Development and validation of a diagnostic nomogram for frailty in cancer patients.

BACKGROUND: The presence of frailty decreases the overall survival of cancer patients. An accurate and operational diagnostic method is needed to help clinicians choose the most appropriate treatment to improve patient outcomes. METHODS: Data were collected from 10 649 cancer patients who were prospectively enrolled in the Investigation on Nutritional Status and its Clinical Outcomes of Common Cancers (INSCOC) project in China from July 2013 to August 2022. The training cohort and validation cohort were randomly divided at a ratio of 7:3. The multivariable logistic regression analysis, multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO) method were used to develop the nomogram. The concordance index and calibration curve were used to assess the diagnostic utility of the nomogram model. RESULTS: The 10 risk factors associated with frailty in cancer patients were age, AJCC stage, liver cancer, hemoglobin, radiotherapy, surgery, hand grip strength (HGS), calf circumference (CC), PG-SGA score and QOL from the QLQ-C30. The diagnostic nomogram model achieved a good C index of 0.847 (95% CI, 0.832-0.862, P < 0.001) in the training cohort and 0.853 (95% CI, 0.83-0.876, P < 0.001) in the validation cohort. The prediction nomogram showed 1-, 3-, and 5-year mortality C indices in the training cohort of 0.708 (95% CI, 0.686-0.731), 0.655 (95% CI, 0.627-0.683), and 0.623 (95% CI, 0.568-0.678). The 1-, 3-, and 5-year C indices in the validation cohort were similarly 0.743 (95% CI, 0.711-0.777), 0.680 (95% CI, 0.639-0.722), and 0.629 (95% CI, 0.558-0.700). In addition, the calibration curves and decision curve analysis (DCA) were well-fitted for both the diagnostic model and prediction model. CONCLUSIONS: The nomogram model provides an accurate method to diagnose frailty in cancer patients. Using this model could lead to the selection of more appropriate therapy and a better prognosis for cancer patients.

The combination of handgrip strength and CONUT predicts overall survival in patients with gastrointestinal cancer: A multicenter cohort study.

BACKGROUND: The controlled nutritional status score (CONUT) and handgrip strength (HGS) were both predictive indexes for the prognosis of cancers. However, the combination of CONUT and HGS for predicting the prognosis of gastrointestinal cancer had not been developed. This study aimed to explore the combination of CONUT and HGS as the potential predictive prognosis in patients with gastric and colorectal cancer. METHODS: A cohort study was conducted with gastric and colorectal cancer patients in multicenter in China. Based on the optimal HGS cutoff value for different sex, the HGS cutoff value was determined. The patients were divided into high and low HGS groups based on their HGS scores. A CONUT score of 4 or less was defined as a low CONUT, whereas scores higher than 4 were defined as high CONUT. The Kaplan-Meier method was used to create survival curves, and the log-rank test was used to compare time-event relationships between groups. A Cox proportional hazard regression model was used to determine independent risk factors for overall survival (OS). RESULTS: A total 2177 gastric and colorectal patients were enrolled in this study, in which 1391 (63.9%) were men (mean [SD] age, 66.11 [11.60] years). Multivariate analysis revealed that patients with high HGS had a lower risk of death than those with low HGS (hazard ratio [HR],0.87; 95% confidence interval [CI], 0.753-1.006, P = 0.06), while high CONUT had a higher risk of death than those with low CONUT (HR, 1.476; 95% CI, 1.227-1.777, P < 0.001). Patients with both low HGS and high CONUT had 1.712 fold increased risk of death (HR, 1.712; 95% CI, 1.364-2.15, P < 0.001). Moreover, cancer type and sex were stratified and found that patients with high CONUT and low HGS had lower survival rate than those with low CONUT and high HGS in both gastric or colorectal cancer, and both male and female. CONCLUSION: A combination of low HGS and high CONUT was associated with poor prognosis in patients with gastrointestinal cancer, which could probably predict the prognosis of gastrointestinal cancer more accurate than HGS or CONUT alone.

Correction: Characterization of LEF1 High Expression and Novel Mutations in Adult Acute Lymphoblastic Leukemia.

[This corrects the article DOI: 10.1371/journal.pone.0125429.].

