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

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.