Biomarker Research最新文献

Background: Detecting and treating precancerous lesions can lower the incidence of esophageal squamous cell carcinoma (ESCC), making it a key preventive strategy. Although endoscopic screening and intervention can significantly reduce mortality associated with ESCC, they have certain shortcomings. We aimed to develop three predictive models: the motif, eight-protein, and combined motif-protein models to identify ESCC and its precancerous lesions.

Methods: Plasma samples were collected for cfDNA sequencing, and nine commonly used clinical protein biomarkers related to the digestive system were measured. Using a total cohort of 199 patients with ESCC, 91 patients with esophageal squamous precancerous lesions (ESPL), and 201 controls, we developed an integrative model based on selected multi-omics biomarkers.

Results: The motif-protein model, integrating 20 principal components of cfDNA terminal motifs with six protein features, outperformed both the motif model and the eight-protein model in distinguishing patients with ESCC and ESPL (area under the curve = 0·90). It achieved an overall sensitivity of 88·5% and a specificity of 75·4%. Notably, it successfully identified 90·9% of high-grade intraepithelial neoplasia cases, 86·8% of stage I ESCC cases, and 87·8% of HGIN or T1aN0 stage ESCC (subset of Stage I) cases who were eligible for endoscopic treatment, highlighting its potential as an effective tool for early diagnosis.

Conclusions: The motif-protein model may serve as an effective tool for the early diagnosis of esophageal lesions. Our findings underscore the clinical potential of the multi-omics liquid biopsy test as a non-invasive method for detecting early esophageal lesions.

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by dysregulated differentiation and uncontrolled proliferation of myeloid precursor cells. AML is the second most common type of leukemia after acute lymphoblastic leukemia, yet it has the lower survival rates, with only approximately 30% of adult patients surviving five years post-diagnosis. Standard treatment regimens typically include intensive chemotherapy, advances in allogeneic hematopoietic stem cell transplantation (allo-HSCT) have significantly improved outcomes in the treatment of AML. Advances in molecular profiling technologies have significantly enhanced our understanding of the genetic and epigenetic alterations that drive AML, revealing numerous novel therapeutic targets. Consequently, targeted molecular therapies and epigenetic treatments are becoming increasingly important. Moreover, immunotherapy represents a promising therapeutic strategy that has demonstrated considerable potential in the context of AML. This review summarizes new strategies and emerging therapeutic targets in AML, with a particular focus on recent advancements in immunotherapy. It also explores the feasibility of integrating these therapeutic approaches into current treatment paradigms and their potential impact on future clinical practices.

Primary liver cancer, particularly hepatocellular carcinoma (HCC), remains a major cause of cancer-related mortality worldwide, with rising incidence and limited treatment options, especially for patients diagnosed at advanced stages. In recent years, metabolic reprogramming has emerged as a hallmark of cancer that enables HCC cells to survive, proliferate, and resist therapy under hostile conditions. HCC cells undergo profound remodeling of glucose, lipid, and amino acid metabolism to adapt to hypoxia and nutrient deprivation. These processes are orchestrated by key signaling cascades, including the PI3K/AKT/mTOR, Ras-Raf-MEK-ERK-cMYC, and LKB1-AMPK pathways, forming a dynamic and integrated metabolic-signaling network. This review comprehensively integrates recent advances in the understanding of metabolic pathways in HCC, with a particular focus on glycolysis, de novo lipogenesis, and glutamine metabolism. We delineate the regulatory mechanisms underlying these pathways and construct an interaction map linking metabolic circuits to clinical phenotypes such as tumor heterogeneity, metastatic potential, and immune modulation. Furthermore, we systematically evaluate the biomarker potential of metabolic intermediates, rate-limiting enzymes, and key regulators in the context of early detection, molecular classification, prognosis prediction, and therapeutic response in HCC. We also highlight cutting-edge technologies, including metabolic imaging, liquid biopsy-based biomarker detection, and metabolism-targeted therapies. The review explores their potential synergy with immunotherapy, chemotherapy, and radiotherapy, aiming to provide a comprehensive framework for individualized HCC management. Our discussion underscores the translational relevance of metabolic biomarkers and offers insights for future research and clinical innovation.

Background: The advent of poly (ADP-ribose) polymerase inhibitors (PARPi) over the past decade has significantly altered the management of epithelial ovarian cancer (EOC). We proposed that the etiology of homologous recombination deficiency (HRD) might underlie the variable responses to PARPi observed across patient populations.

Methods: As part of the phase 2 study of the Chinese HRD Harmonization Project, we developed a genomic methylation sequencing (GM-seq) pipeline facilitated by the TET enzyme for the simultaneous identification of methylated modifications and genetic variations in EOC tumor samples, and compared with established DNA sequencing-based HRD assays.