GLIM Achieves Best Diagnostic Performance in Non-Cancer Patients with Low BMI: A Hierarchical Bayesian Latent-Class Meta-Analysis.

CONTEXT: Global Leadership Initiative on Malnutrition (GLIM) and Patient-Generated Subjective Global Assessment (PG-SGA) are commonly used nutrition assessment tools, whose performance does not reach a consensus due to different and imperfect reference standards. OBJECTIVE: This study aimed to evaluate and compare the diagnostic accuracy of GLIM and PG-SGA, using a hierarchical Bayesian latent class model, in the absence of a gold standard. DATA SOURCES: A systematic search was undertaken in PubMed, Embase, and Web of Science from inception to October 2022. Diagnostic test studies comparing (1) the GLIM and/or (2) PG-SGA with "semi-gold" standard assessment tools for malnutrition were included. DATA EXTRACTION: Two authors independently extracted data on sensitivity, specificity, and other key characteristics. The methodological quality of each included study was appraised according to the criteria in the Quality Assessment of Diagnostic Accuracy Studies-2. DATA ANALYSIS: A total of 45 studies, comprising 20 876 individuals evaluated for GLIM and 11 575 for PG-SGA, were included. The pooled sensitivity was 0.833 (95% CI 0.744 to 0.896) for GLIM and 0.874 (0.797 to 0.925) for PG-SGA, while the pooled specificity was 0.837 (0.780 to 0.882) for GLIM and 0.778 (0.707 to 0.836) for PG-SGA. GLIM showed slightly better performance than PG-SGA, with a higher diagnostic odds ratio (25.791 vs 24.396). The diagnostic performance of GLIM was most effective in non-cancer patients with an average body mass index (BMI) of <24 kg/m2, followed by non-cancer patients with an average age of ≥60 years. PG-SGA was most powerful in cancer patients with an average age of <60 years, followed by cancer patients with an average BMI of <24 kg/m2. CONCLUSION: Both GLIM and PG-SGA had moderately high diagnostic capabilities. GLIM was most effective in non-cancer patients with a low BMI, while PG-SGA was more applicable in cancer patients. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration No. CRD42022380409.

A convenient calf proportion index calculator for survival prediction in overweight and obese patients with cancer.

OBJECTIVE: This study aimed to define the calf proportion index (CPI) and investigate its association with malnutrition and survival in overweight and obese patients with cancer. METHODS: This multicenter observational cohort study included 3499 patients diagnosed with cancer, including 3145 overweight and 354 obese individuals. The CPI was defined as the ratio of the cross-sectional area of the calf circumference (CC) to the body surface area (BSA). A CPI calculator that automatically calculated the CPI and survival probability based on the patient's sex, height, weight, and CC was developed. RESULTS: During a median follow-up of 44.1 months, 935 deaths were recorded. Receiver operating characteristic curves revealed that the CPI was better than CC and BSA as a predictor of survival, with AUCs for the 3-year mortality rate were 0.574, 0.553 and 0.529, respectively. In overweight and obese patients with cancer, the optimal CPI cut-off value was 0.65 % for men and 0.57 % for women. The Kaplan-Meier curve revealed that patients with a low CPI had lower survival. After adjusting confounding factors, a low CPI was an independent risk factor for overweight (hazard ratio [HR]: 1.29, 95 % confidence interval [CI]: 1.11-1.51, P < 0.001) and obesity (HR: 1.92, 95 % CI: 1.20-3.09, P = 0.007) in patients with cancer. The CPI exhibited significant prognostic value in patients with lung and digestive system cancers. The risk of malnutrition was significantly higher in patients with a low CPI (HR: 1.25, 95 % CI: 1.04-1.50, P = 0.019). CONCLUSIONS: The CPI is a useful prognostic indicator in overweight and obese patients with cancer, especially in obese patients.

Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.

BACKGROUND: Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. METHODS: Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. RESULTS: A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. CONCLUSION: The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.

Postinfusion PD-1+ CD8+ CAR T cells identify patients responsive to CD19 CAR T-cell therapy in non-Hodgkin lymphoma.

