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At least 19 recordsLinked to original sources
PubMed2026

Prognostic value of the lactate-to-albumin ratio in adult sepsis: An updated systematic review of prognostic evidence.

BACKGROUND: The lactate-to-albumin ratio (LAR) has emerged as a potential prognostic biomarker in sepsis. This systematic review evaluated the prognostic value of LAR for mortality in adults with sepsis or septic shock. METHODS: PubMed/MEDLINE, Embase, Web of Science, Scopus, and the Cochrane Library were searched from inception through March 2026. Studies evaluating mortality-related prognostic performance of LAR in adults with sepsis or septic shock were included. Risk of bias was assessed using the Quality In Prognosis Studies (QUIPS) tool. Adjusted odds ratios (ORs) and hazard ratios (HRs) were evaluated separately because of methodological heterogeneity. Discrimination was assessed using study-specific area under the curve (AUC), sensitivity, specificity, and LAR thresholds. RESULTS: Fourteen primary studies were included. Higher LAR was consistently associated with increased mortality across emergency department and intensive care populations. AUC values generally ranged from approximately 0.65 to 0.87, although one smaller cohort reported an AUC of 0.976. Several multivariable analyses demonstrated associations between higher LAR and mortality after adjustment for clinical covariates. Adjusted ORs and HRs were not pooled because of differences in LAR scaling, thresholds, mortality endpoints, and adjustment strategies. Considerable variability was observed in reported cut-offs and diagnostic performance. CONCLUSIONS: Higher LAR is associated with mortality in adult sepsis and may provide complementary prognostic information. However, clinical and methodological heterogeneity precludes a universal cut-off or single pooled adjusted effect. Standardized prospective multicenter studies are required before routine clinical implementation.

Humans↗
PubMed1978

[A comparative clinical and pathological study on the classification and prognostic features of 57 thymomas. II. prognostic features (author's transl)].

The most important prognostic determinants of the thymomas are the gross findings at operation (equal to the presence or absence of gross invasion of adjacent tissue) and the presence or absence of the thymoma-associated systemic disease, particulary myasthenia gravis. The histologic type of thymoma had no proof value in predicting prognosis with the exception of the so-called atypical thymomas. Thirty-four of 57 patients with thymomas were females and 23 males. The tumors in 40 cases were non-invasive thymomas, and in 17 cases the tumour were invasive of adjacent tissue. 35.1 percent of patients were asymptomatic, the tumours being discovered on roentgenograms done on a routine basis or for an unrelated porpose. 40.3 percent of patients have had a thymoma-associated systemic disease. The most common presenting symptoms were related to myasthenia gravis (26.3%). The 5-year survival rate was 90 percent for non-invasive thymomas without myastenia gravis and 50 percent for invasive thymomas. The 5-year survival rate for patients with myasthenia gravis and encapsulated (non-invasive) thymomas was approximately 60 percent, whereas that for invasive thymomas with myasthenia gravis was 40 percent. The primary form of therapy for all thymomas is the surgical excision. In cases with invasive and/or metastasizing thymomas, postoperative radiation and adjuvanted chemotherapy is indicated, but in most series, the longterm results of this form of therapy are discouraging.

Adolescent↗
PubMed1978

Six-month prognostic norms derived from studies of the Rorschach Prognostic Rating Scale.

The Rorschach Prognostic Rating Scale (RPRS) was introduced in 1951 by Klopfer. Kirkner, Wisham, and Baker. The predictions of Klopfer et al. are compared to the outcomes in four studies of the RPRS. The originalinterpretation is shown to predict higher percentages of success than revealed by the empirical studies. A second interpretation of the scale is proposed on the basis of the experimental data. This interpretation relates RPRS scores to the chance for substantial improvement within 30 weeks of once weekly therapy by client-centered, rational-emotive, desensitization, aversion, or traditional methods. For any given RPRS score, the chance for substantial improvement is approximately the same for every type of therapy, and increases as the RPRS score increases. The second interpretation is proposed in both tubular and algebraic forms as a stimulus to further research and clinical applications.

