The advanced lung cancer inflammation index is the optimal inflammatory biomarker of overall survival in patients with lung cancer

Combined influence of nutritional and inflammatory status and breast cancer: findings from the NHANES

BACKGROUND

Previous studies have hinted at the benefits of following an anti-inflammatory diet for potentially reducing breast cancer prevalence. However, the combined influence of diet and inflammation on breast cancer remains unclear.

METHODS

The advanced lung cancer inflammation index (ALI) was used to assess inflammation and nutritional status. Statistical methods, such as multivariable logistic regression, eXtreme Gradient Boosting (XGBoost) model, and subgroup analysis, were employed to analyze the impact of ALI on prevalence of BC. Additionally, a two-piece-wise logistic regression model with smoothing was used to determine the ALI threshold for BC prevalence. The study aimed to understand the mechanistic association between ALI levels and BC development.

RESULTS

The mean (SD) age of the study population was 50.0 (17.7) years, with 40.0% of individuals classified as obese. Comparing ALI tertiles to the lowest tertile, the odds ratios (95% CI) for breast cancer (BC) were 0.78 (0.62, 0.98) and 0.68 (0.52, 0.87) for T2-T3. The XGBoost machine learning model was employed to assess the importance of selected factors, revealing ALI as one of the top five variables influencing BC. Subgroup analysis identified a correlation between ALI, alcohol consumption, and menopausal status. Additionally, ALI levels were associated with decreased estradiol (E2) levels, increased total testosterone (TT)/E2 ratio, and TT/sex hormone-binding globulin (SHBG) ratio.

CONCLUSION

This study indicates a potential protective effect of ALI levels against breast cancer, possibly related to sex hormone disruption. The findings support the use of optimal therapeutic strategies for preventing breast cancer.

Predicting prognosis and drug sensitivity in bladder cancer: an insight into Pan-programmed cell death patterns regulated by M6A modifications

The team aimed to explore the possible functional significance of M6A regulation in Pan-programmed cell death (PCD) among patients with bladder cancer (BLCA). In BLCA patients, the analysis was conducted on the13 patterns of programmed cell death (PCD) and the regulation of M6A. Transcriptome, genomics, and clinical data were collected from TCGA-BLCA, GEO32548, and IMvigor210. Consensus clustering analysis, functional enrichment analysis, and other prognostic tools were used to validate the Pan-PCD. Finally, in vitro experiments and transcription sequencing were performed to understand the potential influence of the PI3K pathway on Pan-PCD in BLCA patients. Diverse PCD patterns were simultaneously activated, and M6A regulators exhibited significant variability in bladder malignant tissues. The machine learning algorithm established an 8-gene M6A-related Pan-PCD signature. This signature was validated in three independent datasets, and BLCA patients with higher risk scores had worse prognosis. An unsupervised clustering approach identified activated and suppressed Pan-PCD subgroups of BLCA patients, with distinct responses to immunotherapy and drug sensitivity. In addition, the PI3K pathway was identified as a key mechanism for various forms of programmed cell death, encompassing apoptosis, pyroptosis, autophagy, and cell death dependent on lysosomes. This research revealed that the Pan-PCD model was a more promising approach for BLCA patients under M6A regulation. A new signature from M6A-related Pan-PCD was proposed, with prognostic value for survival or drug sensitivity. The PI3K pathway was a key mechanism for multiple PCDs in BLCA patients.

Monocytes-to-lymphocytes ratio increases the prognostic value of circulating tumor cells in non-small cell lung cancer: a prospective study

Background

Circulating tumor cells (CTCs) has shown important prognostic value in non-small cell lung cancer (NSCLC). However, the present low sensitivity of CTC capture technology restricts their clinical application. This study aims to explore the feasibility of combining the peripheral blood cell (PBC)-derived inflammation-based score with CTCs to increase the prognostic value of CTCs in NSCLC.

Methods

Sixty volunteers diagnosed with NSCLC were recruited. CTC count and six inflammation-based scores were examined and the association with progression-free survival (PFS) and overall survival (OS) was explored. The changes in the CTC counts before and after the immunotherapy were observed.

Results

Multivariate analysis showed that CTCs >7 [hazard ratio (HR) =9.07; 95% confidence interval (CI): 3.68-22.37, P<0.001] and monocytes-to-lymphocytes ratio (MLR) > 0.2 (HR =3.07; 95% CI: 1.21-7.84; P=0.01) were associated with shorter OS and PFS in patients with NSCLC. Patients with CTCs >7 and MLR >0.2 had 12.30 times increased risk of death (P<0.001) and 6.10 times increased risk of disease progression (P=0.002) compared with those with CTCs ≤7 and MLR ≤0.2. Decreased CTC counts after immunotherapy were closely related to disease control (r=0.535, P=0.01).

Conclusions

CTCs and MLR are both independent risk factors for prognosis in patients with NSCLC. The combination of CTCs with MLR significantly increased the prognostic value of CTCs, which would contribute to stratification of NSCLC patients and providing precise treatment. Dynamic monitoring of CTCs efficiently shows the immunotherapy response in NSCLC.

Advanced Lung Cancer Inflammation Index: A Novel Comprehensive Biomarker of Host Status for Patients with Metastatic Colorectal Cancer

Numerous biomarkers that reflect host status have been identified for patients with metastatic colorectal cancer (mCRC). However, there has been a paucity of biomarker studies that comprehensively indicate body composition, nutritional assessment, and systemic inflammation status. The advanced lung cancer inflammation index (ALI), initially introduced as a screening tool for patients with non-small-cell lung cancer in 2013, emerges as a holistic marker encompassing all body composition, nutritional status, and systemic inflammation status. The index is calculated by the simple formula: body mass index × albumin value / neutrophil-to-lymphocyte ratio. Given its accessibility in routine clinical practice, the ALI has exhibited promising clinical utility in prognosticating outcomes for patients with multiple types of cancer. In this review, we focus on the significance of host status and the clinical applicability of the ALI in the treatment and management of patients with malignancies, including mCRC. We also suggest its potential in guiding the formulation of treatment strategies against mCRC and outline future perspectives.

Association between the advanced lung cancer inflammation index and all-cause and cardiovascular mortality in patients with RA: Insights from NHANES data analysis

Background

Rheumatoid arthritis (RA) is associated with significant mortality, which is primarily due to cardiovascular complications. Despite advancements in RA treatment, mortality rates remain high, highlighting the need for reliable prognostic markers. The advanced lung cancer inflammation index (ALI), which integrates inflammatory and nutritional biomarkers, has shown promise in predicting outcomes in various medical conditions. However, its role in RA prognosis remains unclear.

Methods

This study aimed to investigate the associations between the ALI and all-cause mortality, as well as cardiovascular mortality, in RA patients using data from the National Health and Nutrition Examination Survey (NHANES). A total of 1568 RA patients were included, and the ALI was calculated using body mass index (BMI), serum ALB, and the neutrophil-to-lymphocyte ratio. Comprehensive demographic, lifestyle, and metabolic data from the NHANES enabled adjustments for potential confounders. Multivariate Cox regression and sensitivity analyses were conducted to assess the associations between the ALI and mortality outcomes.

Results

Our findings demonstrate an inverse association between the ALI and both all-cause and cardiovascular mortality in RA patients. Furthermore, a nonlinear relationship was observed, with mortality risk increasing significantly below a certain ALI threshold. Stratified analyses revealed a protective effect of the ALI across various demographic and clinical subgroups, underscoring its potential as a prognostic marker in patients with RA.

Conclusion

The ALI holds promise as a valuable prognostic marker for identifying high-risk individuals and guiding personalized management strategies for patients with RA. However, further validation in prospective studies is warranted to confirm its clinical utility. Nonetheless, the potential implications of the ALI for improving the prognosis of patients with RA underscore the importance of its continued investigation in clinical practice.

Establishing and Validating a novel Prognostic Model in the Initial Diagnosis of Non-small Cell Lung Cancer with Bone Metastases

Background: The aim of this research is to establish and validate a prognostic model for predicting prognosis in non-small cell lung cancer (NSCLC) patients with bone metastases. Methods: Overall, 176 NSCLC patients with bone metastases were retrospectively evaluated in the research. We employed the LASSO-Cox regression method to select the candidate indicators for predicting the prognosis among NSCLC patients complicated with bone metastases. We employed the receiver operating characteristic curve (ROC) and the concordance index (C-index) to assess the discriminative ability. Results: Based on the LASSO-Cox regression analysis, 9 candidate indicators were screened to build the prognostic model. The prognostic model had a higher C-index in the training cohort (0.738, 95% CI: 0.680-0.796) and the validation cohort (0.660, 95% CI: 0.566-0.754) than the advanced lung cancer inflammation index (ALI). Furthermore, the AUCs of the 1-, 2-, and 3-year OS predictions for the prognostic model were higher than ALI in both cohorts. Kaplan-Meier curves and the estimated restricted mean survival time (RMST) values showed that the patients in the low-risk subgroup had the lower probabilities of cancer-specific mortality than high-risk subgroup. Conclusions: The prognostic model could provide clinicians with precise information and facilitate individualized treatment for patients with bone metastases.