ABSTRACT: Chimeric antigen receptor (CAR) T-cell therapy has revolutionized treatment for relapsed/refractory B-cell non-Hodgkin lymphoma (NHL). Robust biomarkers and a complete understanding of CAR T-cell function in the postinfusion phase remain limited. Here, we used a 37-color spectral flow cytometry panel to perform high dimensional single-cell analysis of postinfusion samples in 26 patients treated with CD28 costimulatory domain containing commercial CAR T cells for NHL and focused on computationally gated CD8+ CAR T cells. We found that the presence of postinfusion Programmed cell death protein 1 (PD-1)+ CD8+ CAR T cells at the day 14 time point highly correlated with the ability to achieve complete response (CR) by 6 months. Further analysis identified multiple subtypes of CD8+ PD-1+ CAR T cells, including PD-1+ T cell factor 1 (TCF1)+ stem-like CAR T cells and PD-1+ T-cell immunoglobulin and mucin-domain containing-3 (TIM3)+ effector-like CAR T cells that correlated with improved clinical outcomes such as response and progression-free survival. Additionally, we identified a subset of PD-1+ CD8+ CAR+ T cells with effector-like function that was increased in patients who achieved a CR and was associated with grade 3 or higher immune effector cell-associated neurotoxicity syndrome. Here, we identified robust biomarkers of response to CD28 CAR T cells and highlight the importance of PD-1 positivity in CD8+ CAR T cells after infusion in achieving CR.

The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information.

BACKGROUND & AIMS: The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weight loss information, and bias or even failure to recall patients' historical weight can cause misestimates of malnutrition. We aimed to compare the suitability of several screening tools in GLIM diagnosis, and establish machine learning (ML) models to predict malnutrition in CRC patients without weight loss information. METHODS: This multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n = 3365) and a validation cohort (n = 1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance. RESULTS: NRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/). CONCLUSIONS: Nutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.

Humoral immune response to tumor-associated antigen Ubiquilin 1 (UBQLN1) and its tumor-promoting potential in lung cancer.

BACKGROUND: This study aims to investigate the expression of UBQLN1 in lung cancer (LC) tissue and the diagnostic capability of autoantibody to UBQLN1 (anti-UBQLN1) in the detection of LC and the discrimination of pulmonary nodules (PNs). METHODS: Sera from 798 participants were used to discover and validate the level of autoantibodies via HuProt microarray and Enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was applied to establish model. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic potential. Immunohistochemistry was performed to detect UBQLN1 expression in 88 LC tissues and 88 para-tumor tissues. qRT-PCR and western blotting were performed to detect the expression of UBQLN1 at the mRNA and protein levels, respectively. Trans-well assay and cell counting kit-8 (CCK-8) was used to investigate the function of UBQLN1. RESULTS: Anti-UBQLN1 was identified with the highest fold change by protein microarray. The level of anti-UBQLN1 in LC patients was obviously higher than that in NC or patients with benign lung disease of validation cohort 1 (P<0.05). The area under the curve (AUC) of anti-UBQLN1 was 0.610 (95%CI: 0.508-0.713) while reached at 0.822 (95%CI: 0.784-0.897) when combining anti-UBQLN1 with CEA, CYFRA21-1, CA125 and three CT indicators (vascular notch sign, lobulation sign and mediastinal lymph node enlargement) in the discrimination of PNs. UBQLN1 protein was overexpressed in lung adenocarcinoma (LUAD) tissues compared to para-tumor tissues. UBQLN1 knockdown remarkably inhibited the migration, invasion and proliferation of LUAD cell lines. CONCLUSIONS: Anti-UBQLN1 might be a potential biomarker for the diagnosis of LC and the discrimination of PNs.

Comprehensive evaluation of serum hepatic proteins in predicting prognosis among cancer patients with cachexia: an observational cohort study.