Humans↗
PubMed2026

Non-small cell lung cancer and tumor-educated platelets: screening of biomarkers and construction of a prognostic model.

BACKGROUND: Lung cancer is a leading cause of cancer-related mortality worldwide, emphasizing the urgent need for effective early detection strategies. Traditional Chinese medicine (TCM) provides a unique perspective on tumor pathogenesis, focusing on concepts such as "long-term stasis leading to accumulation". Tumor-educated platelets (TEPs) offer potential as biomarkers due to their ability to reflect cancer heterogeneity and facilitate less invasive diagnostic approaches. This study aims to identify TEP-related prognostic biomarkers for non-small cell lung cancer (NSCLC) and to construct and validate a multigene prognostic model by integrating platelet transcriptomic data with tumor tissue datasets. METHODS: We performed comprehensive analysis of gene expression datasets obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories to characterize transcriptomic differences among lung cancer specimens, normal tissue samples, and TEPs. Using R software, we identified Differentially expressed genes (DEGs) and subsequently applied a multi-stage analytical pipeline to TEP-associated DEGs, incorporating univariate Cox proportional hazards regression, least absolute shrinkage and selection operator (LASSO) regression, multivariate Cox regression, and stepwise regression modeling to pinpoint genes with prognostic significance. These prognostically relevant genes served as the foundation for developing a risk stratification model. We computed individual risk scores across both training and validation cohorts, enabling patient stratification into high- and low-risk categories. Model robustness was assessed through internal cross-validation and external validation procedures, while predictive performance was quantified using risk calibration metrics and receiver operating characteristic (ROC) curve analysis. RESULTS: Through systematic bioinformatics screening, we identified a four-gene prognostic signature comprising NELL2, C4orf48, PRAM1, and KLHL35, which served as the foundation for developing our risk stratification algorithm. Rigorous internal cross-validation and external cohort validation substantiated the moderate predictive performance of this signature. Comprehensive clinicopathological correlation analysis revealed that elevated risk indices, advanced pathological staging (stage III-IV), increased primary tumor dimensions, regional lymph node metastasis, and distant organ dissemination each demonstrated statistically significant associations with diminished overall survival (OS) outcomes in lung cancer patients. The clinical nomogram exhibited acceptable calibration, with calibration plots showing reasonable concordance between predicted and observed survival probabilities across all time points. Discriminative capacity assessment via time-dependent ROC analysis yielded area under the curve (AUC) values consistently surpassing 0.6, confirming moderate prognostic discrimination. Furthermore, decision curve analysis (DCA) demonstrated that our integrated multi-gene model conferred potential net clinical benefit compared to individual prognostic variables across the full spectrum of clinically relevant threshold probabilities (0-1 range), thereby establishing its potential utility for risk-informed clinical decision-making. CONCLUSIONS: This study identified NELL2, C4orf48, PRAM1, and KLHL35 as candidate TEP-related prognostic biomarkers for non-small cell lung cancer (NSCLC). The developed prognostic model shows preliminary potential for patient stratification, but its clinical application, particularly as a platelet-based liquid biopsy tool, requires further validation in independent TEP-based cohorts.

Tumor-educated platelets (TEPs)↗
PubMed2026

Development and internal validation of a six-gene prognostic model based on galactose metabolism for overall survival in lung adenocarcinoma.