Efficacy of first-line chemotherapy based on primary site of tumor versus etoposide-platinum in advanced gastroenteropancreatic neuroendocrine carcinoma

Background

Gastroenteropancreatic neuroendocrine carcinomas (GEP-NECs) constitute a rare and aggressive group of malignancies usually with widespread disease. There are limited studies on GEP-NECs, and therefore, we aim to acquire more information on the clinical features, treatment regimens, and prognosis.

Methods

Data from advanced GEP-NECs patients who had not previously received systemic treatment for advanced disease at The First Affiliated Hospital of Nanjing Medical University from 2010 to 2022 were retrospectively collected. Relationships between clinical-pathological features, treatment regimens, and prognosis were investigated using Kaplan-Meier curves and cox regression models.

Results

A total of fifty-four patients were enrolled in the study. The median age was 65.5 years and 79.6% were male. At diagnosis, 51.9% and 3.7% of patients developed liver and brain metastasis respectively. Sixteen (29.6%) patients received chemotherapy according to primary site of tumor (PST), while thirty-eight (70.4%) were treated with etoposide-platinum (EP) regimen, which based on the first-line treatment of advanced small cell lung cancer (SCLC). No significant differences on progression-free survival (PFS) and response rate were observed between these two groups. Univariate survival analysis showed that liver metastasis, elevated baseline serum carcinoembryonic antigen, elevated baseline serum neuron-specific enolase, elevated baseline serum lactate dehydrogenase, and elevated baseline serum neutrophil-to-lymphocyte ratio (NLR) were associated with shorter PFS. After multivariate analysis, elevated NLR was the only factor that remained significantly associated with shorter PFS (P=0.01).

Conclusions

GEP-NECs are aggressive neoplasms, of which elevated NLR is proven to be an independent negative predictor. Treatment regimens based on PST are not inferior to regiments based on SCLC (EP) for GEP-NECs patients. Large-scale, prospective randomized controlled trials are required to establish the standard of care.

Fibrinogen-to-albumin ratio (FAR) is the best biomarker for the overall survival of patients with non-small-cell lung cancer

Objective

The inflammatory response and the nutritional status are associated with overall survival (OS) in patients with non-small cell lung cancer (NSCLC), but it is unclear which biomarkers are better suited to predict prognosis. This study sought to determine which of the commonly existing inflammatory and nutritional indicators best predicted the OS.

Methods

This study included 15 compound indicators based on inflammation or nutrition, with cutoff points obtained through the receiver operating characteristic (ROC) curve. Univariate and multivariate Cox proportional risk models were used to evaluate the relationship between these predictors and OS. Kaplan-Meier curves were used for survival analysis, and log-rank tests were used to compare differences between groups. The C-index was calculated to evaluate the predictive ability of the different indicators.

Results

The study included 899 patients with NSCLC. In the univariate analysis, all 15 measures were significantly associated with the OS of patients (all p < 0.05). The results of the C-index analysis showed that the fibrinogen-to-albumin ratio (FAR), the systemic immune-inflammation index (SII), and the albumin-to-alkaline phosphatase ratio (AAPR) were the three indices with the best predictive performance. Among them, FAR (C-index = 0.639) had the best predictive power for OS in patients with NSCLC. In the different subgroups, FAR had the highest C-index in male, non-smoking, adenocarcinoma, and stage II patients. The C-index of the platelet-to-lymphocyte ratio (PLR) in female patients was the highest. SII was the highest in smokers, in those aged <65 and ≥65 years, and in stage III patients. The C-index of AAPR was the highest in non-adenocarcinomas. The C-index of the pan-immune-inflammation value (PIV) was the highest in stage I patients. In the multivariate Cox regression analysis, among FAR, SII, and AAPR, only FAR was an independent predictor of OS in patients with NSCLC. A high FAR was associated with a higher risk of death in patients with NSCLC (HR = 1.601, 95% CI = 1.028-2.495). In order to further evaluate the potential prognostic value of FAR, SII, and AAPR in patients with different stages, Cox regression analysis was performed for those with stage I-II and stage III NSCLC. The results showed that FAR was an independent prognostic factor for OS in patients with stage I-II NSCLC.

Conclusion

For all patients with NSCLC, the prognostic power of FAR was superior to that of other inflammatory and nutritional indicators.

Exploring the Impact of Adequate Energy Supply on Nutrition, Immunity, and Inflammation in Elderly Patients with Chronic Obstructive Pulmonary Disease

Background

Chronic Obstructive Pulmonary Disease (COPD) progression in the elderly is notably influenced by nutritional, immune, and inflammatory status. This study aimed to investigate the impact of adequate energy supply on these indicators in COPD patients.

Methods

COPD patients meeting specific criteria were recruited and categorized into energy-adequate and energy-deficient groups based on their energy supply. Comparable demographic factors such as age, gender, smoking and drinking history, COPD duration, inhaled drug classification, and home oxygen therapy application were observed. Notable differences were found in BMI and inhaled drug use between the two groups.

Results

The energy-adequate group exhibited significant improvements in various health indicators, including lymphocyte count, hemoglobin, CRP, total cholesterol, prealbumin, albumin, PNI, SII, SIRI, CAR, and CONUT scores in the secondary auxiliary examination. These positive changes suggest a notable enhancement in nutritional, immune, and inflammatory status.

Conclusion

This research highlights the substantial benefits of adequate energy supply in elderly COPD patients. The observed improvements in nutritional, immune, and inflammatory markers underscore the importance of addressing energy needs to positively influence disease-related outcomes in this population. These findings have implications for developing targeted interventions to optimize the well-being of elderly individuals with COPD.

The advanced lung cancer inflammation index (ALI) predicted the postoperative survival rate of patients with non-small cell lung cancer and the construction of a nomogram model

OBJECTIVE

To investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with operable non-small-cell lung carcinoma (NSCLC). By constructing the nomogram model, it can provide a reference for clinical work.

METHODS

A total of 899 patients with non-small cell lung cancer who underwent surgery in our hospital between January 2017 and June 2021 were retrospectively included. ALI was calculated by body mass index (BMI) × serum albumin/neutrophil to lymphocyte ratio (NLR). The optimal truncation value of ALI was obtained using the receiver operating characteristic (ROC) curve and divided into two groups. Survival analysis was represented by the Kaplan-Meier curve. The predictors of Overall survival (OS) were evaluated by the Cox proportional risk model using single factor and stepwise regression multifactor analysis. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling 1 000 times) was used for internal verification of the nomogram model. The concordance index (C-index) was used to represent the prediction performance of the nomogram model, and the calibration graph method was used to visually represent its prediction conformity. The application value of the model was evaluated by decision curve analysis (DCA).

RESULTS

The optimal cut-off value of ALI was 70.06, and the low ALI group (ALI < 70.06) showed a poor survival prognosis. In multivariate analyses, tumor location, pathological stage, neuroaggression, and ALI were independently associated with operable NSCLC-specific survival. The C index of OS predicted by the nomogram model was 0.928 (95% CI: 0.904-0.952). The bootstrap self-sampling method (B = 1000) was used for internal validation of the prediction model, and the calibration curve showed good agreement between the prediction and observation results of 1-year, 2-year, and 3-year OS. The ROC curves for 1-year, 2-year, and 3-year survival were plotted according to independent factors, and the AUC was 0.952 (95% CI: 0.925-0.979), 0.951 (95% CI: 0.916-0.985), and 0.939 (95% CI: 0.913-0.965), respectively. DCA shows that this model has good clinical application value.

CONCLUSION

ALI can be used as a reliable indicator to evaluate the prognosis of patients with operable NSCLC, and through the construction of a nomogram model, it can facilitate better individualized treatment and prognosis assessment.

Comprehensive analysis of the prognostic role of laboratory indices in advanced lung cancer patients

OBJECTIVE

Lung cancer, the most common cause of cancer-related death, is diagnosed mostly in advanced stages, and 5-year survival is approximately 5.8%. It is critical to identify reliable prognostic factors to optimize treatment responses, guide therapeutic strategies and pave the way to new research. In this study, we aimed to investigate the strongest prognostic factors for advanced non-small cell lung cancer (NSCLC).

METHODS

We retrospectively analyzed 278 patients with NSCLC. We evaluated the association between potential prognostic factors and overall survival (OS) times using Kaplan-Meier analysis and Cox regression analysis.