BACKGROUND: Hepatic proteins, including albumin, prealbumin, and transferrin have been confirmed to be prognostic predictors in various cancers. This study aimed to comprehensively assess the prognostic value of these three serum markers in patients with cancer cachexia. METHODS: This multicenter prospective cohort study included 1303 cancer cachexia patients, among whom 592 deaths occurred during a median follow-up of 20.23 months. The definition of cachexia was based on the 2011 international consensus. Concordance index (C-index) and receiver operating characteristic (ROC) curves were applied to compare the prognostic performance. The primary outcome was overall survival, which was calculated using the Kaplan-Meier method generated by log-rank test. A Cox proportional hazard regression model was used to identify independent predictors associated with survival. The secondary outcomes included 90-days mortality and quality of life (QoL). RESULTS: C-index and ROC curves showed that albumin had the most accurate predictive capacity for survival, followed by transferrin and prealbumin. Multivariate Cox analysis confirmed that low albumin (hazard ratio [HR] = 1.51, 95% confidence interval [95%CI] = 1.28-1.80, P < 0.001), prealbumin (HR = 1.42, 95%CI = 1.19-1.69, P < 0.001), and transferrin (HR = 1.50, 95%CI = 1.25-1.80, P < 0.001) were independent risk factors for long-term survival in cancer patients with cachexia. In subgroup analysis, the prognostic value of low albumin was significant in patients with upper gastrointestinal, hepatobiliary and pancreatic, and colorectal cancers; low prealbumin was significant in colorectal cancer; and low transferrin was significant in patients with upper gastrointestinal and colorectal cancer. All three hepatic proteins were valuable as prognostic predictors for patients with advanced (Stage III and IV) cancer with cachexia. The risks of 90-days mortality and impaired QoL were higher in cachexia patients with low albumin, prealbumin, and transferrin levels. CONCLUSION: Low albumin, prealbumin, and transferrin levels were all independent prognostic factors affecting patients with cancer cachexia, especially in patients in the advanced stages. These results highlight the value of routinely checking serum hepatic proteins in clinical practice to predict the prognosis of patients with cancer cachexia.

Integrated neutrophil-to-lymphocyte ratio and handgrip strength better predict survival in patients with cancer cachexia.

OBJECTIVES: Systemic inflammation and skeletal muscle strength play crucial roles in the development and progression of cancer cachexia. In this study we aimed to evaluate the combined prognostic value of neutrophil-to-lymphocyte ratio (NLR) and handgrip strength (HGS) for survival in patients with cancer cachexia. METHODS: This multicenter cohort study involved 1826 patients with cancer cachexia. The NLR-HGS (NH) index was defined as the ratio of neutrophil-to-lymphocyte ratio to handgrip strength. Harrell's C index and receiver operating characteristic (ROC) curve analysis were used to assess the prognosis of NH. Kaplan-Meier analysis and Cox regression models were used to evaluate the association of NH with all-cause mortality. RESULTS: Based on the optimal stratification, 380 women (NH > 0.14) and 249 men (NH > 0.19) were classified as having high NH. NH has shown greater predictive value compared to other indicators in predicting the survival of patients with cancer cachexia according to the 1-, 3-, and 5-y ROC analysis and Harrell's C index calculation. Multivariate survival analysis showed that higher NH was independently associated with an increased risk of death (hazard ratio = 1.654, 95% confidence interval = 1.389-1.969). CONCLUSION: This study demonstrates that the NH index, in combination with NLR and HGS, is an effective predictor of the prognosis of patients with cancer cachexia. It can offer effective prognosis stratification and guidance for their treatment.

De novo transcriptome assembly and molecular response mechanism analysis of a diatom Cyclotella meneghiniana Kützing exposed to cadmium.

Cadmium is a persistent heavy metal commonly found in aquatic ecosystems and has a strong toxic effect on organisms. The sensitivity of phytoplankton to environmental changes and its role as an indicator of aquatic ecosystem health have been well-established. However, the mechanisms by which phytoplankton respond to cadmium remain incompletely understood. In this study, we chose the typical planktonic diatom Cyclotella meneghiniana Kützing, by integrating physiological-biochemical data and transcriptome analysis, to reveal the molecular mechanisms of C. meneghiniana responing to cadmium. Under cadmium stress, the cell density and chlorophyll-a content of C. meneghiniana significantly decreased, while MDA content and SOD activity gradually increased. At 72 h of cadmium stress, we found that at this time point, cell abundance and physiological variation were very significant, therefore we selected 72 h for subsequent analysis. To better understand the cadmium stress response mechanisms of C. meneghiniana, a de novo transcriptome method was used to analyse C. meneghiniana under cadmium stress for 72 h, and 1704 (M vs. CK) and 4788 (H vs. CK) differentially expressed genes were found. Our results showed that the changes in gene expression were closely correlated to the physiological-biochemical changes. Although cadmium stress could promote the nitrogen metabolism pathway, ROS scavenging system, and photosynthesis. While, C. meneghiniana under medium and high concentrations of cadmium can also limit various intracellular metabolic pathways, such as the MAPK pathway and phosphatidylinositol metabolic pathway, and the degree of inhibition increases with the increase of stress concentration. In present study, the complete molecular mechanism of the planktonic diatom response to cadmium has been established, which provided important information for further studies on heavy metal pollutants and the multiple functional genes responsible for cadmium sensitivity and tolerance in planktonic diatoms.

