Search Results (4,035)

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high incidence and mortality rates worldwide. Given the poor prognosis of patients with HCC, it is crucial to explore the molecular mechanisms underlying HCC development and to evaluate prognostic markers. Differential expression analysis followed by univariate Cox, LASSO, and multivariate Cox regression identified four genes (EPO, SOCS2, IL18RAP, and KPNA2), and a Cox-based risk score was evaluated in the TCGA-LIHC cohort and externally in GSE14520 using Kaplan–Meier and time-dependent ROC analyses. Bulk, single-cell, and protein resources provided convergent expression context. Survival machine-learning analysis using observed overall-survival time and censoring status identified Cox–Ridge as the best-performing model in TCGA-LIHC, with more modest performance in GSE14520, and immune profiling revealed risk-group-associated differences in estimated immune and stromal components, immune-cell composition, and immune-checkpoint expression. The oncoPredict/GDSC2 screen highlighted five potential drug candidates for experimental prioritization. Because the drug screen is based on computationally predicted sensitivities, these findings should be regarded as hypothesis-generating and require validation in prospective cohorts and experimental systems before clinical translation. Full article

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide and exhibits considerable biological heterogeneity in both molecular and clinical characteristics. The diverse molecular alterations and clinical manifestations of HCC indicate substantial heterogeneity across patient subgroups. This study aimed to identify novel therapeutic targets and predictive biomarkers associated with HCC using an integrative bioinformatics approach. High-throughput genomic datasets were obtained from the UCSC Xena browser to retrieve mRNA HTSeq-count data from the TCGA-HCC cohort. Gene co-expression network (GCN), protein–protein interaction network (PPIN), and enrichment analyses were performed to identify key dysregulated genes and their biological significance. Integrated network analyses identified three dysregulated hub genes, namely CXCR2, TLR2, and TLR4. Genomic alterations in these genes were further evaluated across tumor samples in the TCGA-HCC cohort. Kaplan–Meier (KM) survival analysis demonstrated that lower CXCR2 mRNA expression was significantly associated with poorer overall survival (OS) and recurrence-free survival (RFS). Furthermore, TIMER and UALCAN analyses revealed significant associations between CXCR2 expression and tumor purity, as well as immune cell infiltration levels, including T cells, macrophages, dendritic cells (DCs), and neutrophils. These findings suggest that CXCR2 is significantly associated with the immune microenvironment of HCC and represents a potential prognostic biomarker whose biological role warrants further mechanistic investigation. Full article

Background/Objectives: Glioblastoma (GBM) is the most aggressive and lethal primary brain tumor, and elucidating the molecular determinants of its malignant progression is essential for improving patient outcomes. NTAN1 encodes N-terminal asparagine amidase 1, a component of the N-degron pathway involved in selective protein turnover; however, its role in GBM remains unclear. This study aimed to investigate the clinical significance and biological function of NTAN1 in GBM. Methods: Integrated analyses of The Cancer Genome Atlas (TCGA) transcriptomic and clinical data were performed, together with tissue microarray validation, to assess the association between NTAN1 expression and prognosis in GBM. Immunohistochemical analysis of GBM specimens was conducted to evaluate NTAN1 protein expression. Functional studies, including NTAN1 knockdown and overexpression experiments, were used to examine its effects on GBM cell proliferation, clonogenic growth, migration, and invasion. An orthotopic GBM model was established to assess the effect of NTAN1 inhibition on tumor growth. Transcriptomic analysis was further performed to explore the molecular changes associated with NTAN1 knockdown. Results: Elevated NTAN1 expression was associated with poor survival in GBM. Immunohistochemical analysis further demonstrated that high NTAN1 protein expression predicted unfavorable prognosis. Functional studies showed that NTAN1 knockdown inhibited GBM cell proliferation, clonogenic growth, migration, and invasion, whereas NTAN1 overexpression promoted these malignant phenotypes. In an orthotopic GBM model, NTAN1 inhibition significantly reduced tumor volume. Transcriptomic analysis showed that NTAN1 knockdown was associated with reduced expression of invasion-related genes, including SPINK1, MMP1, and LIF, and with enrichment changes in inflammation-associated pathways, suggesting that NTAN1 may promote GBM progression through pro-invasive molecular programs. Conclusions: Collectively, these findings identify NTAN1 as a potential promoter of GBM malignant progression and a prognostic biomarker of poor outcome. Full article

