplified, cost-effective, easily accessible, non-invasive, and readily promotive prognostic indicator for patients with early-stage BC and demonstrates superior predictive power than the traditional staging system.

Exploring the molecular mechanisms network of breast cancer by multi-omics analysis

BACKGROUND

Breast cancer (BC), the most prevalent malignancy in women globally, still lacks comprehensive research on its molecular targets and necessitates further investigation into the underlying molecular mechanisms driving its initiation and progression.

METHODS

The GSE20685 Series Matrix File downloaded from the Gene Expression Omnibus database was divided into a high-risk group (n = 49) and a low-risk group (n = 278) to construct the co-expression network.

RESULTS

Four hub genes were identified based on the Weighted Gene Co-expression Network Analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed. Hub gene immune infiltration was investigated using the Tumor Immune Estimation Resource database, and CD4+ T cell expression levels were substantially correlated with hub gene expression. Based on the CancerRxGene database (Genomics of Drug Sensitivity in Cancer database), it was found that the hub genes were highly sensitive to common chemotherapy drugs such as AKT inhibitor VIII and Erlotinib. The expression of Secreted Frizzled-Related Protein 1, melanoma-inhibiting activity (MIA), and Keratin 14 was related to tumor mutation burden, and the expression of MIA also affected the microsatellite instability of the tumor. This study employs multi-omics analysis to investigate the molecular network associated with the prognosis of BC, highlighting its intricate connection with the immune microenvironment.

CONCLUSION

These findings pinpoint four crucial genes in BC progression, offering targets for further research and therapy. Their connections to immune infiltration and chemotherapy sensitivity underscore complex interactions in the tumor microenvironment.

Preoperative Systemic Inflammation Score Predicts the Prognosis of Patients with Upper Tract Urothelial Carcinoma Undergoing Radical Nephroureterectomy

Background: To investigate the prognostic significance of systemic inflammation score (SIS) in upper tract urothelial carcinoma (UTUC) in patients undergoing radical nephroureterectomy (RNU). Methods: A total of 313 UTUC patients who underwent RNU at West China Hospital from May 2014 to June 2019 were retrospectively analyzed. The predictive value of SIS for relevant endpoints, including overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS), was assessed by Kaplan-Meier curves and the Cox proportional hazards model. Results: According to inclusion and exclusion criteria, 218 UTUC patients were ultimately included in this cohort study. Statistical analysis shows that increased SIS was significantly associated with higher TNM stage (p = 0.017), lower BMI (p = 0.037), absence of hemoglobin (p < 0.001), and pathologic necrosis (p = 0.007). Kaplan-Meier survival curves clearly visually stratified survival for the three outcomes. After adjusting for tumor grade, the multivariate Cox proportional hazards model results showed that SIS was an independent risk factor for poor OS and CSS (HR = 1.89, 95% CI: 1.11-3.21, p = 0.0183, HR = 1.89, 95% CI: 1.07-3.33, p = 0.0285) in the advanced group. Conclusions: SIS was an independent risk factor for OS and CSS after RNU in patients with high-grade UTUC. It may be a novel and conducive tool for preoperative risk stratification and guiding individualized therapy for high-risk UTUC patients.

New scoring system combining computed tomography body composition analysis and inflammatory-nutritional indicators to predict postoperative complications in stage II-III colon cancer

BACKGROUND AND AIM

Postoperative complications are important clinical outcomes for colon cancer patients. This study aimed to investigate the predictive value of inflammatory-nutritional indicators combined with computed tomography body composition on postoperative complications in patients with stage II-III colon cancer.

METHODS

We retrospectively collected data from patients with stage II-III colon cancer admitted to our hospital from 2017 to 2021, including 198 patients in the training cohort and 50 patients in the validation cohort. Inflammatory-nutritional indicators and body composition were included in the univariate and multivariate analyses. Binary regression was used to develop a nomogram and evaluate its predictive value.

RESULTS

In the multivariate analysis, the monocyte-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), nutritional risk score (NRS), skeletal muscle index (SMI), and visceral fat index (VFI) were independent risk factors for postoperative complications of stage II-III colon cancer. In the training cohort, the area under the receiver operating characteristic curve of the predictive model was 0.825 (95% confidence interval [CI] 0.764-0.886). In the validation cohort, it was 0.901 (95% CI 0.816-0.986). The calibration curve showed that the prediction results were in good agreement with the observational results. Decision curve analysis showed that colon cancer patients could benefit from the predictive model.

