Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Breast Cancer: A Propensity Score-Matching Study

The value of inflammation-related indicators in chemotherapy efficacy and disease-free survival of triple-negative breast cancer

BACKGROUND

Systemic inflammation is closely correlated with the progression of cancer. Inflammation-related indicators has been recognized as outcome predictors in triple-negative breast cancer (TNBC). Our study aimed to investigate the predictive value of several inflammation-related indicators in TNBC patients underwent chemotherapy (NAC).

METHODS

100 TNBC patients were enrolled in the study. Levels of interleukin-6 (IL-6) were detected by enzyme-linked immunosorbent assays (ELISAs). Platelet-to-lymphocyte ratio (PLR) and immune inflammatory index (SII) were obtained from blood routine. Levels of Ki-67 expression were detected by immunohistochemistry (IHC). Mentioned indicators were divided into two groups based on their median values. The correlation between these indicators and NAC efficacy was analyzed using t tests. Prognostic risk scores were calculated by univariate Cox regression analysis and Lasso-penalized Cox regression. The patients were divided into low- and high-risk groups based on the median risk score. Survival curves were obtained by Kaplan-Meier method. Models for univariate and multivariate Cox regression analyses were performed to determine the independent risk factors. A nomogram was used for the prediction of 1-, 2-, and 3-year disease-free survival (DFS). Accuracy of the prognostic model was validated by receiver operating characteristic curve (ROC).

RESULTS

IL-6, PLR, SII, and Ki-67 levels were associated with neoadjuvant efficacy. IL-6, PLR, SII, Ki-67, and lymphocyte count were associated with DFS. The risk score for each TNBC patient was obtained using LASSO regression analysis to construct a prognostic model. In the prognostic model, patients in the high-risk score group showed worse DFS than those in the low-risk group. Risk score and tumor size were independently correlated with outcomes in multivariate Cox regression analysis. A nomogram was constructed using IL-6, PLR, SII, Ki-67, and Miller-Payne (MP) scores. Calibration curves demonstrated good consistency between the actual and predictive values of the nomogram.

CONCLUSION

A prognostic model was established by combining four prognosis-related indicators in TNBC patients who underwent NAC.

Characterization of stem cell landscape and identification of stemness-relevant prognostic gene signature to aid immunotherapy in breast cancer

A common digestive system cancer with a dismal prognosis and a high death rate globally is breast cancer (BRCA). BRCA recurrence, metastasis, and medication resistance are all significantly impacted by cancer stem cells (CSCs). However, the relationship between CSCs and the tumor microenvironment in BRCA individuals remains unknown, and this information is critically needed. Our research utilized bioinformatics techniques and TCGA data to explore the complex relationship between CSCs and BRCA development. We identified 26 stem cell gene sets from the Stem Checker database and classified BRCA samples into stemness subtypes using consensus clustering. Prognosis, tumor microenvironment (TME) elements, and treatment responses varied across subtypes. Using LASSO, Cox regression, and differential expression analysis, we developed a stemness-risk model. BRCA patients were divided into two groups (Cluster A and Cluster B). Cluster B exhibited an improved prognosis, higher PIK3CA mutation frequency, and increased levels of CD8 T cells and regulatory Tregs. A 5-gene stemness model was constructed, showing that higher stemness scores correlated with poorer prognosis. The model was validated using the METABRIC cohort data from cBioPortal. Our findings identify two stemness-related subgroups with distinct prognoses and TME patterns. Further experimental validation is necessary before this model can be considered for clinical application.

The Clinical Prediction Value of the Ubiquitination Model Reflecting the Microenvironment Infiltration and Drug Sensitivity in Breast Cancer

The ubiquitin-proteasome system influences cancer progression through multiple mechanisms. Due to the extensive proteasomal modifications observed in cancer tissues, ubiquitination is closely related to various biological functions with cancer. However, the roles of ubiquitin-related genes (UbRGs) in breast cancer (BC) have not been thoroughly investigated. In this study, we retrieved 763 reliable UbRGs and identified a potential prognostic signature for breast cancer patients. Additionally, we analyzed eight overall survival-associated UbRGs using univariate Cox proportional hazard regression in the Cancer Genome Atlas (TCGA) database. Subsequently, we used Lasso-Cox risk regression analysis to generate prognostic characteristics of UbRGs associated with overall survival (OS), validated in an external cohort (GSE158309). Next, we compared differences in tumor microenvironment and drug sensitivity between subgroups, describing the potential impact of UbRGs on the landscape of the tumor immune microenvironment and their predictive significance for therapeutic resistance to different strategies. Furthermore, a nomogram model containing eight genes, histology, subtype, T status, N status, and the American Joint Committee on Cancer (AJCC) stage was constructed. Finally, in vitro and in vivo experiments validated the effects of FBXL6 and PDZRN3 on breast cancer development. In conclusion, we demonstrate that ubiquitin-related genes are closely associated with breast cancer prognosis, immune environment, and drug sensitivity. Our results offer a new insight into breast cancer treatment.

Study of the significance of the combination of the fibrinogen-albumin ratio and sarcopenia in predicting the prognosis of laryngeal cancer patients undergoing radical surgery

OBJECTIVE

This study aims to investigate how the impact of preoperative sarcopenia and inflammatory markers for laryngeal cancer patients and develop a new scoring system to predict their prognosis.

MATERIALS AND METHODS

Patients who underwent laryngectomy for laryngeal cancer (LC) from December 2015 to December 2020 at the Second Affiliated Hospital of Fujian Medical University were included. Independent prognostic factors were determined using univariate and multivariate analyses. A new scoring system (SFAR) was established based on FAR and preoperative sarcopenia, and statistically analyzed.

RESULTS

198 cases included in this study that met the admission criteria. Multivariate analysis shown that preoperative sarcopenia, pTNM stage, and FAR were independent prognostic factors for laryngeal cancer. Based on these three indicators, we developed the SFAR scoring system. Multivariate analysis showed that SFAR was an independent predictor of laryngeal cancer (p < 0.001). SFAR was then incorporated into a prognostic model that included T-stage and N-stage, and a column-line graph was generated to accurately predict its survival.

CONCLUSION

Systemic inflammation and sarcopenia are significantly associated with postoperative prognosis in laryngeal cancer. A new scoring system (SFAR) had implications for improving the prognosis of patients undergoing surgery for laryngeal cancer.

Predicting monotherapy resistance risk in patients with low-risk gestational trophoblastic neoplasia: integration of the systemic immune-inflammation index and the prognostic nutritional index

Purpose

Currently, there are no reliable indicators for the early identification of patients with low-risk gestational trophoblastic neoplasia (GTN) who develop resistance to monotherapy. This study aimed to evaluate the effectiveness of combining the Systemic Immune-Inflammation Index (SII) and Prognostic Nutritional Index (PNI) in detecting early resistance to monotherapy in patients with low-risk GTN.

Methods

This retrospective study included 91 patients with low-risk GTN who received initial monotherapy at Fujian Maternal and Child Health Hospital between 2013 and 2021. The SII and PNI before chemotherapy were calculated from prechemotherapy peripheral blood samples, with cut-off values determined by receiver operating characteristic (ROC) curves. The SII-PNI score ranged from 0 to 2 points and was categorized as follows: a score of 2 points indicated a high SII (≥467.02) and a low PNI (≤51.35); a score of 1 point indicated either a high SII or a low PNI; and a score of 0 points indicated neither a high SII nor a low PNI.

Results

Ninety-one patients with low-risk GTN underwent monotherapy, 19 of whom developed resistance, whereas the remaining 72 did not. The SII was significantly greater in chemotherapy-resistant patients than in non-resistant patients (P=0.04), whereas the PNI was markedly lower in chemotherapy-resistant patients (P=0.002). Univariate analysis revealed that cut-off values of 467.02 for the SII (P=0.04) and 51.35 for the PNI (P=0.024) were associated with chemotherapy resistance in patients with low-risk GTN. As the SII-PNI score increased, the proportion of chemotherapy-resistant patients increased (P<0.001), and the time for human chorionic gonadotropin (hCG) normalization correspondingly increased (P<0.001). Multivariate logistic regression analysis indicated that a high SII-PNI score is an independent risk factor for chemotherapy resistance in patients with low-risk GTN (P=0.001).

Conclusion

A high SII and low PNI are linked to chemotherapy resistance in patients with low-risk GTN. The pretreatment SII-PNI score is a key indicator for predicting the sensitivity of patients with low-risk GTN to single-agent chemotherapy, aiding in the early identification of individuals at high risk of resistance.

Value of Contrast-Enhanced Ultrasound Combined with Immune-Inflammatory Markers in Predicting Axillary Lymph Node Metastasis of Breast Cancer

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with immune-inflammatory markers in predicting axillary lymph node metastasis (ALNM) in breast cancer patients.

METHODS

From January 2020 to June 2023, the clinicopathological data and ultrasound features of 401 breast cancer patients who underwent biopsy or surgery were recorded. Patients were randomly divided into a training set (321 patients) and a validation set (80 patients). The risk factors for ALNM were determined using univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, and prediction models were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess their diagnostic performance.

RESULTS

Logistic regression analysis demonstrated that systemic immunoinflammatory index (SII), CA125, Ki67, pathological type, lesion size, enhancement pattern and Breast Imaging Reporting and Data System (BI-RADS) category were significant risk factors for ALNM. Three different models were constructed, and the combined model yielded an AUC of 0.903, which was superior to the clinical model (AUC=0.790) and ultrasound model (AUC=0.781). A nomogram was constructed based on the combined model, calibration curves and DCA demonstrated its satisfactory performance in predicting ALNM.

CONCLUSION

The nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

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.