Differential protein analysis of saline-alkali promoting the oil accumulation in Nitzschia palea.

BACKGROUND: The increasingly severe salinization of the aquatic environment has led to serious damage to the habitats of aquatic organisms. Benthic diatoms are commonly employed as indicator species for assessing water quality and serve as a reflection of the overall health of the aquatic ecosystem. Nitzschia palea is a common diatom found in freshwater, with high oil content, rapid reproductive rate, and it is a commonly dominant species in various rivers. RESULTS: The results showed that after 4 days (d) of saline-alkali stress, the cell density and chlorophyll a content of Nitzschia palea reached their maximum values. Therefore, we selected Nitzschia palea under 4 d stress for Tandem Mass Tag (TMT) quantitative proteomic analysis to explore the molecular adaptation mechanism of freshwater diatoms under saline-alkali stress. Totally, 854 proteins were enriched, of which 439 differentially expressed proteins were identified. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and subcellular fractionation analysis revealed that these proteins were mainly enriched in the photosynthesis pathway, citric acid cycle (TCA cycle), fatty acid synthesis, and glutathione cycle. CONCLUSIONS: This study aims to reveal the physiological, biochemical and proteomic mechanisms of salt and alkali tolerance and molecular adaptation of Nitzschia palea under different saline-alkali concentrations. This study showed that Nitzschia palea is one candidate of the environmental friendly, renewable bioenergy microalgae. Meantime, Nitzschia palea reveals for the proteome of the freshwater and provides the basis, it became a model algal species for freshwater diatoms.

Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study.

OBJECTIVES: The concept of possible sarcopenia (PS) was recently introduced to enable timely intervention in settings without the technologies required to make a full diagnosis of sarcopenia. This study aimed to investigate the association between PS and all-cause mortality in patients with solid cancer. DESIGN: Retrospective observational study. SETTING AND PARTICIPANTS: 13,736 patients with 16 types of solid cancer who were ≥18 years old. MEASUREMENTS: The presence of both a low calf circumference (men <34 cm or women <33 cm) and low handgrip strength (men <28 kg or women <18 kg) was considered to indicate PS. Harrell's C-index was used to assess prognostic value and the association of PS with mortality was estimated by calculating multivariable-adjusted hazard ratios (HRs). RESULTS: The study enrolled 7207 men and 6529 women (median age = 57.8 years). During a median follow-up of 43 months, 3150 deaths occurred. PS showed higher Harrell's C-index (0.549, 95%CI = [0.541, 0.557]) than the low calf circumference (0.541, 95%CI = [0.531, 0.551], P = 0.037) or low handgrip strength (0.542, 95%CI = [0.532, 0.552], P = 0.026). PS was associated with increased mortality risk in both univariate (HR = 1.587, 95%CI = [1.476, 1.708]) and multivariable-adjusted models (HR = 1.190, 95%CI = [1.094, 1.293]). Sensitivity analyses showed that the association of PS with mortality was robust in different covariate subgroups, which also held after excluding those patients who died within the first 3 months (HR = 1.162, 95%CI = [1.060, 1.273]), 6 months (HR = 1.150, 95%CI = [1.039, 1.274]) and 12 months (HR = 1.139, 95%CI = [1.002, 1.296]) after enrollment. CONCLUSION: PS could independently and robustly predict all-cause mortality in patients with solid cancer. These findings imply the importance of including PS assessment in routine cancer care to provide significant prognostic information to help mitigate sarcopenia-related premature deaths.