BACKGROUND: Lung cancer remains a leading cause of cancer incidence and mortality globally. Metabolic reprogramming promotes tumor progression and shapes an immunosuppressive tumor microenvironment. Galactose metabolism is involved in multiple malignancies, but its prognostic value in lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop and internally validate a galactose metabolism-related multigene prognostic model for LUAD. METHODS: A retrospective prognostic model development and internal validation study was performed using RNA sequencing (RNA-seq) and clinical data from 585 LUAD patients in The Cancer Genome Atlas (TCGA). Differential expression, functional enrichment, univariate and multivariate Cox regression were applied to construct a prognostic gene signature. Internal validation was performed using bootstrap resampling. Model performance was evaluated by time-dependent receiver operating characteristic (ROC), C-index, calibration, and Kaplan-Meier analysis. Associations between the model and immune infiltration, immunotherapy responsiveness, and tumor stemness were also analyzed. RESULTS: A six-gene prognostic model (GALT, GANC, PGM1, GALM, B4GALT1, PGM2) was developed. The model showed good discrimination with 1-, 3-, and 5-year area under the curve (AUC) values of 0.719, 0.693, and 0.684, respectively. The low-risk group exhibited significantly longer survival, increased antitumor immune infiltration (CD8+ T cells, M1 macrophages, activated CD4+ memory T cells), higher expression of T cell proliferation-related genes, lower immune checkpoint expression, better predicted immunotherapy response, and lower tumor stemness compared with the high-risk group. CONCLUSIONS: We developed and internally validated a six-gene prognostic model for LUAD based on galactose metabolism. The model shows moderate prognostic performance and is associated with antitumor immunity and tumor stemness. It may be used for prognostic risk stratification and to guide personalized immunotherapy in LUAD.

Galactose metabolism↗
PubMed2026

Construction of molecular signatures based on the co-expression network of NECSO-related gene TRPM4 and its prognostic value in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) demonstrates significant prognostic variability that is not entirely accounted for by traditional staging systems. Necrosis by sodium overload (NECSO) is an emerging programmed cell death pathway, but its clinical relevance in HCC remains undefined. Therefore, this study aimed to identify TRPM4-associated core genes, develop and validate a prognostic signature, and investigate its relationship with the tumor immune microenvironment, tumor mutational burden, and single-cell expression patterns in HCC. METHODS: We integrated transcriptomic, clinical, and mutational datasets from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) (n=421) and Gene Expression Omnibus (GEO) cohorts (n=115) to identify genes co-expressed with TRPM4-a key NECSO mediator-and those differentially expressed in HCC. A prognostic signature was developed using least absolute shrinkage and selection operator (LASSO)-Cox regression and validated through survival analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox regression analysis. The immune landscape was characterized using CIBERSORT, somatic mutation data were used to calculate tumor mutational burden (TMB) and assess its correlation with the risk score, and single-cell RNA sequencing (scRNA-seq) resolved cell-type-specific expression patterns. RESULTS: From 294 TRPM4-associated core genes, we identified an 11-gene signature (BRSK1, MMP1, GRIN2D, GP6, MYOM2, N4BP3, CCDC112, TSEN54, MAP3K9, SPP1, B3GNT4) that independently predicted overall survival (OS) (hazard ratio =5.419, P<0.001) with areas under the curve (AUCs) of 0.779, 0.693, and 0.701 at 1, 3, and 5 years. These values were superior or comparable to conventional clinicopathologic variables after direct comparison. High-risk patients exhibited an immunosuppressive microenvironment, characterized by enrichment of M0 macrophage, a higher M2/M1 ratio (P<0.001) and distinct immune checkpoint profiles. When integrated with TMB, the prognostic stratification was further refined: high-TMB/high-risk patients had poorest outcomes (median OS, 15.3 months), while low-TMB/low-risk patients had the most favorable survival (median OS, 68.7 months). Single-cell analysis revealed that MMP1 was induced in cancer-associated fibroblasts (CAFs) and SPP1 was downregulated in macrophages, single-cell risk scores confirmed TAFs and macrophages as the main contributors to the prognostic model. CONCLUSIONS: The TRPM4-centered 11-gene signature provides robust and independent prognostic stratification in HCC by integrating immune, mutational, and single-cell features. This signature serves as a potential tool for prognostic evaluation and may help inform immunotherapeutic strategies for HCC.

Hepatocellular carcinoma (HCC)↗
PubMed2026

Prognostic value of genes associated with metastasis and propionate metabolism in rectal cancer.