Association of the systemic immune-inflammation index (SII) and severity of diabetic ketoacidosis in patients with type 1 diabetes mellitus: a retrospective cohort study

Background

Diabetic ketoacidosis (DKA) is the most serious metabolic complication of type 1 diabetes mellitus (T1DM). Insulin deficiency and inflammation play a role in the pathogenesis of DKA. The authors aimed to assess the systemic immune-inflammation index (SII) as a marker of severity among T1DM patients with DKA and without infection.

Methods

The authors included T1DM patients older than or equal to 12 years hospitalized because of DKA. The authors excluded patients with infection or any condition that can change SII parameters or cause metabolic acidosis. The authors compared SII, neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR) between severe and non-severe DKA groups. The authors also assessed the need for an ICU, length of stay, and 90-day readmission rate between the groups.

Results

The study included 241 patients with a median age of 17 (14, 24) years, and 44.8% were males. More patients with severe DKA (45%) required ICU admission (P<0.001). Median SII increased with DKA severity, and the difference was significant (P=0.033). No significant difference was observed as regards median NLR or PLR (P=0.380 and 0.852, respectively). SII, but not NLR or PLR, had a significant negative correlation with PH (r=-0.197, P=0.002) and HCO3 level (r=-0.144, P=0.026). Also, being in the highest SII quartile was an independent risk factor for DKA severity (OR, 2.522; 95% CI, 1.063-6.08; P=0.037). The authors estimated an SII cut-off value of 2524.24 to predict DKA severity with high specificity.

Conclusion

Elevated SII is a risk factor for DKA severity in T1DM. It is better than NLR and PLR in prognosticating DKA patients. These findings highlight the role of inflammation in DKA. SII can help as a valuable and simple tool to assess DKA severity.

Prognostic value of the systemic immune-inflammation index in critically ill elderly patients with hip fracture: evidence from MIMIC (2008-2019)

Background

The systemic immune-inflammation index (SII) showed an extensive link between immunological dysfunction and the activation of systemic inflammation. Several studies have confirmed the application of SII to orthopedic diseases. However, the significance of SII in critically ill elderly individuals with hip fracture who require intensive care unit (ICU) admission is not yet known. This study centered on exploring the relationship between SII and clinical outcomes among critically ill elderly hip fracture individuals.

Methods

The study centered around elderly patients experiencing severe illness following hip fractures and requiring admission to the ICU. These patients from the MIMIC-IV database formed the basis of this study's cohort. We stratified them into quartiles according to their SII levels. The results involved the mortality at 30 days and 1 year post-admission. Then we employ Cox proportional hazards regression analysis as well as restricted cubic splines to explore the association between the SII and clinical results in critically ill elderly patients with hip fracture.

Results

The study encompassed 991 participants, among whom 63.98% identified as females. Notably, the mortality rates attributed to any cause within 30 days and 1 year after hospitalization stood at 19.68 and 33.40%, respectively. The multivariate Cox proportional hazards model disclosed a significant correlation between an elevated SII and all-cause mortality. Following adjustments for confounding variables, individuals with a high SII showed a notable correlation with 30-day mortality [adjusted hazard ratio (HR), 1.065; 95% confidence interval (CI), 1.044-1.087; p < 0.001] and 1-year mortality (adjusted HR, 1.051; 95% CI, 1.029-1.074; p < 0.001). Furthermore, the analysis of restricted cubic splines demonstrated a progressive increase in the risk of all-cause death as the SII value rose.

Conclusion

Among critically ill elderly patients with hip fracture, the SII exhibits a non-linear association that positively correlates with both 30-day and 1-year all-cause mortality rates. The revelation indicates that the SII may play a vital role in identifying patients with hip fractures who face an escalated risk of mortality due to any cause.

Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40-70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5-6.5 years post-CBC. Our ridge regression model, incorporating neutrophil-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64-0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

Joint effect of abnormal systemic immune-inflammation index (SII) levels and diabetes on cognitive function and survival rate: A population-based study from the NHANES 2011-2014

OBJECTIVE

The purpose of this study was to investigate whether the combination of abnormal systemic immune-inflammation index (SII) levels and hyperglycemia increased the risk of cognitive function decline and reduced survival rate in the United States.

METHODS

This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) database from 2011-2014 and enrolled 1,447 participants aged 60 years or older. Restricted cubic splines (RCS), linear regression and kaplan-meier(KM) curve were employed to explore the combined effects of abnormal SII and hyperglycemia on cognitive function and survival rate, and subgroup analysis was also conducted.

RESULTS

The RCS analysis revealed an inverted U-shaped relationship between lgSII levels and cognitive function. Linear regression analysis indicated that neither abnormal SII nor diabetes alone significantly contributed to the decline in cognitive function compared to participants with normal SII levels and blood glucose. However, when abnormal SII coexisted with diabetes (but not prediabetes), it resulted to a significant decline in cognitive function. After adjusting for various confounding factors, these results remained significant in Delayed Word Recall (β:-0.76, P<0.05) and Digit Symbol Substitution tests (β:-5.02, P<0.05). Nevertheless, these results showed marginal significance in Total Word Recall test as well as Animal Fluency test. Among all subgroup analyses performed, participants with both abnormal SII levels and diabetes exhibited the greatest decline in cognitive function compared to those with only diabetes. Furthermore, KM curve demonstrated that the combination of abnormal SII levels and diabetes decreased survival rate among participants.

CONCLUSION

The findings suggest that the impact of diabetes on cognitive function/survival rate is correlated with SII levels, indicating that their combination enhances predictive power.

The correlation between systemic inflammatory markers and efficiency for advanced gastric cancer patients treated with ICIs combined with chemotherapy

Currently lacking research to explore the correlation between inflammatory markers and the efficacy of immune checkpoint inhibitors (ICIs) combined with chemotherapy in the treatment of advanced gastric cancer. This study is a retrospective study and included patients with advanced gastric cancer who receiving ICIs combined with chemotherapy from January 2020 to December 2022. We analysed the relationship between systemic inflammatory markers and the efficacy of ICIs combined chemotherapy and constructed a clinical prediction model. A nomogram was constructed based on the results of the bidirectional stepwise regression model. A total of 197 patients were enrolled in the training group, with a median follow-up period of time 26 months. Kaplan Meier analysis showed that the median OS of patients with low systemic immune-inflammatory index (SII) and low platelet to lymphocyte ratio (PLR) was superior to those with high SII and PLR. Univariate and multivariate Cox regression analysis showed that SII, NLR, PLR, and N stage as independent prognostic factors for OS. Adding SII to the conventional model improved the predictive ability of the 12-month OS. A total of 95 patients were included in the validation group, and external validation of the SII-based nomogram showed favourable predictive performance. Baseline SII, PLR, and N stage may serve as independent predictive factors for survival outcomes in advanced gastric cancer patients undergoing ICIs combined with chemotherapy. The SII-based nomogram can provide intuitive and accurate prognosis prediction of individual patients.

Mitochondrial calcium uniporter as biomarker and therapeutic target for breast cancer: Prognostication, immune microenvironment, epigenetic regulation and precision medicine

INTRODUCTION

Mitochondrial calcium uniporter (MCU) is a central subunit of MCU complex that regulate the levels of calcium ions within mitochondria. A comprehensive understanding the implications of MCU in clinical prognostication, biological understandings and therapeutic opportunity of breast cancer (BC) is yet to be determined.

OBJECTIVES

This study aims to investigate the role of MCU in predictive performance, tumor progression, epigenetic regulation, shaping of tumor immune microenvironment, and pharmacogenetics and the development of anti-tumor therapy for BC.

METHODS

The downloaded TCGA datasets were used to identify predictive ability of MCU expressions via supervised learning principle. Functional enrichment, mutation landscape, immunological profile, drug sensitivity were examined using bioinformatics analysis and confirmed by experiments exploiting human specimens, in vitro and in vivo models.

RESULTS

MCU copy numbers increase with MCU gene expression. MCU expression, but not MCU genetic alterations, had a positive correlation with known BC prognostic markers. Higher MCU levels in BC showed modest efficacy in predicting overall survival. In addition, high MCU expression was associated with known BC prognostic markers and with malignancy. In BC tumor and sgRNA-treated cell lines, enrichment pathways identified the involvement of cell cycle and immunity. miR-29a was recognized as a negative epigenetic regulator of MCU. High MCU levels were associated with increased mutation levels in oncogene TP53 and tumor suppression gene CDH1, as well as with an immunosuppressive microenvironment. Sigle-cell sequencing indicated that MCU mostly mapped on to tumor cell and CD8 T-cells. Inter-databases verification further confirmed the aforementioned observation. miR-29a-mediated knockdown of MCU resulted in tumor suppression and mitochondrial dysfunction, as well as diminished metastasis. Furthermore, MCU present pharmacogenetic significance in cellular docetaxel sensitivity and in prediction of patients' response to chemotherapeutic regimen.

CONCLUSION

MCU shows significant implication in prognosis, outcome prediction, microenvironmental shaping and precision medicine for BC. miR-29a-mediated MCU inhibition exerts therapeutic effect in tumor growth and metastasis.

