Univariate Cox Regression Analysis Research Articles

Construction of a novel mitochondrial oxidative stress-related genes prognostic system and molecular subtype characterization for breast cancer

PurposeBreast cancer (BRCA) is the most common malignant tumor among women, characterized by high incidence rates and mortality rates. Oxidative stress and immunity, particularly in relation to mitochondria, have emerged as pivotal factors in breast carcinogenesis. Nonetheless, limited research has explored the specific contribution of mitochondrial oxidative stress to the prognosis of BRCA.MethodIn this study, we conducted univariate and multivariate Cox regression analyses to pinpoint independent prognostic genes associated with mitochondrial oxidative stress (MOSRGs) and their correlation with BRCA clinical outcomes. Subsequently, we developed a robust and accurate MOS scoring system for BRCA patients based on these identified independent prognostic MOSRGs.ResultOur findings were further substantiated by immune infiltration and somatic mutation analyses, providing additional evidence that the MOS scoring system holds predictive value for clinical outcomes in patients and correlates directly with three subtypes of BRCA. In vitro experiments in the MCF7 cell and breast tissue further verified the mRNA and protein expression level of independent prognostic genes, validating the consistency of the MOS prognostic signatures in BRCA.ConclusionThis research has unveiled a novel prognostic scoring system, providing valuable insights for improving patient prognosis assessment and developing individualized treatment strategies in BRCA patients.

The Demographic and Clinical Characteristics, Prognostic Factors, and Survival Outcomes of Head and Neck Carcinosarcoma: A SEER Database Analysis

Background: Head and neck carcinosarcoma (HNCS) is a rare and highly aggressive malignancy with limited research, resulting in an incomplete understanding of disease progression and a lack of reliable prognostic tools. This study aimed to retrospectively analyze the clinical characteristics and outcomes of HNCS patients using data from the Surveillance, Epidemiology, and End Results (SEER) database and to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS). Methods: Patients diagnosed with HNCS from 1975 to 2020 were identified in the SEER database. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic indicators, with the optimal model selected using the minimal Akaike Information Criterion (AIC). The identified prognostic factors were incorporated into nomograms to predict OS and CSS. Model performance was assessed using the concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Survival curves were generated using Kaplan–Meier analysis and compared via the log-rank test. Results: A total of 152 HNCS patients were included, with 108 assigned to the training cohort and 44 to the validation cohort in a 7:3 ratio. Prognostic factors including age, primary tumor site, marital status, radiotherapy, chemotherapy, tumor size, pathological grade, and tumor stage were incorporated into the nomogram models. The models demonstrated strong predictive performance, with C-index values for OS and CSS of 0.757 and 0.779 in the training group, and 0.777 and 0.776 in the validation group, respectively. AUC values for predicting 3-, 5-, and 10-year OS were 0.662, 0.713, and 0.761, and for CSS the values were 0.726, 0.703, and 0.693. Kaplan–Meier analysis indicated significantly improved survival for patients with lower risk scores. The 3-, 5-, and 10-year OS rates for the entire cohort were 54.1%, 45.6%, and 35.1%, respectively, and the CSS rates were 62.9%, 57.5%, and 52.2%, respectively. Conclusions: This study provides validated nomograms for predicting OS and CSS in HNCS patients, offering a reliable tool to support clinical decision-making for this challenging malignancy. These nomograms enhance the ability to predict patient prognosis and personalize treatment strategies.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a metastasis-related gene signature for predicting prognosis in lung adenocarcinoma.