RESULTS

The median OS in all patients was 15.3 months. In univariate analysis, gender, histologic type, performance status, immunotherapy, radiotherapy, hemoglobin level, serum albumin, sodium-globulin ratio (SGR), neutrophil-lymphocyte ratio (NLR), systemic immune inflammation index (SII), hemoglobin-albumin-lymphocyte-platelet score (HALP), and advanced lung cancer index (ALI) were associated with survival. Models were established for multivariate analyses. In the models, NLR, SGR, HALP, immunotherapy, radiotherapy, and Eastern Cooperative Oncology Group (ECOG) performance status showed independent prognostic features (p < 0.001, p = 0.003, p = 0.002, p < 0.001, p = 0.010, and p = 0.025, respectively). In addition, in the subgroup analysis, prognostic indexes (NLR, SGR, and HALP) were found to have a prognostic effect on survival in multiple subgroups.

CONCLUSIONS

Pretreatment NLR, SGR, HALP, immunotherapy, radiotherapy, and ECOG performance status are independent prognostic factors for advanced NSCLC patients. These prognostic factors can be used in clinical practice as easily accessible, simple, and useful tools for clinicians.

Association between advanced lung cancer inflammation index and in-hospital mortality in ICU patients with community-acquired pneumonia: A retrospective analysis of the MIMIC-IV database

Objective

The objective of the present study was to explore the correlation between the advanced lung cancer inflammation index (ALI) and in-hospital mortality among patients diagnosed with community-acquired pneumonia (CAP).

Methods

Data from the Medical Information Mart for Intensive Care-IV database were adopted to analyze the in-hospital mortality of ICU patients with CAP. Upon admission to the ICU, fundamental data including vital signs, critical illness scores, comorbidities, and laboratory results, were collected. The in-hospital mortality of all CAP patients was documented. Multivariate logistic regression (MLR) models and restricted cubic spline (RCS) analysis together with subgroup analyses were conducted.

Results

This study includes 311 CAP individuals, involving 218 survivors as well as 93 nonsurvivors. The participants had an average age of 63.57 years, and the females accounted for approximately 45.33%. The in-hospital mortality was documented to be 29.90%. MLR analysis found that ALI was identified as an independent predictor for in-hospital mortality among patients with CAP solely in the Q1 group with ALI ≤ 39.38 (HR: 2.227, 95% CI: 1.026-4.831, P = 0.043). RCS analysis showed a nonlinear relationship between the ALI and in-hospital mortality, with a turning point at 81, and on the left side of the inflection point, a negative correlation was observed between ALI and in-hospital mortality (HR: 0.984, 95% CI: 0.975-0.994, P = 0.002). The subgroup with high blood pressure showed significant interaction with the ALI.

Conclusion

The present study demonstrated a nonlinear correlation of the ALI with in-hospital mortality among individuals with CAP. Additional confirmation of these findings requires conducting larger prospective investigations.

Energy metabolism as the hub of advanced non-small cell lung cancer management: a comprehensive view in the framework of predictive, preventive, and personalized medicine

Energy metabolism is a hub of governing all processes at cellular and organismal levels such as, on one hand, reparable vs. irreparable cell damage, cell fate (proliferation, survival, apoptosis, malignant transformation etc.), and, on the other hand, carcinogenesis, tumor development, progression and metastazing versus anti-cancer protection and cure. The orchestrator is the mitochondria who produce, store and invest energy, conduct intracellular and systemically relevant signals decisive for internal and environmental stress adaptation, and coordinate corresponding processes at cellular and organismal levels. Consequently, the quality of mitochondrial health and homeostasis is a reliable target for health risk assessment at the stage of reversible damage to the health followed by cost-effective personalized protection against health-to-disease transition as well as for targeted protection against the disease progression (secondary care of cancer patients against growing primary tumors and metastatic disease). The energy reprogramming of non-small cell lung cancer (NSCLC) attracts particular attention as clinically relevant and instrumental for the paradigm change from reactive medical services to predictive, preventive and personalized medicine (3PM). This article provides a detailed overview towards mechanisms and biological pathways involving metabolic reprogramming (MR) with respect to inhibiting the synthesis of biomolecules and blocking common NSCLC metabolic pathways as anti-NSCLC therapeutic strategies. For instance, mitophagy recycles macromolecules to yield mitochondrial substrates for energy homeostasis and nucleotide synthesis. Histone modification and DNA methylation can predict the onset of diseases, and plasma C7 analysis is an efficient medical service potentially resulting in an optimized healthcare economy in corresponding areas. The MEMP scoring provides the guidance for immunotherapy, prognostic assessment, and anti-cancer drug development. Metabolite sensing mechanisms of nutrients and their derivatives are potential MR-related therapy in NSCLC. Moreover, miR-495-3p reprogramming of sphingolipid rheostat by targeting Sphk1, 22/FOXM1 axis regulation, and A2 receptor antagonist are highly promising therapy strategies. TFEB as a biomarker in predicting immune checkpoint blockade and redox-related lncRNA prognostic signature (redox-LPS) are considered reliable predictive approaches. Finally, exemplified in this article metabolic phenotyping is instrumental for innovative population screening, health risk assessment, predictive multi-level diagnostics, targeted prevention, and treatment algorithms tailored to personalized patient profiles-all are essential pillars in the paradigm change from reactive medical services to 3PM approach in overall management of lung cancers. This article highlights the 3PM relevant innovation focused on energy metabolism as the hub to advance NSCLC management benefiting vulnerable subpopulations, affected patients, and healthcare at large.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00357-5.

Identification of cachexia in lung cancer patients with an ensemble learning approach

Objective

Nutritional intervention prior to the occurrence of cachexia will significantly improve the survival rate of lung cancer patients. This study aimed to establish an ensemble learning model based on anthropometry and blood indicators without information on body weight loss to identify the risk factors of cachexia for early administration of nutritional support and for preventing the occurrence of cachexia in lung cancer patients.

Methods

This multicenter study included 4,712 lung cancer patients. The least absolute shrinkage and selection operator (LASSO) method was used to obtain the key indexes. The characteristics excluded weight loss information, and the study data were randomly divided into a training set (70%) and a test set (30%). The training set was used to select the optimal model among 18 models and verify the model performance. A total of 18 machine learning models were evaluated to predict the occurrence of cachexia, and their performance was determined using area under the curve (AUC), accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC).

Results

Among 4,712 patients, 1,392 (29.5%) patients were diagnosed with cachexia based on the framework of Fearon et al. A 17-variable gradient boosting classifier (GBC) model including body mass index (BMI), feeding situation, tumor stage, neutrophil-to-lymphocyte ratio (NLR), and some gastrointestinal symptoms was selected among the 18 machine learning models. The GBC model showed good performance in predicting cachexia in the training set (AUC = 0.854, accuracy = 0.819, precision = 0.771, recall = 0.574, F1 score = 0.658, MCC = 0.549, and kappa = 0.538). The abovementioned indicator values were also confirmed in the test set (AUC = 0.859, accuracy = 0.818, precision = 0.801, recall = 0.550, F1 score = 0.652, and MCC = 0.552, and kappa = 0.535). The learning curve, decision boundary, precision recall (PR) curve, the receiver operating curve (ROC), the classification report, and the confusion matrix in the test sets demonstrated good performance. The feature importance diagram showed the contribution of each feature to the model.

Conclusions

The GBC model established in this study could facilitate the identification of cancer cachexia in lung cancer patients without weight loss information, which would guide early implementation of nutritional interventions to decrease the occurrence of cachexia and improve the overall survival (OS).

Inflammation-based lung adenocarcinoma molecular subtype identification and construction of an inflammation-related signature with bulk and single-cell RNA-seq data

The role of inflammation is increasingly understood to have a central influence on therapeutic outcomes and prognosis in lung adenocarcinoma (LUAD). However, the detailed molecular divisions involved in inflammatory responses are yet to be fully elucidated. Our study identified two main inflammation-oriented LUAD grades: the inflammation-low (INF-low) and the inflammation-high (INF-high) subtypes. Both presented with unique clinicopathological features, implications for prognosis, and distinctive tumor microenvironment profiles. Broadly, the INF-low grade, marked by its dominant immunosuppressive tumor microenvironment, was accompanied by less favorable prognostic outcomes and a heightened prevalence of oncogenic mutations. In contrast, the INF-high grade exhibited more optimistic clinical trajectories, underscored by its immune-active environment. In addition, our efforts led to the conceptualization and empirical validation of an inflammation-centric predictive model with considerable predictive potency. Our study paves the way for a refined inflammation-centric LUAD classification and fosters a deeper understanding of tumor microenvironment intricacies.