BACKGROUND: Research indicates that alterations in propionate metabolic pathways play a critical role in cancer development and invasion. Postoperative metastatic recurrence remains a major cause of mortality in patients with rectal cancer. However, propionate metabolism-related genes (PMRGs) in rectal cancer remain insufficiently characterized. Therefore, this study aimed to identify prognostic biomarkers associated with lymph node metastasis and propionate metabolism and construct a risk&#x2011;prediction model for rectal cancer via bioinformatic analyses. METHODS: The Cancer Genome Atlas-Rectum Adenocarcinoma (TCGA-READ) and GSE87211 datasets, together with a curated PMRGs gene set, were used in this study. Pearson correlation analysis was performed to assess associations between overlapping genes (differentially expressed genes between READ and normal tissues, as well as between N0 and N1-N2 stages) and PMRGs, leading to the identification of candidate genes. Functional enrichment analyses were subsequently conducted to characterize the biological roles of these candidates. Prognostic biomarkers were identified using univariate Cox regression combined with least absolute shrinkage and selection operator (LASSO) regression, and a prognostic model was constructed accordingly. Independent prognostic validation was then performed. In addition, immune checkpoint profiling and immunotherapy response analyses were conducted across risk subgroups. Single-gene Gene Set Enrichment Analysis (GSEA) was applied to elucidate the pathways associated with the identified biomarkers. Finally, drug sensitivity analyses were performed. RESULTS: A total of 157 candidate genes were identified through the analytical pipeline. Functional enrichment analysis indicated that these genes were primarily involved in inflammatory response regulation and tumor necrosis factor (TNF) signaling pathways. Five prognostic biomarkers were subsequently identified and incorporated into a predictive model. External validation using the GSE87211 cohort confirmed the robustness of the model. Risk score and disease status were identified as independent prognostic factors. Six immune checkpoint molecules exhibited differential expression between risk groups. Correlation analyses revealed that the risk score was positively associated with most immune checkpoint genes. Single-gene GSEA demonstrated that the biomarkers were mainly enriched in ribosomal biogenesis and cell adhesion molecule-related pathways. Furthermore, 51 therapeutic agents exhibited significantly different half-maximal inhibitory concentration (IC50) values between risk subgroups. CONCLUSIONS: This study identified five biomarkers (CCL24, IGFBP3, ODC1, PYGM, and VKORC1) associated with lymph node metastasis and propionate metabolism pathways, providing a potential foundation for prognostic prediction in patients with rectal cancer.

Rectal cancer↗
PubMed2026

Integrative analysis of the roles and prognostic value of RNA-binding proteins in papillary renal cell carcinoma.

RNA-binding proteins (RBPs) serve essential roles in various cancer types, but their functions in papillary renal cell carcinoma (pRCC) have not been elucidated to date. In our work, differentially expressed RBPs in pRCC were identified after acquisition of RNA-sequencing and clinical data related to pRCC from The Cancer Genome Atlas database(TCGA). Functional enrichment analysis and protein interaction network analysis, along with univariate and multivariate Cox regression analyses, were performed to uncover potential biological effects of the identified RBPs and screen the hub RBPs for pRCC prognosis. We identified 251 up-regulated and 129 down-regulated RBPs, and filtered out seven hub RBPs, namely, SRSF8, CD3EAP, HBS1L, ELAC2, MRPL34, NOP2 and IGF2BP2, for their prognostic relevance. A prognostic risk score model for overall survival of pRCC patients was constructed based on the seven hub RBPs. Further analysis showed that the low-risk group had higher survival rate than the high-risk group in both training and validation cohorts. The predictive accuracy was verified in the Human Protein Atlas database.In addition, we introduced the GSE15641 dataset from the Gene Expression Omnibus (GEO) database for independent external validation, and confirmed the expression levels of HBS1L, MRPL34 and IGF2BP2 through real-time quantitative PCR (RT-qPCR) and Western blotting (WB) using human renal tubular epithelial cell line HK-2 and human papillary renal cell carcinoma cell line Caki-2. In pRCC, CD3EAP was significantly elevated, while ELAC2, IGF2BP2, MRPL34, SRSF8 and HBS1L were significantly reduced. There was no significant difference between tumor and normal tissues in NOP2 expression. Risk score and tumor grade were independent prognostic factors associated with overall survival. In addition, we established a nomogram based on the seven prognostic RBPs to help predict overall survival at 1-3 years. In conclusion, seven differentially expressed hub RBPs were identified as potential prognostic biomarkers for pRCC. Our prognostic model might serve as a support for better treatment decision-making. Our work could provide potential new ideas for diagnosis and research on targeted drugs for pRCC.