Association between a novel nutrition-inflammation prognostic grading system and overall survival in hospitalized adults with cancer: A retrospective cohort study.

BACKGROUND: Malnutrition and increased systemic inflammatory responses are highly prevalent in patients with cancer and they have a negative effect on prognosis. We aimed to develop a nutrition-inflammation prognostic grading system (NIPGS) for patients with cancer, which incorporates the Nutritional Risk Screening 2002 (NRS 2002) and C-reactive protein (CRP) levels. METHODS: This multicenter retrospective cohort study totally included 6891 patients diagnosed with cancer. A 4 × 4 matrix incorporating the four NRS 2002 categories within each of the four CRP categories was constructed. Groups with approximate hazard ratios (HRs) were clustered into one grade. The NIPGS consists of four grades, with the survival rate gradually decreasing from Grades 1 to 4. The primary outcome was overall survival (OS) and comprehensive survival analyses were performed. RESULTS: During a median follow-up of 18.70 months, 2818 death cases occurred. Kaplan-Meier curve showed the survival rate decreased from Grades 1 to 4 of NIPGS (P < 0.001). The NIPGS was an independent risk factor associated with OS adjusting for confounders, with HRs increasing from 1.22 (95% confidence interval [CI], 1.09-1.36; P < 0.001) in Grade 2, 1.58 (95% CI, 1.39-1.80; P < 0.001) in Grade 3 to 1.92 (95% CI, 1.73-2.13; P < 0.001) in Grade 4. A high NIPGS grade was also associated with an increased risk of short-term mortality, poor quality of life, and longer hospital stay and expenses. Two internal validation cohorts confirmed the results of our study. CONCLUSION: The NIPGS could be an effective prognostic tool for patients with cancer.

Assessing the causal association between dietary vitamin intake and lymphoma risk: a Mendelian randomisation study.

Observational studies of diet-related vitamins and lymphoma risk results were inconsistent. Our study aimed to estimate the causality between dietary vitamin intake and lymphoma through a Mendelian randomisation (MR) study. We enrolled dietary-related retinol, vitamin C, vitamin E, vitamin B6 and vitamin B12 as exposures of interest, with Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) as the outcome. The causal effects were estimated using inverse variance weighting (IVW), MR-Egger regression analysis and weighted median, supplemented by sensitivity analyses. The results revealed that genetically predicted dietary vitamin B12 intake was associated with a reduced HL risk (OR = 0.22, 95% CI 0.05-0.91, p = 0.036). The Q test did not reveal heterogeneity, the MR-Egger test showed no significant intercepts, and the leave-one-out (LOO) analysis did not discover any SNP that affect the results. No causal relationship about dietary vitamin intake on the NHL risk was observed.

Matrine suppresses hepatocellular carcinoma tumorigenesis by modulating circ_0055976/miR-1179/lactate dehydrogenase A axis.

BACKGROUND: Matrine has been identified to have anticancer activity in hepatocellular carcinoma (HCC). Circ_0055976 was highly expressed in HCC. Here, we investigated the function and relationship of Matrine and circ_0055976 in HCC tumorigenesis. METHODS: Cell proliferation and invasion were detected using Cell Counting Kit-8, 5-Ethynyl-2'-deoxyuridine (EdU), colony formation and transwell assays, respectively. Cell aerobic glycolysis was evaluated by detecting glucose consumption, lactate production, and the ratios of ATP/ADP. Levels of genes and proteins were detected by quantitative real-time polymerase chain reaction and Western blotting. The target relationship between miR-1179 and circ_0055976 or lactate dehydrogenase A (LDHA) was analyzed by dual-luciferase reporter assay. The mouse xenograft model was established to conduct the in vivo assay. RESULTS: Matrine suppressed HCC cell proliferation, invasion and anaerobic glycolysis in vitro. Circ_0055976 was highly expressed in HCC tissues and cells, and was reduced by Matrine treatment. Moreover, overexpression of circ_0055976 reversed the anticancer effects of Matrine in HCC cells. Mechanistically, circ_0055976/miR-1179/LDHA formed an axis. Circ_0055976 knockdown or miR-1179 overexpression impaired HCC cell proliferation, invasion, and anaerobic glycolysis, which were reversed by miR-1179 inhibition or LDHA overexpression. Meanwhile, forced expression of LDHA abolished the regulatory effects of Matrine on HCC cells. In the clinic, Matrine impeded HCC tumor growth in vivo, and this effect was boosted after circ_0055976 silencing. CONCLUSION: Matrine suppressed HCC cell proliferation, invasion, and anaerobic glycolysis via circ_0055976/miR-1179/LDHA axis, providing a new insight into the clinical application of Matrine in HCC treatment.