Metastasis has been documented as an independent and significant prognostic feature of lung adenocarcinoma (LUAD) patients. However, the underlying genetic and molecular mechanisms responsible for LUAD metastasis and their prognostic significance are not exactly defined. The single-cell transcriptomic profiles of primary and metastatic LUAD samples were integrated as a whole dataset. Enrichment analysis and pseudotime trajectory analysis were performed to illustrate the cellular origins and changes during the metastatic process. The LUAD metastasis-related genes (LMRGs) molecular cluster and signature was constructed through unsupervised consensus clustering and ten machine-learning algorithms in The Cancer Genome Atlas (TCGA) LUAD cohort using ten machine-learning algorithms. Validation of the signature was conducted using four independent cohorts from the Gene Expression Omnibus (GEO) database. Kaplan-Meier, ROC, univariate and multivariate Cox-regression analyses were performed to test the stability of the signature. The gene CCT6A was subjected to knockdown, followed by validation through western blot analysis, flow cytometry, wound healing and transwell-migration assays to determine its potential significance. First, the signaling pathway networks remodeling and metabolic reprogramming were demonstrated to be involved in the metastasis of malignant LUAD cells, which facilitate their extravasation and adaptation to other organs. Furthermore, distinct subtypes of malignant LUAD cells exhibit tissue-specific patterns. Then, two distinct molecular patterns of LMRGs were established, which showed diverse prognoses. A LUAD metastasis-related gene signature (LMRGS) was constructed via a multiple machine-learning-based integrative procedure, which possesses distinctly superior accuracy than most common clinical features and 69 published prognostic signatures. The patients stratified by the signature into high-risk group had a significantly poorer prognosis compared to those in the low-risk group, and this was well validated across different clinical subgroups. In addition, the risk score calculated by LMRGS remained an independent prognostic parameter in both univariate and multivariate Cox regression. Notably, knockdown of CCT6A gene promoted cell apoptosis and decelerated the cell migration obviously. LMRGS could serve as a novel and promising tool to improve clinical outcomes for individual LUAD patients.

Targeting LLT1 as a potential immunotherapy option for cancer patients non-responsive to existing checkpoint therapies in multiple solid tumors

BackgroundHigh levels of LLT1 expression have been found in several cancers, where it interacts with CD161 on NK cells to facilitate tumor immune escape. Targeting LLT1 could potentially relieve this inhibitory signal and enhance anti-tumor responses mediated through NK cells. Using the ‘The Cancer Genome Atlas’ (TCGA) database, we investigated the role of LLT1 in the tumor microenvironment (TME) across various cancers. Identifying such biomarkers could create new therapeutic options for patients in addition to complementing existing immunotherapies.MethodsLLT1 expression was evaluated in 33 cancers using TCGA transcriptome data. Univariate Cox regression analysis was employed to assess the correlation of LLT1 expression with patient survival. The relationship between LLT1 expression with immune infiltrates, immune gene signatures, and cancer genomic biomarkers (TMB, MSI, and MMR) was also investigated. Immunofluorescence studies were conducted to validate LLT1 expression in tumors. Furthermore, using the CRI iAtlas data, we evaluated LLT1 distribution and its correlation with other immune checkpoint genes in patients non-responsive to existing immune checkpoint therapies across multiple solid cancers.ResultsHigh expression of LLT1 was observed in 12 cancers, including BRCA, CHOL, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, STAD, SARC, and PCPG. In certain cancers like COAD, KICH, and KIRC, high LLT1 expression was associated with poor prognosis. Further analysis revealed that upregulated LLT1 was associated with an abundance of NK and T cell infiltrates in the TME, as well as exhaustive immune biomarkers, and inversely associated with pro-inflammatory and tumor suppressor signatures. High LLT1 expression is also positively correlated with genomic biomarkers in certain cancers. Immunofluorescence studies confirmed moderate to high LLT1 expression in immune-resistant prostate cancer, glioma, ovarian cancer, and immune-sensitive liver cancer cell lines. An independent assessment of clinical cohorts from CRI iAtlas showed a correlation of upregulated LLT1 with multiple immunosuppressive genes in patients non-responsive to current ICIs.ConclusionsThe biomarker analysis revealed a clear association between elevated LLT1 expression and an immunosuppressive TME in patient cohorts from TCGA and clinical databases. Therefore, this study provides a foundation for utilizing LLT1 as a potential target to improve clinical responses in ICI non-responsive patients with upregulated LLT1.

Development of a nomogram for predicting pancreatic portal hypertension in patients with acute pancreatitis: a retrospective study

IntroductionPancreatic portal hypertension (PPH) is a rare complication of acute pancreatitis (AP) that can lead to severe gastrointestinal bleeding. The risk factors associated with PPH, as well as the overall...

Molecular characterization of PANoptosis-related genes associated with immune infiltration and prognosis in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic pulmonary disease with unknown pathogenesis and poor prognosis. PANoptosis, a newly identified form of inflammatory programmed cell death, has been implicated in various inflammatory lung diseases. This study aimed to identify differentially expressed PANoptosis-related genes (PRDEGs) associated with immune infiltration and prognosis in IPF, while also establishing a novel prognostic prediction model. A total of 63 PRDEGs were identified from GSE110147 dataset, with 31 exhibiting consistent expression trends in GSE213001. Enrichment analysis indicated that the majority of these PRDEGs were enriched in inflammatory and immune-related pathways. Three key PRDEGs—NLRP3, ATM, and VEGFA—were selected through univariate and multivariable Cox regression analyses. The prognostic prediction model developed from these key PRDEGs demonstrated robust predictive performance. Furthermore, the expression of most PRDEGs was positively correlated with pro-inflammatory immune cells, including macrophages, neutrophils, and CD4+ T cells. Validation of the expression levels of these key PRDEGs was conducted in fibrotic mouse lung tissue. This study suggests that PANoptosis plays a role in IPF, potentially linked to the infiltration of pro-inflammatory immune cells, and may influence disease progression through the regulation of inflammatory immune signaling pathway. A new prognostic prediction model for IPF based on PRDEGs was successfully developed.

