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Background: Tongue squamous cell carcinoma (TSCC) is a prevalent tumor that affects many people worldwide. Radiotherapy is a common treatment option, but its efficacy varies greatly. This study seeks to validate the identified gene signature associated with radiosensitivity in TSCC, and its potential in predicting radiotherapy response and prognosis. Methods: We analyzed 122 TSCC patients from TCGA database using the radiosensitivity signature and classified them into radiosensitive (RS) and radioresistant (RR) groups. Immune infiltration analysis methods were applied to investigate the immune status between different subgroups. Immunophenotype Score (IPS) and pRRophetic algorithm were employed to estimate the efficiency of treatment. A radioresistant TSCC cell line was established by gradually increasing radiation doses. Cell radiosensitivity was evaluated using the CCK-8 and colony formation assays. The expression of radiosensitivity-related genes was validated by qRT-PCR. Results: Our study validated the predictive capacity of a previously identified "31-gene signature" in the TCGA-TSCC cohort, which effectively stratified patients into RS and RR groups. We observed that the RS group exhibited superior overall survival and progression-free survival rates relative to the RR group when treated with radiotherapy. The RS group was significantly enriched in most immunerelated hallmark pathways, and may therefore benefit from immune checkpoint inhibitors. However, the RS group displayed lower sensitivity to first-line chemotherapy. A radioresistant TSCC cell line (CAL-27R) exhibited increased clonogenic potential and cell viability following irradiation, accompanied by downregulation of three radiosensitivity-related genes compared to its parental non-resistant cell (CAL-27). In addition, we constructed and validated a radiosensitivity-related prognostic index (PI) using 4 radiosensitivity-related genes associated with TSCC prognosis. Conclusion: We assessed the ability of the radiosensitivity gene signature to predict outcomes in TSCC patients. our research provided valuable insights into the molecular pathways associated with radiosensitivity in TSCC and offered clinicians a practical tool to predict patient radiotherapy effectiveness and prognosis. [ABSTRACT FROM AUTHOR]

TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels. [ABSTRACT FROM AUTHOR]

Abstract TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels.

Despite considerable progress has been made in the understanding of the genetics and molecular biology of renal cell carcinoma (RCC), therapeutic options of patients with papillary renal cell carcinoma (PRCC) are limited. Immunotherapy based on immune checkpoint inhibitors (ICIs) has become a hot point in researching new drug for tumor and been tested in a number of human clinical trials. In this study, an immune-related gene prognostic index (IRGPI) was developed and provided a comprehensive and systematic analysis of distinct phenotypic and molecular portraits in the recognition, surveillance, and prognosis of PRCC. The reliability of the IRGPI was evaluated using independent datasets from GEO database and the expression levels of the genes in the IRGPI detected by real-time PCR. Collectively, the currently established IRGPI could be used as a potential biomarker to evaluate the response and efficacy of immunotherapy in PRCC. [ABSTRACT FROM AUTHOR]

Background: Hematopoietic stem cell transplantation (HSCT) is a procedure with high morbidity and mortality. Identifying patients for maximum benefit and risk assessment is crucial in the decision‐making process. This has led to the development of predictive risk models for HSCT in adults, which have limitations when applied to pediatric population. Our goal was to develop an automatic learning algorithm to predict survival in children with malignant disorders undergoing HSCT. Methods: We studied allogenic HSCTs performed on children with malignant disorders at a third‐level hospital between 1991 and 2021. Survival was analyzed using the Kaplan–Meier method, log‐rank test for the univariate analysis, and Cox regression for the multivariate analysis. A prognostic index was constructed based on these findings. Lastly, we constructed a predictive model using a random forest algorithm to forecast 1‐year survival after HSCT. Results: We analyzed 229 HSCTs in 201 patients with a median follow‐up of 1.64 years. Variables that impacted on the multivariate analysis were older age (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.12–1.76, p =.003), oldest period of HSCT (HR 0.46, 95% CI 0.29–0.73, p <.001), and mismatched donor (HR 2.65, 95% CI 1.51–4.65, p =.001). Our prognostic index was associated with 3‐year overall survival (OS; p <.001). A random forest was developed using as variables: diagnosis, age, year of HSCT, time from diagnosis to HSCT, disease stage, donor type, and conditioning. This achieved 72% accuracy in predicting 1‐year OS. Conclusions: Our index and random forest was effective in predicting 1‐year survival. However, further validation in diverse populations is necessary to establish their generalizability. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to explore the clinical outcomes and prognostic factors for PTCL-NOS patients in the real world. Methods: Clinical data were retrospectively collected from adult patients with PTCL-NOS treated at a single center in Taiwan. Results: 104 PTCL-NOS patients with a median age of 53.0 years were enrolled. Patients with the International Prognostic Index (IPI) or prognostic index for peripheral T-cell lymphoma (PIT) scores of zero had a longer overall survival (OS) and progression free survival (PFS), while patients with IPI or PIT scores ≥1 did poorly. For patients who are eligible for transplantation, the use of pralatrexate as salvage chemotherapy has shown better OS (2-year OS 83.3% vs. 24.4%, P = 0.011) compared to patients who did not. By multivariate analysis, age >60 years, male, B symptoms, ECOG >1, lung involvement, and thrombocytopenia were independent adverse factors for OS. Incorporating factors in multivariate analysis, we established a novel predictive index for PTCL-NOS which efficiently stratifies patients into low (0–1 factor), intermediate-1 (2 factors), intermediate-2 (3 factors), and high risk (4–6 factors) groups with 2-year OS rates of 81.5%, 32.9%, 8.8%, and 0%, respectively (P

Background: Cognitive changes in Huntington's disease (HD) precede motor manifestations. ENROLL‐HD platform includes four cognitive measures of information processing speed (IPS). Our group is eager to seek clinical markers in the life stage that is as close as possible to the age of onset (ie, the so called prodromal HD phase) because this is the best time for therapeutic interventions. Objectives: Our study aimed to test whether cognitive scores in prodromal ENROLL‐HD mutation carriers show the potential to predict the severity of motor and behavioral changes once HD became fully manifested. Methods: From the global ENROLL‐HD cohort of 21,343 participants, we first selected a premanifest Cohort#1 (ie, subjects with Total Motor Score (TMS) <10 and Diagnostic Confidence Level (DCL) <4, N = 1.222). From this cohort, we then focused on a prodromal Cohort#2 of subjects who were ascertained to phenoconvert into manifest HD at follow‐up visits (ie, subjects from 6 ≤ TMS≤9 and DCL <4 to TMS≥10 and DCL = 4, n = 206). Results: The main results of our study showed that low IPS before phenoconversion in Cohort#2 predicted the severity of motor and behavioral manifestations. By combining the four IPS cognitive measures (eg, the Categorical Verbal Fluency Test; Stroop Color Naming Test; Stroop Word Reading; Symbol Digit Modalities Test), we generated a Composite Cognition Score (CCS). The lower the CCS score the higher the TMS and the apathy scores in the same longitudinally followed‐up patients after phenoconversion. Conclusions: CCS might represent a clinical instrument to predict the prognosis of mutation carriers who are close to manifesting HD. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the relationship between peripheral blood indices and the efficacy and prognosis of advanced esophageal squamous cell carcinoma (ESCC) patients treated with camrelizumab. Patients and Methods: We retrospectively analyzed 64 patients who received camrelizumab for advanced ESCC at the Second People's Hospital of Lianyungang City between July 2020 and June 2022. The study included examination of the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the systemic inflammation index (SII), the lymph-to-monocytes ratio (LMR), the absolute lymphocyte count (ALC), and lactate dehydrogenase (LDH). We used multivariate logistic regression analysis to explore the link existing between peripheral blood and the efficacy of treatment. Determination of potential prognostic factors for Progression-free survival (PFS) and Overall survival (OS) using Cox regression analysis. The nomogram model was developed based on the results of the Cox multivariate analysis. Patients were divided into three groups according to the reduction in LDH and LDL levels before treatment, and the Kaplan-Meier survival curves for the three groups were compared and ROC curves for LDH combined with PLR were plotted. Results: Lower LDH (OR=6.237, 95% CI: 1.625– 23.944) were independently associated with disease control rates(DCR). LDH was independently correlated with PFS (HR: 0.227 95% CI: 0.099– 0.517). LDH and PLR were independently linked to OS. The C index of the nomogram model is 0.819, indicating good predictive performance. Kaplan-Meier Survival Curve suggested better OS in patients with reduced pretreatment LDH and PLR. The area under the ROC curve showed that the LDH index combined with the PLR index predicts patient survival better than the index alone. Conclusion: LDH combined with PLR predicted prognosis in patients with ESCC treated with camrelizumab. [ABSTRACT FROM AUTHOR]