Background/Objectives: This study assessed the impact of variations in autophagy-related genes (ATG) on baseline characteristics of cutaneous melanoma, laboratory parameters (including inflammatory biomarkers), response to therapy, and survival in patients treated with immune checkpoint inhibitors (ICIs) as first-line therapy. Methods: DNA was extracted from blood samples of 144 melanoma patients. Genotyping of ATG5 (rs2245214 and rs510432), ATG10 (rs1864183 and rs1864182), and ATG16L1 (rs2241880) was performed using an allelic discrimination method on the StepOnePlus^TM Real-Time PCR System. Correlations with laboratory parameters, response to therapy, and survival were assessed only in the subgroup of patients who received ICIs in first-line treatment (n = 74). Statistical significance was calculated, and p values were adjusted for multiple testing using the Benjamini–Hochberg False Discovery Rate (FDR). Results: Considering baseline characteristics of 144 patients, ATG5 rs2245214 showed a trend with regression (p = 0.042) and lymphovascular invasion (p = 0.05), while ATG16L1 rs2241880 was associated with lymphovascular invasion (p = 0.056), with corrected FDR q value for all histopathological characteristics of 0.076. In patients who received ICIs in first-line, ATG5 rs2245214 genotypes were associated with LDH (p = 0.001, q = 0.004) and the number of metastatic sites (p < 0.001, q = 0.004). Also, ATG5 rs2245214 was associated with neutrophil-to-lymphocyte ratio (NLR) (p = 0.036, q = 0.045) systemic immune-inflammation (SII) index (p = 0.038, q = 0.048) and pan-immune-inflammation value (PIV) (p = 0.031,q = 0.041), while ATG10 rs1864183 was associated with PIV (p = 0.047, q = 0.047). The association of ATG genotypes with disease control rate (DCR) was demonstrated for ATG5 rs2245214 (p = 0.013, q = 0.029) and ATG16L1 rs2241880 (p = 0.032,q = 0.032). Progression-free survival (PFS) was significantly associated with ATG10 rs1864183 (p = 0.023, q = 0.046). The significance of the ATG10 rs1864183 C/T genotype as a prognostic marker for progression was confirmed in both univariate and multivariate Cox proportional hazards regression analyses (p = 0.025, q = 0.028 and p = 0.022, respectively). Conclusions: This study shows that ATG5 rs2245214, ATG10 rs1864183, and ATG16L1 rs2241880 correlate with systemic inflammation, response to ICIs, and had a trend toward melanoma characteristics. However, these findings should be confirmed in larger patient cohorts. Full article

Breast cancer (BRCA) possesses prominent molecular heterogeneity, where aberrant expression of genes annotated to asparagine metabolism networks drives malignant progression and therapeutic resistance. However, systematic construction of prognostic signatures from a holistic asparagine metabolic pathway perspective remains scarce, limiting the clinical translation of metabolic insights into prognostic tools. We integrated TCGA and GEO BRCA transcriptomic datasets to screen asparagine metabolism-related differentially expressed genes and build a prognostic model. Six biomarkers, SLC35A2, SRD5A2, NT5E, CEL, IFNG and CNR1, were selected via univariate Cox, LASSO and multivariate Cox regression. SRD5A2 and IFNG were enriched in low-risk patients, while the other four genes were upregulated in high-risk subgroups. This signature reliably stratifies patient prognosis, with risk scores correlating strongly with pathway activity, immune infiltration, immune checkpoints, mutation landscapes and drug responsiveness. Bioinformatic results were validated via TCGA cohort analysis, in vitro cellular assays and Western blot. Two in vivo models were established: 4T1 xenografts in 6-week-old BALB/c mice and DMBA/hormone-induced spontaneous breast tumors in 8-week-old SD rats. Tumors were generated by cell injection or DMBA gavage plus cyclic hormone treatment, and tissue sections were processed for immunohistochemistry. Consistent differential expression of the six core genes was validated across all in vitro and in vivo systems. In conclusion, this asparagine metabolism-associated signature offers candidate biomarkers for personalized prognosis and provides preclinical evidence for metabolism-targeted BRCA therapy. Full article