Characterization of blood inflammatory markers in patients with non-small cell lung cancer

OBJECTIVE

To investigate the differences and correlation between blood inflammatory indexes such as monocytes (MONO), lymphocytes (LYM), haemoglobin (HGB), neutrophils (NEU), platelets (PLT), ultrasensitive C-reactive protein, albumin and platelet/lymphocyte ratio (PLR), NEU/LYM ratio (NLR), MONO/LYM ratio (MLR) and clinicopathologic characteristics of patients with non-small cell lung cancer (NSCLC).

METHODS

187 patients with NSCLC who were first diagnosed in 2017-2023 and 102 with healthy check-ups during the same period (control group) were retrospectively selected as study subjects to compare the differences in inflammatory indexes between the two groups and the levels of inflammatory indexes in NSCLC patients with different clinicopathologic characteristics.

RESULTS

Correlation analysis between blood inflammatory indexes and clinicopathologic features in NSCLC group showed that C-reactive protein, CAR, and PLR values were different in different pathologic types (P<0.05). The values of NEU, MONO, C-reactive protein, MLR, NLR, CAR and albumin were different among various degrees of differentiation (P<0.05). There were differences in LYM, albumin, MLR, NLR, CAR, and C-reactive protein among M stage subgroups (P<0.05). Analysis of the efficacy of early diagnosis of non-small cell lung cancer has been shown, the AUC of NLR was 0.796, sensitivity of 0.679, specificity of 0.176, 95% CI=0.743-0.849 (P<0.001). The AUC of albumin was 0.977, the sensitivity was 0.941, the specificity was 0.941, and 95% CI was 0.959-0.994 (P<0.001).

CONCLUSION

Blood inflammatory indexes are closely associated with NSCLC and vary according to pathologic features. Blood inflammatory indices can predict tumor pathologic staging and guide treatment for patients with NSCLC.

Prognostic value of the advanced lung cancer inflammation index in patients with gastric cancer after radical gastrectomy: a propensity-score matching cohort study and meta-analysis

BACKGROUND

Insufficient evidence existed about the prognostic role of the advanced lung cancer inflammation index (ALI) for gastric cancer patients who underwent curative resection. The aim of this study was to identify the predictive ability of ALI for survival after curative gastrectomy.

METHODS

We retrospectively analyzed 328 gastric cancer patients who received curative gastrectomy from the database of Chongqing University Cancer Hospital, and investigated the prognostic role of the preoperative ALI compared with clinicopathological variables and other serum biomarkers, such as preoperative neutrophil-to-lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and Lymphocyte-monocyte ratio (LMR). To minimize intergroup differences, propensity score matching (PSM) analysis was employed. Additionally, we performed a meta-analysis of four cohort studies published up to October 2023 following the PRISMA guidelines.

RESULTS

In the overall cohort, patients in the low ALI group had a significantly worse overall survival compared to those in the high ALI group (P < 0.0001). Subgroup analysis identified that ALI maintained its prognostic significance across different subgroups. In addition, ROC analysis showed that ALI had a higher AUC value for 3-year overall survival compared to NLR, PLR, and LMR (0.576 vs. 0.573 vs. 0.557 vs. 0.557). Multivariate analysis indicated that ALI, other than other serum biomarkers, was an independent risk factor for decreased overall survival in GC patients following curative surgery (HR = 1.449; 95%CI: 1.028-2.045; P = 0.034). Consistently, PSM analysis supported all of these findings. The meta-analysis including 4 studies evaluating 2542 patients, confirmed the association between the low ALI and poor survival outcomes.

CONCLUSION

The preoperative ALI was an independent prognostic factor for survival in gastric cancer patients who underwent curative gastrectomy.

Clinical significance of the advanced lung cancer inflammation index in patients with limited-stage small cell lung cancer treated with chemoradiotherapy

The aim of the study was to investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with limited-stage small-cell lung cancer (LS-SCLC) undergoing definite chemo-radiotherapy (CRT). We included 87 patients with LS-SCLC from South Korea, treated between 2005 and 2019 with definite CRT. ALI was calculated using body mass index, serum albumin, and neutrophil-lymphocyte ratio. We categorized 38 patients into the high ALI group (ALI ≥ 44.3) and 48 into the low ALI group (ALI < 44.3). Patients in the high ALI group exhibited longer overall survival (OS) than patients in the low ALI group. In multivariate analysis, prophylactic cranial irradiation (hazard ratio [HR] = 0.366, 95% confidence interval [CI] 0.20-0.66, P = 0.0008), and high ALI (HR = 0.475, 95% CI 0.27-0.84, P = 0.0103) were identified as independent prognostic factors for predicting better OS. Notably, a high ALI score was particularly indicative of longer survival in patients treated with the combination of etoposide and cisplatin. In conclusion, this study demonstrated that a high pretreatment ALI was significantly associated with better OS in patients with LS-SCLC undergoing definite CRT. This suggests that ALI could be a useful tool for predicting prognosis and guiding chemotherapy regimen selections in clinical practice for LS-SCLC.

Prognostic Biomarkers of Systemic Inflammation in Non-Small Cell Lung Cancer: A Narrative Review of Challenges and Opportunities

Non-small cell lung cancer (NSCLC) is a common malignancy and is associated with poor survival outcomes. Biomarkers of systemic inflammation derived from blood tests collected as part of routine clinical care offer prognostic information for patients with NSCLC that may assist clinical decision making. They are an attractive tool, as they are inexpensive, easily measured, and reproducible in a variety of healthcare settings. Despite the wealth of evidence available to support them, these inflammatory biomarkers are not yet routinely used in clinical practice. In this narrative review, the key inflammatory indices reported in the literature and their prognostic significance in NSCLC are described. Key challenges limiting their clinical application are highlighted, including the need to define the optimal biomarker of systemic inflammation, a lack of understanding of the systemic inflammatory landscape of NSCLC as a heterogenous disease, and the lack of clinical relevance in reported outcomes. These challenges may be overcome with standardised recording and reporting of inflammatory biomarkers, clinicopathological factors, and survival outcomes. This will require a collaborative approach, to which this field of research lends itself. This work may be aided by the rise of data-driven research, including the potential to utilise modern electronic patient records and advanced data-analysis techniques.

Clinical impact of inflammatory and nutrition index based on metabolic tumor activity in non‑small cell lung cancer treated with immunotherapy

The aim of the present study was to explore the relationship between tumor metabolic glycolysis and inflammatory or nutritional status in patients with advanced non-small cell lung cancer (NSCLC) who received programmed death-1 (PD-1) blockade. A total of 186 patients were registered in the present study. All of patients underwent 18F-FDG PET imaging before initial PD-1 blockade, and maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were assessed as indicators of 18F-FDG uptake. As inflammatory and nutritional index, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ration (PLR), systemic immune inflammation index (SII), prognostic nutritional index (PNI), advanced lung cancer inflammation index (ALI) and Glasgow prognostic score (GPS) were evaluated based on previous assessment. 18F-FDG uptake by MTV and TLG significantly correlated with the scores of NLR, PLR, SII, PNI and ALI, in addition to the level of albumin, lactate dehydrogenase, C-reactive protein, white blood cells, neutrophils, lymphocytes and body mass index. The count of NLR, PLR and SII was significantly higher in patients with <1 year overall survival (OS) compared with in those with ≥1 year OS, and that of PNI and ALI was significantly lower in those with <1 year OS compared with those with ≥1 year OS. High MTV under the high PLR, SII and low ALI were identified as significant factors for predicting the decreased PFS and OS after PD-1 blockade in a first-line setting. In second or more lines, high MTV was identified as a significant prognostic predictor regardless of the levels of PLR, SII, ALI and GPS. In conclusion, metabolic tumor glycolysis determined by MTV was identified as a predictor for the outcome of PD-1 blockade under the high inflammatory and low nutritional conditions, in particular, when treated with a first-line PD-1 blockade. A high MTV under high PLR and SII and low ALI in the first-line setting could be more predictive of ICI treatment than other combinations.

Systemic inflammatory response index is a predictor of prognosis in gastric cancer patients: Retrospective cohort and meta-analysis

BACKGROUND

The systemic inflammatory response index (SIRI) has been demonstrated to make a significant difference in assessing the prognosis of patients with different solid neoplasms. However, research is needed to ascertain the accuracy and reliability of applying the SIRI to patients who undergo robotic radical gastric cancer surgery.

AIM

To validate the applicability of the SIRI in assessing the survival of gastric cancer patients and evaluate the clinical contribution of preoperative SIRI levels to predicting long-term tumor outcomes in patients, who received robotic radical gastric cancer surgery.