Background: To construct an effective prognostic index to predict overall survival (OS) and triplet regimen efficacy for advanced gastric cancer (AGC) patients treated with platinum-based and fluorouracil-based chemotherapy. Objectives: Between 2011 and 2021, 679 patients from two randomized phase III trials and one phase II trial were enrolled. Designs: We collected 11 baseline clinicopathological and 14 hematological parameters to establish a prognostic index. Methods: Univariate and multivariate Cox analyses were used to screen prognostic factors, and a prognostic index nomogram was conducted. Results: Seven prognostic factors were identified: primary tumor site in the non-proximal gastric area, signet-ring cell carcinoma (SRCC)/mucinous carcinoma, peritoneal metastasis, neutrophil count higher than the upper limit of normal value (ULN), lymphocyte count lower than the lower limit of normal value, lactate dehydrogenase level higher than the ULN, and alkaline phosphatase level higher than the ULN as significant for prognosis. A prognostic nomogram named the Fudan advanced gastric cancer prognostic risk score (FARS) index was constructed, and patients in the high-risk group had significantly shorter OS than those in the low-risk group (median OS, 15.5 versus 8.0 months, p < 0.001). The areas under the curve of the FARS index for 1-, 2-, and 3-year OS were 0.70, 0.72, and 0.77, respectively. A validation and external cohort verified the prognostic value of the FARS index. Moreover, three triplet regimen efficacy parameters were identified: SRCC/mucinous adenocarcinoma, primary tumor location in the non-proximal gastric area, and peripheral neutrophil count higher than the ULN; a TRIS index was subsequently conducted. In patients with any two of the three parameters, the triplet regimen showed significantly longer OS than the doublet regimen (p = 0.018). Conclusion: The constructed FARS index to predict the OS of AGC patients and the TRIS index to screen out the dominant population for triplet regimens can be used to aid clinical decision-making and individual risk stratification. Plain language summary: A prognostic index in locally advanced and metastatic gastric cancer To date, no recognized systematic prognostic score has been established for advanced gastric cancer (AGC). Our research aims to construct an effective prognostic index to predict overall survival (OS) for AGC patients to aid clinical decision-making and individual risk stratification. In our research, seven prognostic factors were identified: primary tumor site in the non-proximal gastric area, signet-ring cell carcinoma (SRCC)/mucinous carcinoma, peritoneal metastasis, neutrophil count higher than the upper limit of normal value (ULN), lymphocyte count lower than the lower limit of normal value, lactate dehydrogenase level higher than the ULN, and alkaline phosphatase level higher than the ULN as significant for prognosis. A prognostic index named the Fudan advanced gastric cancer prognostic risk score (FARS) index was constructed, and patients in the high-risk group had significantly shorter OS than those in low-risk group (median OS, 15.5 months vs. 8.0 months, P < 0.001). Moreover, three triplet regimen efficacy parameters were identified: SRCC/mucinous adenocarcinoma, primary tumor location in the non-proximal gastric area, and peripheral neutrophil count higher than the ULN; a TRIS index was subsequently conducted. In patients with any two of the three parameters, the triplet regimen showed significantly longer OS than the doublet regimen (P = 0.018). [ABSTRACT FROM AUTHOR]

Background & Aims: Fatty liver disease with metabolic dysfunction (MAFLD) is a new concept proposed to replace the previous concept of Non-Alcoholic Hepatic Steatosis (NAFLD). We developed and internally validated a prognostic model to predict the likelihood of death in a cohort of subjects with MAFLD. Methods: Our work involved two steps: the first was the construction of a bootstrapped multivariable Cox model for mortality risk prognosis and the second was its validation. Results: The study cohort included 1506 subjects, of which 907 were used for internal validation. Discriminant measures for the final model were R2D 0.6845 and Harrell's C 0.8422 in the development and R2D 0.6930 and Harrell's C 0.8465 in the validation. We used the nine independent prognostic factors selected by the LASSO Cox procedure and fitted by the bootstrap Cox survival model, and observed β were: Gender 0.356 1.42 (p < 0.008), Age 0.146 (p < 0.001), Glycemia 0.004 (p < 0.002), Total Cholesterol −0.0040 (p < 0.009), Gamma Glutamyl Transpeptidase 0.009 (p < 0.001), SBP 0.009 (p < 0.036), DBP −0.016 (p < 0.041), ALP 0.008 (p < 0.071) and Widowhood 0.550 (p < 0.001). Conclusions: We produced and validated a model to estimate the probability of death in subjects with MAFLD. The instruments we used showed satisfactory predictive capabilities. [ABSTRACT FROM AUTHOR]

We aimed to explore the clinical outcomes and prognostic factors for PTCL-NOS patients in the real world. Clinical data were retrospectively collected from adult patients with PTCL-NOS treated at a single center in Taiwan. 104 PTCL-NOS patients with a median age of 53.0 years were enrolled. Patients with the International Prognostic Index (IPI) or prognostic index for peripheral T-cell lymphoma (PIT) scores of zero had a longer overall survival (OS) and progression free survival (PFS), while patients with IPI or PIT scores ≥1 did poorly. For patients who are eligible for transplantation, the use of pralatrexate as salvage chemotherapy has shown better OS (2-year OS 83.3% vs. 24.4%, P = 0.011) compared to patients who did not. By multivariate analysis, age >60 years, male, B symptoms, ECOG >1, lung involvement, and thrombocytopenia were independent adverse factors for OS. Incorporating factors in multivariate analysis, we established a novel predictive index for PTCL-NOS which efficiently stratifies patients into low (0–1 factor), intermediate-1 (2 factors), intermediate-2 (3 factors), and high risk (4–6 factors) groups with 2-year OS rates of 81.5%, 32.9%, 8.8%, and 0%, respectively (P < 0.001). PTCL-NOS patients have a dismal prognosis in Taiwan. Novel agents may improve the outcomes of PTCL-NOS patients. The usefulness of the novel prognostic index for PTCL-NOS needs further validation. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer is among the most prevalent malignancies worldwide. This study explores the relationships between angiogenesis-related genes (ARGs) and immune infiltration, and assesses their implications for the prognosis and treatment of cervical cancer. Additionally, it develops a diagnostic model based on angiogenesis-related differentially expressed genes (ARDEGs). Methods: We systematically evaluated 15 ARDEGs using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA). Immune cell infiltration was assessed using a single-sample gene-set enrichment analysis (ssGSEA) algorithm. We then constructed a diagnostic model for ARDEGs using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and evaluated the diagnostic value of this model and the hub genes in predicting clinical outcomes and immunotherapy responses in cervical cancer. Results: A set of ARDEGs was identified from the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and UCSC Xena database. We performed KEGG, GO, and GSEA analyses on these genes, revealing significant involvement in cell proliferation, differentiation, and apoptosis. The ARDEGs diagnostic model, constructed using LASSO regression analysis, showed high predictive accuracy in cervical cancer patients. We developed a reliable nomogram and decision curve analysis to evaluate the clinical utility of the ARDEG diagnostic model. The 15 ARDEGs in the model were associated with clinicopathological features, prognosis, and immune cell infiltration. Notably, ITGA5 expression and the abundance of immune cell infiltration (specifically mast cell activation) were highly correlated. Conclusion: This study identifies the prognostic characteristics of ARGs in cervical cancer patients, elucidating aspects of the tumor microenvironment. It enhances the predictive accuracy of immunotherapy outcomes and establishes new strategies for immunotherapeutic interventions.