CONCLUSIONS

A nomogram combining MLR, SII, NRS, SMI, and VFI with good accuracy and reliability in predicting postoperative complications in patients with stage II-III colon cancer was established, which can help guide treatment decisions.

The pretherapeutic systemic inflammation score is a prognostic predictor for elderly patients with oesophageal cancer: a case control study

BACKGROUND

The systemic inflammation score (SIS), based on serum albumin (Alb) and lymphocyte-to-monocyte ratio (LMR), is a novel prognostic tool for some tumours. Studies indicate that the SIS can be used as a postoperative prognostic marker. However, its predictive value in elderly oesophageal squamous cell carcinoma (ESCC) patients treated with radiotherapy is unclear.

METHODS

In total, 166 elderly ESCC patients who received radiotherapy with or without chemotherapy were included. Based on different combinations of Alb and LMR levels, the SIS was divided into 3 groups, SIS = 0 (n = 79), SIS = 1 (n = 71) and SIS = 2 (n = 16). The Kaplan-Meier method was used for survival analysis. Univariate and multivariate analyses were performed to assess prognosis. Time-dependent receiver operating characteristic (t-ROC) curves were used to compare the prognostic accuracy of the SIS with that of Alb, LMR, neutrophil-to lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammatory index (SII).

RESULTS

Decreased Alb and LMR were both associated with shorter OS, whereas a lower SIS was significantly associated with better outcomes. The OS of SIS = 0, SIS = 1 and SIS = 2 was 28.0 ± 2.9, 16.0 ± 2.8 and 10.0 ± 7.0 months, respectively (p = 0.000). Similar results were also observed for PFS. Multivariate analysis of the model with SIS revealed that the SIS was a significant independent biomarker for predicting OS and PFS. The nomogram showed that the C-index was improved to 0.677 when the SIS factor was incorporated. Furthermore, the 3-year OS rates for patients in the SIS-high group (SIS = 1 and SIS = 2) undergoing concurrent radiotherapy with a single agent (CCRT-1) and concurrent radiotherapy with two agents (CCRT-2) were 42% and 15%, respectively (p = 0.039). The t-ROC curve showed that the SIS was more sensitive than other prognostic factors for predicting overall survival.

CONCLUSION

The SIS may be a useful prognostic marker in elderly patients with ESCC receiving radiotherapy alone or chemoradiotherapy. The SIS showed a better predictive ability for OS than the continuous variable Alb and could stratify patient prognosis in different therapeutic regimens. CCRT-1 may be the best treatment for SIS-high patients.

The Systemic Inflammation Score is Associated with the Survival of Patients with Prostate Cancer

Background

The systemic inflammation score (SIS) based on the albumin (Alb) level and lymphocyte-to-monocyte ratio (LMR), has been associated with survival in some cancers. However, its prognostic role in prostate cancer (PCa) remains unclear.

Methods

The associations between the SIS and the clinicopathological features of PCa were evaluated. The correlations between the SIS and overall survival (OS) and progression-free survival (PFS) were assessed using Kaplan-Meier analysis and the Log rank test. Univariate and multivariate Cox analyses were conducted to determine the prognostic factors for PCa. Hazard ratios and 95% confidence intervals were calculated.

Results

A total of 253 patients with PCa were included in this study. The Kaplan-Meier analysis and Log rank test suggested that patients with a higher Alb level, higher LMR, or a lower SIS had better 5-year OS and PFS compared with patients with a lower Alb level or lower LMR or higher SIS. Univariate and multivariate Cox analyses showed that drinking, prostate-specific antigen level >100 ng/mL, and neutrophil-to-lymphocyte ratio >2.09 were significant prognostic factors for OS and PFS in patients with PCa. Nomograms for 5-year OS and PFS were established with concordance index values of 0.888 and 0.824, respectively. The calibration curve was consistent between the actual observations and the prediction nomogram for OS and PFS probability at 5 years.

Conclusion

A high SIS is associated with unfavorable survival in patients with PCa. The SIS serves as a novel independent prognostic factor for OS in patients with PCa.

