Preoperative systemic immune-inflammation index as a prognostic indicator for patients with urothelial carcinoma

Clinical Significance of the Serum 3'tRF-AlaAGC, Neutrophil to High-Density Lipoprotein Ratio, and Lymphocyte-to-Monocyte Ratio in Breast Cancer with Lymph Node Metastasis

Objective

Breast cancer (BC) is a common malignant tumor among women, the local recurrence, lymph node metastasis (LNM), and distant metastasis are the key factors affecting the prognosis of patients. tRNA-derived small RNAs (tDRs) are non-coding small RNA fragments [16-40 nucleotides (nt) in length] that play an important role in carcinogenesis and can serve as novel biological markers for the diagnosis and prognosis of various tumors. Accumulating evidence suggests that blood-based inflammatory indicators are linked with the pathogenesis of BC. However, the clinical significance of the combination of tDRs and inflammatory indicators in BC patients with LNM is still unclear.

Methods

The serum samples were collected from 175 patients with BC admitted to our hospital during June 2021 and May 2024, and 94 age-matched healthy women, and the clinical data of the research subjects were recorded. Serum 3'tRF-AlaAGC levels were measured using quantitative real-time PCR (qRT-PCR) and the blood-based inflammatory indicators were calculated from peripheral blood samples. Lasso-cox regression and multiple logistic regression were employed for variable selection. Receiver operating characteristic (ROC) was used to calculate the cut-off value of variables. Spearman correlation test was used to examine the correlation between 3'tRF-AlaAGC levels and neutrophil to HDL-C ratio (NHR), lymphocyte-to-monocyte ratio (LMR). A nomogram model for risk assessment of LNM in BC was established by using the rms package of R software.

Results

Serum 3'tRF-AlaAGC levels in BC patients with LNM were significantly higher than that in without LNM [5.17 (1.79, 16.55) vs 11.68 (2.64, 58.74), P=0.009]. The variables screened by Lasso-cox regression including 3'tRF-AlaAGC, NHR and LMR, with optimal cut-off values of 18.78, 2.94 and 5.41, respectively. NHR levels were significantly negatively associated with LMR in low 3'tRF-AlaAGC expression groups (r=-0.209, P=0.021). Multivariate logistic regression analysis confirmed that 3'tRF-AlaAGC (OR: 3.242, 95% CI: 1.583-6.641, P=0.001), NHR (OR: 3.305, 95% CI: 1.543-7.079, P=0.002), and LMR (OR: 0.329, 95% CI: 0.150-0.723, P=0.006) were independent risk factors of BC with LNM. The C-statistic of the nomograms model was 0.704, with a sensitivity of 57.14% and a specificity of 77.14%.

Conclusion

3'tRF-AlaAGC >18.78, NHR > 2.94, and LMR ≤ 5.41 were the independent risk factors of BC with LNM. The nomogram model incorporating 3'tRF-AlaAGC, NHR and LMR can effectively predict the risk of LNM of BC patients.

Predictive value of cumulative SII for MACE in STEMI patients after PCI

The systemic immune-inflammation index (SII) has been used effectively to effectively assess the prognosis of patients with a variety of diseases. But few evidence on the relationship between SII and long-term prognosis of myocardial infarction. We thus aimed to evaluate the relationships of cumulative exposure to SII and its accumulation time course with major adverse cardiovascular events (MACE) events in patients with acute myocardial infarction after percutaneous coronary intervention. To evaluate the predictive value of SII in MACE events in patients with acute myocardial infarction. A total of 480 patients with acute ST-elevation myocardial infarction who underwent emergency coronary angiography at the Department of Cardiology, Affiliated Hospital of Hebei University from August 2022 to August 2023 were enrolled in this study. Eighteen patients were lost to follow-up, with a loss rate of 3.8%. Time-weighted cumulative SII was calculated as the weighted sum of the mean SII value for each time interval, then normalized by total exposure duration, the exposure duration was from hospitalization to 1-year follow-up. Duration of high SII exposure was defined as the duration with high SII and ranged from hospitalization to 1-year follow-up. The time course of SII accumulation was categorized by the combination of time-weighted cumulative SII < or ≥ median and SII slope. At 1-year follow-up, after adjusting for potential confounders, the time-weighted cumulative SII was divided into 2 groups. The S2 group which is above the median had a higher risk of MACE (hazard ratio, 1.090; 95% confidence interval 1.035-1.149), the high time-weighted cumulative SII group with a positive slope had a higher risk of MACE (hazard ratio, 4.096; 95% confidence interval 1.851-9.065). Long-term cumulative exposure to SII increases the risk of MACE in patients with acute ST-elevation myocardial infarction undergoing coronary angiography, and late high SII results in a higher risk of MACE events at the same time-weighted cumulative SII, underscoring the importance of late inflammation control.