Background: This study introduces a novel prognostic tool, the Disulfidoptosis-Related lncRNA Index (DRLI), integrating the molecular signatures of disulfidoptosis and long non-coding RNAs (lncRNAs) with the cellular heterogeneity of the tumor microenvironment, to predict clinical outcomes in patients with clear cell renal cell carcinoma (ccRCC). Methods: We analyzed 530 tumor and 72 normal samples from The Cancer Genome Atlas (TCGA), employing k-means clustering based on disulfidoptosis-associated gene expression to stratify ccRCC samples into prognostic groups. lncRNAs correlated with disulfidoptosis were identified and used to construct the DRLI, which was validated by Kaplan-Meier and receiver operating characteristic curves. We utilized single-cell deconvolution analysis to estimate the proportion of immune cell types within the tumor microenvironment, while the ESTIMATE and TIDE algorithms were employed to assess immune infiltration and potential response to immunotherapy. Results: The Disulfidoptosis-Related lncRNA Index (DRLI) effectively stratified ccRCC patients into high and low-risk groups, significantly impacting survival outcomes (P

Background: Inflammation play a key role for COVID-19 infection and its adverse outcomes. Objectives: The study aimed to evaluate different systemic inflammatory indices as a predictor of COVID-19 infection. Patients and Methods: A cross-sectional study conducted on patients suspected of having COVID-19. PCR was done and laboratory tests including CBC, coagulation profile, D-dimer, CRP, and diverse of established systemic inflammatory ratios were evaluated on admission [Neutrophil/lymphocyte ratio(NLR), platelet/lymphocyte ratio(PLR), lymphocyte/monocyte ratio(LMR), systemic immune-inflammation index(SII), lymphocyte-to-CRP ratio (LCR), prognostic index (PI), Neutrophil platelet score (NPS), systemic inflammatory response syndrome (SIRS), and platelet/monocyte Neutrophil ratio. Results: We included 128 patients, 56 males (43.75%) and 72 females (56.25%), 48 (37.5%) had positive PCR, and 80 (62.5%) had negative PCR. PCR (+) patients had significantly higher NLR (median: 7.37, range: 0.5:158), MLR (median: 0.56, range: 0.03:4.7), and PLR (median: 274.15, range: 13.2:1475) compared to PCR (-) patients (median: 3.46, range: 0.2:24), (median: 0.333, range: 0.03:3.5), (median: 116.7, range: 50.8:878.1) respectively, (P=0.001). PCR (+) patients had a significantly higher PI (p< 0.00001) and NPS (P=0,004). D-Dimer had the highest Area Under Curve (0.979), followed by CRP (0.950), INR (0.919), PC (0.920), PT (0.831), Neutrophil % (0.710), and inflammatory indices PLR (0.748), SII (0.724), NLR (0.698), SIRS (0.674), LMR (0.668), LCR (0.651), and P2/MS (0.605). Conclusion: The calculated inflammatory indices, and the prognostic index can be used to estimate the degree of COVID-19 disease, and combined assessments for multiple inflammatory scores are more accurate in predicting disease severity and offer clinical benefits.

Abstract Background Gastric cancer (GC) is a heterogeneous malignancy with variable clinical outcomes. The immune system has been implicated in GC development and progression, highlighting the importance of immune-related gene expression patterns and their prognostic significance. Objective This study aimed to identify differentially expressed immune-related genes (DEIRGs) and establish a prognostic index for GC patients using comprehensive bioinformatic analyses. Methods We integrated RNA sequencing data from multiple databases and identified DEIRGs by overlapping differentially expressed genes with immune-related genes. Functional enrichment analysis was performed to uncover the biological processes and signaling pathways associated with DEIRGs. We conducted a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules related to with GC. Cox regression analysis was conducted to determine independent prognostic DEIRGs for overall survival prediction. Based on these findings, we developed an immune-related gene prognostic index (IRGPI) based on these findings. The prognostic value of the IRGPI was validated using survival analysis and an independent validation cohort. Functional enrichment analysis, gene mutation analysis, and immune cell profiling were performed to gain insights into the biological functions and immune characteristics associated with the IRGPI-based subgroups. Results We identified 493 DEIRGs significantly enriched in immune-related biological processes and signaling pathways associated with GC. WGCNA analysis revealed a significant module (turquoise module) associated with GC, revealing potential therapeutic targets. Cox regression analysis identified RNASE2, CGB5, CTLA4, and DUSP1 as independent prognostic DEIRGs. The IRGPI, incorporating the expression levels of these genes, demonstrated significant prognostic value in predicting overall survival. The IRGPI-based subgroups exhibited distinct biological functions, genetic alterations, and immune cell compositions. Conclusion Our study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies.

Jiaming He,1,2,&ast; Tiankuo Luan,3,&ast; Gang Zhao,4 Yingxue Yang5 1Laboratory of Stem Cells and Tissue Engineering, Department of Histology and Embryology, Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 2Institute of Life Sciences, Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 3Department of Anatomy, Chongqing Medical University, Chongqing, People’s Republic of China; 4Department of Gastroenterology, Wushan County People’s Hospital of Chongqing, Chongqing, 404700, People’s Republic of China; 5Department of Gastroenterology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yingxue Yang, Email yangyingxue@hospital.cqmu.edu.cnBackground: The objective is to create an IRGPI (Immune-related genes prognostic index), which could predict the survival and effectiveness of immune checkpoint inhibitor (ICI) treatment for lung adenocarcinoma (LUAD).Methods: By applying weighted gene co-expression network analysis (WGCNA), we ascertained 13 genes associated with immune functions. An IRGPI was constructed using four genes through multicox regression, and its validity was assessed in the GEO dataset. Next, we explored the immunological and molecular attributes and advantages of ICI treatment in subcategories delineated by IRGPI. The model genes were also validated by the random forest tree, and functional experiments were conducted to validate it.Results: The IRGPI relied on the genes CD79A, IL11, CTLA-4, and CD27. Individuals categorized as low-risk exhibited significantly improved overall survival in comparison to those classified as high-risk. Extensive findings indicated that the low-risk category exhibited associations with immune pathways, significant infiltration of CD8 T cells, M1 macrophages, and CD4 T cells, a reduced rate of gene mutations, and improved sensitivity to ICI therapy. Conversely, the higher-risk group displayed metabolic signals, elevated frequencies of TP53, KRAS, and KEAP1 mutations, escalated levels of NK cells, M0, and M2 macrophage infiltration, and a diminished response to ICI therapy. Additionally, our study unveiled that the downregulation of IL11 effectively impedes the proliferation and migration of lung carcinoma cells, while also inducing cell cycle arrest.Conclusion: IRGPI is a biomarker with significant potential for predicting the effectiveness of ICI treatment in LUAD patients and is closely related to the microenvironment and clinicopathological characteristics. Keywords: lung adenocarcinoma, immune-related genes, bioinformatics, prognostic index, IL11

Objective: We introduce the comprehensive inflammatory prognostic index (CIPI), a novel prognostic tool for critically ill cerebral infarction patients, designed to meet the urgent need for timely and convenient clinical decisionmaking in this high-risk patient group. Methods: Using exploratory factor analysis on selected indices—neutrophil to lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and systemic immune inflammation index (SIII)—we derived CIPI, a latent variable capturing their combined predictive power. Data from 1,022 patients in the Medical Information Mart for Intensive Care (MIMIC)-IV database were used to develop CIPI-based survival models, with the robustness and area under the receiver operating characteristic curve (AUC) performance of CIPI validated against an independent dataset of 326 patients from the MIMIC-III CareVue subset. The CIPI’s predictive power for in-hospital and intensive care unit (ICU) mortality was assessed through Kaplan–Meier analysis, univariate and multivariate Cox regression models, and time-dependent AUC analysis. Linearity, subgroup sensitivity analyses and interaction effects with CIPI were also evaluated. Results: CIPI was an independent prognostic factor, demonstrating a statistically significant association with in-hospital and ICU mortality, when assessed as a continuous and a categorical variable. It showed a linear relationship with mortality rates and demonstrated stability across most subgroups, with no significant interactions observed. Its predictive capabilities for in-hospital and ICU mortality among critically ill cerebral infarction patients matched those of established prognostic indices in the MIMIC database. Conclusion: Our study indicates that CIPI is a reliable and effective prognostic tool for critically ill cerebral infarction patients in predicting in-hospital and ICU mortality. Its straightforward calculation, rooted in routine blood tests, enhances its practicality, promising significant utility in clinical settings. [ABSTRACT FROM AUTHOR]