Liquid-Liquid Phase Separation in the Prognosis of Lung Adenocarcinoma: An Integrated Analysis.

Lung adenocarcinoma (LUAD) is a highly lethal malignancy. Liquid-Liquid Phase Separation (LLPS) plays a crucial role in targeted therapies for lung cancer and in the progression of lung squamous cell carcinoma. However, the role of LLPS in the progression and prognosis of LUAD remains insufficiently explored. This study employed a multi-step approach to identify LLPS prognosis-related genes in LUAD. First, differential analysis, univariate Cox regression analysis, Random Survival For-est (RSF) method, and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regres-sion analysis were utilized to identify five LLPS prognosis-related genes. Subsequently, LASSO Cox regression was performed to establish a prognostic score termed the LLPS-related progno-sis score (LPRS). Comprehensive analyses were then conducted based on the LPRS, including survival analysis, clinical feature analysis, functional enrichment analysis, and tumor microenvi-ronment assessment. The LPRS was integrated with additional clinicopathological factors to de-velop a prognostic nomogram for LUAD patients. Immunohistochemical validation was per-formed on clinical tissue samples to further validate the findings. Finally, the relationship be-tween KRT6A, one of the identified genes, and epidermal growth factor receptor (EGFR) muta-tions was investigated. The LPRS was established using five LLPS-related genes: IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. Higher LPRS was closely associated with poor survival outcomes, gender, progression-free survival (PFS), and advanced TNM stage. Furthermore, LPRS emerged as an independent prognostic factor for LUAD. A nomogram integrating LPRS, TNM stage, and age demonstrated remarkable predictive accuracy for prognosis among patients with LUAD. LLPS likely influences LUAD prognosis through the activity of IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. KRT6A exhibits significant upregulation in LUAD, particularly in patients with EGFR mutations. This study introduces a novel LPRS model that demonstrates high accuracy in pre-dicting the clinical prognosis of LUAD. Moreover, the findings suggest that KRT6A may play a critical role in the LLPS-mediated malignant progression of LUAD.

Construction and validation of a prognostic signature based on microvascular invasion and immune-related genes in hepatocellular carcinoma

Background: Microvascular invasion (MVI) is an independent risk factor of poor prognosis in hepatocellular carcinoma (HCC) and can be used to guide the diagnosis and treatment of HCC. The immune system serves as an integral role in the incidence and progression of HCC. However, the molecular biology correlation between MVI and tumor immunity and the value of combining the two parameters to predict patient prognosis and HCC response to treatment remain to be evaluated. Results: In this study, we used univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis to establish the MVI and immune-related gene index (MIRGPI) including eight genes. We demonstrated that the MIRGPI was an independent risk factor in predicting the prognosis of HCC. Subsequently, our research established a nomogram model combining pathologic characteristics and verified its good clinical application value. In addition, our study found that the TP53 gene had a higher mutation frequency and a lower degree of immune infiltration in the high-risk group. The low-risk group had higher sensitivity to immunotherapy, sorafenib, and TACE treatment, and the high-risk group had higher sensitivity to common chemotherapeutic agents. Finally, SEMA3C was found to facilitate the proliferation, migration and invasive ability of HCC by in vitro and in vivo experiments, and its mechanism may be associated with the activation of the NF-Κb/EMT signaling pathway. Conclusions: In summary, the MIRGPI signature we developed is a reliable marker for the prediction of prognosis and treatment response, and is important for the prognostic assessment and individualized treatment of HCC.