Systemic immune-inflammation index as a prognostic marker in HER2-positive breast cancer patients undergoing trastuzumab therapy

The prognostic value of SII (Systemic Immune-Inflammation Index) in HER-2-positive breast cancer (BC) patients, regardless of whether they receive trastuzumab treatment, and its potential value to distinguish patients who may benefit from trastuzumab therapy, warrant further investigation. Clinical data was collected from 797 HER-2-positive BC patients between July 2013 and March 2018. Baseline data differences were adjusted with propensity score matching. Univariate and multivariate analyses explored the correlation between clinical pathological factors, SII, and DFS. Four groups were established. Based on the baseline SII values of the participants, patients who did not receive trastuzumab treatment were divided into Group 1 (Low-SII) and Group 2 (High-SII), where SII had no predictive value for prognosis between groups. Participants who received trastuzumab treatment were also divided into two groups: the Low-SII group (Group 3) and the High-SII group (Group 4). The 5-year DFS was significantly higher in Group 3 than in Group 4 (91.76% vs. 82.76%, P = 0.017). Furthermore, multivariate analysis demonstrated a significant association between high SII and shorter DFS (HR = 3.430, 95% CI = 1.830-6.420, P < 0.001). In HER-2-positive BC patients treated with trastuzumab, those with lower SII showed a longer DFS, suggesting that SII may help in identifying patients who benefit from trastuzumab therapy.

A novel machine learning prediction model for metastasis in breast cancer

BACKGROUND

Breast cancer (BC) metastasis is the common cause of high mortality. Conventional prognostic criteria cannot accurately predict the BC metastasis risk. The machine learning technologies can overcome the disadvantage of conventional models.

AIM

We developed a model to predict BC metastasis using the random survival forest (RSF) method.

METHODS

Based on demographic data and routine clinical data, we used RSF-recursive feature elimination to identify the predictive variables and developed a model to predict metastasis using RSF method. The area under the receiver operating characteristic curve (AUROC) and Kaplan-Meier survival (KM) analyses were plotted to validate the predictive effect when C-index was plotted to assess the discrimination and Brier scores was plotted to assess the calibration of the predictive model.

RESULTS

We developed a metastasis prediction model comprising three variables (pathological stage, aspartate aminotransferase, and neutrophil count) selected by RSF-recursive feature elimination. The model was reliable and stable when assessed by the AUROC (0.932 in training set and 0.905 in validation set) and KM survival analyses (p < .0001). The C-indexes (0.959) and Brier score (0.097) also validated the good predictive ability of this model.

CONCLUSIONS

This model relies on routine data and examination indicators in real-time clinical practice and exhibits an accurate prediction performance without increasing the cost for patients. Using this model, clinicians can facilitate risk communication and provide precise and efficient individualized therapy to patients with breast cancer.

Identification of breast cancer subgroups and immune characterization based on glutamine metabolism-related genes

Immunotherapy is a promising treatment for breast cancer (BC). However, due to individual differences and tumor heterogeneity, immunotherapy is only applicable to some BC patients. Glutamine metabolism plays a role in inhibiting immunotherapy, but its role in BC is limitedly studied. Therefore, we aimed to identify different BC subgroups based on glutamine metabolism and characterize the features of different subgroups to provide guidance for personalized immunotherapy for BC patients. Using unsupervised clustering analysis, we classified BC patients in The Cancer Genome Atlas (TCGA) with glutamine metabolism-related genes and obtained low-risk (LR) and high-risk (HR) subgroups. Survival analysis revealed that prognosis of LR subgroup was notably better than HR subgroup. Through ssGSEA and CIBERSORT methods, we disclosed that infiltration levels of B cells, Mast cells, T helper cells, and Th2 cells, and Type II IFN Response immune function were notably higher in LR subgroup than in HR subgroup. The Wilcox algorithm comparison denoted that DEPTH of LR subgroup was significantly lower than HR subgroup. The TIDE of LR subgroup was significantly higher than HR subgroup. Functional annotation of differentially expressed genes revealed that channel activity and the Estrogen signaling pathway may be related to BC prognosis. Ten hub genes were selected between the subgroups through the STRING database and Cytoscape, and their correlation with drugs was predicted on the CellMiner website. This study analyzed the immune characteristics of BC subgroups based on glutamine metabolism and provided reference for prognosis prediction and personalized immunotherapy.

Peripheral blood inflammatory indexes in breast cancer: A review

Immune and inflammatory responses play an important role in tumorigenesis and metastasis. Inflammation is an important component of the tumor microenvironment, and the changes in inflammatory cells may affect the occurrence and development of tumors. Complete blood count at the time of diagnosis and treatment can reflect the inflammatory status within the tumor. Studies have shown that the number of certain inflammatory cells in peripheral blood and their ratios are important prognostic factors for many malignancies, including neutrophil, lymphocyte, monocyte, and platelet counts, as well as neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, systemic immune-inflammation index, systemic inflammation response index and pan-immune-inflammation-value. The value of peripheral blood inflammation indexes in predicting the efficacy and prognosis of breast cancer neoadjuvant therapy is worth recognizing. This review details the application of peripheral blood inflammation indexes in the evaluation of efficacy and prediction of prognosis in neoadjuvant therapy for breast cancer, aiming to provide a more comprehensive reference for the comprehensive diagnosis and treatment of breast cancer.

Comprehensive evaluation and advanced modification of polymethylmethacrylate cement in bone tumor treatment

Bone tumors are invasive diseases with a tendency toward recurrence, disability, and high mortality rates due to their grievous complications. As a commercial polymeric biomaterial, polymethylmethacrylate (PMMA) cement possesses remarkable mechanical properties, injectability, and plasticity and is, therefore, frequently applied in bone tissue engineering. Numerous positive effects in bone tumor treatment have been demonstrated, including biomechanical stabilization, analgesic effects, and tumor recurrence prevention. However, to our knowledge, a comprehensive evaluation of the application of the PMMA cement in bone tumor treatment has not yet been reported. This review comprehensively evaluates the efficiency and complications of the PMMA cement in bone tumor treatment, for the first time, and introduces advanced modification strategies, providing an objective and reliable reference for the application of the PMMA cement in treating bone tumors. We have also summarized the current research on modifications to enhance the anti-tumor efficacy of the PMMA cement, such as drug carriers and magnetic hyperthermia.

Independent and Combined Associations of Blood Manganese, Cadmium and Lead Exposures with the Systemic Immune-Inflammation Index in Adults

Manganese (Mn), cadmium (Cd) and lead (Pb) have toxic effects on the immune system. However, their independent and combined effects on immune-inflammation responses are unclear. In recent years, the systemic immune-inflammation index (SII) has been developed as an integrated and novel inflammatory indicator. A retrospective cross-sectional study of 2174 adults ≥20 years old from the National Health and Nutrition Examination Survey (NHANES) 2015-2016 was conducted. Generalized linear models were used to evaluate the independent and combined associations of SII with blood Mn, Cd and Pb levels. As continuous variables, both blood Cd and Mn showed dose-dependent relationships with the SII before and after adjusting for all potential confounding factors. Metal concentrations were then converted into categorical variables. Compared with the adults in the lowest Cd or Mn tertile, those in the highest tertile had higher risks of elevated SII. Furthermore, co-exposure to Mn and Cd also showed a positive relationship with the SII after adjusting for all confounding factors. However, the single effect of Pb exposure and the joint effect of Pb and other metal exposures on the SII were not observed. This study provides important epidemiological evidence of the associations of SII with single and co-exposure effects of blood Mn, Cd, and Pb.

Absolute monocyte count has a diagnostic role in distinguishing tumor marker-negative TGCT from benign testicular tumor via CCL2 regulation

Clinically, for testicular tumor patients with negative tumor markers, how to distinguish the malignant from the benign is a difficult problem. This study aimed to assess the clinical significance of the absolute monocyte count (AMC) in differential diagnosis of testicular germ cell tumor with stage S0 (TGCTS0) and benign testicular tumor. In this retrospective single-center study, a total of 90 patients newly diagnosed with benign testicular tumor or TGCTS0 were reviewed. All patients received surgical intervention as the primary treatment method. AMC and other clinicopathological parameters were analyzed. Receiver operating characteristic (ROC) curves were constructed to assess the diagnostic power of investigated parameters, and to determine the optimal cutoff values. Kaplan-Meier curve analysis was used to study the survival of patients with TGCTS0. qRT-PCR and immunohistochemistry (IHC) were performed to examine the expression of C-C motif chemokine ligand 2 (CCL2) mRNA and protein respectively. Differential gene expression and functional enrichment analysis were performed using Gene Expression Omnibus and the Cancer Genome Atlas databases. The mean preoperative AMC in patients with TGCTS0 was significantly higher than that in patients with benign testicular tumor (P = .020). AMC > 0.485*10^9/L was identified to be associated with the presence of TGCTS0 (hazard ratio [HR] = 3.074, P = .026), and patients with higher AMC level had worse progression free survival (PFS) (P = .047). Furthermore, AMC combined with lactate dehydrogenase (LDH) achieved a better diagnostic efficacy for TGCTS0 (area under curve [AUC] = 0.695). Tumor-associated macrophages (TAMs) signature gene CCL2 was highly expressed in TGCT compared with normal testicular tissue. Functional enrichment analysis showed that CCL2 is closely involved in the Extracellular Matrix Organization pathway and positively correlated with the expression of various matrix metalloproteinases (MMPs). Elevated AMC may serve as a predictor of higher risk of TGCTS0, and CCL2 mediated TAMs infiltration and MMPs secretion is essential for the tumorigenesis of TGCT.

High Levels of SII and PIV are the Risk Factors of Axillary Lymph Node Metastases in Breast Cancer: A Retrospective Study

Purpose

To investigate the predictive value of systemic immune-inflammation-index (SII) and Pan-Immune-Inflammation-value (PIV) for axillary lymph node (ALN) metastasis in patients with breast cancer.

Patients and Methods

We retrospectively collected data of 247 patients with invasive breast from the Affiliated Hospital of Jiangnan University. The state of axillary lymph node (ALN) metastasis was confirmed by pathological diagnosis. Clinicopathological data (age, ER, PR, HER2, Ki67 expression levels, diapause status, weight, histological grade, vascular invasion, and state of axillary lymph node) were compared between differences of SII and PIV groups and an association between clinical indexes and ALN metastasis was evaluated.