The biomarkers related to immune infiltration to predict distant metastasis in breast cancer patients

Background: The development of distant metastasis (DM) results in poor prognosis of breast cancer (BC) patients, however, it is difficult to predict the risk of distant metastasis. Methods: Differentially expressed genes (DEGs) were screened out using GSE184717 and GSE183947. GSE20685 were randomly assigned to the training and the internal validation cohort. A signature was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Gene set enrichment analysis (GSEA) was used for functional analysis. Finally, a nomogram was constructed and calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity. The clinical benefit of this nomogram was revealed by decision curve analysis (DCA). Finally, we explored the relationships between candidate genes and immune cell infiltration, and the possible mechanism. Results: A signature containing CD74 and TSPAN7 was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Mechanistically, the signature reflect the overall level of immune infiltration in tissues, especially myeloid immune cells. The expression of CD74 and TSPAN7 is heterogeneous, and the overexpression is positively correlated with the infiltration of myeloid immune cells. CD74 is mainly derived from myeloid immune cells and do not affect the proportion of CD8+T cells. Low expression levels of TSPAN7 is mainly caused by methylation modification in BC cells. This signature could act as an independent predictive factor in patients with BC (p = 0.01, HR = 0.63), and it has been validated in internal (p = 0.023, HR = 0.58) and external (p = 0.0065, HR = 0.67) cohort. Finally, we constructed an individualized prediction nomogram based on our signature. The model showed good discrimination in training, internal and external cohort, with a C-index of 0.742, 0.801, 0.695 respectively, and good calibration. DCA demonstrated that the prediction nomogram was clinically useful. Conclusion: A new immune infiltration related signature developed for predicting metastatic risk will improve the treatment and management of BC patients.

Prognostic value of a modified systemic inflammation score in breast cancer patients who underwent neoadjuvant chemotherapy

BACKGROUND AND PURPOSE

The modified systemic inflammation score (mSIS) system, which is constructed based on the neutrophil to lymphocyte ratio (NLR) and albumin (Alb), has not been applied to evaluate the prognosis of malignant breast cancer patients who underwent neoadjuvant chemotherapy (NAC). The present study aimed to explore the relationship between the mSIS and overall survival (OS), disease-free survival (DFS) and pathological complete response (pCR).

METHODS

A total of 305 malignant breast tumor patients who underwent NAC were incorporated into this retrospective analysis. We determined OS and DFS using K-M survival curves and the log-rank test. The relationship between the mSIS and OS and DFS was evaluated by a Cox regression model. A nomogram was constructed based on Cox regression analysis.

RESULTS

Patients in the mSIS low-risk group had better 5- and 8-year OS rates than those in the mSIS high-risk group (59.8% vs. 77.0%; 50.1% vs. 67.7%; X² = 8.5, P = 0.0035, respectively). Patients in the mSIS (1 + 2 score) + pCR subgroup had the highest 5- and 8-year OS and disease-free survival (DFS) rates (OS: 55.0% vs. 75.7% vs. 84.8, 42.8% vs. 65.7% vs. 79.8%, X² = 16.6, P = 0.00025; DFS: 38.8% vs. 54.7% vs. 76.3%, 33.3% vs. 42.3 vs. 72.1%, X2 = 12.4, P = 0.002, respectively). Based on the mSIS, clinical T stage and pCR results, the nomogram had better predictive ability than the clinical TNM stage, NLR and Alb.

CONCLUSIONS

mSIS is a promising prognostic tool for malignant breast tumor patients who underwent NAC, and the combination of mSIS and pCR is helpful in enhancing the ability to predict a pCR.

Predictive value of modified systemic inflammation score for postoperative unplanned ICU admission in patients with NSCLC

Background

The purpose of this study was to investigate the predictive value of the modified systemic inflammation score (mSIS) in postoperative unplanned admission to the intensive care unit (ICU) in patients with non-small-cell lung cancer (NSCLC).

Methods

The clinical data of 1,321 patients with NSCLC treated with thoracic surgery in our hospital from August 2019 to June 2021 were analyzed retrospectively. The preoperative mSIS, which takes into account the serum albumin (ALB) level and lymphocyte-to-monocyte ratio (LMR), was recorded as 0, 1 or 2 and then was used to identify high-risk patients with unplanned admission to the ICU. The independent risk factors for unplanned admission to the ICU in patients with NSCLC after surgery were identified by multivariate logistic regression analysis.