METHODS

Initially, an exhaustive retrieval was performed in the PubMed, the Cochrane Library, EMBASE, Web of Science, and Scopus databases to identify relevant studies. Subsequently, a meta-analysis was executed on 6 cohort studies identifying the value of the SIRI in assessing the survival of gastric cancer patients. Additionally, the clinical data of 161 patients undergoing robotic radical gastric cancer surgery were retrospectively analyzed to evaluate their clinicopathological characteristics and relevant laboratory indicators. The association between preoperative SIRI levels and 5-year overall survival (OS) and disease-free survival (DFS) was assessed.

RESULTS

The findings demonstrated an extensive connection between SIRI values and the outcome of patients with gastric cancer. Preoperative SIRI levels were identified as an independent hazard feature for both OS and DFS among those who received robotic surgery for gastric cancer. SIRI levels in gastric cancer patients were observed to be associated with the presence of comorbidities, T-stage, carcinoembryonic antigen levels, the development of early serious postoperative complications, and the rate of lymph node metastasis.

CONCLUSION

SIRI values are correlated with adverse in the gastric cancer population and have the potential to be utilized in predicting long-term oncological survival in patients who undergo robotic radical gastric cancer surgery.

Advanced lung cancer inflammation index predicts overall survival of hepatocellular carcinoma after hepatectomy

Aim

Limited data are available regarding ALI's clinical relevance and prognostic value in patients with hepatocellular carcinoma (HCC) after hepatectomy.

Materials and methods

HCC patients who received hepatectomy at the Meizhou People's Hospital from May 2011 to February 2022 were enrolled in the study cohort. The ALI was calculated as follows: ALI = BMI (kg/m2) × ALB (g/dL)/(absolute neutrophil count/absolute lymphocyte count). The primary outcome was overall survival (OS). The secondary outcome was cancer-specific survival (CSS). Univariate and multivariate Cox regression analyses were performed, followed by nomogram construction and decision curve analysis (DCA).

Results

425 HCC patients were enrolled for analyses. Lower preoperative ALI was significantly correlated with incomplete tumor capsule and advanced tumor stage. Lower preoperative ALI was an adverse independent prognostic factor for OS (HR: 1.512, 95% CI: 1.122-2.039, P 0.007) and CSS (HR: 1.754, 95% CI: 1.262-2.438, P <0.001) in HCC patients. The nomogram plot was built based on three (including age, TNM stage, and ALI) and two (including TNM stage and ALI) independent prognostic factors for OS and CSS, respectively. Further analyses indicated that the nomogram had better predictive value and some net benefit than the traditional TNM stage alone, especially in long-term OS.

Conclusions

Our study further indicated that ALI could be a prognostic marker for OS and CSS in HCC patients after hepatectomy, especially in long-term OS.

Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration

Hepatocellular carcinoma (HCC) stands as the prevailing manifestation of primary liver cancer and continues to pose a formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group demonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups. In summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.

Association between platelet‑to‑lymphocyte ratio and serum prostate specific antigen

There is evidence that the systemic inflammatory response may have an impact on prostate-specific antigen (PSA) levels. However, the relationship between the platelet-to-lymphocyte ratio (PLR) and PSA remains unclear. As a result, the relationship between PLR and PSA using the National Health and Nutrition Examination Survey (NHANES) database was examined. After the screening, 6,638 participants out of 52,186 in the NHANES survey conducted between 2001 to 2010 were suitable for the present study. The PLR was the independent variable in the present study, and PSA was the dependent variable. The selected subjects in the present study had an average age of 58.563±11.848 years. After controlling for covariates, the results showed that with every increase in PLR, the PSA concentration increased by 0.004 ng/ml (0.001, 0.007). This difference was statistically significant. Furthermore, a smoothing curve based on a fully adjusted model was created to investigate the possibility of a linear relationship between PLR and PSA concentration in men from USA. In men from USA, an independent and positive correlation between PLR and PSA was identified, which could potentially result in overdiagnosis of asymptomatic prostate cancer in populations with higher PLR levels.

Evaluating the predictive significance of systemic immune-inflammatory index and tumor markers in lung cancer patients with bone metastases

Objective

This study aims to develop a predictive model for identifying lung cancer patients at elevated risk for bone metastases, utilizing the Unified Immunoinflammatory Index and various tumor markers. This model is expected to facilitate timely and effective therapeutic interventions, especially in the context of the growing significance of immunotherapy for lung cancer treatment.

Methods

A retrospective analysis was conducted on 324 lung cancer patients treated between January 2019 and January 2021. After meeting the inclusion criteria, 241 patients were selected, with 56 exhibiting bone metastases. The cohort was divided into a training group (169 patients) and a validation group (72 patients) at a 7:3 ratio. Lasso regression was employed to identify critical variables, followed by logistic regression to construct a Nomogram model for predicting bone metastases. The model's validity was ascertained through internal and external evaluations using the Concordance Index (C-index) and Receiver Operating Characteristic (ROC) curve.

Results

The study identified several factors influencing bone metastasis in lung cancer, such as the Systemic Immune-Inflammatory Index (SII), Carcinoembryonic Antigen (CEA), Neuron Specific Enolase (NSE), Cyfra21-1, and Neutrophil-to-Lymphocyte Ratio (NLR). These factors were incorporated into the Nomogram model, demonstrating high validation accuracy with C-index scores of 0.936 for internal and 0.924 for external validation.

Conclusion

The research successfully developed an intuitive and accurate Nomogram prediction model utilizing clinical indicators to predict the risk of bone metastases in lung cancer patients. This tool can be instrumental in aiding clinicians in developing personalized treatment plans, thereby optimizing patient outcomes in lung cancer care.

Peroxisome proliferator-activated receptors signature reveal the head and neck squamous cell carcinoma energy metabolism phenotype and clinical outcome

BACKGROUND

Peroxisome proliferator activating receptors (PPARs) are important regulators of nuclear hormone receptor function, and they play a key role in biological processes such as lipid metabolism, inflammation and cell proliferation. However, their role in head and neck squamous cell carcinoma (HNSC) is unclear.

METHODS

We used multiple datasets, including TCGA-HNSC, GSE41613, GSE139324, PRJEB23709 and IMVigor, to perform a comprehensive analysis of PPAR-related genes in HNSC. Single-cell sequencing data were preprocessed using Seurat packets, and intercellular communication was analyzed using CellChat packets. Functional enrichment analysis of PPAR-related genes was performed using ClusterProfile and GSEA. Prognostic models were constructed using LASSO and Cox regression models, and immunohistochemical analyses were performed using human protein mapping (The Human Protein Atlas).

RESULTS

Our single-cell RNA sequencing analysis revealed distinct cell populations in HNSC, with T cells having the most significant transcriptome differences between tumors and normal tissues. The PPAR features were higher in most cell types in tumor tissues compared with normal tissues. We identified 17 PPAR-associated differentially expressed genes between tumors and normal tissues. A prognostic model based on seven PPAR-associated genes was constructed with high accuracy in predicting 1, 2 and 3 year survival in patients with HNSC. In addition, patients with a low risk score had a higher immune score and a higher proportion of T cells, CD8+ T cells and cytotoxic lymphocytes. They also showed higher immune checkpoint gene expression, suggesting that they might benefit from immunotherapy. PPAR-related genes were found to be closely related to energy metabolism.

CONCLUSIONS

Our study provides a comprehensive understanding of the role of PPAR related genes in HNSC. The identified PPAR features and constructed prognostic models may serve as potential biomarkers for HNSC prognosis and treatment response. In addition, our study found that PPAR-related genes can differentiate energy metabolism and distinguish energy metabolic heterogeneity in HNSC, providing new insights into the molecular mechanisms of HNSC progression and therapeutic response.

CT-based deep learning radiomics and hematological biomarkers in the assessment of pathological complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma: A two-center study

PURPOSE

To evaluate and validate CT-based models using pre- and posttreatment deep learning radiomics features and hematological biomarkers for assessing esophageal squamous cell carcinoma (ESCC) pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT).

MATERIAL AND METHODS

This retrospective study recruited patients with biopsy-proven ESCC who underwent nCRT from two Chinese hospitals between May 2017 and May 2022, divided into a training set (hospital I, 111 cases), an internal validation set (hospital I, 47 cases), and an external validation set (hospital II, 33 cases). We used minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) as feature selection methods and three classifiers as model construction methods. The assessment of models was performed using area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA).

RESULTS

A total 190 patients were included in our study (60.8 ± 7.08 years, 133 men), and seventy-seven of them (40.5 %) achieved pCR. The logistic regression (LR)-based combined model incorporating neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio, albumin, and radscores performed well both in the internal and external validation sets with AUCs of 0.875 and 0.857 (95 % CI, 0.776-0.964; 0.731-0.984, P <0.05), respectively. DCA demonstrated that nomogram was useful for pCR prediction and produced clinical net benefits.