Bioinformatics↗
PubMed2026

Stratifying lung adenocarcinoma: a novel prognostic model based on mitochondrial outer membrane permeabilization activity.

UNLABELLED: Mitochondrial outer membrane permeabilization (MOMP) is a core apoptotic regulatory event that dictates mitochondrial integrity, where full activation drives cell death and sublethal dysregulation contributes to tumor genomic instability. We used the Cancer Genome Atlas lung adenocarcinoma cohort (TCGA-LUAD) as the training cohort and the Gene Expression Omnibus dataset GSE42127 as the validation cohort to identify prognostic genes related to MOMP activity in lung adenocarcinoma (LUAD) and to evaluate their potential biological significance. By intersecting MOMP-related genes with differentially expressed genes, combined with survival analysis, Mendelian randomization analysis, and 101 machine-learning algorithm combinations, seven prognostic genes, namely BIRC5, PSMD11, TNFRSF13C, YWHAZ, YWHAG, CYCS, and LTB, were identified. Next, an optimal prognostic model was constructed based on the gradient boosting machine (GBM) algorithm. Based on the risk score, LUAD patients were stratified into high- and low-risk groups, and patients in the high-risk group exhibited poorer overall survival in both the training and validation cohorts. Furthermore, a nomogram integrating the risk score and clinicopathological factors was developed and showed favorable predictive performance for 1-, 3-, and 5-year survival. Meanwhile, functional and immune analyses revealed that the high-risk group was enriched in DNA replication-related pathways and demonstrated a higher tumor mutation burden (TMB). Correlation analysis indicated that TNFRSF13C was positively correlated with activated B cells, whereas BIRC5 was negatively correlated with eosinophils, suggesting that MOMP-related genes might be involved in remodeling the immune microenvironment of LUAD. Drug sensitivity analysis showed differences in predicted half-maximal inhibitory concentration (IC50) values between the risk groups, suggesting the potential value of this model in assisting therapeutic stratification. Single-cell RNA sequencing (scRNA-seq) further identified T lymphocytes as a key cell type, with numerous prognostic genes exhibiting differential expression in T cells or dynamic changes during differentiation. We suggest that the MOMP-related signature established in this study may provide a reference for prognostic stratification in LUAD and offers candidate prognostic genes for subsequent experimental and clinical validation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13205-026-05058-6.

Lung adenocarcinoma↗
PubMed2026

Targeting RELA and STAT3 regulates TNFRSF10A-mediated apoptosis in a novel apoptosis-based prognostic model for clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal malignancy and remains a major cause of cancer-related mortality worldwide. Although advances in surgery, targeted therapy, and immunotherapy have improved outcomes for patients, reliable biomarkers for predicting prognosis remain limited. Therefore, robust gene-based prognostic models are urgently needed to improve risk stratification and guide individualized treatment strategies. METHODS: We developed a novel prognostic model integrating apoptosis and immune - related genes (AIRGs) to predict overall survival (OS) in patients with ccRCC. RESULT: Using Gene Set Enrichment Analysis (GSEA) combined with least absolute shrinkage and selection operator (LASSO) Cox regression, we identified 7 key prognostic genes, namely, CCR4, TNFRSF10A, TEK, TGFA, CD14, IFITM1, and SEMA3G, that collectively demonstrated strong predictive performance in TCGA cohort with c-index&#x2009;=&#x2009;0.711. Functional enrichment analyses revealed that apoptosis, immune regulation, and multiple oncogenic signaling pathways were significantly associated with the risk score, highlighting the critical role of the tumor microenvironment in ccRCC progression. Transcription factor binding analysis based on the JASPAR database suggested that RELA and STAT3 with scores of 0.829 and 0.951, respectively are potential upstream regulators within the prognostic network, particularly influencing TNFRSF10A expression. External validation using the International Cancer Genome Consortium (ICGC) dataset confirmed the robustness of the prognostic model with c-index&#x2009;=&#x2009;0.612 Furthermore, in vitro experiments demonstrated that RELA and STAT3 regulate TNFRSF10A-mediated apoptotic signaling in ccRCC cells, providing mechanistic support for the bioinformatic findings. CONCLUSION: This study establishes a biologically informed and clinically relevant prognostic framework for ccRCC. Our findings highlight the therapeutic potential of targeting the RELA/STAT3-TNFRSF10A axis and contribute to the advancement of precision medicine in ccRCC.