Simple Summary: Advanced soft tissue sarcomas (aSTSs) have scarce treatment options due to their low incidence, being considered by the World Health Organization as a rare disease; low investment by principal actors in trial designs; and high variability in treatment responses. The combination of gemcitabine and dacarbazine has been demonstrated to be effective in aSTSs in terms of progression-free survival and overall survival in three phase I–II clinical trials. Some pathological, clinical, and analytical variables have been analyzed as possible prognostic and predictive factors, providing a clue for clinicians to select the most optimal treatment in this setting. Background: The combination of gemcitabine and dacarbazine has exhibited efficacy in terms of progression-free survival (PFS) and overall survival (OS) for aSTSs, albeit without robust confirmation from larger clinical trials. Methods: We conducted a retrospective study in a single institution involving aSTS patients treated with gemcitabine and dacarbazine. Results: 95 patients were assessed, pointing to a benefit in PFS of 3.5 months and an OS of 14.2 months. Patients with translocated histotypes had better PFS, while those with platelet–lymphocyte ratios (PLRs) surpassing a specific threshold or lower albumin levels had poorer overall survival. Conclusions: This study validates previous findings from three phase I–II trials, affirming the utility of this treatment approach in routine clinical practice. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to examine the factors influencing the prognosis of end‐stage head and neck squamous cell carcinoma (HNSCC) and to develop a more useful prognostic index. Patients and methods: This retrospective observational study included 79 patients with end‐stage HNSCC. Patients were grouped by patient factors and prognostic indicators, and overall survival from the start of Best Supportive Care was compared for each group. In addition, overall survival was compared between patients with palliative prognostic index (PPI) ≥6 and neutrophil‐to‐lymphocyte ratio (NLR) ≥10 and patients with PPI <6 or NLR <10. Results: PPI ≥6 and NLR ≥10 were associated with significantly more frequent poor prognoses (p = 0.01 and p = 0.002, respectively). The median survival was 32 days in cases with PPI ≥6 and NLR ≥10, and 64 days for PPI <6 or NLR <10 (p < 0.001). Conclusion: The combination of PPI and NLR is a useful indicator for predicting the prognosis of patients with end‐stage HNSCC. [ABSTRACT FROM AUTHOR]

The Graded Prognostic Assessment (GPA) score has the best accuracy among prognostic scales for patients with brain metastases (BM). A wide range of GPA-derived scales have been established to different types of primary tumor BM. However, there is a high variability between them, and their characteristics have not been described altogether yet. We aim to summarize the features of the existent GPA-derived scales and to compare their predictor factors and their uses in clinical setting. Medline was searched from inception until January 2023 to identify studies related to the development, update, or validation of GPA. The initial search yielded 1,083 results. 16 original studies and 16 validation studies were included, comprising a total of 33,348 patients. 13 different scales were assessed, including: GPA, Diagnosis-Specific GPA, Extracranial Score, Lung-molGPA, Updated Renal GPA, Updated Gastrointestinal GPA, Modified Breast GPA, Integrated Melanoma GPA, Melanoma Mol GPA, Sarcoma GPA, Hepatocellular Carcinoma GPA, Colorectal Cancer GPA, and Uterine Cancer GPA. The most prevalent prognostic predictors were age, Karnofsky Performance Status, number of BM, and presence or absence of extracranial metastases. Treatment modalities consisted of whole brain radiation therapy, stereotactic radiosurgery, surgery, cranial radiotherapy, gamma knife radiosurgery, and BRAF inhibitor therapy. Median survival rates with no treatment and with a specific treatment ranged from 6.1 weeks to 33 months and from 3.1 to 21 months, respectively. Original GPA and GPA-derived scales are valid prognostic tools, but with heterogeneous survival results when compared to each other. More studies are needed to improve scientific evidence of these scales. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is a heterogeneous malignancy with variable clinical outcomes. The immune system has been implicated in GC development and progression, highlighting the importance of immune-related gene expression patterns and their prognostic significance. Objective: This study aimed to identify differentially expressed immune-related genes (DEIRGs) and establish a prognostic index for GC patients using comprehensive bioinformatic analyses. Methods: We integrated RNA sequencing data from multiple databases and identified DEIRGs by overlapping differentially expressed genes with immune-related genes. Functional enrichment analysis was performed to uncover the biological processes and signaling pathways associated with DEIRGs. We conducted a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules related to with GC. Cox regression analysis was conducted to determine independent prognostic DEIRGs for overall survival prediction. Based on these findings, we developed an immune-related gene prognostic index (IRGPI) based on these findings. The prognostic value of the IRGPI was validated using survival analysis and an independent validation cohort. Functional enrichment analysis, gene mutation analysis, and immune cell profiling were performed to gain insights into the biological functions and immune characteristics associated with the IRGPI-based subgroups. Results: We identified 493 DEIRGs significantly enriched in immune-related biological processes and signaling pathways associated with GC. WGCNA analysis revealed a significant module (turquoise module) associated with GC, revealing potential therapeutic targets. Cox regression analysis identified RNASE2, CGB5, CTLA4, and DUSP1 as independent prognostic DEIRGs. The IRGPI, incorporating the expression levels of these genes, demonstrated significant prognostic value in predicting overall survival. The IRGPI-based subgroups exhibited distinct biological functions, genetic alterations, and immune cell compositions. Conclusion: Our study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies. [ABSTRACT FROM AUTHOR]

Prognosis of diffuse large B cell lymphoma (DLBCL) can be predicted by various factors. The most widely used tool for prediction is the international prognostic index (IPI). β2-microglobulin is a tumor marker commonly used in hematological malignancies. β2-microglobulin is well correlated with outcome of DLBCL. It has been used as an adjunctive tool in some scoring systems for prognostication of DLBCL. In this study, we collected data of patients with diagnosis of DLBCL between 2015 and 2019 in our institute. For each patient, IPI was calculated according to published literature. At diagnosis, serum levels of β2-microglobulin were measured in the clinical laboratory and the results were retrieved from medical records. A total of 516 patients (269 male and 247 female) were enrolled for retrospective analysis. The median age was 64 (range 22–96). The median follow-up period was 32.2 months. The median level of β2-microglobulin was 2319 μg/L (normal range < 2366 μg/L in the clinical laboratory). Level of β2-microglobulin was significantly different between survivors and patients who succumbed to the disease. β2-microglobulin level was correlated with tumor stage, extranodal involvement, B symptoms and IPI, suggesting that it may be a good surrogate marker for disease severity and outcome prediction. We selected the intermediate-risk patients for further analysis. Patients with intermediate-risk IPI and high β2-microglobulin levels have overall survival comparable to patients with high-risk IPI, suggesting an important role of β2-microglobulin in subdivision of DLBCL patients. In conclusion, β2-microglobulin levels correlated with outcome of DLBCL. It may be used independently as a prognostic factor. Subdivision of patients with intermediate-risk IPI may identify a group of high-risk patients, which can be helpful in refining plans of treatment and follow-up. [ABSTRACT FROM AUTHOR]

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Abstract Background N6-methyladenosine (m6A) modification is involved in tumorigenesis and progression as well as closely correlated with stem cell differentiation and pluripotency. Moreover, tumor progression includes the acquisition of stemness characteristics and accumulating loss of differentiation phenotype. Therefore, we integrated m6A modification and stemness indicator mRNAsi to classify patients and predict prognosis for LGG. Methods We performed consensus clustering, weighted gene co-expression network analysis, and least absolute shrinkage and selection operator Cox regression analysis to identify an m6A regulation- and mRNAsi-related prognostic index (MRMRPI). Based on this prognostic index, we also explored the differences in immune microenvironments between high- and low-risk populations. Next, immunotherapy responses were also predicted. Moreover, single-cell RNA sequencing data was further used to verify the expression of these genes in MRMRPI. At last, the tumor-promoting and tumor-associated macrophage polarization roles of TIMP1 in LGG were validated by in vitro experiments. Results Ten genes (DGCR10, CYP2E1, CSMD3, HOXB3, CABP4, AVIL, PTCRA, TIMP1, CLEC18A, and SAMD9) were identified to construct the MRMRPI, which was able to successfully classify patients into high- and low-risk group. Significant differences in prognosis, immune microenvironment, and immunotherapy responses were found between distinct groups. A nomogram integrating the MRMRPI and other prognostic factors were also developed to accurately predict prognosis. Moreover, in vitro experiments illustrated that inhibition of TIMP1 could inhibit the proliferation, migration, and invasion of LGG cells and also inhibit the polarization of tumor-associated macrophages. Conclusion These findings provide novel insights into understanding the interactions of m6A methylation regulation and tumor stemness on LGG development and contribute to guiding more precise immunotherapy strategies.