Background: Pyrotinib, an irreversible pan-HER tyrosine kinase inhibitor, has demonstrated substantial clinical efficacy in patients with HER2-positive breast cancer (BC). However, intrinsic and acquired resistance remain important challenges limiting therapeutic benefit, and reliable biomarkers for predicting pyrotinib response are currently unavailable. This study aimed to identify molecular determinants associated with pyrotinib resistance and uncover their underlying mechanisms. Methods: Pre-treatment tumour samples from an exploratory discovery cohort of 12 patients with HER2-positive BC receiving pyrotinib-containing neoadjuvant therapy were analysed by proteomic profiling. Differentially expressed proteins (DEPs) between the pathological complete response (pCR) and non-pCR groups were integrated with weighted gene co-expression network analysis and protein–protein interaction network analysis to identify candidate proteins. The prognostic relevance of the candidate genes was subsequently evaluated using 127 machine-learning strategies across three independent BC cohorts. Models were ranked according to the mean area under the receiver operating characteristic curve (AUC) across two evaluation cohorts, and SHapley Additive exPlanations (SHAP) analysis was performed separately in both cohorts to prioritise a candidate for subsequent investigation. In vitro and in vivo experiments were then conducted to evaluate the biological role of the prioritised candidate and its association with pyrotinib sensitivity. Finally, the association between pre-treatment SLC27A2 expression and pCR was evaluated in an independent, non-overlapping retrospective cohort of 103 patients receiving pyrotinib-containing neoadjuvant therapy. Results: Exploratory proteomic profiling of 12 pre-treatment tumour samples identified 617 DEPs between the pCR and non-pCR groups. Among 127 machine-learning strategies used to evaluate the prognostic relevance of the candidate genes, the glmBoost–random forest model achieved the highest mean AUC across the two evaluation cohorts (mean AUC = 0.678). SHAP analysis showed that SLC27A2 ranked second in GSE16446 and first in GSE48390 according to mean absolute SHAP values, supporting its prioritisation for subsequent investigation. Functional experiments showed that SLC27A2 promoted proliferation, migration, invasion, and epithelial–mesenchymal transition in HER2-positive BC cells. SLC27A2 knockdown enhanced pyrotinib sensitivity in vitro. In the xenograft experiment using female BALB/c nude mice, both SLC27A2 knockdown and pyrotinib treatment reduced tumour growth, and a significant interaction between the two factors was observed for endpoint tumour weight (p for interaction = 0.041). Mechanistically, SLC27A2 knockdown reduced lipid accumulation and PPARα expression, whereas pharmacological activation of PPARα partially attenuated the increase in pyrotinib sensitivity induced by SLC27A2 knockdown. Clinical validation further showed that high-pre-treatment SLC27A2 expression was independently associated with a lower likelihood of achieving pCR after pyrotinib-containing neoadjuvant therapy (OR = 0.10, 95% CI: 0.03–0.31, p < 0.001). Conclusions: SLC27A2 is a candidate factor associated with BC prognosis and reduced pyrotinib sensitivity in HER2-positive BC. Preclinical findings suggested that PPARα-related fatty acid metabolism may contribute to the association between SLC27A2 and pyrotinib response, while clinical validation showed that high-pre-treatment SLC27A2 expression was independently associated with a lower likelihood of achieving pCR following pyrotinib-containing neoadjuvant therapy. These findings support SLC27A2 as a candidate response-associated biomarker and potential therapeutic target, although further mechanistic investigation and external clinical validation are required. Full article