Results

The cut-off values of SII and PIV were 320.04 and 92.01, respectively. The significant difference between vascular invasion (P=0.023) and axillary lymph node metastases (P<0.001) in the high and low SII levels. Significant differences were observed in tumor size (p=0.024), PR expression level (P=0.033) and the status of axillary lymph node metastases (p<0.001) between the high PIV group and the low PIV group. Univariate analysis showed that vascular invasion, tumor size, Ki67 expression level, SII, and PIV were significantly correlated with axillary lymph node metastases (p<0.05). Then, multivariate analysis revealed that the vascular invasion (p<0.001), HER2 expression levels (p<0.047), SII (p<0.001) and PIV (p<0.030) were risk factors for axillary lymph node metastases.

Conclusion

High levels of SII, PIV, LVI, and HER2 are the risk factors for axillary lymph node metastases in breast cancer patients.

A novel circadian cycle-related gene signature for prognosis prediction of patients with breast cancer

The extensive and intricate relationships between circadian rhythm and cancer have been reported in numerous studies. However, in breast cancer (BC), the potential role of circadian clock-related genes (CCRGs) in prognosis prediction has not been fully clarified. The transcriptome data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. A CCRGs-based risk signature was established by differential expression analysis, univariate, Lasso and multivariate Cox regression analyses. we conducted a gene set enrichment analysis (GSEA) between groups. A nomogram integrating independent clinical factors and risk score was generated and evaluated by calibration curves and decision curve analysis (DCA). Differentially expression analysis revealed 80 differentially expressed CCRGs, and 27 of them were significantly associated with the overall survival (OS) of BC. BC can be classified into 4 molecular subtypes with significant differences in prognosis based on the 27 CCRGs. Three prognostic CCRGs, including desmocollin 1 (DSC1), LEF1, and protocadherin 9 (PCDH9), were identified to be independent risk factors of BC prognosis and were used to construct a risk score model. BC patients were divided into high- and low-risk groups, and there were significant differences in prognosis between the 2 groups both in the training and validation cohorts. It was found that patients in different groups of race, status, or T stage had significant levels of risk score. Furthermore, patients of different risk levels exhibit varying degrees of sensitivity to vinorelbine, lapatinib, metformin, and vinblastine. GSEA showed that in the high-risk group, immune response-related activities were dramatically repressed whereas cilium-related processes were significantly stimulated. Cox regression analysis demonstrated that age, N stage, radiotherapy and the risk score were independent prognostic risk factors of BC, and a nomogram was established based on these variables. The nomogram exerted a favorable concordance index (0.798) as well as calibration performance, which strongly supports the clinical application of the nomogram. Our study indicated the disruption of the expression of CCRGs in BC and built a favorable prognostic risk model based on 3 independent prognostic CCRGs. These genes may be applied as candidate molecular targets for the diagnosis and therapy of BC.

Systematic pan-cancer analysis identifies SLC35C1 as an immunological and prognostic biomarker

GDP-amylose transporter protein 1 (SLC35C1) plays an important role in many types of cancer. Therefore, it is clinically important to further investigate the expression profile of SLC35C1 in human tumors to provide new molecular clues for the pathogenesis of glioma. In this study, we performed a comprehensive pan-cancer analysis of SLC35C1 using a series of bioinformatics approaches and validated its differential tissue expression and biological function. The results showed that SLC35C1 was aberrantly expressed in different types of tumors and significantly correlated with overall survival (OS) and progression-free interval (PFI). More importantly, the expression level of SLC35C1 was closely correlated with Tumor Microenvironment (TME), immune infiltration and immune-related genes. In addition, we found that SLC35C1 expression was also closely related to Tumor Mutation Burden (TMB), Microsatellite Instability (MSI) and antitumor drug sensitivity in various cancer types. Functional bioinformatics analysis indicated that SLC35C1 may be involved in multiple signaling pathways and biological processes in glioma. Based on SLC35C1 expression, a risk factor model was found to predict OS of glioma. In addition, in vitro experiments showed that SLC35C1 knockdown significantly inhibited the proliferation, migration and invasive ability of glioma cells, while SLC35C1 overexpression promoted proliferation, migration, invasion and colony formation of glioma cells. Finally, quantitative real-time PCR confirmed that SLC35C1 was highly expressed in gliomas.

Support Tools in the Differential Diagnosis of Salivary Gland Tumors through Inflammatory Biomarkers and Radiomics Metrics: A Preliminary Study

BACKGROUND

The purpose of this study was to investigate how the systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR), and radiomic metrics (quantitative descriptors of image content) extracted from MRI sequences by machine learning increase the efficacy of proper presurgical differentiation between benign and malignant salivary gland tumors.

METHODS

A retrospective study of 117 patients with salivary gland tumors was conducted between January 2015 and November 2022. Univariate analyses with nonparametric tests and multivariate analyses with machine learning approaches were used.

RESULTS

Inflammatory biomarkers showed statistically significant differences (p < 0.05) in the Kruskal-Wallis test based on median values in discriminating Warthin tumors from pleomorphic adenoma and malignancies. The accuracy of NLR, PLR, SII, and SIRI was 0.88, 0.74, 0.76, and 0.83, respectively. Analysis of radiomic metrics to discriminate Warthin tumors from pleomorphic adenoma and malignancies showed statistically significant differences (p < 0.05) in nine radiomic features. The best multivariate analysis result was obtained from an SVM model with 86% accuracy, 68% sensitivity, and 91% specificity for six features.

CONCLUSIONS

Inflammatory biomarkers and radiomic features can comparably support a pre-surgical differential diagnosis.

Integrated Evaluation of Inflammatory, Nutritional, and Sarcopenia Markers to Predict Survival in Metastatic Breast Cancer Patients

BACKGROUND/AIM

The prognosis of a cancer patient is influenced by the tumor-related factors, as well as by various patient-related factors. We evaluated the association between inflammatory and nutritional factors and their outcomes, including the prognosis and therapeutic course, in patients with metastatic breast cancer.

PATIENTS AND METHODS

In this observational retrospective study, we evaluated 35 patients. The inflammatory and nutritional markers before systemic therapy included the lymphocyte count, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammatory index (SII), systemic inflammatory response index (SIRI), pan-immuno-inflammatory values (PIV), prognostic nutritional index (PNI), Glasgow prognostic score (GPS), and psoas muscle index (PMI).

RESULTS

Triple-negative, low PNI, and GPS 2 were correlated with worse overall survival in the univariable analysis. The GPS was the only independent predictor of overall survival [hazard ratio=5.85, 95% confidence interval=1.15-29.68, p<0.01]. The time to treatment failure of first-line therapy in patients with GPS 2 was significantly shorter than that in patients with GPS 0/1 (p<0.01).

CONCLUSION

The GPS was an independent predictive marker for overall survival in patients with metastatic breast cancer.

Prognostic value of the pretreatment systemic immune-inflammation index in patients with prostate cancer: a systematic review and meta-analysis

BACKGROUND

The systemic immune-inflammation index (SII) is a novel biomarker to predict the prognosis of some malignant tumors based on neutrophil, platelet, and lymphocyte counts. Evidence is scarce about the prognostic value of SII for prostate cancer patients. This systematic review and meta-analysis was conducted to explore the prognostic value of the SII in prostate cancer.

METHODS

The PubMed, Embase, Web of Science, and Cochrane Library (CENTRAL) databases were searched to determine eligible studies from inception to August 15, 2022. Hazard ratios (HRs) with 95% confidence intervals (CIs) were extracted to pool the results. Statistical analyses were conducted by using Stata 17.0 software.

RESULTS

A total of 12 studies with 8083 patients were included. The quantitative synthesis showed that a high SII was related to poor overall survival (OS) (HR = 1.44, 95% CI 1.23-1.69, p < 0.001). Furthermore, a subgroup analysis showed that a high SII was associated with poor OS in the groups of any ethnicity, tumor type, and cutoff value. An increased SII was also associated with inferior progression-free survival (PFS) (HR = 1.80, 95% CI 1.27-2.56, p = 0.001). In the subgroup analysis, a high SII value was related to poor PFS in Asian patients (HR = 4.03, 95% CI 1.07-15.17, p = 0.04) and a cutoff value > 580 (HR = 1.19, 95% CI 1.04-1.36, p = 0.01).

CONCLUSION

Based on the current evidence, a high pretreatment SII may be associated with poor OS and PFS. The SII may serve as an important prognostic indicator in patients with prostate cancer. More rigorously designed studies are needed to explore the SII and the prognosis of prostate cancer.

Could systemic immune inflammation index be a new parameter for diagnosis and disease activity assessment in systemic lupus erythematosus?

PURPOSE

Systemic immune inflammation index (SII) has been used as a marker of inflammatory status in various diseases, but its role in systemic lupus erythematosus (SLE) is unknown. We aimed to investigate the role of SII in SLE and its association with disease activity and renal involvement.

METHODS

In this retrospective study, 76 patients with SLE were compared with 76 age- and gender-matched healthy control group in terms of SII, neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR). SLE Disease Activity Index 2000 system (SLEDAI-2 K) was used to divide the SLE patients into an inactive group (SLEDAI-2 K < 9) and an active group (SLEDAI-2 K ≥ 9). Correlations between the ratios and both disease activity and renal involvement were analyzed.

RESULTS

SLE patients had a higher level of SII compared with controls. The ability of SII in predicting SLE (AUC = 0.626) was lower than NLR (AUC = 0.723) and PLR (AUC = 0.666). SII was positively correlated with C-reactive protein (r = 0.288. p = 0.01), but no association between SII and SLEDAI-2 K scores was found. Significantly higher values of NLR, but not SII, were detected in patients with nephritis (p = 0.04). The best NLR cut-off value to predict SLE patients with nephritis was 2.32, with 78.5% sensitivity and 56.2% specificity.