Application of CT-based foundational AI and radiomics models for prediction of outcome for non-small-cell lung cancer patients treated on the NRG/RTOG 0617 clinical trial

Abstract Objectives To apply CT-based foundational artificial intelligence (AI) and radiomics models for predicting overall survival (OS) for patients with locally advanced non-small cell lung cancer (NSCLC). Methods Data for 449 patients retrospectively treated on the NRG Oncology/Radiation Therapy Oncology Group (RTOG) 0617 clinical trial were analyzed. Foundational AI, radiomics and clinical features were evaluated using univariate cox regression and correlational analyses to determine independent predictors of survival. Several models were fit using these predictors and model performance was evaluated using nested cross-validation and unseen independent test datasets via area under receiver-operator-characteristic curves, AUC’s. Results For all patients, the combined foundational AI and clinical models achieved AUCs of 0.67 for the Random Forest (RF) model. The combined radiomics and clinical models achieved RF AUCs of 0.66. In the low-dose arm, foundational AI alone achieved AUC of 0.67, while AUC for the ensemble radiomics and clinical models was 0.65 for the support vector machine (SVM). In the high-dose arm, AUC values were 0.67 for combined radiomics and clinical models and 0.66 for the foundational AI model. Conclusions This study demonstrated encouraging results for application of foundational AI and radiomics models for prediction of outcomes. More research is warranted to understand the value of ensemble models toward improving performance via complementary information. Advances in knowledge Using foundational AI and radiomics-based models we were able to identify significant signatures of outcomes for NSCLC patients retrospectively treated on a national cooperative group clinical trial. Associated models will be important for application toward prospective patients.

An Innovative Telomere-associated Prognosis Model in AML: Predicting Immune Infiltration and Treatment Responsiveness.

To build an innovative telomere-associated scoring model to predict prognosis and treatment responsiveness in acute myeloid leukemia (AML). AML is a highly heterogeneous malignant hematologic disorder with a poor prognosis. While telomere maintenance is frequently observed in tumors, investigations into telomere-related genes (TRGs) in AML remain limited. This study aimed to identify prognostic TRGs using the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression, evaluate their predictive value, explore the association between TRG scores and immune cell infiltration, and assess the sensitivity of high-scoring AML patients to chemotherapeutic agents. Univariate Cox regression analysis was conducted on the TCGA cohort to identify prognostic TRGs and to develop the TRG scoring model using LASSO-Cox and multivariate Cox regression. Validation was performed on the GSE37642 cohort. Immune cell infiltration patterns were assessed through computational analysis, and the sensitivity to chemotherapeutic agents was evaluated. Thirteen prognostic TRGs were identified, and a seven-TRG scoring model (including NOP10, OBFC1, PINX1, RPA2, SMG5, MAPKAPK5, and SMN1) was developed. Higher TRG scores were associated with a poorer prognosis, as confirmed in the GSE37642 cohort, and remained an independent prognostic factor even after adjusting for other clinical characteristics. The high-score group was characterized by elevated infiltration of B cells, T helper cells, natural killer cells, tumor-infiltrating lymphocytes, regulatory T (Treg) cells, M2 macrophages, neutrophils, and monocytes, along with reduced infiltration of gamma delta T cells, CD4- T cells, and resting mast cells. Moreover, high infiltration of M2 macrophages and Tregs was associated with poor overall survival compared to low infiltration. Notably, high-risk AML patients were resistant to Erlotinib, Parthenolide, and Nutlin-3a, but sensitive to AC220, Midostaurin, and Tipifarnib. Additionally, using RT-qPCR, we observed significantly higher expression of two model genes, OBFC1 and SMN1, in AML tissues compared to control tissues. This innovative TRG scoring model demonstrates considerable predictive value for AML patient prognosis, offering valuable insights for optimizing treatment strategies and personalized medicine approaches. The identified TRGs and associated scoring models could aid in risk stratification and guide tailored therapeutic interventions in AML patients.

Development and validation of a tumor size-stratified prognostic nomogram for patients with gastric signet ring cell carcinoma.