CONCLUSION

The incorporation of radscores and hematological biomarkers into LR-based model improved pCR prediction after nCRT in ESCC. Enhanced pCR predictability may improve patients selection before surgery, providing clinical application value for the use of active surveillance.

Geriatric Nutritional Risk Index Combined with Calf Circumference Can be a Good Predictor of Prognosis in Patients Undergoing Surgery for Gastric or Colorectal Cancer

OBJECTIVES

To explore the effect of combined hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancer and to screen for the best prognostic indicators.

INTRODUCTION

Gastric and colorectal cancer is a widespread health concern worldwide and one of the major contributors to cancer-related death. The hematological and physical measurement indicators have been shown to associate with the prognosis of patients undergoing surgery for gastric or colorectal cancer, respectively, but it is still unclear whether the combination of the two can reflect the prognosis more effectively.

METHODS

Thirteen hematological indicators and 5 physical measurement indicators were selected in this study, and the most promising ones were screened using LASSO regression. Then, the best prognostic indicators were selected by time-ROC curves. Survival curves were constructed using the Kaplan-Meier method, and the effects of hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancers were evaluated by Cox proportional risk regression analysis. In addition, the relationship between hematological and physical measurement indicators on secondary outcomes, including length of stay, hospitalization costs, intensive care unit (ICU) admission, and patients' subjective global assessment scores (PGSGA), was explored.

RESULTS

After initial screening, among the hematological indicators, the geriatric nutritional risk index (GNRI) showed the highest mean area under the curve (AUC) values. Among body measures, calf circumference (CC) showed the highest mean AUC value. Further analyses showed that the combination of combined nutritional prognostic index (GNRI) and calf circumference (CC) (GNRI-CC) had the best performance in predicting the prognosis of patients undergoing surgery for gastric or colorectal cancers. Low GNRI, low CC, and low GNRI-low CC increased the risk of death by 44%, 48%, and 104%, respectively. Sensitivity analyses showed the same trend. In addition, low GNRI-low CC increased the risk of malnutrition by 17%.

CONCLUSION

This study emphasizes that a combination of blood measures and body measures is essential to accurately assess the prognosis of patients undergoing surgery for gastric or colorectal cancers. The GNRI-CC is a good prognostic indicator and can also assess the risk of possible malnutrition.

Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations

Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.

Cholesterol-modified prognostic nutritional index (CPNI) as an effective tool for assessing the nutrition status and predicting survival in patients with breast cancer

BACKGROUND

Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients.

METHODS

This prospective study analyzed data from 776 breast cancer patients enrolled in the "Investigation on Nutritional Status and its Clinical Outcome of Common Cancers" (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan-Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients.

RESULTS

Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality (p < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese.

CONCLUSIONS

The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer.

Effect of dynamic platelet-to-lymphocyte ratio on the prognosis of patients with esophageal squamous cell carcinoma receiving chemoradiotherapy

Systemic inflammatory load affects the long-term developmental outcomes in patients with malignancy. The purpose of this study was to investigate the effect of the dynamic levels of platelet-to-lymphocyte ratio (PLR) at different treatment stages on the prognosis of patients with esophageal squamous cell carcinoma (ESCC) undergoing chemoradiotherapy. This study included 168 patients who received chemoradiotherapy between 2012 and 2018. PLR levels at different treatment stages were calculated based on blood test results. The association between PLR and overall survival (OS) was determined using the Kaplan-Meier method and Cox proportional regression models. The cutoff values of PLR before and after treatment of 168 patients with ESCC were 195.7 and 403.6, respectively. The 5-year OS rates of patients in the low and high pre-PLR groups were 42.1% and 21.7%, respectively. The overall 5-year OS rate of all patients was 27.1%. Multivariate analysis results showed that patient age (hazard ratio [HR] = 1.736; 95% confidence interval (CI) = 1.129-2.669; P = .012), alcohol consumption (HR = 1.622; 95%CI = 1.050-2.508; P = .029), T stage (HR = 12.483; 95%CI = 3.719-41.896; P < .001), pre-PLR (HR = 1.716; 95%CI = 1.069-2.756; P = .025), post-PLR (HR = 1.664; 95%CI = 1.106-2.503; P = .015) were independent factors of the prognosis of patients with ESCC. PLR at different treatment stages can be used to effectively evaluate the prognosis of patients with ESCC undergoing chemoradiotherapy.

Prognostic Value of Combination of Lymphocyte-to-Monocyte-Ratio and CYFRA 21-1 in Esophageal Squamous Cell Carcinoma

BACKGROUND

This study aimed to investigate the potential of a combined score based on CYFRA 21-1 level and LMR as a prognostic predictor for patients with ESCC.

METHODS

A total of 460 patients who underwent esophagectomy were analyzed, and three groups were established based on the CA-LMR score. OS and RFS were evaluated using the Kaplan-Meier analysis, and associated factors were analyzed by univariate and multivariate Cox analysis. A mpStage system was also established based on the CA-LMR score.

RESULTS

The allocation of CA-LMR score of 0, 1, and 2 was 107 (23.3%), 280 (60.9%), and 73 (15.9%). There was a significant association between CA-LMR and male gender (P = 0.001), lower BMI (P = 0.035), longer tumor lesions (P = 0.002), and high pT, pN, pStage (P < 0.001, P = 0.011, P = 0.001). The 5-year OS rates for CA-LMR scores of 0, 1, and 2 were 75.4%, 60.2%, and 32.8%, respectively (P < 0.001). Multivariate analysis showed that CA-LMR score (P = 0.011) was an independent prognostic factor for OS. The proposed mpStage system, based on CA-LMR score, demonstrated superior discriminatory ability, monotonicity, homogeneity, and prognosis prediction ability over AJCC 8th pStage system.

CONCLUSIONS

The CA-LMR score, combined with tumor marker and inflammatory index, could use as a potential prognostic indicator; moreover, our modified pStage system exhibited superior stratification and prognostic accuracy for patients with ESCC.

Superiority of CRP-albumin-lymphocyte index as a prognostic biomarker for patients with gastric cancer

OBJECTIVES

The new C-reactive protein (CRP)-albumin-lymphocyte (CALLY) index is an immune nutrition scoring system based on serum CRP) serum albumin, and lymphocyte counts. The aim of this study was to verify the prognostic value of the CALLY index in patients with gastric cancer and to evaluate the superiority of this new system.

METHODS

We retrospectively analyzed the data of patients with gastric cancer who were followed up from the INSCOC database between May 2013 and December 2018. Through simple random sampling, patients with gastric cancer were placed into one of two groups: the training group (n = 684) or the verification group (n = 290) in a ratio of 7:3. Correlation analysis, Kaplan-Meier method, and cubic spline function were used to analyze the relationship between the CALLY index and overall survival (OS) in these patients. Based on the results of Cox regression analysis of the training cohort, a nomogram model for predicting 1 -, 2 -, 3-, and 5-y OS was established and verified internally. The prediction accuracy and benefit of the nomogram in gastric cancer were evaluated by calibration and clinical decision curve and compared with the traditional TNM gastric cancer staging system.

RESULTS

The CALLY index was negatively correlated with the age of patients with gastric cancer (men, r = -0.1; women, r = -0.1), but positively correlated with body mass index (BMI; men, r = 0.063; women, r = 0.058), and the cutoff value of the CALLY index was determined as 1.12. The OS of patients with gastric cancer and a CALLY index >1.12 was significantly higher than that of patients with gastric cancer and a CALLY index ≤1.12 (P < 0.0001). There was an L-shaped dose-response relationship between the CALLY index and OS in patients with gastric cancer, and age, TNM stage, surgical treatment, chemotherapy, BMI, and the CALLY index were significantly correlated with the prognosis of patients with gastric cancer. Tumor TNM stage, BMI, and the CALLY index were independent risk factors affecting the prognosis of patients with gastric cancer. The CALLY index was a protective factor in the following patient factors: diagnosis of gastric cancer; <65 y of age; male; TNM 3 stage; BMI 18.5 to 23.9 kg/m2; smoker; consumer of alcohol; no radio- or chemotherapy; surgery; presence of diabetes, hypertension, or both; no family history of cancer; experienced a significant interaction with chemotherapy and surgery. A nomogram based on TNM staging, BMI, and the CALLY index has good predictive ability and clinical application value. Compared with traditional TNM staging systems, the nomogram has better resolution and accuracy in predicting 1 -, 2 -, 3-, and 5-year OS.

CONCLUSION

The CALLY index can be used as an independent prognostic factor for patients with gastric cancer, and constructs a nomogram prediction model combining TNM staging, BMI, and CALLY index, which yields better predictions than traditional TNM staging.