Triple-negative breast cancer (TNBC) is characterized by marked immune microenvironment heterogeneity and variable chemotherapy response, yet the epithelial transcriptional programs governing cytotoxic immune activation remain poorly understood. We performed an exploratory, integrative analysis using single-cell RNA sequencing of 31,962 cells from eight TNBC patients operationally stratified into Good and Bad Prognosis groups based on pathological lymphoid infiltration, a discovery grouping subsequently validated against pathological complete response (pCR) in three independent bulk RNA-seq cohorts. This analysis identified four epithelial transcriptional states. The G5 DNA damage subpopulation—predominantly restricted to Good Prognosis tumors (29.2% vs. 0%)—and the G4 Metabolism subpopulation—2.4-fold enriched in Bad Prognosis—were the primary prognostic signatures. Machine learning validation using nested leave-one-cohort-out (LOCO) cross-validation across 614 samples demonstrated that G4 + G5 raw genes with random forest yielded the largest observed mean AUC of 0.653, though these results are exploratory and do not establish a validated clinical classifier. CellChat ligand–receptor interaction analysis revealed that G5 DNA-damage epithelial cells are the dominant immune activators in Good Prognosis TNBC, predominantly engaging CD8 cytotoxic T cells through MHC-I antigen presentation via HLA-A/B/C/E/F → CD8A/CD8B interactions, the highest-probability signaling pathway identified. Spatial transcriptomics independently validated significantly higher DNA damage and CD8 T-cell scores in Good Prognosis tissue. Together, these exploratory findings suggest a framework in which tumor-intrinsic DNA damage signaling is associated with MHC-I antigen presentation upregulation and CD8 cytotoxic T-cell engagement, supporting further investigation of this axis and its potential implications for combining DNA-damaging chemotherapy with immune checkpoint blockade in TNBC. Full article

MCT1 (encoded by SLC16A1) is a key regulator of cellular metabolism, mediating proton-coupled transport of lactate, pyruvate, ketone bodies, and other monocarboxylates across biological membranes. Long recognised for its canonical role in metabolic homeostasis and the lactate shuttle, MCT1 is now implicated in tumour-promoting processes, including metabolic symbiosis, angiogenesis, immune evasion, and therapy resistance. Aberrant plasma membrane MCT1 (PM MCT1) expression is observed in diverse malignancies, where it may carry prognostic or predictive value, making it an attractive therapeutic target. This review integrates established metabolic functions of MCT1 with emerging evidence showing its unexpected nuclear localisation (nMCT1) and potential to modulate chromatin state through metabolite-driven epigenetic regulation. In particular, we discuss how PM MCT1 substrates such as lactate, pyruvate, and ketone bodies may influence histone modifications and gene regulation through direct or indirect metabolic mechanisms. We also examine reports of nuclear or nuclear-associated MCT1 (nMCT1) staining in immune and cancer contexts, while emphasising that functions of nMCT1 remain insufficiently validated. By distinguishing established transport biology from substrate-mediated epigenetic effects, this review highlights both the therapeutic promise of MCT1 targeting and the experimental gaps that must be addressed. We conclude by outlining priorities for future research, including orthogonal validation of putative nMCT1, improved patient stratification based on MCT1 expression and metabolic phenotype, and rational combination strategies for MCT1-directed therapies. Full article

Background/Objectives: Cardiorenal syndrome (CRS) is associated with substantially higher morbidity and mortality than either isolated cardiac or renal dysfunction. The application of classical and novel biomarkers has been tested in prompt diagnosis and monitoring of patients with CRS. Methods: This is a comprehensive literature review following a structured approach. We searched MEDLINE and Embase databases from January 2000 to December 2025. Results: The pathophysiology of CRS is complex, and the present review attempts to shed light on the clinical interpretation of the most widely used biomarkers as indices of diagnosis and prognosis. Among them, troponin is elevated in CRS and its absolute levels retain prognostic value, while changes in its levels over time may assist in the diagnosis of acute coronary syndrome. Natriuretic peptides are highly influenced by coexistence of chronic kidney disease (CKD) and in this context have considerable diagnostic and prognostic value. The combination of cystatin C, a biomarker of renal dysfunction, with cardiac biomarkers may create a powerful risk algorithm. Most recently, gene profiling and proteomics have the potential to stratify patients with CRS; however, more data from large cohorts are required for their validation. The therapeutic modulation of biomarkers in CRS patients may help elucidate the underlying pathophysiologic mechanisms. Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have emerged as first-line therapy for patients with CRS, despite the fact their mechanisms are mostly unknown. Significant changes in the aforementioned biomarkers and the inflammatory factors may explain their emerging beneficial effects on both heart failure and CKD. Conclusions: The use of biomarkers has increased rapidly in recent years for diagnosis, surveillance and prognostic stratification in CRS. Full article