CONCLUSION

For the first, we demonstrated that SII level was elevated in patients with SLE. However, NLR is a better marker than SII in predicting SLE and could serve as an indicator of nephritis. Larger-scale studies should be carried out to confirm our results.

The pan-immune-inflammation value is associated with clinical outcomes in patients with advanced TNBC treated with first-line, platinum-based chemotherapy: an institutional retrospective analysis

Background

Advanced triple-negative breast cancer (aTNBC) has a poor prognosis; thus, there is a need to identify novel biomarkers to guide future research and improve clinical outcomes.

Objectives

We tested the prognostic ability of an emerging, complete blood count (CBC)-based inflammatory biomarker, the pan-immune-inflammation value (PIV), in patients with aTNBC treated with first-line, platinum-based chemotherapy.

Design

This was a retrospective, monocentric, observational study.

Methods

We included consecutive aTNBC patients treated with platinum-based, first-line chemotherapy at our Institution, and for whom baseline (C1) CBC data were available. We collected CBC data early on-treatment, when available. PIV was calculated as: (neutrophil count × platelet count × monocyte count)/lymphocyte count. Patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer (aBC) were included in a control, non-TNBC cohort.

Results

A total of 78 aTNBC patients were included. When evaluated as a continuous variable, PIV-C1 was associated with worse overall survival (OS; p < 0.001) and progression-free survival (PFS; p < 0.001). On the other hand, when PIV-C1 was assessed on the basis of its quantile distribution, patients with 'high PIV-C1' experienced worse OS [adjusted hazard ratio (HR): 4.46, 95% confidence interval (CI): 2.22-8.99; adjusted p < 0.001] and PFS (adjusted HR: 2.03, 95% CI: 1.08-3.80; adjusted p = 0.027) when compared to patients with 'low PIV-C1'. Higher PIV-C1 was also associated with primary resistance to chemotherapy. Similarly, a higher PIV calculated from CBC at C2D1 (PIV-C2) was associated with worse survival outcomes. We also created a PIV-based score combining information about both PIV-C1 and PIV-C2 and allowing the stratification of patients at low, intermediate, and high risk of death. No association was observed between PIV-C1 and clinical outcomes of HR+/HER2- aBC patients.

Conclusion

PIV has a promising prognostic discrimination ability in aTNBC patients treated with first-line, platinum-based chemotherapy. Both baseline and early on-treatment PIV are associated with clinical outcomes and may be exploited for creating PIV-based risk classifiers if further validated.

Potential Impact of Preoperative Circulating Biomarkers on Individual Escalating/de-Escalating Strategies in Early Breast Cancer

The research on non-invasive circulating biomarkers to guide clinical decision is in wide expansion, including the earliest disease settings. Several new intensification/de-intensification strategies are approaching clinical practice, personalizing the treatment for each patient. Moreover, liquid biopsy is revealing its potential with multiple techniques and studies available on circulating biomarkers in the preoperative phase. Inflammatory circulating cells, circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), and other biological biomarkers are improving the armamentarium for treatment selection. Defining the escalation and de-escalation of treatments is a mainstay of personalized medicine in early breast cancer. In this review, we delineate the studies investigating the possible application of these non-invasive tools to give a more enlightened approach to escalating/de-escalating strategies in early breast cancer.

Novel peripheral blood parameters as predictors of neoadjuvant chemotherapy response in breast cancer

The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune severity index (SII), and prognostic nutritional index (PNI) are associated with the prognosis of gastric, lung, and breast cancers. However, the predictive value of pathological complete response (pCR) rates in patients with breast cancer treated with neoadjuvant chemotherapy (NAC) remains unclear. This retrospective study explored the correlation between each index and the efficacy of neoadjuvant chemotherapy in patients with breast cancer and assessed the relationship between changes before and after neoadjuvant chemotherapy. We enrolled 95 patients with locally advanced breast cancer who received neoadjuvant therapy for breast cancer at the Second Affiliated Hospital of Fujian Medical University from April 2020 to April 2022. Based on postoperative pathology, patients were divided into pCR and non-pCR groups. Between-group differences and efficacy prediction ability of NLR, PLR, SII, and PNI were analyzed. Patient characteristics and changes in NLR, PLR, SII, and PNI before and after neoadjuvant chemotherapy (NAC) were compared between groups. Patients were divided into two groups according to the optimal diagnostic thresholds of the SII before treatment. Between-group differences in terms of neoadjuvant therapy efficacy and patient characteristics were evaluated. The pCR exhibited significantly lower ER (χ 2 = 10.227, P = 0.001), PR (χ 2 = 3.568, P = 0.049), pretreatment NLR (χ 2 = 24.930, P < 0.001), pretreatment PLR (χ 2 = 22.208, P < 0.001), pretreatment SII (χ 2 = 26.329, P < 0.001), and post-treatment PNI (P = 0.032), but higher HER-2 (χ 2 = 7.282, P = 0.007) and ΔNLR (P = 0.015) than the non-pCR group. ROC curve analysis revealed that the areas under the curve (AUC) of pretreatment SII, NLR, and PLR for predicting pCR of NAC for breast cancer were 0.827, 0.827, and 0.810, respectively, indicating a higher predictive value for response to NAC in patients with breast cancer. According to the Youden index, the optimal cut-off value of SII pretreatment was 403.20. Significant differences in age (χ 2 = 6.539, P = 0.01), ER (χ 2 = 4.783, P = 0.029), and HER-2 (χ 2 = 4.712, P = 0.030) were observed between high and low-SII groups. In conclusion, pretreatment NLR, PLR, and SII can be used as predictors of pCR in patients with breast cancer receiving neoadjuvant chemotherapy. The predictive value of pretreatment SII is higher, and patients with low SII are more likely to achieve pCR.

Leveraging the Dynamic Immune Environment Triad in Patients with Breast Cancer: Tumour, Lymph Node, and Peripheral Blood

During the anti-tumour response to breast cancer, the primary tumour, the peripheral blood, and the lymph nodes each play unique roles. Immunological features at each site reveal evidence of continuous immune cross-talk between them before, during and after treatment. As such, immune responses to breast cancer are found to be highly dynamic and truly systemic, integrating three distinct immune sites, complex cell-migration highways, as well as the temporal dimension of disease progression and treatment. In this review, we provide a connective summary of the dynamic immune environment triad of breast cancer. It is critical that future studies seek to establish dynamic immune profiles, constituting multiple sites, that capture the systemic immune response to breast cancer and define patient-selection parameters resulting in more significant overall responses and survival rates for breast cancer patients.

An insight into the invasion of breast ductal carcinoma in situ based on clinical, pathological and hematological data

Background

Ductal carcinoma in situ (DCIS) has become a non-negligible part of breast cancers owing to the greatly increased incidence. While its natural history was not fully elucidated, which is the reason for current controversies in clinical treatment. Exploration of this issue from a clinical perspective is meaningful.

Methods

Medical records of 389 patients diagnosed with DCIS or DCIS with invasive ductal carcinoma (IDC) were reviewed. All of them received appropriate medical care in our center. All 324 patients in training cohort were divided into invasion and non-invasion groups based on pathology. Differences in DCIS immunohistochemical markers and hematological indicators between them were analyzed. In the invasion group, differences between DCIS and matched IDC were compared to explore changes in the tumor heterogeneity during invasion. Conclusions are validated in the validation cohort of 65 patients.

Results

Patients in invasion and non-invasion groups were balanced in baseline characteristics and no statistically significant differences were noticed for DCIS immunohistochemical markers. For hematological indicators, high expression of platelet >291.50) (odds ratio, 2.46; CI [1.35-4.46]; p = 0.003) and SII (>347.20) (odds ratio, 2.54; CI [1.56-4.12]; p < 0.001) were established as independent predictors for invasion by logistic analysis and were validated in the validation cohort. Ki-67 of IDC was significantly higher than that of matched DCIS (p < 0.001). HER2 expression and histological grade of DCIS were separately linearly related to those of IDC.

Conclusion

The change in hematological indicators is an independent predictor for invasion and can be incorporated into the treatment decision-making process for DCIS. Invasion tumor cells exhibit a stronger proliferative capacity compared with the in-situ ones. There are linear relationships in HER2 expression and histological grades between DCIS and matched IDC. DCIS subclones with different histological grades will develop into invasive carcinomas separately.

Axillary management for early invasive breast cancer patients: Who will truly benefit?

Background

The implementation of sentinel lymph node biopsy (SLNB) and further completion axillary lymph node dissection (cALND) after positive sentinel lymph nodes (SLNs) on early invasive breast cancer patients should be cautiously tailored. Identifying predictors for SLN and non-sentinel lymph node (nSLN) metastases can help surgeons make better surgical decisions.

Methods

A retrospective case-control study was designed and a total of 560 eligible patients were enrolled consecutively. They were all diagnosed in our center and received appropriate medical care. According to the metastasis of SLN and nSLN, they were divided into metastatic and non-metastatic groups on two successive occasions to investigate the relationship between clinical factors, pathological factors, hematological factors and lymph node metastasis.

Results

In total, 101 (18.04%) patients developed SLN metastases, including 98 patients with macro-metastases and 3 patients with micro-metastases. Out of 97 patients receiving further cALND, 20 patients (20.62%) developed nSLN metastases. Multivariate analysis revealed that “high expression of Ki-67” and “lymphatic invasion” predicted a higher risk of SLN metastasis; and “increased number of positive SLNs” and “increased systemic inflammation index (SII)” predicted a higher risk of nSLN metastasis.

Conclusion

Surgery for early invasive breast cancer patients should be more customized and precise. Appropriate axillary management is necessary for patients with the associated predictors.