Humans↗
PubMed2026

Prognostic significance of DNA damage response-related markers in esophageal squamous cell carcinoma using machine learning approaches.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) lacks reliable prognostic biomarkers. Homologous recombination deficiency (HRD) has been implicated in genomic instability across multiple cancers, but its prognostic significance in ESCC remains unexplored. This study aimed to evaluate HRD score as a prognostic biomarker and develop a machine learning-based predictive model for ESCC. METHODS: Transcriptomic and clinical data from 78 ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and randomly split into training (70%) and test (30%) cohorts. Prognostic models were constructed using 112 machine learning algorithm combinations based on DNA damage response (DDR)-related genes. Gene set enrichment analysis (GSEA), somatic mutation profiling, and immune cell infiltration estimation via CIBERSORT were performed to characterize HRD-associated molecular features. RESULTS: High HRD scores were significantly associated with poorer overall survival (P<0.05). Among 112 algorithm combinations, the survival support vector machine (Survival-SVM) model demonstrated optimal performance [training concordance index (C-index): 0.741; test C-index: 0.708], identifying six hub genes: PARP1, MBD4, TELO2, NSMCE3, SMUG1, and BABAM1. A nomogram incorporating risk score (RS) and clinical variables achieved strong predictive accuracy for 1- to 3-year survival [area under the curve (AUC) >0.7]. High-HRD tumors exhibited distinct mutational patterns (TP53 and TTN) and enriched glutathione metabolism and cytochrome P450 pathways. Immune infiltration analysis revealed significant differences in plasma cell and neutrophil infiltration between risk groups (P<0.05), suggesting HRD-associated immune microenvironment remodeling. CONCLUSIONS: We developed a novel HRD-based prognostic model incorporating six DDR-related genes that demonstrates robust predictive performance in ESCC. HRD score is identified as an independent prognostic factor associated with genomic instability, immune microenvironment alterations, and clinical outcomes. These findings provide a theoretical basis for personalized treatment strategies, including potential applications of PARP inhibitors and immunotherapy in ESCC.

Esophageal squamous cell carcinoma (ESCC)↗
PubMed2026

Established and emerging prognostic factors in mycosis fungoides and S&#xe9;zary syndrome.

Mycosis fungoides (MF) and S&#xe9;zary syndrome (SS) are the most common subtypes of cutaneous T-cell lymphoma (CTCL), characterized by heterogeneous clinical behavior and variable prognosis. Accurate prognostication is essential for risk-adapted management. This review synthesizes emerging evidence on prognostic factors in MF and SS, with a focus on recent genomic advances. Traditional prognostic frameworks are outlined, highlighting the prognostic impact of demographics, stage, clinical features, and histologic findings. More advanced prognostics are then outlined, including genomic alterations and impact of the tumor microenvironment. We highlight realms in which integration of traditional and more biological prognostic frameworks can support more individualized treatment strategies.