Background: In recent decades, the incidence of colorectal cancer (CRC) has been rising worldwide. CRC ranks second in cancer-related mortality. The identification of reliable biomarkers for early diagnosis and prognosis prediction, along with a deeper understanding of the underlying molecular events, holds substantial promise for improving patient outcomes. The tissue inhibitor of the metalloproteinase 1 (TIMP1) gene is overexpressed in various gastrointestinal malignancies and contributes to tumor progression. However, its role in regulating the CRC tumor immune microenvironment (TIME) and its potential as a clinically actionable prognostic biomarker remain unclear. Methods: To probe how TIMP1 acts as a prognosis-related candidate biomarker in colorectal carcinoma, TCGA-derived datasets were adopted to conduct Kaplan–Meier survival assessment. We also investigated the connection between the expression abundance of TIMP1 and the infiltration of immune populations and intratumoral lymphocytes; furthermore, immune checkpoint-related genes were systematically assessed across multiple tumor types via the TISIDB and TIMER2.0 platforms, with particular emphasis on CRC. We adopted the ESTIMATE scoring system to figure out how TIMP1 gene expression correlates with the phenotypic properties of the colorectal-cancer TIME. We relied on the limma toolkit for the screening of differential transcripts from high-TIMP1 and low-TIMP1 cohorts. Enrichment assessments covering Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes entries were then carried out to predict the potential biological pathways associated with TIMP1. We constructed the protein–protein interaction map for TIMP1-interacting partners via the STRING repository. To further explore TIMP1-correlated genes, we performed Venn diagram intersection analysis combined with Spearman’s correlation test. Finally, quantitative reverse-transcription PCR was then implemented to detect TIMP1 messenger-RNA abundance inside the RKO colorectal carcinoma cell line as well as normal colonic epithelial CCD-18Co cells, which offered in vitro experimental verification for our bioinformatic outcomes. Results: According to outcome data, TIMP1 transcripts were markedly up-regulated in CRC specimens and cell lines relative to normal samples. Elevated TIMP1 expression served as a poor-prognosis indicator for overall survival (hazard ratio [HR] = 0.43, 95% confidence interval [CI] = 0.29–0.64, p < 0.001) and disease-specific survival (HR = 0.39, 95% CI = 0.22–0.68, p = 0.001) among colorectal-carcinoma patients. TIMP1-high and TIMP1-low groups exhibited notable differences in immune cell infiltration (CD8⁺ T, macrophage, mast, neutrophil, B, monocyte, dendritic, and CD4⁺ T cells). TIMP1 expression was also significantly correlated with tumor-infiltrating lymphocytes, key immune checkpoint genes (e.g., CD274 [PD-L1] and CTLA4), and immunomodulatory chemokines (e.g., CCL3 and CCL5). Twelve TIMP1-interacting DEGs were selected: COL5A1, FN1, PRG4, and a cluster of nine MMPs (MMP1/2/3/7/8/9/11/13/14), all of which showed significant positive correlations with TIMP1 (r = 0.31–0.63, all p < 0.001). Conclusions: TIMP1 expression correlates with features of the tumor immune microenvironment and extracellular matrix remodeling in CRC, suggesting that TIMP1 shows potential as a candidate biomarker. However, its potential as a therapeutic target warrants further experimental investigation. Full article