Correlation of serum NLR, PLR and HALP with efficacy of neoadjuvant chemotherapy and prognosis of triple-negative breast cancer

OBJECTIVE

To investigate the correlation of blood neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and hemoglobin and albumin levels and lymphocyte and platelet counts (HALP) with the efficacy of neoadjuvant chemotherapy (NAC) and the prognosis of triple-negative breast cancer (TNBC).

METHODS

In this retrospective study, clinical data of 92 patients with TNBC were analyzed. The patients were treated with NAC in the Department of Gynecology of the People's Hospital of Zhuji from January 2015 to December 2018. According to treatment efficacy of NAC, patients were divided into a pathologic complete response (pCR) group (n=37) and a non-pathologic complete response (non-pCR) group (n=55). The pathological and clinical data of patients were collected, and the efficacy of NAC and influencing factors were statistically analyzed. The predicting performances of NLR, PLR and HALP for the efficacy of NAC in patients with TNBC were investigated. Patients were followed up for 3 years to obtain the all-cause mortality so as to analyze the correlation of NLR, PLR and HALP with survival time.

RESULTS

Multivariate regression analysis showed that TNM stage III (OR (95% CI): 1.742 (1.209-2.631), P=0.003), lymph nodes metastasis (OR (95% CI): 1.922 (1.492-2.983), P =0.005), high NLR (OR (95% CI): 2.261 (1.625-2.754), P<0.001), high PLR (OR (95% CI): 2.062 (1.692-2.791), P<0.001) and low HALP (OR (95% CI): 0.518 (0.365-0.734), P<0.001) were risk factors of poor NAC efficacy for TNBC. The mortality of patients in the non-pCR group was higher than that in the pCR group within 3 years (P<0.05). Survival analysis showed that the 3-year survival rate of the non-pCR group was lower than that of the pCR group (P<0.05). Furthermore, patients with high NLR, high PLR and low HALP had a lower 3-year survival rate than those with low NLR, low PLR and high HALP (P<0.05).

CONCLUSIONS

Lymph node metastasis, TNM stage III, high NLR, high PLR and low HALP are risk factors for the poor efficacy of NAC for TNBC. High expression of NLR, PLR and low expression of HALP may indicate a poor prognosis of TNBC patients who failed NAC.

Pan-Immune-Inflammation Value: A New Prognostic Index in Operative Breast Cancer

Background

To build a predictive scoring model based on simple immune and inflammatory parameters to predict postoperative survival in patients with breast cancer.

Methods

We used a brand-new immuno-inflammatory index-pan-immune-inflammation value (PIV)-to retrospectively evaluate the relationship between PIV and overall survival (OS), and based on the results of Cox regression analysis, we established a simple scoring prediction model based on several independent prognostic parameters. The predictive accuracy of the model was evaluated and independently validated.

Results

A total of 1,312 patients were included for analysis. PIV was calculated as follows: neutrophil count (10⁹/L) × platelet count (10⁹/L) × monocyte count (10⁹/L)/lymphocyte count (10⁹/L). According to the best cutoff value of PIV, we divided the patients into two different subgroups, high PIV (PIV > 310.2) and low PIV (PIV ≤ 310.2), associated with significantly different survival outcomes (3-year OS, 80.26% vs. 86.29%, respectively; 5-year OS, 62.5% vs. 71.55%, respectively). Six independent prognostic factors were identified and used to build the scoring system, which performed well with a concordance index (C-index) of 0.759 (95% CI: 0.715-0.802); the calibration plot showed good calibration.

Conclusions

We have established and verified a simple scoring system for predicting prognosis, which can predict the survival of patients with operable breast cancer. This system can help clinicians implement targeted and individualized treatment strategies.

Prognosis and Dissection of Immunosuppressive Microenvironment in Breast Cancer Based on Fatty Acid Metabolism-Related Signature

Fatty acid metabolism has been deciphered to augment tumorigenesis and disease progression in addition to therapy resistance via strengthened lipid synthesis, storage, and catabolism. Breast cancer is strongly associated with the biological function of fatty acid metabolism owing to the abundant presence of adipocytes in breast tissue. It has been unraveled that tumor cells exhibit considerable plasticity based on fatty acid metabolism, responding to extra-tumoral and a range of metabolic signals, in which tumor microenvironment plays a pivotal role. However, the prognostic significance of fatty acid metabolism in breast cancer remains to be further investigated. Alongside these insights, we retrieved 269 reliable fatty acid metabolism-related genes (FMGs) and identified the landscape of copy number variations and expression level among those genes. Additionally, 11 overall survival-related FMGs were clarified by univariate Cox hazards regression analysis in The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Subsequently, a prognostic signature based on 6 overall survival (OS)-related FMGs was generated using Lasso Cox hazards regression analysis in TCGA dataset and was validated in two external cohorts. The correlation between the signature and several essential clinical parameters, including T, N, and PAM50 subtypes, was unveiled by comparing the accumulating signature value in various degrees. Furthermore, an optimal nomogram incorporating the signature, age, and American Joint Committee on Cancer (AJCC) stage was constructed, and the discrimination was verified by C-index, the calibration curve, and the decision curve analysis. The underlying implications for immune checkpoints inhibitors, the landscape of tumor immune microenvironment, and the predictive significance in therapy resistance to diverse strategies were depicted ultimately. In conclusion, our findings indicate the potential prognostic connotation of fatty acid metabolism in breast cancer, supporting novel insights into breast cancer patients’ prognosis and administrating effective immunotherapy.

WASF2 Serves as a Potential Biomarker and Therapeutic Target in Ovarian Cancer: A Pan-Cancer Analysis

Background

Wiskott-Aldrich syndrome protein family member 2 (WASF2) has been shown to play an important role in many types of cancer. Therefore, it is worthwhile to further study expression profile of WASF2 in human cancer, which provides new molecular clues about the pathogenesis of ovarian cancer.

Methods

We used a series of bioinformatics methods to comprehensively analyze the relationship between WASF2 and prognosis, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and tried to find the potential biological processes of WASF2 in ovarian cancer. Biological behaviors of ovarian cancer cells were investigated through CCK8 assay, scratch test and transwell assay. We also compared WASF2 expression between epithelial ovarian cancer tissues and normal ovarian tissues by using immunohistochemical staining.

Results

In the present study, we found that WASF2 was abnormally expressed across the diverse cancer and significantly correlated with overall survival (OS) and progression-free interval (PFI). More importantly, the WASF2 expression level also significantly related to the TME. Our results also showed that the expression of WASF2 was closely related to immune infiltration and immune-related genes. In addition, WASF2 expression was associated with TMB, MSI, and antitumor drugs sensitivity across various cancer types. Functional bioinformatics analysis demonstrated that the WASF2 might be involved in several signaling pathways and biological processes of ovarian cancer. A risk factor model was found to be predictive for OS in ovarian cancer based on the expression of WASF2. Moreover, in vitro experiments, it was demonstrated that the proliferative, migratory and invasive capacity of ovarian cancer cells was significantly inhibited due to WASF2 knockdown. Finally, the immunohistochemistry data confirmed that WASF2 were highly expressed in ovarian cancer.

Conclusions

Our study demonstrated that WASF2 expression was associated with a poor prognosis and may be involved in the development of ovarian cancer, which might be explored as a potential prognostic marker and new targeted treatments.

Combined systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) predicts chemotherapy response and prognosis in locally advanced gastric cancer patients receiving neoadjuvant chemotherapy with PD-1 antibody sintilimab and XELOX: a prospective study

BACKGROUND

Previous studies have confirmed that systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) can predict the prognosis and chemotherapy efficacy of various malignant tumors. However, to the best of our knowledge, no study investigated the SII combined with PNI score to predict the efficacy of anti-programmed death 1 (anti-PD-1) antibody sintilimab and XELOX regimen (capecitabine plus oxaliplatin) in the treatment of locally advanced gastric cancer. This study aims to evaluate the predictive value of pre-treatment SII-PNI score on the sensitivity of sintilimab immunotherapy combined with XELOX chemotherapy in patients with locally advanced gastric cancer.

METHODS

We registered a prospective clinical study involving 30 locally advanced gastric cancer patients from March 2020 to July 2021. The pre-treatment SII and PNI were calculated from peripheral blood samples, and the cut-off value was calculated by receiver operating characteristic. The SII-PNI score ranged from 0 to 2 and were categorized into the following: score of 2, high SII (≥ 568.5) and low PNI (≤ 52.7); score of 1, either high SII or low PNI; score of 0, no high SII nor low PNI.

RESULTS

All patients were evaluated by RECIST1.1 criteria after four cycles of sintilimab immunotherapy combined with XELOX chemotherapy, including 5 patients with TRG 3 and 25 patients with non-TRG 3. The SII-PNI score of non-TRG 3 patients was significantly lower than that of TRG 3 patients (P = 0.017). The medial progression free survival of patients with low SII-PNI score was significantly better than that of patients with high SII-PNI score (P < 0.001). Multivariate analysis showed that SII-PNI score was an independent prognostic factor for predicting progression-free survival (P = 0.003).

CONCLUSION

The pre-treatment SII-PNI score is a significant indicator for predicting chemosensitivity of locally advanced patients after sintilimab immunotherapy combined with XELOX chemotherapy, which can help to identify high-risk groups and predict prognosis.

TRIAL REGISTRATION

The registered name of the trial is "Prospective clinical study of sintilimab combined with chemotherapy for neoadjuvant therapy in locally advanced gastric cancer". Its Current Controlled Trials number is ChiCTR2000030414. Its date of registration is 01/03/2020.