S&#xe9;zary syndrome↗
PubMed2026

Comprehensive In Silico Analysis Identifies MSTO1 and LIG1 as Candidate Biomarkers With Diagnostic and Prognostic Relevance in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and remains a major cause of cancer-related mortality worldwide. Its poor clinical outcomes are largely attributed to late-stage diagnosis and the limited accuracy of currently available diagnostic and prognostic biomarkers. Therefore, identifying novel molecular markers with improved sensitivity, specificity, and therapeutic relevance is essential for enhancing early detection and guiding personalized treatment strategies. AIMS: To identify and prioritize novel candidate HCC biomarkers with diagnostic and prognostic value and potential therapeutic vulnerability using integrated multi-omics, survival, functional dependency, and tumor microenvironment analyses. METHODS AND RESULTS: We examined the mRNA and protein expression levels of 8 DEGs in HCC tissues in the TCGA and CPTAC datasets using UALCAN, which showed that MSTO1 and LIG1 were overexpressed consistently in HCC relative to normal liver tissues. Moreover, elevated expression levels of these genes were significantly associated with higher tumor grade and advanced stage. Kaplan-Meier plotter survival data confirmed that increased expression of MSTO1 and LIG1 was associated with poorer overall survival. The DepMap CRISPR knockout data confirmed a functional dependency of both genes in HCC cell lines. CBioPortal analyses provided characterization of genomic alterations and enabled enrichment analysis of co-expressed genes, and the TCGA-UALCAN pan-cancer analyses supported the assessment of tissue specificity across tumor types. TIMER3 analyses linked candidate gene expression with immune cell infiltration patterns. Diagnostic performance by ROC analysis showed excellent discrimination for MSTO1 (AUC&#x2009;=&#x2009;0.987) and good discrimination for LIG1 (AUC&#x2009;=&#x2009;0.897). Multivariate Cox regression with Benjamini-Hochberg FDR correction across the eight genes supported MSTO1 as a candidate independent prognostic factor after adjustment for tumor stage, grade, etiology, age, and sex (HR&#x2009;=&#x2009;1.29, p&#x2009;=&#x2009;0.035), whilst LIG1 showed no independent prognostic value. Promoter methylation of MSTO1 and ADH4, assessed via UALCAN, showed that both genes were significantly differentially methylated in the promoter region of primary HCC tissues compared with normal liver tissues. Our study also confirmed the biological and clinical relevance of established HCC biomarkers: TERT, IRAK1, and ADH4. CONCLUSION: MSTO1 and LIG1 emerged as candidate diagnostic biomarkers in HCC. Additionally, MSTO1 showed a candidate prognostic association with overall survival that remained significant after adjusting for tumor stage, grade, and etiology, as well as patients' age, but not after further adjustment for AFP status. Functional data also highlighted MSTO1 as a candidate therapeutic dependency. On the other hand, LIG1 showed no independent prognostic association in either multivariate model. Their differential expression and functional essentiality in HCC cell lines highlighted their value for further experimental and independent-cohort validation before potential integration into biomarker development pipelines aimed at improving early detection and targeted therapy in HCC.

Humans↗
PubMed1979

Acquired hydrocephalus. II. Diagnostic and prognostic value of quantitative isotope ventriculography (QIV), lumbar isotope cisternography (LIC), pneumoencephalography, and continuous intraventricular pressure recording (CIP).

The diagnostic and prognostic values of quantitative isotope ventriculography (QIV), lumbar isotope cisternography (LIC), pneumoencephalography (PEG), and continuous intraventricular pressure recording (CIP) were assessed on the basis of the clinical course in 160 patients suspected of having acquired hydrocephalus. The diagnostic value of a given method is defined as its reliability in the diagnosis of hydrocephalus. The reliability in the selection of hydrocephalic patients who will improve on shunt operation is designated as the prognostic value of the method concerned. As the diagnoses are based on isotopic procedures, the diagnostic values of these procedures cannot be assessed. QIV is of greater prognostic value than the other methods of examination. The presence of plateau waves in CIP is a rare, but prognostically valuable sign. The prognostic value of B waves is also good, whereas their diagnostic value is slight. Both from diagnostic and prognostic points of view, PEG is of less value is communicating hydrocephalus on account of the many false findings.

Cerebral Ventricles↗