Objective: To investigate whether the use of a simple baseline measurement predicts venous leg ulcer healing at 12 and 24 weeks. Method: This was a secondary analysis of a cohort of four randomised controlled trials (RCTs) of treatments adjuvant to compression. Self-reported ulcer duration, and measured ulcer length and width, to calculate estimated ulcer area, were used to obtain a Margolis index score. The score created three prognostic strata for likelihood to heal within 24 weeks, and the number of participants healed and time-to-healing were compared. Results: There were a total of 802 participants across the four RCTs—408 (50.9%) in two 12-week trials and 394 (49.1%) in two 24-week trials. The mean age of participants was 63.7±17.6 years, and 372 were female (46.4%). The Margolis index score at baseline was 0 for 320 participants (predicted normal healing); 1 for 334 participants; and 2 for 148 participants (both 1 and 2 predicted slow-to-heal). Overall, 248 (77.5%) of those participants who scored 0 at baseline healed within 24 weeks, compared with 182 (54.5%) of participants who scored 1, and 30 (20.3%) participants who scored 2. The median time-to-healing was 40 (24–62) days, 57 (35–100) days and 86.5 (56–151) days, respectively. The area under the receiver operating characteristic curve was 0.69 and 0.77, respectively, for the 12 and 24 week trials. Conclusion: A simple baseline index identifies participants with normal or slow-to-heal wounds and could be used to demonstrate prognostic balance between treatment groups in trials. This approach could also be used in clinical practice to assist with managing expectations and for early identification of patients who may best benefit from adjuvant treatments. [ABSTRACT FROM AUTHOR]

Background: We performed a meta-analysis to investigate the association of the systemic inflammation response index (SIRI) with long-term survival outcomes in patients with gastrointestinal malignancy. Methods: PubMed, Web of Science and Embase were searched for relevant studies evaluating the prognostic significance of the SIRI in gastrointestinal malignancies until May 2023. Results: 30 studies with 10,091 patients were included. The pooled results identified that patients in the high SIRI group had a worse overall survival and disease-free survival, which was observed across various tumor types, tumor stages and primary treatments. Conclusion: An elevated SIRI is negatively associated with worse survival outcomes of gastrointestinal malignancy patients and can be used as a risk stratification index for gastrointestinal malignancies. [ABSTRACT FROM AUTHOR]

Abstract Background The tumor microenvironment (TME) has a central role in the oncogenesis of osteosarcomas. The composition of the TME is essential for the interaction between tumor and immune cells. The aim of this study was to establish a prognostic index (TMEindex) for osteosarcoma based on the TME, from which estimates about patient survival and individual response to immune checkpoint inhibitor (ICI) therapy can be deduced. Methods Based on osteosarcoma samples from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, the ESTIMATE algorithm was used to estimate ImmuneScore and StromalScore. Combined differentially expressed gene analysis, weighted gene co-expression network analyses, the Least Absolute Shrinkage and Selection Operator regression and stepwise regression to construct the TMEindex. The prognostic role of TMEindex was validated in three independent datasets. The molecular and immune characteristics of TMEindex and the impact on immunotherapy were then comprehensively investigated. The expression of TMEindex genes in different cell types and its effects on osteosarcoma cells were explored by scRNA-Seq analysis and molecular biology experiments. Results Fundamental is the expression of MYC, P4HA1, RAMP1 and TAC4. Patients with high TMEindex had worse overall survival, recurrence-free survival, and metastasis-free survival. TMEindex is an independent prognostic factor in osteosarcoma. TMEindex genes were mainly expressed in malignant cells. The knockdown of MYC and P4HA1 significantly inhibited the proliferation, invasion and migration of osteosarcoma cells. A high TME index is related to the MYC, mTOR, and DNA replication-related pathways. In contrast, a low TME index is related to immune-related signaling pathways such as the inflammatory response. The TMEindex was negatively correlated with ImmuneScore, StromalScore, immune cell infiltration, and various immune-related signature scores. Patients with a higher TMEindex had an immune-cold TME and higher invasiveness. Patients with a low TME index were more likely to respond to ICI therapy and achieve clinical benefit. In addition, the TME index correlated with response to 29 oncologic drugs. Conclusions The TMEindex is a promising biomarker to predict the prognosis of patients with osteosarcoma and their response to ICI therapy, and to distinguish the molecular and immune characteristics.

Background & Aims: Fatty liver disease with metabolic dysfunction (MAFLD) is a new concept proposed to replace the previous concept of Non-Alcoholic Hepatic Steatosis (NAFLD). We developed and internally validated a prognostic model to predict the likelihood of death in a cohort of subjects with MAFLD. Methods: Our work involved two steps: the first was the construction of a bootstrapped multivariable Cox model for mortality risk prognosis and the second was its validation. Results: The study cohort included 1506 subjects, of which 907 were used for internal validation. Discriminant measures for the final model were R2D 0.6845 and Harrell’s C 0.8422 in the development and R2D 0.6930 and Harrell’s C 0.8465 in the validation. We used the nine independent prognostic factors selected by the LASSO Cox procedure and fitted by the bootstrap Cox survival model, and observed β were: Gender 0.356 1.42 (p < 0.008), Age 0.146 (p < 0.001), Glycemia 0.004 (p < 0.002), Total Cholesterol −0.0040 (p < 0.009), Gamma Glutamyl Transpeptidase 0.009 (p < 0.001), SBP 0.009 (p < 0.036), DBP −0.016 (p < 0.041), ALP 0.008 (p < 0.071) and Widowhood 0.550 (p < 0.001). Conclusions: We produced and validated a model to estimate the probability of death in subjects with MAFLD. The instruments we used showed satisfactory predictive capabilities.

Background: Progression of disease within 24 months (POD24) is a risk factor for poor survival in follicular lymphoma (FL), and there is currently no optimal prognostic model to accurately predict patients with early disease progression. How to combine traditional prognostic models with new indicators to establish a new prediction system, to predict the early progression of FL patients more accurately is a future research direction. Methods: This study retrospectively analyzed patients with newly diagnosed FL patients in Shanxi Provincial Cancer Hospital from January 2015 to December 2020. Data from patients undergoing immunohistochemical detection (IHC) were analyzed using c2 test and multivariate Logistic regression. Also, we built a nomogram model based on the results of LASSO regression analysis of POD24, which was validated in both the training set and validation set, and additional external validation was performed using a dataset (n = 74) from another center, Tianjin Cancer Hospital. Results: The multivariate Logistic regression results suggest that high-risk PRIMA-PI group, Ki-67 high expression represent risk factors for POD24 (P<0.05). Next, PRIMA-PI and Ki67 were combined to build a new model, namely, PRIMA-PIC to reclassify high and low-risk groups. The result showed that the new clinical prediction model constructed by PRIMA-PI with ki67 has a high sensitivity to the prediction of POD24. Compared to PRIMA-PI, PRIMA-PIC also has better discrimination in predicting patient's progression-free survival (PFS) and overall survival (OS). In addition, we built nomogram models based on the results of LASSO regression (histological grading, NK cell percentage, PRIMA-PIC risk group) in the training set, which were validated using internal validation set and external validation set, we found that C-index and calibration curve showed good performance. Conclusion: As such, the new predictive model-based nomogram established by PRIMA-PI and Ki67 could well predict the risk of POD24 in FL patients, which boasts clinical practical value. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is a highly prevalent and fatal cancer. The role of PANoptosis, a novel form of programmed cell death, in HCC is yet to be fully understood. This study focuses on identifying and analyzing PANoptosisassociated differentially expressed genes in HCC (HPAN_DEGs), aiming to enhance our understanding of HCC pathogenesis and potential treatment strategies. Methods: We analyzed HCC differentially expressed genes from TCGA and IGCG databases and mapped them to the PANoptosis gene set, identifying 69 HPAN_DEGs. These genes underwent enrichment analyses, and consensus clustering analysis was used to determine three distinct HCC subgroups based on their expression profiles. The immune characteristics and mutation landscape of these subgroups were evaluated, and drug sensitivity was predicted using the HPANindex and relevant databases. Results: The HPAN_DEGs were mainly enriched in pathways associated with the cell cycle, DNA damage, Drug metabolism, Cytokines, and Immune receptors. We identified three HCC subtypes (Cluster_1, SFN+PDK4-; Cluster_2, SFN-PDK4+; Cluster_3, SFN/PDK4 intermediate expression) based on the expression profiles of the 69 HPAN_DEGs. These subtypes exhibited distinct clinical outcomes, immune characteristics, and mutation landscapes. The HPAN-index, generated by machine learning using the expression levels of 69 HPAN_DEGs, was identified as an independent prognostic factor for HCC. Moreover, the high HPAN-index group exhibited a high response to immunotherapy, while the low HPAN-index group showed sensitivity to small molecule targeted drugs. Notably, we observed that the YWHAB gene plays a significant role in Sorafenib resistance. Conclusion: This study identified 69 HPAN_DEGs crucial to tumor growth, immune infiltration, and drug resistance in HCC. Additionally, we discovered three distinct HCC subtypes and constructed an HPAN-index to predict immunotherapeutic response and drug sensitivity. Our findings underscore the role of YWHAB in Sorafenib resistance, presenting valuable insights for personalized therapeutic strategy development in HCC. [ABSTRACT FROM AUTHOR]