Results

A total of 1,321 patients, including 549 (41.6%) males and 772 (58.4%) females, were included. The median age was 57 years (range 16–95 years). The incidence of unplanned admission to the ICU in patients with mSIS = 2 was significantly higher than that in those with mSIS = 0 and mSIS = 1. The multivariate analysis showed that an mSIS of 2 (OR = 3.728; P = 0.004; 95% CI, 1.520–9.143), an alcohol consumption history (OR = 2.791, P = 0.011; 95% CI, 1.262–6.171), intraoperative infusion volume (OR = 1.001, P = 0.021; 95% CI, 1.000–1.001) and preoperative underlying diseases (OR = 3. 57, P = 0.004; 95% CI, 1.497–8.552) were independent risk factors for unplanned admission to the ICU after lung cancer surgery. In addition, the multivariate logistic regression model showed that the C-statistic value was 0.799 (95% CI: 0.726∼0.872, P &lt; 0.001).

Conclusions

The mSIS scoring system can be used as a simplified and effective predictive tool for unplanned ICU admission in patients with NSCLC.

Development and validation of a nomogram to predict survival for advanced male breast cancer

Male breast cancer is a rare disease. Many experiences of male breast cancer were derived of female breast cancer. However, there are huge differences between two groups. We conducted this study to find a reliable prognostic model for advanced male breast cancer. The cohort was selected from the Surveillance, Epidemiology, and End Results database. The enrolled patients were randomly divided into training and validation group. The univariate and multivariate analyses were used for prognostic assessment and a nomogram was built. Calibration curves and concordance index were compiled to determine predictive and discriminatory capacity. The time-dependent receiver operating curves and the decision curve analysis was used to verify the model's ability. Two hundred and eighty individuals were enrolled. The cumulative rates of 1-, 3- and 5-year overall survival (OS) rates were 98.6%, 72% and 57.9%. The C-indexes for OS were 0.835 (95%CI, 0.777-0.893) in the training group and 0.765 (95%CI, 0.668-0.862) in the validation group. The calibration curves confirmed the consistency of the nomogram both in the training and validation group. The time-dependent receiver operating curves and decision curve analysis demonstrated that the nomogram had better prediction capacity than TNM stage system for advanced male breast cancer. The nomogram we built was a reliable and solid predictive model.

Development and Validation of a Nomogram Model Based on Hematological Indicators for Predicting the Prognosis of Diffused Gliomas

Background

Diffused gliomas are aggressive malignant brain tumors. Various hematological factors have been proven to predict the prognosis of patients with gliomas. The aim of this study is to integrate these hematological markers and develop a comprehensive system for predicting the prognosis of patients with gliomas.

Method

This retrospective study included 723 patients pathologically diagnosed with diffused gliomas. Hematological indicators were collected preoperatively, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), albumin globulin ratio (AGR), platelet distribution width (PDW), red blood cell distribution width (RDW), fibrinogen (FIB), and prognostic nutritional index (PNI). Least absolute shrinkage and selection operator (LASSO) Cox was applied to screen the hematological indicators for a better prediction of patients' prognosis and to build an inflammation-nutrition score. A nomogram model was developed to predict the overall survival (OS), which included age, tumor grade, IDH-1 mutations, and inflammation-nutrition score.

Result

Patients were randomly divided into a primary cohort (n = 509) and a validation cohort (n = 214). There was no difference in age and IDH-1 mutation frequency between the cohorts. In the primary cohort, NLR, LMR, AGR, FIB, and PNI were selected to build an inflammation nutrition score. Patients with a high-risk inflammation-nutrition score had a short median OS of 17.40 months compared with 27.43 months in the low-risk group [HR 2.54; 95% CI (1.91–3.37); p < 0.001]. Moreover, age, tumor grade, IDH-1 mutations, and inflammation-nutrition score were independent prognostic factors in the multivariate analysis and thus were included in the nomogram model. The nomogram model showed a high prediction value with a Harrell's concordance index (C-index) of 0.75 [95% CI (0.72–0.77)]. The validation cohort supported these results.

Conclusion

The prognostic nomogram model provided a high prognostic predictive power for patients with gliomas.

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

Purpose

We attempted to explore the prognostic value of baseline inflammatory and nutritional biomarkers at diagnosis in patients with early-stage breast cancer and develop a novel scoring system, the inflammatory-nutritional prognostic score (INPS).