Relationship between advanced lung cancer inflammation index and long-term all-cause, cardiovascular, and cancer mortality among type 2 diabetes mellitus patients: NHANES, 1999-2018

Background

Type 2 diabetes mellitus (T2DM) was a major global health threat. As a chronic low-grade inflammatory disease, the prognosis of diabetes was associated with inflammation. The advanced lung cancer inflammation index (ALI) served as a comprehensive index to assess inflammation. This study aimed to estimate the association between ALI and all-cause, cardiovascular disease (CVD), and cancer mortality in T2DM patients.

Methods

We extracted cohort data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999-2018 for analysis. The weighted Kaplan-Meier analysis and multivariate-adjusted Cox analysis were utilized to evaluate the relationship between ALI and all-cause, CVD, and cancer mortality in T2DM patients. Restricted cubic spline (RCS) analysis was employed to assess their non-linear relationship. Stratified analysis and interaction analysis were conducted to enhance the robustness of the results.

Results

The study incorporated a total of 3,888 patients. An increase in ALI was associated with a reduced risk of all-cause and CVD mortality in T2DM patients, but not related to cancer mortality. There were J-shaped and L-shaped non-linear relationships between ALI and all-cause, CVD mortality in T2DM patients, respectively. The inflection points were 90.20 and 93.06, respectively. For values below the inflection point, every 10U increase in ALI, both all-cause and CVD mortality risk decreased by 9%. Beyond the inflection point, all-cause mortality rose by 3%, while CVD mortality remained unaffected. Gender-stratified RCS analysis indicated a linear negative relationship between CVD mortality and ALI in female T2DM patients, whereas the trend in males aligned with the overall population.

Conclusion

Our research initially identified a significant correlation between increased ALI levels with decreased all-cause and CVD mortality in T2DM patients. There were J-shaped and L-shaped non-linear relationships between ALI and all-cause, CVD mortality in T2DM patients, respectively. For female patients, there was a linear negative relation between CVD mortality and ALI, whereas the trend in males aligned with the overall population. These findings suggested that maintaining ALI (for example, control body weight and keep albumin in the normal range) within a certain range in the clinical settings was crucial for improving all-cause and CVD mortality in T2DM patients.

Nickel chloride promotes lung cancer invasion and metastasis by up-regulating the expression of E3 ubiquitin ligase TRIM31 through the IL-6/STAT3 signaling axis

Nickel compounds are widely used in industries and daily life as important industrial products. Long-term exposure to nickel compounds has been associated with increased incidence and poor prognosis of lung cancer. However, the molecular mechanism by which exposure to nickel compounds induces the malignant phenotype of lung cancer cells remains unclear. In this study, we confirmed that nickel chloride (NiCl2) exposure promotes invasion and metastasis through IL-6/STAT3 both in vitro and vivo. Mechanistically, we found that NiCl2 mediated the transcriptional regulation of E3 ubiquitin ligase TRIM31 by SATAT3 phosphorylation, and promoted its up-regulation. Overexpression TRIM31 is an independent risk factor for lung cancer patients, and it promotes the invasion and metastasis of lung cancer cells. In addition, E3 ubiquitination ligase TRIM31 binds to its substrate TP53 protein in the RING region and accelerates TP53 protein ubiquitination and degradation. Functional recovery experiments showed that NiCl2 exposure promotes the invasion and metastasis ability of lung cancer and ubiquitination-mediated degradation of TP53 protein through the STAT3/TRIM31 axis. These findings reveal the role and mechanism of NiCl2 in lung cancer progression, indicating that STAT3 and TRIM31 may be promising targets for the treatment of lung cancer.

Peripheral blood nutrient indices as biomarkers for anti‑PD‑1 therapy efficacy and prognosis in patients with advanced gastric cancer

Immunotherapy offers survival benefits for patients with advanced gastric cancer, but not all populations can benefit from immunotherapy. Good nutritional status is fundamental to a patient's immune function and may have an impact on the efficacy of immunotherapy. The present study aimed to investigate changes in prognostic nutritional index (PNI), advanced lung cancer inflammation index (ALI) and albumin-globulin ratio (AGR) values before and after immunotherapy in patients with advanced gastric cancer. The study also aimed to determine the potential association of the aforementioned values with patient outcomes and prognosis. Body mass index (BMI), serum albumin, total protein, peripheral blood lymphocyte, neutrophil, carcinoembryonic antigen (CEA), carbohydrate antigen19-9 (CA19-9) and a-fetoprotein (AFP) data were collected from 195 patients with advanced gastric cancer who underwent immunotherapy from January 2020 to October 2021. In addition, PNI, ALI and AGR values were calculated based on variables in blood collected from the patients within 3 days prior to immunotherapy and 3 weeks after immunotherapy. The results demonstrated that low PNI was associated with elevated CEA levels. Moreover, low ALI levels were associated with reduced BMI levels, elevated AFP levels, PD-L1 negative and first-line treatment. Comparison of responding and non-responding groups revealed that patients who responded to immunotherapy had higher PNI and AGR values than patients who did not respond, both before and after treatment, but had lower CEA and CA19-9 levels after treatment. Furthermore, in the non-responding group, PNI and AGR values were decreased and CEA values were increased following treatment compared with those prior to treatment. The objective response and disease control rates were higher in the high PNI and AGR groups compared with the low PNI and AGR groups, respectively. Moreover, PNI and AGR were found to be independent predictors of the short-term efficacy of immunotherapy for advanced gastric cancer, with cut-off values of 47.18 and 1.29, respectively. Univariate analysis revealed that ALI was associated with the progression-free survival (PFS) of patients, while multivariate analysis demonstrated that baseline PNI and AGR were independent predictors of PFS. In conclusion, tumor progression leads to a decline in the nutritional level of patients, and the present study indicated that effective immunotherapy may alleviate this deterioration to a certain extent. Furthermore, PNI and AGR exhibit potential in predicting the efficacy of immunotherapy and the prognosis of patients with advanced gastric cancer, and may exhibit potential as biomarkers in clinical practice.

Advanced Lung Cancer Inflammation Index for Predicting Prognostic Risk for Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

Purpose

The decreased advanced lung cancer inflammation index (ALI), defined as body mass index (BMI) * albumin (Alb)/neutrophil-to-lymphocyte ratio (NLR), is an independent prognostic risk factor for overall survival in gastric, lung, and colorectal cancers. This study aimed to investigate the value of ALI in predicting the risk of major adverse cardiovascular events (MACEs) in patients with acute coronary syndrome (ACS).

Patients and Methods

A total of 1624 patients with ACS undergoing percutaneous coronary intervention (PCI) were consecutively enrolled between January 2016 and December 2018. Follow-up data were collected at 1, 3, 6, and 12 months and annually thereafter. The primary endpoints were MACEs. All endpoints were defined as all-cause mortality, recurrent angina pectoris, restenosis/intra stent thrombosis, stroke, heart failure, and all-cause bleeding.

Results

The MACEs group and non-MACEs group showed significant differences in patients with age >65 years (28 [50.0%] vs 319 [23.7%]), history of heart failure (16 [28.6%] vs 127 [9.4%]), history of ischemic stroke (14 [25.0%] vs 186 [13.8%]), history of cardiogenic shock (6 [10.71%] vs 16 [1.19%]), left ventricular ejection fraction <40% (8 [14.29%] vs 33 [2.46%]), and ALI <343.96 (44 [78.65%] vs 680 [50.60%]) (all p<0.001). The optimal cut-off value for ALI was 334.96. The area under the curve (AUC) of the 1-, 2-, 3-, and 5-year was 0.560, 0.577, 0.665, and 0.749, respectively. The survival rate was significantly lower in the low ALI group than in the high ALI group (log-rank p<0.001). Low ALI was an independent risk factor for the long-term prognosis of patients with ACS after PCI, univariate HR: 3.671, 95% CI: 1.938-6.953, p<0.001; multivariate HR: 3.009, 95% CI: 1.57-5.769, p=0.001.

Conclusion

ALI score less than 334.96 is an independent prognostic risk factor for patients with ACS undergoing PCI and may be a novel marker for clinical practice.

Common nutritional/inflammatory indicators are not effective tools in predicting the overall survival of patients with small cell lung cancer undergoing first-line chemotherapy

Objective

Various studies have investigated the predictive significance of numerous peripheral blood biomarkers in patients with small cell lung cancer (SCLC). However, their predictive values have not been validated. This study assessed and evaluated the ability of common nutritional or inflammatory indicators to predict overall survival (OS) in patients with SCLC who received first-line chemotherapy.

Methods

Between January 2008 and July 2019, 560 patients with SCLC were enrolled at the Sichuan University West China Hospital. Eleven nutritional or inflammatory indices obtained before chemotherapy were evaluated. The cutoff values of continuous peripheral blood indices were confirmed through maximally selected rank statistics. The relationship of peripheral blood indices with OS was investigated through univariate and multivariate Cox regression analyses. Harrell's concordance (C-index) and time-dependent receiver operating characteristic curve were used to evaluate the performance of these indices.