Modified Controlling Nutritional Status (mCONUT) serves as a promising prognostic factor in patients with cancer cachexia.

BACKGROUND: Malnutrition and systemic inflammation are considered 2 hallmarks of cancer cachexia. Our study aimed to construct a modified Controlling Nutritional Status by introducing C-reactive protein as an inflammatory parameter and investigate its prognostic value in patients with cancer cachexia. METHODS: This multicenter cohort study included 5221 patients with cancer, among whom 1719 were diagnosed with cachexia. Concordance index and receiver operating characteristic curves were used to compare prognostic values between the 2 systems. The primary outcome was overall survival, and comprehensive survival analyses were performed. The secondary outcomes were short-term survival, malnutrition, and quality of life. RESULTS: During the median follow-up of 17.47 mo, 813 deaths were recorded. The modified Controlling Nutritional Status was more accurate than Controlling Nutritional Status in predicting survival in patients with cancer cachexia. Patients in the high Controlling Nutritional Status/modified Controlling Nutritional Status group had a significantly shorter overall survival. Multivariate Cox analysis confirmed high Controlling Nutritional Status (hazard ratio = 1.34, 95% CI, 1.13-1.58; P < 0.001) and modified Controlling Nutritional Status (hazard ratio = 1.46; 95% CI, 1.26-1.69; P < 0.001) were independent risk factors for survival, adjusting for confounders. In subgroup analyses, a high modified Controlling Nutritional Status score had a significantly negative effect on survival in cachexia patients with upper gastrointestinal and colorectal cancer, especially for advanced-stage (stages III and IV) patients. The risk of short-term mortality and experiencing malnutrition rose to 1.48 and 1.13 times, respectively, in the high modified Controlling Nutritional Status group, as well as that for poorer life quality. CONCLUSION: The modified Controlling Nutritional Status group comprehensively reflects nutritional, immune, and inflammatory status and serves as a powerful prognostic scoring system in patients with cancer cachexia.

Discovery of distinct cancer cachexia phenotypes using an unsupervised machine-learning algorithm.

OBJECTIVES: Cancer cachexia is a debilitating condition with widespread negative effects. The heterogeneity of clinical features within patients with cancer cachexia is unclear. The identification and prognostic analysis of diverse phenotypes of cancer cachexia may help develop individualized interventions to improve outcomes for vulnerable populations. The aim of this study was to show that the machine learning-based cancer cachexia classification model generalized well on the external validation cohort. METHODS: This was a nationwide multicenter observational study conducted from October 2012 to April 2021 in China. Unsupervised consensus clustering analysis was applied based on demographic, anthropometric, nutritional, oncological, and quality-of-life data. Key characteristics of each cluster were identified using the standardized mean difference. We used logistic and Cox regression analysis to evaluate 1-, 3-, 5-y, and overall mortality. RESULTS: A consensus clustering algorithm was performed for 4329 patients with cancer cachexia in the discovery cohort, and four clusters with distinct phenotypes were uncovered. From clusters 1 to 4, the clinical characteristics of patients showed a transition from almost unimpaired to mildly, moderately, and severely impaired. Consistently, an increase in mortality from clusters 1 to 4 was observed. The overall mortality rate was 32%, 40%, 54%, and 68%, and the median overall survival time was 21.9, 18, 16.7, and 13.6 mo for patients in clusters 1 to 4, respectively. Our machine learning-based model performed better in predicting mortality than the traditional model. External validation confirmed the above results. CONCLUSIONS: Machine learning is valuable for phenotype classifications of patients with cancer cachexia. Detection of clinically distinct clusters among cachexic patients assists in scheduling personalized treatment strategies and in patient selection for clinical trials.