Predictive Effect of Systemic Immune-Inflammation Index Combined With Prognostic Nutrition Index Score on Efficacy and Prognosis of Neoadjuvant Intraperitoneal and Systemic Paclitaxel Combined With Apatinib Conversion Therapy in Gastric Cancer Patients With Positive Peritoneal Lavage Cytology: A Prospective Study

Background

Gastric cancer with only peritoneal lavage cytology (GC-CY₁) is a special type of gastric cancer, which is defined as stage IV. The pre-treatment systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) are representative blood indexes of systemic inflammatory response and nutritional status. However, the clinical significance of combined detection of these two indexes is still unclear. This study aims to evaluate the clinical value of the new score system by combining SII and PNI (SII-PNI score) as a predictor of efficacy and prognosis after neoadjuvant intraperitoneal and systemic (NIPS) paclitaxel combined with Apatinib conversion therapy for GC-CY₁ patients.

Methods

We registered a prospective clinical study involving 36 GC-CY₁ patients from April 2018 to August 2019 (NCT03718624). All patients underwent re-laparoscopic exploration after treatment. According to free cancer cells (FCCs) status, these patients were divided into FCCs group and non-FCCs group. The SII-PNI score ranged from 0 to 2 as follows: score of 2, high SII (≥512.1) and low PNI (≤52.9); score of 1, either high SII or low PNI; score of 0, no high SII nor low PNI.

Results

All patients underwent re-laparoscopic exploration after 3 cycles of NIPS paclitaxel and Apatinib conversion therapy. Among them, 28 cases (77.78%) were in non-FCCs group, and 8 cases (22.22%) were in FCCs group. The SII-PNI score of non-FCCs patients was significantly lower than that of FCCs patients (p=0.041). The prognosis of patients with high SII-PNI score was significantly worse than that of patients with low SII-PNI score (p<0.001). Multivariate analysis showed that SII-PNI score was an independent prognostic factor for predicting overall survival and progression-free survival (p=0.001, 0.002).

Conclusion

Pretreatment SII-PNI score is an important predictor for the efficacy of GC-CY₁ patients after NIPS paclitaxel combined with Apatinib conversion therapy, which can help to identify high-risk groups and predict prognosis.

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.

Association between systemic immune-inflammation index and neoadjuvant chemotherapy efficacy as well as prognosis in triple-negative breast cancer

OBJECTIVES

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer with a relatively poor prognosis. Neoadjuvant chemotherapy (NAC) is the main treatment method. Due to the heterogeneity of the tumor, the chemotherapy response of TNBC patients is significantly different. Inflammation is closely related to the occurrence and development of cancer. The systemic immune-inflammation index (SII) is an indicator that can comprehensively reflect the state of systemic inflammation. This study aims to explore the association between SII and the NAC efficacy as well as the prognosis in TNBC.

METHODS

The data of TNBC patients who underwent NAC and systemic treatment in Xiangya Hospital of Central South University from January 2015 to June 2019 were collected. According to the inclusion and exclusion criteria, 231 TNBC patients were finally included. The pre-NAC SII was calculated according to the blood routine results of the patients at 1 week before chemotherapy, and the patients were divided into a pre-NAC low SII group (SII<412, 115 cases) and a pre-NAC high SII group (SII≥412, 116 cases). The SII after chemotherapy was calculated according to the blood routine results of the patients at 2 to 3 months after the end of chemotherapy, and the patients were divided into a low SII group after chemotherapy (SII<474, 115 cases) and a high SII group after chemotherapy (SII≥474, 116 cases). Pearson's chi-square test was used to analyze the relationship between SII and other clinical characteristics of TNBC patients, and the relationship between the NAC efficacy and clinical characteristics of TNBC patients. Binary logistic regression analysis was used to find independent factors that affect the efficacy of NAC in TNBC patients. Kaplan-Meier curve analysis was used to analyze factors affecting the prognosis of TNBC patients. Cox regression model was used to find independent factors affecting the prognosis of TNBC patients.

RESULTS

Before NAC, the differences in SII between groups with different ages and tumor sizes were significant (P=0.007 and P=0.002, respectively); after chemotherapy, there were no significant differences in SII between different ages, tumor sizes, histological grades, lymph node staging, and Ki-67 groups (all P>0.05). There were 115 patients with low SII before NAC, with a pathological complete response (pCR) rate of 15.7%; there were 116 patients with high SII before NAC, with a pCR rate of 6.0%. Patients with low SII before NAC had a higher pCR rate than patients with high SII before NAC, and the difference was statistically significant (P=0.019).There were 156 patients with lymph node staging pN0, with a pCR rate of 14.7%; and there were 75 patients with lymph node staging pN1-pN2, with a pCR rate of 2.7%. Patients with lymph node staging pN0 had a higher pCR rate than those with lymph node staging pN1-pN2, and the difference was significant (P=0.006). During the follow-up, 34 patients had local recurrence or distant metastasis. The Kaplan-Meier survival curve showed that the 3-year disease-free survival (DFS) rates for patients with low SII before NAC and high SII before NAC were 87.8% and 82.8%, respectively, and the former was significantly higher than the latter (P=0.005); the 3-year DFS rates for patients with tumor sizes of T1-T2 and T3 were 89.0% and 67.5%, respectively, and the former was significantly higher than the latter (P=0.001); the 3-year DFS rates for patients with lymph node staging of pN0 and pN1-pN2 were 87.8% and 82.8%, respectively, and the former was significantly higher than the latter (P=0.009). Cox analysis showed that SII before NAC and tumor size were independent influencing factors of patients' DFS (P=0.038, P=0.010, respectively).

CONCLUSIONS

SII has important clinical significance in predicting the efficacy and prognosis of NAC in TNBC patients, and it has the potential to be a biomarker.

Prognostic Value of Inflammatory Biomarkers in Patients With Stage I Lung Adenocarcinoma Treated With Surgical Dissection

Objective

Inflammation plays a crucial role in tumorigenesis and progression. Our purpose was to investigate the prognostic value of neutrophil-to-lymphocyte ratio (NLR), systemic inflammation response index (SIRI) and systemic immune-inflammation index (SII), and develop a nomogram to predict the cancer-specific survival (CSS) and disease-free survival (DFS) of stage I lung adenocarcinoma patients.

Methods

1431 patients undergoing surgical resection with pathologically confirmed stage I lung adenocarcinoma were reviewed. The optimal cut-off values for NLR, SII, and SIRI were defined by the receiver operating characteristic (ROC) curve. Cox proportional hazards regression analyses were performed to recognize factors significantly correlated with CSS and DFS to construct the nomogram. The value of adjuvant chemotherapy on model-defined high-risk and low-risk patients was further explored.

Results

The cohort had a median follow-up time of 63 months. Multivariate analysis revealed that higher NLR (≥2.606), higher SIRI (≥0.705), higher SII (≥580.671), later T stage, histological pattern with solid or micropapillary components and radiologic features with solid nodules were significantly associated with worse CSS and DFS. The concordance index (C-index) of the nomogram established by all these factors was higher than that of the TNM staging system both in CSS (validation set 0.778 vs 0.652) and DFS (validation set 0.758 vs 0.695). Furthermore, the value of the established nomogram on risk stratification in stage I lung adenocarcinoma patients was validated.

Conclusions

Higher NLR, SII and SIRI pretreatment were associated with worse survival outcomes. A practical nomogram based on these three inflammatory biomarkers may help clinicians to precisely stratify stage I lung adenocarcinoma patients into high- and low-risk and implement individualized treatment.

Assessment of Systemic Inflammation and Nutritional Indicators in Predicting Recurrence-Free Survival After Surgical Resection of Gastrointestinal Stromal Tumors

Background

Recent studies have shown that the systemic inflammation and nutritional indicators are prognostic for a variety of malignancies. However, only limited data have so far demonstrated their usefulness in gastrointestinal mesenchymal tumors (GIST).

Methods

We retrospectively analyzed the data of GIST patients who underwent radical surgery in Beijing hospital from October 2004 to July 2018. The area under the receiver operating characteristic curve (AUC) was used to compare several commonly used inflammatory and nutritional indicators. The indicators with largest AUC were further analysis. Optimal cut-off values of those indicators in predicting recurrence-free survival (RFS) were determined. Kaplan-Meier curve and the time-dependent receiver operating characteristic (ROC) curve were used to assess the prognostic values. We then used univariate and multivariate Cox regression analyses to identify prognostic factors that were associated with RFS.

Results

In total, 160 patients who underwent surgery for GIST were included in the study. The median survival time was 34.5 months, with 1-, 3-, and 5-year RFS rates of 96.1%, 84.7%, and 80.8%, respectively. The inflammatory and nutritional indicators with largest AUC were Systemic immunoinflammatory Index (SII) and Geriatric Nutrition Risk Index (GNRI), reached 0.650 and 0.713, respectively. The optimal cutoff of GNRI and SII were 98.3, and 820.0, respectively. Univariate analysis showed that GNRI, SII, KI67, surgery method, tumor location, tumor size, and mitotic index were all significant prognostic indicators of RFS. After multivariate Cox analysis, independent prognostic factors for RFS in GIST included tumor location, mitotic index, tumor size, and GNRI (HR=2.802,95% CI: 1.045 to 7.515, p = 0.041). Besides, SII also tended to be associated with RFS (HR = 2.970, 95% CI: 0.946 to 9.326, p = 0.062).

Conclusions

High GNRI is an independent prognostic factor for RFS in GIST, while SII can be considered as a prognostic factor. GNRI and SII can be used as tools to evaluate the prognosis of patients before surgery, helping doctors to better treat high-risk patients.