Patients and Methods

We collected clinicopathological and baseline laboratory data of 1259 patients with early-stage breast cancer between December 2010 and November 2012 from Sun Yat-sen University Cancer Center. Eligible patients were randomly divided into training and validation cohorts (n = 883 and 376, respectively) in a 7:3 ratio. We selected the most valuable biomarkers to develop INPS by the least absolute shrinkage and selection operator (LASSO) Cox regression model. A prognostic nomogram incorporating INPS and other independent clinicopathological factors was developed based on the stepwise multivariate Cox regression method. Then, we used the concordance index (C-index), calibration plot, and time-dependent receiver operating characteristic (ROC) analysis to evaluate the prognostic performance and predictive accuracy of the predictive nomogram.

Results

Four inflammatory-nutritional biomarkers, including neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), prognostic nutritional index (PNI), and albumin-alkaline phosphatase ratio (AAPR), were selected using the LASSO Cox analysis to construct INPS, which remained an independent prognostic indicator per the multivariate Cox regression analysis. Patients were stratified into low- and high-INPS groups based on the cutoff INPS determined by the maximally selected rank statistics. The prognostic model for overall survival consisting of INPS and other independent clinicopathological indicators showed excellent discrimination with C-indexes of 0.825 (95% confidence interval [CI]: 0.786–0.864) and 0.740 (95% CI: 0.657–0.822) in the training and validation cohorts, respectively. The time-dependent ROC curves showed a higher predictive accuracy of our prognostic nomogram than that of traditional tumor-node-metastasis staging.

Conclusion

Baseline INPS is an independent indicator of OS in patients with early-stage breast cancer. The INPS-based prognostic nomogram could be used as a practical tool for individualized prognostic predictions.

Nomogram of Combining CT-Based Body Composition Analyses and Prognostic Inflammation Score: Prediction of Survival in Advanced Epithelial Ovarian Cancer Patients

PURPOSE

To investigate the value of body composition changes measured by quantitative computer tomography (QCT) in evaluating the prognosis of advanced epithelial ovarian cancer (AEOC) patients who underwent primary debulking surgery (PDS) and adjuvant platinum-based chemotherapy, and constructed a nomogram model for predicting survival in combination with prognostic inflammation score (PIS).

METHOD

Fifty-seven patients with AEOC between 2012 and 2016 were retrospectively enrolled. Pre- and post-treatment CT images were used to analyze the body composition biomarkers. The subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), cross-sectional area of paraspinal skeletal muscle area (PMA), skeletal muscle density (SMD), body mineral density (BMD) were measured from two sets of CT images.

RESULTS

In multivariate analyses, VFA gain, PMA loss, BMD loss, and PIS were independent risk factors of overall survival (OS) (HR = 3.7, 3.0, 2.8, 1.9, respectively, all p < 0.05). Receiver operating characteristic (ROC) curves showed that the prognostic model combining body composition changes (BCC) and PIS had the highest predictive performance (area under the curve = 0.890). The concordance index (C-index) of the prognostic nomogram was 0.779 (95% CI, 0.673-0.886). Decision curve analysis (DCA) demonstrated the prognostic nomogram had a great distinguishing performance.

CONCLUSION

CT-based body composition analyses and PIS were associated with poor OS for AEOC patients who underwent PDS and adjuvant platinum-based chemotherapy. The prognostic nomogram with a combination of BCC and PIS was dependable in predicting survival for AEOC patients during treatment.

A Systemic Inflammation Response Score for Prognostic Prediction of Breast Cancer Patients Undergoing Surgery

Background: Systemic inflammatory response is related to the occurrence, progression, and prognosis of cancers. In this research, a novel systemic inflammation response score (SIRS) was calculated, and its prognostic value for postoperative stage I-III breast cancer (BC) patients was analyzed. Methods: 1583 BC patients were included in this research. Patients were randomly divided into a training cohort (n = 1187) and validation cohort (n = 396). SIRS was established in the training cohort based on independent prognostic hematological indicator, its relationship between prognosis and clinical features was analyzed. Then, a nomogram consisted of SIRS and clinical features was established, its performance was examined by calibration plots and receiver operating characteristic curve analysis. Results: The SIRS was an independent prognostic indicator for BC patients, and a high-SIRS was related to multifocality, advanced N stage, and worse prognosis. Incorporating SIRS into a nomogram could accurately predict the prognosis of BC patients, the results of receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of nomogram was up to 0.806 in training cohort and 0.905 in the validation cohort. Conclusion: SIRS was associated with the prognosis of patients with breast cancer. Nomogram based on SIRS can accurately predict the prognosis of breast cancer patients.