Gastric signet ring cell carcinoma (GSRC) is a rare malignancy without a commonly acknowledged prognostic assessment and treatment system. This study aimed to determine the optimal cut-off value of tumor size (TS), and construct a prognostic nomogram in combination with other independent prognostic factors (PFs) to predict 3year and 5year overall survival (OS) in GSRC patients. From the Surveillance, Epidemiology, and End Results (SEER) database, this study collected 4744 patients diagnosed with GSRC. These patients were randomized into a training cohort (n = 2320,) and a validation cohort (n = 1142). A restricted cubic spline (RCS) was used to determine the cut-off value for TS, and univariate and multivariate Cox regression analyses were performed in the training cohort to identified significant predictors. A prognostic nomogram was constructed to predict OS at 3 and 5years. Concordance index (C index), receiver operating characteristics curve (ROC curve), area under curve (AUC), and calibration curve were used to test the predictive accuracy of the model. A non-linear relationship was observed between TS and the risk of OS in GSRC, with TS thresholds at 4.4cm and 9.6cm. Survival was significantly lower in GSRC patients with TS > 4.4cm. Age, marriage, chemotherapy, surgery, TS, SEER stage, regional lymph node status, and total number were independent predictors of OS. The C index in the training cohort was 0.748, and the AUC values for both 3- and 5-year OS were higher than 0.80. Similar results were observed in the validation cohort. In addition, the calibration curves showed good agreement between the predicted 3year and 5year OS and the actual OS. TS is a key prognostic factor for patients with GSRC, and patients with a TS of 4.4-9.6cm and > 9.6cm may have a poorer prognosis than those with a TS of < 4.4cm. The TS-stratified nomogram we constructed and validated has favorable accuracy and calibration precision, and may be helpful in predicting the survival rate of patients.

Comprehensive Analysis and Verification of the Prognostic Significance of Cuproptosis-Related Genes in Colon Adenocarcinoma.

Colon adenocarcinoma (COAD) is a frequently occurring and lethal cancer. Cuproptosis is an emerging type of cell death, and the underlying pathways involved in this process in COAD remain poorly understood. Transcriptomic and clinical data for COAD patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We investigated alterations in DNA and chromatin of cuproptosis-related genes (CRGs) in COAD. In order to identify predictive differentially expressed genes (DEGs) and various molecular subtypes, we used consensus cluster analysis. Through univariate, multivariate, and Lasso Cox regression analyses, four CRGs were identified. A risk prognostic model for cuproptosis characteristics was constructed based on four CRGs. This study also examined the association between the risk score and the tumor microenvironment (TME), the immune landscape, and drug sensitivity. We distinguished two unique molecular subtypes using consensus clustering analysis. We discovered that the clinical characteristics, prognosis, and TME cell infiltration characteristics of patients with multilayer CRG subtypes were all connected. The internal and external evaluations of the predicted accuracy of the prognostic model built using data derived from a cuproptosis risk score were completed at the same time. A nomogram and a clinical pathological analysis make it more useful in the field of medicine. A significant rise in immunosuppressive cells was observed in the high cuproptosis risk score group, with a correlation identified between the cuproptosis risk score and immune cell infiltration. Despite generally poor prognoses, the patients with a high cuproptosis risk but low tumor mutation burden (TMB), cancer stem cell (CSC) index, or microsatellite instability (MSI) may still benefit from immunotherapy. Furthermore, the cuproptosis risk score positively correlated with immune checkpoint gene expression. Analyzing the potential sensitivity to medications could aid in the development of clinical chemotherapy regimens and decision-making. CRGs are the subject of our in-depth study, which exposed an array of regulatory mechanisms impacting TME. In addition, we performed additional data mining into clinical features, prognosis effectiveness, and possible treatment medications. COAD's molecular pathways will be better understood, leading to more precise treatment options.

Prognostic role of prostate specific antigen kinetics in primary high volume metastatic hormonal sensitive prostate cancer treated with novel hormonal therapy agents

The prognostic value of prostate-specific antigen (PSA) kinetics in primary high-volume metastatic hormone-sensitive prostate cancer (mHSPC) patients treated with novel hormonal therapy agents is still unclear. Here, we retrospectively reviewed the data of 102 patients with primary high-volume mHSPC who received novel hormonal therapy agents. The median follow-up was 32.25 ± 14.51 months and the median nadir PSA (nPSA) was 0.20 (0.06, 11.71) ng/mL after treatment. The mean time to nPSA was 10.82 ± 7.27 months and 55 patients (53.9%) had a PSA-density (PSA-D) ≤ 0.08 at 3-months. Univariate and multivariate Cox regression analyses showed that the absence of visceral metastases, nPSA ≤ 0.2 and PSA-D ≤ 0.08 were independent prognostic factors for better PFS and OS (all P < 0.05). Moreover, patients with nPSA ≤ 0.2 and PSA-D ≤ 0.08 had the best PFS and OS, and the combination of the nPSA and PSA-D had a better predictive accuracy for PFS and OS than nPSA and PSA-D alone. Thus, Visceral metastases, nPSA and PSA-D were independent prognostic factors for primary high-volume mHSPC patients treated with novel hormonal therapy agents. Patients with lower nPSA and PSA-D had a best survival outcome, and the combination of nPSA and PSA-D had a better effect on prognosis predicting.