Prognostic significance of the systemic immune-inflammation index in pancreatic carcinoma patients: A meta-analysis

Background: Systemic immune-inflammation index (SII) is a prognostic indicator for several malignancies, including pancreatic carcinoma; however, there is no consensus on its significance. In the current study, a systematic meta-analysis was used to explore the correlation between SII and prognosis in pancreatic carcinoma patients.

Methods: PubMed, Embase and Cochrane Library databases were screened from inception to May 2020. Studies describing the prognostic role of SII in pancreatic carcinoma were then retrieved. The pooled hazard ratio (HR) and 95% confidence interval (CI) was calculated using random- or fixed-effects models to determine the correlation between SII and prognosis.

Results: A total of four studies, comprising 1749 patients, met the inclusion criteria of the study and were therefore included in this meta-analysis. The meta-analysis showed that high SII indicated was correlated with worse overall survival (OS) in patients with pancreatic carcinoma (HR: 1.43, 95% CI: 1.24–1.65, P<0.001). These findings were validated through subgroup analyses, stratified by the American Joint Committee on Cancer (AJCC) stage. In addition, patients with high SII showed poorer cancer-specific survival (HR: 2.32, 95% CI: 1.55–3.48, P<0.001). However, analysis showed no significant correlations between SII and disease-free and relapse-free survival (RFS).

Conclusion: These findings indicate that SII is a potential non-invasive and a promising tool for predicting clinical outcomes of pancreatic carcinoma patients. However, the current research did not explore whether neoadjuvant therapy has an effect on the prognostic value of SII. Further studies using adequate designs and larger sample sizes are required to validate these findings.

Time-Sequencing of the Neutrophil-to-Lymphocyte Ratio to Predict Prognosis of Triple-Negative Breast Cancer

Since triple-negative breast cancers (TNBCs) have varying prognoses, it is important to identify subgroups with particularly poor prognosis. The aim of this study was to assess whether changes in the neutrophil-to-lymphocyte ratio (NLR) during the treatment process were associated with poor prognosis in TNBC patients. This study included 600 TNBC patients who underwent surgery from January 2005 to December 2016. The associations of the NLR and clinicopathologic factors with breast cancer recurrence and survival in patients who underwent both definitive local treatment (total mastectomy or breast-conserving surgery with radiotherapy) and systemic chemotherapy were analyzed. The NLRs at four time points (before surgery, before chemotherapy, before radiotherapy, and 1 year after surgery) were assessed. The univariate analysis showed that changes in the NLR before the start of radiotherapy (odds ratio: 1.115, confidence interval: 1.011-1.229) and 1 year after surgery (odds ratio: 1.196, confidence interval: 1.057-1.354) significantly increased the risk of recurrence or death. In multivariate analysis, T stage, N stage, and changes in the NLR were significant factors. A time-sequenced NLR may reflect the prognosis of TNBC patients. A poor prognosis is expected in patients whose NLR increases during treatment compared to the preoperative NLR, and additional treatment is needed.

Prognostic significance of preoperative systemic immune-inflammation index in combined hepatocellular-cholangiocarcinoma

BACKGROUND

Inflammation-based prognostic scores have been increasingly used for prognosis prediction in malignant tumors. However, no existing study has comprehensively evaluated these scores in combined hepatocellular-cholangiocarcinoma (cHCC-CCA).

OBJECTIVE

This study aimed to identify a robust inflammation-based prognostic predictor for cHCC-CCA.

METHODS

We retrospectively analyzed 220 patients pathologically confirmed as Allen type C cHCC-CCA. The univariate and multivariate analyses were used to explore the associations between clinical variables and prognosis of cHCC-CCA. The propensity score-matching (PSM) was performed to reduce the effects of potential cofounders and selection bias. Finally, the predictive values of different inflammation-based indices were compared by using time-dependent receiver operating characteristic (ROC) curves.

RESULTS

The systemic immune-inflammation index (SII) and aspartate aminotransferase to platelet ratio index (APRI) were identified as independent prognostic predictors in multivariate analysis. After PSM, the survival differences were still significant between SII-high group and SII-low group (P= 0.016 for RFS and P= 0.001 for OS). Further ROC analysis showed that the SII harbored the largest 1-, 3- and 5-year area under the curves (AUC) values as compared with other scores.

CONCLUSIONS

The SII may serve as a preferable predictor of both recurrence-free survival (RFS) and overall survival (OS) in patients with cHCC-CCA.

Systemic immune-inflammation index independently predicts poor survival of older adults with hip fracture: a prospective cohort study

Background

The systemic immune-inflammation index (SII), based on peripheral platelet, neutrophil and lymphocyte counts, has been proven to be a promising prognostic indicator in various diseases. Hip fracture is a common injury among the older adults, and has become a global public health problem with high mortality and disability rates. However, the relationship between SII and the prognosis of hip fracture is not yet well-known. The aim of the this study was to explore the predictive value of SII in older adults with hip fracture undergoing surgery.

Methods

This was a prospective cohort study performed from January 2014 to December 2018 at a orthopaedic center, China. The SII was calculated as platelet×neutrophil/lymphocyte counts. Univariable and multivariable Cox proportional hazard models were used to assess the association between SII and all-cause mortality.

Results

A total of 290 older adults with hip fracture were included, and the mean (SD) age was 77.6 (8.6) years, and 189 (65.2%) were female. The median (IQR) SII was 759.4 (519.0–1128.7) × 10⁹/L. After a median follow-up time of 33.4 months, 13 (4.5%), 26 (9.0%) and 54 (18.6%) patients died within the 30-day, 1-year and last follow-up, respectively. Multivariable Cox analysis revealed that each increase of 100 units of SII was associated with a 8% increased hazard of death at 1-year follow-up (HR = 1.08, 95% CI: 1.01–1.17, p = 0.033), and 9% increased hazard of death at last follow-up (HR = 1.09, 95% CI: 1.03–1.15, p = 0.003).

Conclusions

SII is associated with poor all-cause mortality in older adults with hip fracture undergoing surgery, and deserves further investigation and application in clinical practice.

Association Between Systemic Immune-Inflammation Index and Diabetic Depression

Background

Depression is highly prevalent in patients with diabetes mellitus (DM). Diabetic depression has been shown to be associated with low-grade systemic inflammation. In recent years, the systemic immune-inflammation (SII) index has been developed as an integrated and novel inflammatory indicator. The aims of this study were to investigate the relationship between diabetic depression and SII levels, adjusting for a wide range of potential confounding factors, to examine the potential of SII in predicting diabetic depression.

Methods

The present cross-sectional study was conducted among adults with DM in the National Health and Nutrition Examination Survey between 2009 and 2016, the SII level was calculated as the platelet counts × neutrophil counts/lymphocyte counts. Patient Health Questionnaire‐9 was used to measure depression in patients with DM. Multivariable logistic regression and propensity score-matched analysis were used to analyze the association between SII levels and depression.

Results

A total of 2566 patients with DM were included in the study, of which 370 (13.3%) were diagnosed with depression. Multivariable logistic regression showed that high SII level was an independent risk factor for diabetic depression (OR = 1.347, 95% CI: 1.031–1.760, P = 0.02882) after adjusting for covariates. The relationship between SII and diabetic depression was further verified by propensity score-matched analysis.

Conclusion

Our data suggest that SII is a risk factor for depression in patients with DM. The SII may be an easily accessible and cost-effective strategy for identifying depression in patients with DM. More studies are warranted to further analyze the role of SII in depression in diabetic patients.

Prognostic value of the systemic immune-inflammation index in patients with breast cancer: a meta-analysis

Background

Although previous studies have evaluated the prognostic role of the systemic immune-inflammation index (SII) in patients with breast cancer, the results were inconsistent. Therefore, in this context, we aimed to identify the prognostic and clinicopathological value of the SII in patients with breast cancer by performing a meta-analysis.

Methods

A literature search was using PubMed, Web of Science, EMBASE, and Cochrane Library databases for relevant articles, from their inception to May 12, 2020. The prognostic value of the SII in breast cancer was assessed by pooling the hazard ratios (HRs) with 95% confidence intervals (CIs). The clinical outcomes included the overall survival (OS), disease-free survival (DFS), recurrence-free survival (RFS), and distant metastasis-free survival (DMFS). The methodological quality of all the included studies was evaluated using the Newcastle–Ottawa quality assessment scale. The odds ratios (ORs) with 95% CIs were combined to evaluate the correlation between the SII and clinicopathological characteristics of patients with breast cancer. Publication bias was evaluated using the Begg funnel plot and the Egger linear regression test. All statistical analyses were performed using Stata software, version 12.0 (Stata Corporation, College Station, TX, USA). A p value of < 0.05 was considered statistically significant.

Results

Eight studies involving 2642 patients were included in the current meta-analysis. The combined data showed that patients with a high SII had worse OS (HR = 1.79, 95% CI 1.33–2.42, p < 0.001), poorer DFS/RFS (HR = 1.79, 95% CI 1.31–2.46, p < 0.001), and inferior DMFS (HR = 1.64, 95% CI 1.32–2.03, p < 0.001) than patients with a low SII. In addition, a high SII was correlated with the presence of lymph node metastasis (OR = 1.38, 95% CI 1.12–1.69, p = 0.002), higher T stage (OR = 1.49, 95% CI 1.17–1.89, p < 0.001), advanced TNM stage (OR = 1.37, 95% CI 1.07–1.77, p = 0.014), and higher histological grade (OR = 3.71, 95% CI 1.00–13.73, p = 0.049). However, there was no significant association between the SII and the pathological type (OR = 0.82, 95% CI 0.55–1.23, p = 0.345) or lymphatic invasion (OR = 1.30, 95% CI 0.82–2.08, p = 0.266).

Conclusions

The results of our meta-analysis suggest that an elevated SII predicts poor survival outcomes and is associated with clinicopathological features that indicate tumor progression of breast cancer.