Background: N6-methyladenosine (m6A) modification is involved in tumorigenesis and progression as well as closely correlated with stem cell differentiation and pluripotency. Moreover, tumor progression includes the acquisition of stemness characteristics and accumulating loss of differentiation phenotype. Therefore, we integrated m6A modification and stemness indicator mRNAsi to classify patients and predict prognosis for LGG. Methods: We performed consensus clustering, weighted gene co-expression network analysis, and least absolute shrinkage and selection operator Cox regression analysis to identify an m6A regulation- and mRNAsi-related prognostic index (MRMRPI). Based on this prognostic index, we also explored the differences in immune microenvironments between high- and low-risk populations. Next, immunotherapy responses were also predicted. Moreover, single-cell RNA sequencing data was further used to verify the expression of these genes in MRMRPI. At last, the tumor-promoting and tumor-associated macrophage polarization roles of TIMP1 in LGG were validated by in vitro experiments. Results: Ten genes (DGCR10, CYP2E1, CSMD3, HOXB3, CABP4, AVIL, PTCRA, TIMP1, CLEC18A, and SAMD9) were identified to construct the MRMRPI, which was able to successfully classify patients into high- and low-risk group. Significant differences in prognosis, immune microenvironment, and immunotherapy responses were found between distinct groups. A nomogram integrating the MRMRPI and other prognostic factors were also developed to accurately predict prognosis. Moreover, in vitro experiments illustrated that inhibition of TIMP1 could inhibit the proliferation, migration, and invasion of LGG cells and also inhibit the polarization of tumor-associated macrophages. Conclusion: These findings provide novel insights into understanding the interactions of m6A methylation regulation and tumor stemness on LGG development and contribute to guiding more precise immunotherapy strategies. [ABSTRACT FROM AUTHOR]

Bayes linear kinematics and Bayes linear Bayes graphical models provide an extension of Bayes linear methods so that full conditional updates may be combined with Bayes linear belief adjustment. The use of Bayes linear kinematics eliminates the problem of non-commutativity which was observed in earlier work involving moment-based belief updates. In this paper we describe this approach and investigate its application to the rapid computation of prognostic index values in survival when a patient's values may only be available for a subset of covariates. We consider the use of covariates of various kinds and introduce the use of non-conjugate marginal updates. We apply the technique to an example concerning patients with non-Hodgkin's lymphoma, in which we treat the linear predictor of the lifetime distribution as a latent variable and use its expectation, given whatever covariates are available, as a prognostic index. [ABSTRACT FROM AUTHOR]

Introduction: The Chronic Lymphocytic Leukemia International Prognostic Index (CLL-IPI) is a powerful prognostic tool validated in multiple Western populations. However, its utility in the young Middle Eastern population is unknown. Methodology: We conducted a retrospective analysis of 152 unselected patients with chronic lymphocytic leukemia (CLL) diagnosed between 2008 and 2022 at the Kuwait Cancer Control Center, which serves as the sole cancer center in Kuwait. The evaluation of the CLL-IPI was based on the assessment of event-free survival (EFS) across the entire cohort. Subsequently, we compared the CLL-IPI with the International Prognostic Score for Early-stage patients (IPS-E) in order to predict the time to first treatment specifically within the subgroup of patients diagnosed with early-stage disease. Results: The median age of the study cohort was 59.9 years (IQR, 53.1–68.8). The 5-year EFS rates for the low, intermediate, and high/very high-risk categories were approximately 82%, 34%, and 23%, respectively, p < 0.001 (C-statistic = 0.67). On multivariate analysis, advanced stage and unmated IGHV status were independent prognostic factors of EFS. In those with early-stage disease, cumulative 5-year treatment incidence rates for the low, intermediate, and high/very high-risk categories based on the CLL-IPI score were approximately 8%, 55%, and 55%, respectively, p = 0.001 (C-statistic = 0.70). However, based on the IPS-E score, the cumulative 5-year treatment incidence rates for the low, intermediate, and high-risk categories were approximately 0%, 10%, and 60%, respectively, p < 0.001 (C-statistic = 0.73). Conclusions: The CLL-IPI and the IPS-E are valid stratification tool in our young Middle Eastern population. [ABSTRACT FROM AUTHOR]

Key messages 1. Development a prognostic index of poor renal evolution in patients with LN. 2. We use clinical, histological and laboratory factors 6 months after diagnosis and treatment 3. Effective tool in the early detection and management, easy to use in clinical practice

Background: The tumor microenvironment (TME) has a central role in the oncogenesis of osteosarcomas. The composition of the TME is essential for the interaction between tumor and immune cells. The aim of this study was to establish a prognostic index (TMEindex) for osteosarcoma based on the TME, from which estimates about patient survival and individual response to immune checkpoint inhibitor (ICI) therapy can be deduced. Methods: Based on osteosarcoma samples from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, the ESTIMATE algorithm was used to estimate ImmuneScore and StromalScore. Combined differentially expressed gene analysis, weighted gene co-expression network analyses, the Least Absolute Shrinkage and Selection Operator regression and stepwise regression to construct the TMEindex. The prognostic role of TMEindex was validated in three independent datasets. The molecular and immune characteristics of TMEindex and the impact on immunotherapy were then comprehensively investigated. The expression of TMEindex genes in different cell types and its effects on osteosarcoma cells were explored by scRNA-Seq analysis and molecular biology experiments. Results: Fundamental is the expression of MYC, P4HA1, RAMP1 and TAC4. Patients with high TMEindex had worse overall survival, recurrence-free survival, and metastasis-free survival. TMEindex is an independent prognostic factor in osteosarcoma. TMEindex genes were mainly expressed in malignant cells. The knockdown of MYC and P4HA1 significantly inhibited the proliferation, invasion and migration of osteosarcoma cells. A high TME index is related to the MYC, mTOR, and DNA replication-related pathways. In contrast, a low TME index is related to immune-related signaling pathways such as the inflammatory response. The TMEindex was negatively correlated with ImmuneScore, StromalScore, immune cell infiltration, and various immune-related signature scores. Patients with a higher TMEindex had an immune-cold TME and higher invasiveness. Patients with a low TME index were more likely to respond to ICI therapy and achieve clinical benefit. In addition, the TME index correlated with response to 29 oncologic drugs. Conclusions: The TMEindex is a promising biomarker to predict the prognosis of patients with osteosarcoma and their response to ICI therapy, and to distinguish the molecular and immune characteristics. [ABSTRACT FROM AUTHOR]

Background: The efficacy of nivolumab and atezolizumab in advanced pre-treated NSCLC was documented in prospective trials. We aim to confirm the benefits and indicate predictive factors for immunotherapy in daily practice. Methods: This study was a retrospective analysis. The median PFS and OS were estimated using the Kaplan-Meier method. The log-rank test was used for comparisons. Multivariate analyses were performed using the Cox regression method. Results: A total of 260 patients (ECOG 0-1) with advanced NSCLC (CS III-IV) were eligible to receive nivolumab or atezolizumab as second-line treatment. Median PFS and OS were three months (95% confidence interval [CI] 2.57–3.42) and 10 months (95% CI 8.03–11.96), respectively, for the overall population. The median OS for the atezolizumab arm was eight months (95% CI 5.89–10.1), while for the nivolumab group, it was 14 months (95% CI 10.02–17.97) (p = 0.018). The sum of all measurable changes >100.5 mm (p = 0.007; HR = 1.003, 95% CI 1.001–1.005), PLT > 281.5 G/l (p < 0.001; HR = 1.003, 95% CI 1.001–1.003) and bone metastases (p < 0.004; HR = 1.58, 95% CI 1.04–2.38) were independent negative prognostic factors for OS in multivariate analysis. Based on preliminary analyses, a prognostic index was constructed to obtain three prognostic groups. Median OS in the subgroups was 16 months (95% CI 13.3–18.7), seven months (95% CI 4.83–9.17) and four months (95% CI 2.88–5.13), respectively (p < 0.001). Conclusions: Nivolumab and atezolizumab provided clinical benefit in real life. Clinical and laboratory factors may help to identify subgroups likely to benefit. The use of prognostic indices may be valuable in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia was recently identified as a poor prognostic factor in patients with malignant tumors. The present study investigated the effect of the preoperative albumin–globulin score (AGS), skeletal muscle index (SMI), and combination of AGS and SMI (CAS) on short- and long-term survival outcomes following deceased donor liver transplantation (DDLT) for hepatocellular carcinoma (HCC) and aimed to identify prognostic factors. Methods: A total of 221 consecutive patients who underwent DDLT for HCC were enrolled in this retrospective study between January 2015 and December 2019. The skeletal muscle cross-sectional area was measured by CT (computed tomography). Clinical cutoffs of albumin (ALB), globulin (GLB), and sarcopenia were defined by receiver operating curve (ROC). The effects of the AGS, SMI, and CAS grade on the preoperative characteristics and long-term outcomes of the included patients were analyzed. Results: Patients who had low AGS and high SMI were associated with better overall survival (OS) and recurrence-free survival (RFS), shorter intensive care unit (ICU) stay, and fewer postoperative complications (grade ≥ 3, Clavien–Dindo classification). Stratified by CAS grade, 46 (20.8%) patients in grade 1 were associated with the best postoperative prognosis, whereas 79 (35.7%) patients in grade 3 were linked to the worst OS and RFS. The CAS grade showed promising accuracy in predicting the OS and RFS of HCC patients [areas under the curve (AUCs) were 0.710 and 0.700, respectively]. Male recipient, Child–Pugh C, model for end-stage liver disease (MELD) score > 20, and elevated CAS grade were identified as independent risk factors for OS and RFS of HCC patients after DDLT. Conclusion: CAS grade, a novel prognostic index combining preoperative AGS and SMI, was closely related to postoperative short-term and long-term outcomes for HCC patients who underwent DDLT. Graft allocation and clinical decision making may be referred to CAS grade evaluation. [ABSTRACT FROM AUTHOR]