Construction of lysosome-related prognostic signature to predict the survival outcomes and selecting suitable drugs for patients with HNSCC.

Lysosomes are digestive organelles responsible for endocytosis and autophagy. Recently, the malignancy and invasiveness head and neck squamous cell carcinoma (HNSCC) has been increasingly studied with the role of lysosomes. A list of lysosome-related genes were obtained from MSigDB. A Spearman correlation and univariate Cox regression analyses combined with differential expression analysis were conducted to detect differentially expressed lysosome-related genes related to prognosis. The prediction of prognostic signature was evaluated by plotting survival curve, ROC, and by developing a nomogram. Immune subtypes, infiltration of immune cells, GSVA, TIDE, IC50 of common chemotherapy and targeted therapy, GO, and KEGG function enrichment analyses were carried out to explore the immune microenvironment of the signature. We constructed a lysosome-related prognostic signature that could function as an independent prognostic indicator for patients with HNSCC. High-risk patients were better suited to receive Doxorubicin, Mitomycin C, Pyrimethamine, anti-PD-L1 and anti-CTLA-4 immunotherapy, whereas low-risk patients had sensitivity to Lapatinib. GO functional enrichment analysis showed that prognostic features were strongly associated with epidermis-related functions (e.g., epidermal cell differentiation, epidermal development, and keratinization). In addition, a KEGG function enrichment analysis revealed a potential relationship between the risk assessment model and cardiomyopathy. We constructed a prognostic signature based on lysosome-related genes and successfully predicted the survival outcome of HNSCC patients, which not only provides potential guidance for personalized treatment but also provides a new idea for precision treatment of HNSCC.

A preliminary study of developing an MRI-based model for postoperative recurrence prediction and treatment direction of intrahepatic cholangiocarcinoma.

To establish an MRI-based predictive model for postoperative recurrence in intrahepatic cholangiocarcinoma (iCCA) and further to evaluate the model utility in treatment direction for neoadjuvant and adjuvant therapies. Totally 114 iCCA patients with curative surgery were retrospectively included, including 38 patients in the neoadjuvant treatment, traditional surgery, and adjuvant treatment groups for each. Predictive variables associated with postoperative recurrence were identified using univariate and multivariate Cox regression analyses, and a prognostic model was formulated. Recurrence-free survival (RFS) curves were compared using log-rank test between MRI-predicted high-risk and low-risk iCCAs stratified by the optimal threshold. Tumor multiplicity (hazard ratio (HR) = 1.671 [95%CI 1.036, 2.695], P = 0.035), hemorrhage (HR = 2.391 [95%CI 1.189, 4.810], P = 0.015), peri-tumor diffusion-weighted hyperintensity (HR = 1.723 [95%CI 1.085, 2.734], P = 0.021), and positive regional lymph node (HR = 2.175 [95%CI 1.295, 3.653], P = 0.003) were independently associated with postoperative recurrence; treatment group was not significantly related to recurrence (P > 0.05). Independent variables above were incorporated for the recurrence prediction model, the 1-year and 2-year time-dependent area under the curve values were 0.723 (95%CI 0.631, 0.815) and 0.725 (95%CI 0.634, 0.816), respectively. After risk stratification, the MRI-predicted high-risk iCCA patients had higher cumulative incidences of recurrence and worse RFS than the low-risk patients (P < 0.001 for both). In the MRI-predicted high-risk patients, neoadjuvant therapy was associated with improved all-stage RFS (P = 0.034), and adjuvant therapy was associated with improved RFS after 4months (P = 0.014). The MRI-based iCCA recurrence predictive model may serve as a decision-making tool for both personalized prognostication and therapy selection.

Predictive Value of GINI and ALBI Grades in Esophageal Cancer Receiving Chemoradiotherapy