Results

A total of 560 patients with SCLC were enrolled in the study. All the patients received first-line chemotherapy. In the univariate Cox analysis, all indices, except the Naples score, were related to OS. In the multivariate analysis, albumin-globulin ratio was an independent factor linked with prognosis. All indices exhibited poor performance in OS prediction, with the area under the curve ranging from 0.500 to 0.700. The lactic dehydrogenase (LDH) and prognostic nutritional index (PNI) were comparatively superior predictors with C-index of 0.568 and 0.550, respectively. The LDH showed incremental predictive values, whereas the PNI showed diminishing values as survival time prolonged, especially for men or smokers. The LDH with highest sensitivity (0.646) and advanced lung cancer inflammation index (ALI) with highest specificity (0.952) were conducive to identifying death and survival at different time points.

Conclusion

Common inflammatory or nutritional biomarkers are only marginally useful in predicting outcomes in patients with SCLC receiving first-line chemotherapy. Among them, LDH, PNI, and ALI are relatively promising biomarkers for prognosis evaluation.

Systemic inflammation influences the prognosis of patients with radically resected non-small cell lung cancer and correlates with the immunosuppressive microenvironment

The impact of host condition on prognosis of non-small cell lung cancer (NSCLC) and the interaction between host and NSCLC remain unclear. This study investigated the association between systemic inflammation and prognosis and characteristics of radically resected NSCLC. This study consisted of a cohort study and an exploratory study of institutional prospective databases. All participants underwent video-assisted thoracoscopic lobectomy as the primary treatment. Systemic inflammation was assessed before surgery using the advanced lung cancer inflammation index and the systemic inflammation response index. Next-generation sequencing and multiplex immunofluorescence analysis were conducted to delineate tumor characteristics. In the cohort study including 1507 participants, high inflammation was associated with poor disease-free survival and overall survival before and after propensity score matching and in multivariable analysis. Systemic inflammation showed good prognostic value for stage IA-IB NSCLC, and the prognostic value diminished with upstaging of NSCLC. In the exploratory study including 217 adenocarcinomas, tumor microenvironment of high inflammation group showed a greater abundance of PDL1+ tumor cells and immune cells, which were independent from driver gene mutations and clinicopathological characteristics. Spatial analysis demonstrated a higher frequency of immune-suppressed cellular neighborhood, increased avoidance between immune cells and PDL1- tumor cells and compromised immune killing and presentation in tumor microenvironment of high inflammation group. Systemic inflammation showed limited association with genomic mutations. Systemic inflammation may influence the prognosis of NSCLC at both the systematic level and the local immune response. The correlation between high inflammation and immunosuppressive microenvironment indicates a novel thread for anticancer treatment.

The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non-small cell lung cancer

BACKGROUND

Systemic inflammation, the most representative tumour-host interaction, plays a crucial role in disease progression and prognosis in patients with non-small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study.

METHODS

The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C-statistics. Inter-group differences in survival were assessed using the log-rank test and visualized using the Kaplan-Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90-day outcomes, length of hospitalization, hospitalization expenses and cachexia.

RESULTS

The inflammatory burden index (IBI) had the highest C-statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log-rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L-shaped dose-response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131-1.335, P < 0.001). A high IBI was an independent predictor of 90-day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489-2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256-1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195-1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374-2.207, P < 0.001) in patients with NSCLC.

CONCLUSIONS

The IBI was independently associated with overall survival, 90-day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC.

Triceps skinfold-albumin index significantly predicts the prognosis of cancer cachexia: A multicentre cohort study

BACKGROUND

The fat mass and nutritional status play important roles in the onset and progression of cancer cachexia. The present study evaluated the joint prognostic value of the fat mass, as indicated by the triceps skinfold thickness (TSF), and the serum albumin level, for mortality in patients with cancer cachexia.

METHODS

We performed a multicentre cohort study including 5134 patients with cancer cachexia from January 2013 to April 2019. The sum of the TSF (mm) and serum albumin (g/L) was defined as the triceps skinfold-albumin index (TA). Harrell's C index, a time-dependent receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the prognostic performance of the TA and other indices. Optimal stratification was used to identify the thresholds to define a low TA, and the association of the TA with all-cause mortality was evaluated using Kaplan-Meier analysis and Cox proportional hazard regression models.

RESULTS

The study enrolled 2408 women and 2726 men with a median age of 58.6 years and a median follow-up of 44 months. A total of 607 women (TA < 49.9) and 817 men (TA < 45.6) were classified as having a low TA. The TA showed better discrimination performance (C index = 0.621, 95% confidence interval [CI] = 0.607-0.636) to predict mortality in patients with cancer cachexia than the handgrip strength, the nutritional risk index, the prognostic nutritional index, the controlling nutritional status index, the systemic immune-inflammation index, the modified Glasgow prognostic score, and the TSF or albumin alone in the study population (all P < 0.05). The 1-, 3- and 5-year time-dependent ROC analyses (AUC = 0.647, 0.625 and 0.630, respectively) showed that the TA had the highest prognostic value among all indices investigated (all P < 0.05). Univariate analysis showed that a lower TA was associated with an increased death hazard (hazard ratio [HR] = 1.859, 95% CI = 1.677-2.062), regardless of the sex and cancer type. Multivariable survival analysis showed that a lower TA was independently associated with an increased death hazard (HR = 1.381, 95% CI = 1.223-1.560). This association was significantly strengthened in patients who did not receive curative chemotherapy (HR = 1.491, 95% CI = 1.298-1.713), those who had higher serum total protein levels (HR = 1.469, 95% CI = 1.284-1.681) and those with better physical performance (HR = 1.453, 95% CI = 1.271-1.662).

CONCLUSIONS

This study defined and evaluated a new prognostic index, the TA, which may improve the selection of intervention strategies to optimize the survival of patients with cancer cachexia.

Sex differences in the association of phase angle and lung cancer mortality

Background

Lung cancer is a lethal malignant tumor that is common worldwide and is associated with a high incidence of malnutrition. Phase angle (PA) is a simple, objective, and non-invasive indicator of body composition that has increasingly attracted attention as an indicator of the nutritional status and prognosis of patients with malignant tumors. This study aimed to investigate the association between the PA and overall survival in patients with lung cancer.

Methods

This study prospectively analyzed 804 lung cancer patients in the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) project from 40 hospitals in China. We used a restricted cubic spline to analyze the sex-specific association between PA and mortality in men and women with lung cancer. Cox regression analysis was used to evaluate the independent association between PA and mortality in men and women. Sensitivity analysis was performed. The Kaplan-Meier method was used to evaluate the survival of patients with high and low PA values.

Results

There was an L-shaped association between PA and survival in both men and women with lung cancer (p = 0.019 and p = 0.121, respectively). Kaplan-Meier survival analysis suggested that patients with a high PA showed a better survival than patients with a low PA (p = 0.007 for men and p < 0.001 for women). Multivariate-adjusted Cox regression analysis showed that PA was an independent risk factor for mortality in men (HR = 0.79, 95% CI = 0.65-0.95, p = 0.015), but not in women (HR = 0.83, 95% CI = 0.67-1.04, p = 0.105).

Conclusion

Phase angle is an independent risk factor for the mortality of male lung cancer patients. However, its role in predicting the mortality of female lung cancer patients seems to be limited.

The geriatric nutritional risk index is an effective tool to detect GLIM-defined malnutrition in rectal cancer patients

Background

This study aimed to investigate the value of the Geriatric Nutritional Risk Index (GNRI), prognostic nutritional index (PNI), and advanced lung cancer inflammation index (ALI) scores in detecting malnutrition in patients with rectal cancer; the Global Leadership Initiative on Malnutrition (GLIM) was used as the reference criterion.

Materials and methods

This study included patients with rectal cancer who underwent proctectomy. GNRI, PNI, and ALI were calculated to detect the GLIM-defined malnutrition using the Receiver operating characteristic (ROC) curves. Univariate and multivariate logistic regression analyses were used to evaluate the association between the nutritional tools and postoperative complications. Kaplan-Meier survival curves, log-rank tests, and univariate and multivariate Cox regression analyses were used to clarify the relationship between nutritional tools and overall survival (OS).

Results

This study enrolled 636 patients with rectal cancer. The GNRI demonstrated the highest sensitivity (77.8%), pretty specificity (69.0%), and the largest AUC (0.734). The GNRI showed good property in predicting major postoperative complications. All three nutritional tools were independent predictors of OS.

Conclusion

The GNRI can be used as a promising alternative to the GLIM and is optimal in perioperative management of patients with rectal cancer.