Background: Accurate prognosis in diffuse large B-cell lymphoma (DLBCL) is limited by biological heterogeneity and the absence of formal per-patient uncertainty quantification for treatment-response prediction. This study introduces a multi-layer evidence fusion framework combining gene expression profiling and clinical features with distribution-free uncertainty quantification. Methods: The proposed framework integrates four evidence layers—WGCNA co-expression eigengenes, ssGSEA pathway scores, bootstrap-stable prognostic genes, and the International Prognostic Index—through AUC-proportional reliability discounting and sequential Dempster–Shafer fusion. The primary endpoint was three-year overall survival (OS₃yr) as a surrogate for R-CHOP treatment response. Inductive conformal prediction (ICP, ε = 0.10) was applied to provide per-patient uncertainty sets with a distribution-free coverage guarantee. Training used GSE10846 (n = 223, Affymetrix); external validation used GSE181063 (n = 479, Illumina). Results: The proposed framework achieved internal AUC = 0.808 (95% CI [0.750, 0.863]), significantly outperforming logistic stacking (AUC = 0.786, p = 0.0009) and unweighted DS fusion (AUC = 0.767, p = 0.037). External AUC = 0.791 was statistically comparable to logistic stacking (DeLong p = 0.21). AUC-proportional discounting reduced inter-source conflict K^- by 75% (0.093→0.023). ICP achieved 90.1% internal and 94.6% external coverage; 43.5% of training patients received uncertain predictions ({S,R}). Conclusions: The proposed framework provides an uncertainty-aware approach for multi-layer genomic–clinical evidence fusion in DLBCL, with cross-platform discrimination validated on an independent Illumina cohort. Full article

Background: Elevated plasma homocysteine (HCY) is a risk factor for Alzheimer’s disease (AD), but its relationship with structural brain changes across the AD continuum remains unclear. The choroid plexus (CP) regulates cerebrospinal fluid homeostasis and may interface with peripheral metabolic signals. Whether HCY relates to CP structural alterations and disease progression remains unknown. Methods: We analyzed 819 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (229 cognitively normal (CN), 397 with mild cognitive impairment (MCI), and 193 with AD dementia). Multinomial logistic regression assessed associations between HCY and diagnosis under stepwise covariate adjustment. Phenotype-wide structural magnetic resonance imaging (MRI) mapping identified HCY-associated signals. Cox models evaluated associations of CP volume (CPV) with CN-to-MCI and MCI-to-AD dementia conversion and whether CPV added prognostic discrimination beyond baseline disease-severity markers. Independent human CP single-nucleus and spatial transcriptomic datasets were reanalyzed to characterize epithelial expression states and their spatial organization in a hypothesis-generating analysis. Results: Higher HCY was associated with MCI and AD dementia; however, the AD association attenuated after adjustment for renal function, vitamin B12, and medications, whereas the MCI association remained stable. CPV was among the HCY-associated MRI signals that persisted after progressive covariate adjustment. Right and bilateral CPV showed model-dependent associations with MCI-to-AD dementia conversion. In the disease-severity sensitivity analysis, larger right and bilateral CPV remained associated with a higher risk of progression from MCI to AD dementia. Single-nucleus analysis identified two CP epithelial states with relatively high expression of one-carbon metabolism-related genes, termed one-carbon metabolism-enriched epithelial state A (OCM-Epi-A) and state B (OCM-Epi-B). Donor-level pseudobulk analysis did not identify pathway enrichment after false discovery rate correction, whereas OCM-Epi-A–like spots were located near endothelial spots more often than expected by chance in three of the four spatial samples. Conclusions: Circulating HCY was associated with larger CPV, and larger CPV showed model-dependent associations with MCI-to-AD dementia progression. Independent transcriptomic reanalysis identified one-carbon metabolism-enriched epithelial states and their spatial organization in postmortem CP tissue, providing hypothesis-generating tissue-level context for the ADNI associations. Full article