Objectives: The principal objective of this study was to assess the predictive efficacy of the global immune–nutrition–inflammation index (GINI) and the albumin–bilirubin (ALBI) score among patients receiving chemoradiotherapy for esophageal cancer. Methods: A retrospective analysis was conducted on 46 patients who received definitive or neoadjuvant radiotherapy for esophageal cancer at our institution. Blood samples were collected from these patients prior to the initiation of radiotherapy to measure the biomarkers, including the C-reactive protein (CRP), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), monocyte–lymphocyte ratio (MLR), the global immune–nutrition–inflammation index (GINI), and the albumin–bilirubin (ALBI) grade. The predictive significance of these biomarkers for progression-free survival (PFS) and overall survival (OS) was evaluated using both univariate and multivariate Cox regression analyses. Results: The median follow-up time for this study was 19.5 months (range: 2.6–166.3 months). Univariate analysis revealed that the platelet count (p = 0.003) and monocyte count (p = 0.04) were significant predictors of PFS. In the multivariate analysis, only the platelet count (p = 0.005) remained an independent predictor of PFS. Univariate analysis demonstrated that the neutrophil count (p = 0.04), lymphocyte count (p = 0.01), NLR (p = 0.005), PLR (p = 0.004), CRP (p = 0.02), ALBI grade (p = 0.01), and GINI (p = 0.005) were significant predictors of OS. Multivariate analysis identified the GINI as a predictor of OS, approaching statistical significance (p = 0.08). Conclusion: The results of our study indicate that the pretreatment GINI and ALBI grades are significantly and independently associated with the OS rates in patients with esophageal cancer who are undergoing chemoradiotherapy.

Surgical management and prognostic factors for Endolymphatic Sac Tumor: a single-institute experience with a systematic review

OBJECTIVETo evaluate the clinical features, surgical outcomes, and predictors of progression-free survival (PFS) in patients with Endolymphatic Sac Tumors (ELSTs). METHODSThis retrospective study analyzed 15 cases from Beijing Tiantan Hospital and 237 from literature (1988-2023), focusing on patients with pathologically confirmed intracranial or skull ELSTs who had comprehensive treatment and follow-up records. Univariate and multivariate Cox regression analyses were employed to identify factors influencing PFS. RESULTSCases from our institute comprised 10 males and 5 females with an average age of 39.1 years. Among these, 86.7% underwent gross total resection (GTR). During the follow-up period, two patients (13.3%) were lost to follow-up. After a mean follow-up of 74.9 months, one patient experienced recurrence and another died from unrelated causes. A review of literature identified 237 additional patients, including 134 females (56.5%), with an average age of 39.8 years; 22.8% of these patients had von Hippel-Lindau disease. The GTR rate was 69.2%. After a mean follow up of 53.2 months, 33 recurrences occurred, and the median PFS was 48 months. Additionally, eight patients died during the follow-up period, none of the deaths were attributed to ELSTs. Multivariate analysis identified GTR (HR 0.279, 95% CI 0.086-0.903, p = 0.033) as a significant protective factor against recurrence among the pooled cases. CONCLUSIONSGTR is crucial for improving PFS in ELST patients, emphasizing the need for advanced surgical techniques and long-term follow-up due to potential recurrences.

Five-gene prognostic model based on autophagy-dependent cell death for predicting prognosis in lung adenocarcinoma

Non-small cell lung adenocarcinoma (LUAD) is the predominant form of lung cancer originating from lung epithelial cells, making it the most prevalent pathological type. Currently, reliable indicators for predicting treatment efficacy and disease prognosis are lacking. Despite extensive validation of autophagy-dependent cell death (ADCD) in solid tumor studies and its correlation with immunotherapy effectiveness and cancer prognosis, systematic research on ADCD-related genes in LUAD is limited. We utilized AddModuleScore, ssGSEA, and WGCNA to identify genes associated with ADCD across single-cell and bulk transcriptome datasets. The TCGA dataset, comprising 598 cases, was randomly divided into training and validation sets to develop an ADCD-related LUAD prediction model. Internal validation was performed using the TCGA validation set. For external validation, datasets GSE13213 (119 LUAD samples), GSE26939 (115 LUAD samples), GSE29016 (39 LUAD samples), and GSE30219 (86 LUAD samples) were employed. We evaluated the model’s accuracy and effectiveness in predicting prognostic risk. Additionally, CIBERSORT, ESTIMATE, and ssGSEA techniques were used to explore immunological characteristics, drug response, and gene expression in LUAD. Real-time RT-PCR was conducted to assess variations in mRNA expression levels of the gene XCR1 between cancerous and normal tissues in 10 lung cancer patients. We identified 249 genes associated with autophagy-dependent cell death (ADCD) at both single-cell and bulk transcriptome levels. Univariate COX regression analysis revealed that 18 genes were significantly associated with overall survival (OS). Using LASSO-Cox analysis, we developed an ADCD signature based on five genes (BIRC3, TAP1, SLAMF1, XCR1, and HLA-DMB) and created the ADCD-related risk scoring system (ADCDRS). Validation of this model demonstrated its ability to predict disease prognosis and its correlation with clinical characteristics, immune cell infiltration, and the tumor microenvironment. To enhance clinical applicability, we integrated an ADCDRS nomogram. Furthermore, we identified potential drugs targeting specific risk subgroups. We successfully identified a model based on five ADCD genes to predict disease prognosis and treatment efficacy in LUAD, as well as to assess the tumor immune microenvironment. An efficient and practical ADCDRS nomogram was designed.