Peripheral blood immunoinflammatory biomarkers: prospective predictors of postoperative long-term survival and chronic postsurgical pain in breast cancer

Background

Tumor progression and chronic postsurgical pain (CPSP) in patients with breast cancer are both significantly influenced by inflammation. The associations between immunoinflammatory biomarkers and long-term survival, as well as CPSP, remain ambiguous. This study examined the predictive value of immunoinflammatory biomarkers for both long-term survival and CPSP.

Methods

Data on the clinicopathological characteristics and perioperative peripheral blood immunoinflammatory biomarkers of 80 patients who underwent breast cancer surgery were retrospectively collected. Optimal cut-off values for preoperative immunoinflammatory biomarkers, including the preoperative systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), neutrophil-to-lymphocyte ratio (NLR), and pan-immune-inflammation value (PIV), were established via receiver operating characteristic (ROC) curves. Kaplan-Meier curves and Cox regression analysis were used to evaluate the relationships between preoperative immunoinflammatory biomarkers and long-term survival. The relationships among the perioperative neutrophil count (NEU), monocyte count (MONO), lymphocyte count (LYM), platelet count (PLT), SII, SIRI, NLR, PIV, dynamic changes in peripheral blood cell counts, and CPSP were further assessed using logistic regression analysis.

Results

Kaplan-Meier curves revealed a considerable prolongation of disease-free survival (DFS) and overall survival (OS) in the low preoperative SII, SIRI, NLR, and PIV groups. Multivariate Cox regression analysis revealed that only an elevated preoperative SIRI was an independent risk factor for postoperative DFS (HR=8.890, P=0.038). The incidence of CPSP was 28.75%. Univariate logistic regression analysis revealed that body mass index (BMI), postoperative NEU, MONO, SIRI, and PIV were negatively correlated with the occurrence of CPSP, whereas subsequent multivariate logistic regression analysis revealed that only BMI was independently associated with CPSP (OR=0.262, P=0.023).

Conclusion

Elevated preoperative SIRI was an independent risk factor for poor DFS in breast cancer patients after surgery. In contrast, perioperative immunoinflammatory biomarkers had limited potential for predicting CPSP in patients who underwent breast cancer surgery.

The Clinical Value of the Combined Detection of Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and Prognostic Nutritional Index (PNI) in Early Diagnosis of Gastric Cancer

Objective

Gastric cancer (GC) is a common malignant tumor of the digestive tract. Accumulating studies suggest that inflammation is linked with the pathogenesis of GC. The study delves into novel hematological inflammatory markers, such as systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and prognostic nutritional index (PNI), to explore their potential applications in early diagnosis of GC.

Methods

From October 2020 and August 2024, 1339 GC patients admitted to our hospital were enrolled in this study. The pre-treatment SII, SIRI, and PNI was calculated from peripheral blood samples. Univariate and multivariate logistic regression analyses were utilized to verify independent risk factors for patients, and constructed the nomograms. The correlation between hematological indicators and tumor-node-metastasis (TNM) stage was assessed through Spearman's analysis.

Results

Eligible patients and healthy controls were grouped by gender. The diagnostic ability of PNI was significantly superior to other indicators to diagnose male GC (area under the curve [AUC]=0.908, 95% CI: 0.892-0.925) and female GC (AUC=0.890, 95% CI: 0.865-0.914). Besides, the combination of hematological indicators is more effective in diagnosing GC patients, especially for male patients (AUC=0.916, 95% CI: 0.901-0.932, sensitivity: 84.98%, specificity: 84.29%). The C-statistic of Nomogram model was 0.917 for males and 0.875 for females. In both male and female cohorts, CEA, SII, and SIRI were positively correlated with TNM stage, while PNI was negatively correlated. The AUC of CEA, SII, SIRI, and PNI combined for the diagnosis in the early stage of male GC patients was 0.897 (95% CI: 0.875-0.918, sensitivity: 86.57%, specificity: 80.30%) is higher than that of in the advanced stage (AUC: 0.745, 95% CI: 0.710-0.780, sensitivity: 56.53%, specificity: 82.86%).

Conclusion

The combined CEA, SII, PNI, and SIRI could be used as screening biomarkers in diagnosing GC, especially in the early stage of male GC patients.