Background: The combination of gemcitabine and dacarbazine has exhibited efficacy in terms of progression-free survival (PFS) and overall survival (OS) for aSTSs, albeit without robust confirmation from larger clinical trials. Methods: We conducted a retrospective study in a single institution involving aSTS patients treated with gemcitabine and dacarbazine. Results: 95 patients were assessed, pointing to a benefit in PFS of 3.5 months and an OS of 14.2 months. Patients with translocated histotypes had better PFS, while those with platelet–lymphocyte ratios (PLRs) surpassing a specific threshold or lower albumin levels had poorer overall survival. Conclusions: This study validates previous findings from three phase I–II trials, affirming the utility of this treatment approach in routine clinical practice.

ObjectiveDespite viral suppression and immune response on antiretroviral therapy, people with HIV infection experience excess mortality compared with uninfected individuals. The Veterans Aging Cohort Study (VACS) Index incorporates clinical biomarkers of general health with age, CD4 cell count, and HIV-1 RNA to discriminate mortality risk in a variety of HIV-positive populations. We asked whether additional biomarkers further enhance discrimination.Design and methodsUsing patients from VACS for development and from the Antiretroviral Therapy Cohort Collaboration (ART-CC) for validation, we obtained laboratory values from a randomly selected visit from 2000 to 2014, at least 1 year after antiretroviral therapy initiation. Patients were followed for 5-year, all-cause mortality through September 2016. We fitted Cox models with established predictors and added new predictors based on model fit and Harrell's c-statistic. We converted all variables to continuous functional forms and selected the best model (VACS Index 2.0) for validation in ART-CC patients. We compared discrimination using c-statistics and Kaplan-Meier plots.ResultsAmong 28 390 VACS patients and 12 109 ART-CC patients, 7293 and 722 died, respectively. Nadir CD4, CD8, and CD4 : CD8 ratio did not improve discrimination. Addition of albumin, white blood count, and BMI, improved c-statistics in VACS from 0.776 to 0.805 and in ART-CC from 0.800 to 0.831. Results were robust in all nine ART-CC cohorts, all lengths of follow-up and all subgroups.ConclusionVACS Index 2.0, adding albumin, white blood count, and BMI to version 1.0 and using continuous variables, provides improved discrimination and is highly transportable to external settings.

ObjectiveDespite viral suppression and immune response on antiretroviral therapy, people with HIV infection experience excess mortality compared with uninfected individuals. The Veterans Aging Cohort Study (VACS) Index incorporates clinical biomarkers of general health with age, CD4 cell count, and HIV-1 RNA to discriminate mortality risk in a variety of HIV-positive populations. We asked whether additional biomarkers further enhance discrimination.Design and methodsUsing patients from VACS for development and from the Antiretroviral Therapy Cohort Collaboration (ART-CC) for validation, we obtained laboratory values from a randomly selected visit from 2000 to 2014, at least 1 year after antiretroviral therapy initiation. Patients were followed for 5-year, all-cause mortality through September 2016. We fitted Cox models with established predictors and added new predictors based on model fit and Harrell's c-statistic. We converted all variables to continuous functional forms and selected the best model (VACS Index 2.0) for validation in ART-CC patients. We compared discrimination using c-statistics and Kaplan-Meier plots.ResultsAmong 28 390 VACS patients and 12 109 ART-CC patients, 7293 and 722 died, respectively. Nadir CD4, CD8, and CD4 : CD8 ratio did not improve discrimination. Addition of albumin, white blood count, and BMI, improved c-statistics in VACS from 0.776 to 0.805 and in ART-CC from 0.800 to 0.831. Results were robust in all nine ART-CC cohorts, all lengths of follow-up and all subgroups.ConclusionVACS Index 2.0, adding albumin, white blood count, and BMI to version 1.0 and using continuous variables, provides improved discrimination and is highly transportable to external settings.

Aim: Palliative medicine provides holistic care to increase the quality of life of patients. Predicting prognosis is critical for a personalized treatment plan. Therefore, we aimed to investigate the survival prediction properties of routine biochemistry tests, complete blood count (CBC) and neutrophil/lymphocyte ratios, in addition to biomarker-based indices (the mGPS, PI, and PNI). Materials and methods: The laboratory parameter values, prognostic factor scores, diagnoses and survival time of 139 palliative care patients in the last 5 weeks of their life were evaluated retrospectively. Results: Ninety-one (65.5%) patients were male and the average age was 65.9 years (28–91 years). Results of all three prognostic indices (the mGPS, PI, and PNI) investigated in this study were found to have a statistically significant relationship with the survival time of our patients. In addition, NLR, neutrophil percent, hemoglobin, serum albumin and CRP values were seen to have a statistically significant relationship with the scores of all three prognostic indices. Moreover, hand it is detected that WBC, NLR, albumin, and CRP values were associated with overall survival. Conclusion: The results of our study, which showed that these indices have prognostic markers in palliative care patients, will guide the management of the clinical process. We would like to emphasize the importance of palliative care clinicians in developing personalized treatment plans for patients using prognostic tools for survival prediction.

Yong Luo,1 Guian Zhang2 1Department of Urology, the Second People’s Hospital of Foshan, Affiliated Foshan Hospital of Southern Medical University, Foshan, 528000, People’s Republic of China; 2School of Medicine, South China University of Technology, Guangzhou, 510006, People’s Republic of ChinaCorrespondence: Yong Luo, Department of Urology, the Second People’s Hospital of Foshan, Affiliated Foshan Hospital of Southern Medical University, 78 Weiguo Road, Foshan, 528000, People’s Republic of China, Tel +86-15625093895, Fax +86-0757-88032009, Email luomou5@outlook.com Guian Zhang, School of Medicine, South China University of Technology, Guangzhou, 510006, People’s Republic of China, Tel +86-13246808932, Email machfine@gmail.comBackground: Kidney renal clear cell carcinoma (KIRC) is one of the most common aggressive malignancies in the genitourinary system with the high degree of immune infiltration. However, the role of necroptosis-related genes in the immune infiltration of KIRC and the impact on overall survival have not been adequately studied.Methods: Differentially expressed necroptosis-related genes were identified based on The Cancer Genome Atlas (TCGA). Then, we constructed a necroptosis-related prognostic index (NRPI) through Lasso Cox regression analysis. The KIRC patients were divided into NRPI-high and NRPI-low groups by the median. Univariate and multivariate Cox regression analyses were used to determine NRPI as an independent prognostic factor. The role of NRPI was assessed through nomogram, GO/KEGG enrichment analyses, and immune cells infiltration. The efficacy of immunotherapy in KIRC patients was evaluated by TIDE. The immunohistochemistry was performed to verify the difference in protein expression between tumor samples and normal tissues from our hospital.Results: We found that NRPI-high patients had higher mortality. The multivariate Cox regression between the signature and multiple clinicopathological characteristics proved that NRPI could effectively and independently predict the prognosis of KIRC. The protein expression of three necroptosis-related genes constituting NRPI was significantly different between tumor and normal tissues. NRPI was closely related to immunologically relevant pathways and functions. The tumor microenvironment and immune infiltrating cells showed clear distinctions in NRPI-high and NRPI-low patients. The analysis of clinical treatments found that NRPI-low patients responded better to immunotherapy, while NRPI-high patients were more sensitive to targeted therapy. Furthermore, we identified a lncRNAs/miRNA/mRNA regulatory axis for KIRC.Conclusion: In general, NRPI was a promising biomarker in predicting the prognosis and responses to treatments in KIRC.Keywords: kidney renal clear cell carcinoma, necroptosis, prognostic index, immune infiltration, immunotherapy