[Artículo traducido] Adherencia al tratamiento con quimioterapia oral en pacientes con cáncer de pulmón no microcítico localmente avanzado o metastásico: protocolo para un estudio de vida real realizado en farmacia hospitalaria

ObjectiveThis article describes a study protocol for evaluating adherence to oral chemotherapy (OCT) in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) in Spain. MethodsThis multicenter, observational, prospective study will be conducted by six hospital pharmacists from six Spanish hospitals. The study will include men and women aged 18 years or older with a diagnosis of locally advanced or metastatic NSCLC who are being treated or have been prescribed OCT. Once included, the patient will be active and prospectively followed up for 3 months, including four study visits to record information on sociodemographic variables, antineoplastic treatment and adherence, pharmaceutical care, clinical variables, and patient-reported outcomes (PRO) (the 3-level version of EQ-5D, the EORTC Core Quality of Life Questionnaire, the Brief Illness Perception Questionnaire, the Treatment Satisfaction with Medicines Questionnaire, and the PRO version of Common Terminology Criteria for Adverse Events). Twelve months after patient inclusion, we will record information on the disease progression status and dispensed prescriptions. The primary outcome is the percentage of treatment adherence that will be calculated based on the pill count as follows: the difference between the number of pills dispensed minus the number of unused pills will be divided by the number of days of treatment multiplied by the number of pills/day prescribed by the oncologist; this quotient will be multiplied by 100 to obtain the percentage of adherence. Based on the that pill count reconciliation, those with a percentage adherence greater than80% will be primarily categorized as adherent. Secondarily, treatment adherence will be also calculated based on the proportion of days covered and the 4-items Morisky Green Levine Medication Adherence Scale. To analyze the impact of patients' and treatment characteristics on adherence, bivariate analyses will be performed using different adherence cut-off points. To evaluate the impact of adherence on treatment efficacy as evaluated by progression-free survival, we will be using the Kaplan–Meier method and compare it with the log-rank test and univariate Cox regression analysis. ConclusionsWe expect that our study will provide initial information on key aspects of adherence to OCT (measurement, facilitators, and barriers) and its relationship with patients' and clinically relevant outcomes in the setting of NSCLC, and that this information will help in designing pharmaceutical interventions to improve adherence.

Shared and specific competing endogenous RNAs network mining in four digestive system tumors

BackgroundDigestive system malignancies, including esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), liver hepatocellular carcinoma (LIHC), and colon adenocarcinoma (COAD), pose significant global health challenges. Identifying shared and distinct regulatory mechanisms across these cancers can lead to improved therapies. This study aims to construct and compare competing endogenous RNA (ceRNA) networks across ESCA, STAD, LIHC, and COAD to identify RNA biomarkers that could serve as precision therapeutic targets to enhance clinical outcomes and advance personalized cancer care. MethodsClinical and transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed to predict differentially expressed RNAs using edgeR package. The ceRNA networks were constructed using the miRcode and ENCORI databases. Functional enrichment analysis and prognostic RNA screening were performed with ConsensusPathDB and univariate Cox regression analysis. Resultswe identified 6, 88, 55, and 41 RNA biomarkers in ESCA, STAD, LIHC, and COAD, respectively. Network analysis revealed shared and specific elements, with shared nodes enriched in cell cycle and mitotic processes. Several biomarkers, including HMGB3 and RGS16 (ESCA), COL4A1 and COL6A3 (STAD), CDCA5 and CDCA8 (LIHC), and LIMK1 and OSBPL3 (COAD), were consistent with prior studies, while novel biomarkers, such as C3P1 (ESCA), P2RY6 (STAD), and N4BP2L1 and PPP1R3B (LIHC), were discovered. Based on RNA correlation analysis, 1, 23, and 2 potential ceRNA regulatory axes were identified in STAD (PVT1/miR-490-3p/HMGA2), LIHC (DLX6-AS1/miR-139-5p/TOP2A, etc.), and COAD (STRCP1 & LINC00488/miR-142-3p/GAB1), respectively. ConclusionsThis study advances the understanding of ceRNA networks in digestive cancers, highlighting RNA biomarkers with potential as therapeutic targets for personalized treatment strategies.