Prognostic model for predicting recurrence in hepatocellular carcinoma patients with high systemic immune-inflammation index based on machine learning in a multicenter study

Introduction

This study aims to use machine learning to conduct in-depth analysis of key factors affecting the recurrence of HCC patients with high preoperative systemic immune-inflammation index (SII) levels after receiving ablation treatment, and based on this, construct a nomogram model for predicting recurrence-free survival (RFS) of patients.

Methods

This study included clinical data of 505 HCC patients who underwent ablation therapy at Beijing You'an Hospital from January 2014 to January 2020, and accepted 65 HCC patients with high SII levels from Beijing Ditan Hospital as an external validation cohort. 505 patients from Beijing You'an Hospital were divided into low SII and high SII groups based on the optimal cutoff value of SII scores. The high SII group was further randomly divided into training and validation cohorts in a 7:3 ratio. eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox regression analysis, were used to explore the factors affecting the post-ablation RFS of HCC patients. Based on the identified key factors, a nomogram model were developed to predict RFS in HCC patients, and their performance were evaluated using the concordance index (C index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). The optimal cutoff value for nomogram scores was used to divide patients into low- and high-risk groups, and the effectiveness of the model in risk stratification was evaluated using Kaplan-Meier (KM) survival curves.

Results

This study confirmed that age, BCLC stage, tumor number, and GGT level were independent risk factors affecting RFS in HCC patients. Based on the selected risk factors, an RFS nomogram was successfully constructed. The C-index, ROC curve, calibration curve, and DCA curve each demonstrated the discrimination, accuracy, and decision-making utility of the nomogram, indicating that it has good predictive performance. KM curve revealed the nomogram could significantly differentiate patient populations with different recurrence risk.

Conclusion

We developed a reliable nomogram that can accurately predict the 1-, 3-, and 5-year RFS for HCC patients with high SII levels following ablation therapy.

C-reactive Protein Is a Prognostic Factor for Survival in Metastatic Upper Tract Urothelial Carcinoma Patients Receiving Pembrolizumab

BACKGROUND/AIM

The number of available treatment options for urothelial carcinoma has increased recently. Upper tract urothelial carcinoma (UTUC) is relatively rare compared with bladder cancer. There are few reports on the efficacy of immune checkpoint inhibitors (ICIs) for metastatic UTUC, and ICIs may occasionally show less efficacy and cause severe side effects. Therefore, it is important to predict the treatment response and change the treatment strategy as appropriate. We investigated the prognostic factors for treatment response in patients with metastatic UTUC treated with pembrolizumab at our hospital.

PATIENTS AND METHODS

Patients who received pembrolizumab for UTUC between January 2018 and June 2023 were analyzed. Patients who presented with bladder cancer complications at initial diagnosis were excluded. The primary endpoints assessed were overall survival (OS) and progression-free survival (PFS). Statistical analyses were conducted using laboratory values obtained before and after pembrolizumab administration. The relationship between cancer and inflammation is important. Therefore, we analyzed this relationship using prognostic factors for urothelial carcinoma as previously reported. Specifically, pretreatment C-reactive protein (CRP) level, neutrophil-to-lymphocyte ratio (NLR), and NLR/albumin values were examined.

RESULTS

Forty-seven patients were analyzed. The median PFS was 66 days (24-107 days), and the median OS was 164 days (13-314 days). A CRP level <1 before the first cycle was a useful factor in the multivariate analysis for both OS and PFS [OS: p=0.004, hazard ratio (HR)=3.244, 95% confidence interval (CI)=1.464-7.104; PFS: p=0.003, HR=2.998, 95%CI=1.444-6.225].

CONCLUSION

CRP level is a prognostic factor for pembrolizumab treatment response in patients with UTUC.

Prognostic value of systemic immune-inflammation index in patients with metastatic renal cell carcinoma treated with systemic therapy: a meta-analysis

Objective

A novel systemic immune-inflammation index (SII), based on the neutrophils, lymphocytes, and platelet counts, is associated with the prognosis of several cancers, including non-metastatic renal cell carcinoma (RCC). In the present study, we evaluate the prognostic significance of SII in patients with metastatic RCC (mRCC) treated with systemic therapy.

Method

Relevant studies were searched comprehensively from Web of Science, PubMed, Embase and the Cochrane Library up to January 2024. The pooled hazard ratio (HR) and 95% confidence interval (CI) were extracted from each study to evaluate the prognostic value of SII in patients with mRCC treated with tyrosine kinase inhibitor (TKI) or immune checkpoint inhibitor (ICI).