Abstract Background Senescent cells have been identified in the aging prostate, and the senescence-associated secretory phenotype might be linked to prostate cancer (PCa). Thus, we established a cellular senescence-related gene prognostic index (CSGPI) to predict metastasis and radioresistance in PCa. Methods We used Lasso and Cox regression analysis to establish the CSGPI. Clinical correlation, external validation, functional enrichment analysis, drug and cell line analysis, and tumor immune environment analysis were conducted. All analyses were conducted with R version 3.6.3 and its suitable packages. Results We used ALCAM and ALDH2 to establish the CSGPI risk score. High-risk patients experienced a higher risk of metastasis than their counterparts (HR: 10.37, 95% CI 4.50–23.93, p

Aims: As sex hormone-dependent tumors, it remains to be clarified whether there is a common genetic signature and its value between breast and endometrial cancers. The aim of this study was to establish the shared sex hormone metabolism-related gene prognostic index (SHMRGPI) between breast and endometrial cancers and to analyze its potential role in the therapeutic and prognostic assessment of endometrial cancers. Methods: Using transcriptome data from TCGA, tumor-associated gene modules were identified by weighted gene co-expression network analysis, and the intersection of module genes with female sex hormone synthesis and metabolism genes was defined as sex hormone metabolism-related gene. SHMRGPI was established by the least absolute shrinkage and selection operator and Cox regression. Its prognostic value of patients with endometrial cancer was validated, and a nomogram was constructed. We further investigated the relationship between SHMRGPI groups and clinicopathological features, immune infiltration, tumor mutation burden, and drug sensitivity. Results: A total of 8 sex hormone metabolism-related gene were identified as key genes for the construction of prognostic models. Based on SHMRGPI, endometrial cancer patients were divided into high and low SHMRGPI groups. Patients in the low SHMRGPI group had longer overall survival (OS) compared with the high group (P< 0.05). Furthermore, we revealed significant differences between SHMRGPI groups as regards tumor immune cell infiltration, somatic mutation, microsatellite instability and drug sensitivity. Patients with low SHMRGPI may be the beneficiaries of immunotherapy and targeted therapy. Conclusions: The SHMRGPI established in this study has prognostic power and may be used to screen patients with endometrial cancer who may benefit from immunotherapy or targeted therapy [ABSTRACT FROM AUTHOR]

Background: Currently we do not have an ideal biomarker in lupus nephritis (LN) that should help us to identify those patients with SLE at risk of developing LN or to determine those patients at risk of renal progression. We aimed to evaluate the development of a prognostic index for LN, through the evaluation of clinical, analytical and histological factors used in a cohort of lupus. We have proposed to determine which factors, 6 months after the diagnosis of LN, could help us to define which patients will have a worse evolution of the disease and may be, more aggressive treatment and closer follow-up. Methods: A retrospective study to identify prognostic factors was carried out. We have included patients over 18 years of age with a clinical diagnosis of systemic lupus erythematosus (SLE) and kidney involvement confirmed by biopsy, who are followed up in our centre during the last 20 years. A multi-step statistical approach will be used in order to obtain a limited set of parameters, optimally selected and weighted, that show a satisfactory ability to discriminate between patients with different levels of prognosis. Results: We analysed 92 patients with LN, although only 73 have been able to be classified according to whether or not they have presented poor renal evolution. The age of onset (44 vs. 32; p = 0.024), the value of serum creatinine (1.41 vs. 1.04; p = 0.041), greater frequency of thrombocytopenia (30 vs. 7%; p = 0.038), higher score in the renal chronicity index (2.47 vs. 1.04; p = 0.015), proliferative histological type (100%) and higher frequency of interstitial fibrosis (67 vs. 32%; p = 0.017) and tubular atrophy (67 vs. 32%; p = 0.018) was observed between two groups. The multivariate analysis allowed us to select the best predictive model for poor outcome at 6 months based on different adjustment and discrimination parameters. Conclusion: We have developed a prognostic index of poor renal evolution in patients with LN that combines demographic, clinical, analytical and histopathological factors, easy to use in routine clinical practice and that could be an effective tool in the early detection and management. Key messages: Development a prognostic index of poor renal evolution in patients with LN. We use clinical, histological and laboratory factors 6 months after diagnosis and treatment Effective tool in the early detection and management, easy to use in clinical practice [ABSTRACT FROM AUTHOR]

Purpose Prognostic Index for Natural Killer Lymphoma (PINK) is the most widely accepted prognostic model for patients with extranodal natural killer/T-cell lymphoma (ENKTL) treated with non-anthracycline–based therapy. We aimed to evaluate the prognostic implications of serum β-2 microglobulin (β2M) in the context of PINK and proposed a new prognostic model. Materials and Methods A total of 138 patients who were newly diagnosed with ENKTL and treated with non-anthracycline-based chemotherapy were identified. The cut-off value of high serum β2M was calculated by maximal-chi square methods (4.1 mg/L). A new prognostic model incorporating serum β2M into PINK was proposed and validated in an independent validation cohort (n=88). Results The patients’ median age was 53.5 years (range, 19 to 80 years). Patients with high serum β2M levels had significantly worse overall survival (OS) and progression-free survival (PFS). In multivariate analysis, high serum β2M was an independent adverse prognostic factor for OS. A new PINK-B (Prognostic Index for Natural Killer Lymphoma-serum β-2 microglobulin) model stratified patients into three groups with distinct OS and PFS in the training cohort (3-year OS, 84.1% [95% confidence interval, 75.1 to 94.2], 46.8% [36.1 to 60.8] and 17.6% [6.3 to 49.2] for the low-, intermediate, and high-risk groups, respectively; 3-year PFS, 70.6% [59.4 to 83.8], 35.9% [25.9 to 49.8], and 7.35% [1.1 to 46.7] for the low-, intermediate-, and high-risk groups, respectively). The PINK-B model was further validated in an independent cohort. Conclusion Serum β2M is an independent prognostic factor for ENKTL patients. The new serum β2M-based prognostic model may be useful for identifying ultra-high-risk patients, and it can easily be adopted into daily clinical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: The landscape of treatment of patients with melanoma brain metastases (MBM) is continually evolving. We report the real-world data on 531 internationally treated patients with MBM before and after 2017 and their prognosis and treatment outcomes before the introduction of combined immunotherapy. We aimed to analyze trends in survival probability and their relevance to the currently used prognostic index, melanoma mol-GPA, and to some presumed prognostic and predictive factors not included in mol-GPA, such as symptoms occurrence and use of steroids. We have observed significant improvement in the survival of patients with the poorest mol-GPA prognosis. In our prognostic model, the presence of symptoms associated with brain metastases predicted a worse response to immune checkpoint inhibitors; however, symptoms without steroid use did not have prognostic significance. The prognosis of patients with MBM has been improving over the years due to the introduction of modern local and systemic treatment options across all mol-GPA prognostic groups. Stage IV melanoma patients develop melanoma brain metastases (MBM) in 50% of cases. Their prognosis is improving, and its understanding outside the context of clinical trials is relevant. We have retrospectively analyzed the clinical data, course of treatment, and outcomes of 531 subsequent stage IV melanoma patients with BM treated in five reference Italian and Polish melanoma centers between 2014 and 2021. Patients with MBM after 2017 had a better prognosis, with a significantly improved median of overall survival (OS) after 2017 in the worst mol-GPA prognostic groups (mol-GPA ≤ 2): a median OS >6 months and HR 0.76 vs. those treated before 2017 (CI: 0.60–0.97, p = 0.027). In our prognostic model, mol-GPA was highly predictive for survival, and symptoms without steroid use did not have prognostic significance. Local therapy significantly improved survival regardless of the year of diagnosis (treated before or after 2017), with median survival >12 months. Systemic therapy improved outcomes when it was combined with local therapy. Local surgery was associated with improved OS regardless of the timing related to treatment start (i.e., before or after 30 days from MBM diagnosis). Local and systemic treatment significantly prolong survival for the poorest mol-GPA prognosis. Use of modern treatment modalities is justified in all mol-GPA prognostic groups. [ABSTRACT FROM AUTHOR]