Background: Telomerase is a ribonucleic multimeric reverse transcriptase complex protecting the chromosomal ends from erosion and thereby from cellular senescence. The prognostic value of the components of this complex and their interrelationships with the immune system are not well understood. Objectives: We aimed to examine 15 telomerase-related genes across 7489 tumor samples from the TCGA database. Methods: The mRNA expression of these genes was analyzed using Kaplan–Meier statistics and hierarchical clustering analyses, alone or in combination with tumor infiltration counts for 11 immune cell types. As an additional analysis, univariable and multivariable Cox regression analyses have been performed. Results: Thirteen of 21 tumor types showed significant associations between gene expression in tumors and survival times of patients. Most gene correlations were found in hepatocellular carcinoma and renal clear cell carcinoma. In hepatocellular carcinoma, a high expression of DKC1, NHP2, GAR1, WRAP53, and ACD was associated with shorter survival. In renal clear cell carcinoma, TERT, DKC1, and PARN correlated with shorter survival, and NAF1, TERF2, POT1, and TINF2 with longer survival. DKC1 was the only gene significantly associated with poor prognosis in both tumor types. The telomerase-related genes correlated with patterns of immune cell infiltration, which influenced the survival of patients. The associations of mutation burden and neoantigen load with survival varied depending on the gene and patient groups. In renal clear cell carcinoma, TERT, DKC1, and PARN showed strong interactions with immune cell infiltration and neoantigen load. Conclusions: The combination of telomerase-related gene expression and immune-cell infiltration was associated with overall survival in hepatocellular carcinoma and renal clear cell carcinoma and warrants further evaluation as prognostic markers. Full article

Background: It is well documented that Black and African American (AA) patients have the highest risk of recurrence of breast cancer. However, less is known about racial differences in gene expression profiling tests such as Oncotype DX that predicts cancer recurrence. This study aimed to assess the predictive differences in Oncotype DX scores for breast cancer outcomes between Black/AA and non-Hispanic White patients. Methods: We identified Oncotype DX testing status in patients ages 18–75 years who were newly diagnosed with breast cancer, assessed race and other factors among patients with different testing statuses, and described the distribution of the Oncotype DX score. Additionally, we assessed treatment, and multifactorial impact on outcomes by Oncotype DX score, including: receipt of chemotherapy, endocrine therapy, stage, and disease-related recurrence and mortality. Results: We identified 2577 eligible patients for the analysis. Black/AA patients had a 49% greater risk/hazard of mortality than non-Hispanic White (NHW) patients (multivariable model p-value 0.0444). Oncotype DX score differences were associated with mortality in the overall sample (multivariable model, p-value = 0.0281). Specifically, in the Oncotype DX-specific models, Black/AA patients with an Oncotype DX score of 0–16 had 73% greater risk of mortality than NHW patients (multivariable model p-value 0.0409). While all other Oncotype DX score categories showed elevated risks of mortality, they were not statistically significant. The Oncotype DX-specific multivariable models showed that, compared to NHW patients, recurrence was lower for Black/AA patients in the lower categories and higher in the upper categories, but results were not statistically significant. Conclusions: This study assessed recurrence and mortality outcomes by the recurrence score and found significantly higher rates of mortality among Black/AA patients in the lowest recurrence score category. Other studies have shown that recurrence scores add prognostic value during diagnosis. However, examining performance of these tools among diverse populations is imperative. Full article

Senescence-associated secretory phenotype (SASP) signaling, along with key markers such as P16^INK4a/CDKN2A and LMNB1, has not been systematically studied in circulating lymphocyte subsets in head and neck squamous cell carcinoma (HNSCC). This study aimed to evaluate SASP-related genes and senescence markers in peripheral CD4⁺, CD8⁺, and CD19⁺ cells and assess their clinical relevance. Expression of IL-6, IL-1β, TNFα, CXCL1, P16^INK4a/CDKN2A, and LMNB1 was measured by RT-qPCR in sorted lymphocytes from 58 HNSCC patients at baseline, 31 post-treatment, and 13 controls. Statistical analyses included nonparametric tests, correlation analyses, and survival models (Kaplan–Meier, Cox regression). In the results, LMNB1 was significantly upregulated in all lymphocyte subsets of HNSCC patients. IL-6, CXCL1, and IL-1β were elevated in CD4⁺ T cells. A coordinated co-expression network involving IL-6, CXCL1, IL-1β, P16^INK4a/CDKN2A, and LMNB1 was observed. Clinically, IL-6 in CD8⁺ T cells was associated with higher nodal stage and worse survival, while CXCL1 in CD19⁺ B cells independently predicted survival. No differences were found between pre- and post-treatment samples. Circulating lymphocytes in HNSCC display coordinated expression of selected senescence-associated genes, with IL-6 and CXCL1 as candidate prognostic biomarkers linked to tumor progression that warrant further validation. Full article