Results

A total of 12 studies including 4,238 patients were included in the final analysis. High SII was significantly correlated to poor overall survival (OS, HR = 1.88; 95% CI 1.60-2.21; P < 0.001) and progression-free survival (PFS, HR = 1.66; 95% CI 1.39-1.99; P < 0.001). Stratified by therapy, high SII was also related to the poor OS (TKI: HR = 1.63, P < 0.001; ICI: HR = 2.27, P < 0.001) and PFS (TKI: HR = 1.67, P < 0.001; ICI: HR = 1.88, P = 0.002).

Conclusion

In conclusion, high SII could serve as an unfavorable factor in patients with mRCC treated with systemic therapy. Stratified by therapies, the elevated SII was also associated with worse prognosis. Whereas, more prospective and large-scale studies are warranted to validate our findings.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024522831, identifier CRD42024522831.

Prognostic value of the systemic immune-inflammation index in patients with acute respiratory distress syndrome: A retrospective study

Background

Inflammation is critical in the etiology and progression of acute respiratory distress syndrome (ARDS). This study aims to rigorously assess the predictive capacity of systemic immune-inflammation index (SII) in determining the outcomes of patients with ARDS.

Methods

Patient data were extracted from version 2.2 of the Medical Information Mart for Intensive Care IV (MIMIC-IV). The Receiver Operating Characteristic (ROC) curve was deployed to determine the optimal cutoff value for the SII, facilitating the stratification of participants into distinct cohorts based on SII levels. The relationship between SII and survival outcomes was rigorously evaluated using Cox proportional hazards models. The association between SII and patient survival was rigorously examined using Cox proportional-hazard models. The impact of varying SII levels on mortality was quantitatively assessed through these models, with the results articulated as hazard ratios (HRs) and 95% confidence intervals (CIs). Three distinct models were formulated for this analysis: Model 1 employed univariate Cox regression to relate SII with mortality; Model 2 introduced adjustments for age and sex; and Model 3 extended these adjustments to include age, sex, race, SAPS II, APSIII, Hemoglobin, Albumin, Pneumonia, SpO2, and SBP.

Results

Post-application of the inclusion criteria, a cohort of 976 eligible patients was delineated for detailed examination. Univariate analysis focusing on 30-day mortality within the SII ≥1694, the hazard ratio (HR) was 1.42 (95% confidence interval (CI): 1.11, 1.81). However, after adjusting for confounding factors such as age, sex, race, Simplified Acute Physiology Score II (SAPS II), Acute Physiology Score (APS) III, Hemoglobin, Albumin, Pneumonia, SpO2, and Systolic Blood Pressure (SBP), an SII value of ≥1694 was identified as an independent and significant risk factor for mortality in patients with ARDS, with an HR of 1.38 (95% CI: 1.08-1.77, P = 0.0016). This trend was consistent for 90-day and one-year mortality rates.

Conclusions

SII surfaced as an autonomous determinant of mortality in ARDS patients, affirming its status as an accessible and dependable prognostic indicator for individuals newly diagnosed with this critical condition. Additional research is imperative to further elucidate the prognostic implications of SII in the therapeutic management of patients with ARDS.

Identification of an inflammation-related risk signature for prognosis and immunotherapeutic response prediction in bladder cancer

Tumor inflammation is one of the hallmarks of tumors and is closely related to tumor occurrence and development, providing individualized prognostic prediction. However, few studies have evaluated the relationship between inflammation and the prognosis of bladder urothelial carcinoma (BLCA) patients. Therefore, we constructed a novel inflammation-related prognostic model that included six inflammation-related genes (IRGs) that can precisely predict the survival outcomes of BLCA patients. RNA-seq expression and corresponding clinical data from BLCA patients were downloaded from The Cancer Genome Atlas database. Enrichment analysis was subsequently performed to determine the enrichment of GO terms and KEGG pathways. K‒M analysis was used to compare overall survival (OS). Cox regression and LASSO regression were used to identify prognostic factors and construct the model. Finally, this prognostic model was used to evaluate cell infiltration in the BLCA tumor microenvironment and analyze the effect of immunotherapy in high- and low-risk patients. We established an IRG signature-based prognostic model with 6 IRGs (TNFRSF12A, NR1H3, ITIH4, IL1R1, ELN and CYP26B1), among which TNFRSF12A, IL1R1, ELN and CYP26B1 were unfavorable prognostic factors and NR1H3 and ITIH4 were protective indicators. High-risk score patients in the prognostic model had significantly poorer OS. Additionally, high-risk score patients were associated with an inhibitory immune tumor microenvironment and poor immunotherapy response. We also found a correlation between IRS-related genes and bladder cancer chemotherapy drugs in the drug sensitivity data. The IRG signature-based prognostic model we constructed can predict the prognosis of BLCA patients, providing additional information for individualized prognostic judgment and treatment selection.