Results: Somatic mutation and HRD scores were confounded by low tumor purity in our cohort of 98 locally advanced/advanced EOC patients. In samples with tumor purity ≥ 30% (n = 45), the GM-seq pipeline showed high consistency with DNA sequencing-based HRD assay, identifying genetic variations in homologous recombination repair (HRR) genes and HRD score with 92.6% (25/27) and 97.1% (33/34) consistency respectively, in addition to conducting methylation profiling. Moreover, different underlying mechanisms of HRD were associated with varying degrees of PARPi efficacy, with BRCA1/2 LOH group having the best efficacy (median PFS, undefined), followed by BRCA1 methylation group (median PFS, 23.4 months), and those with unknown etiology of HRD having the worst efficacy (median PFS, 8.8 months, p < 0.001).

Conclusion: Our findings underscore the importance of considering HRD etiology when evaluating PARPi efficacy in EOC patients. The GM-seq pipeline, represents a significant advancement in HRD detection, enabling more accurate predictions of PARPi response.

This study aimed to identify the ultrastructural features of sperm in Chinese asthenozoospermia patients and to evaluate their clinical implications. A total of 139 individuals, including 106 asthenozoospermia patients and 33 fertile controls, were recruited from multiple centers across China. Sperm ultrastructural defects were examined using transmission electron microscopy (TEM), while conventional sperm quality was assessed by light microscopy. Compared with the control group, the asthenozoospermia group showed significant ultrastructural defects, particularly in the sperm tail, including mitochondrial and axonemal defects. Based on tail ultrastructure, participants were further categorized into four groups (normal ultrastructure, simple abnormal axonemes, simple abnormal mitochondria, and both abnormality in axonemes and mitochondria). All three abnormal groups indicated significantly lower value in sperm (motile/kinematic) quality compared to the normal group. However, no statistically significant differences in sperm quality were observed among the three abnormal groups. These nationwide findings indicated that TEM could remedy the limitations of conventional light microscopy, which is difficult to localize organelle-specific defects. This capability may ultimately inform individualized diagnostic and therapeutic strategies.

Renal cancer, particularly clear cell renal cell carcinoma (ccRCC), is characterized by significant intratumoral heterogeneity, which poses challenges for diagnosis and treatment. Single-cell sequencing (SCS) provides unprecedented insights into the cellular landscape of renal cancer, allowing for detailed characterization of tumor heterogeneity at the single-cell level. This review highlights how SCS has been instrumental in elucidating the origins of different renal cancer subtypes, understanding mechanisms of tumor initiation and progression, and dissecting the complex tumor microenvironment (TME). It discusses the identification of novel biomarkers and therapeutic targets, as well as the potential of SCS to inform personalized treatment strategies. The review also explores the integration of SCS with spatial omics technologies, which enhances the understanding of cellular interactions within their spatial context. Moreover, it addresses the challenges and future directions in applying SCS to clinical practice, emphasizing its significance in advancing renal cancer biology and improving clinical interventions.

Cellular therapies for several blood cancers particularly of lymphoid origin have made remarkable leaps forward. In parallel, blood proteomics, specifically quantitative proteomics, has been a powerful tool for identifying and quantifying protein biomarkers associated with cellular therapies, providing insights into treatment efficacy and toxicity. Both mass spectrometry (MS)-based proteomics and large-scale affinity-based platforms such as Olink and SomaScan have been increasingly implemented in research and clinical laboratories to identify and quantify candidate biomarkers in the blood. Biomarkers are used for risk stratification, early diagnosis, prognosis, and for treatment response prediction and monitoring in context of treatment efficacy and toxicity. These biomarkers might facilitate timely and selective therapeutic intervention and understand pathogenesis mechanisms of responses and adverse events. They are anticipated to undergo faster transition from bench to bedside soon. This review article summarizes recent technical progresses in clinical proteomics. The review also provides current information on validated biomarkers in the field of cellular therapies.

Objectives: To investigate the predictive value of machine-learning-based Radiomics models for postoperative overall survival (OS) of endometrial cancer (EC) patients and their biological mechanisms.

Methods: Data from 469 patients with endometrial cancer in three Centers (271 in Center 1, 154 in Center 2, and 44 in Center 3) were retrospectively and 90 patients in Center 1 were prospectively analyzed. Three-dimensional Radiomics parameters of the primary lesion and its surrounding 5 mm region in T2WI were collected from all patients. Ten machine learning methods were used to calculate the optimal Radiomics score (Radscore), whose incremental value to the available clinical indexes, pathomics, transcriptomics, and proteomics were revealed. Eventually, TCGA and CPTAC were used for the exploration of biological mechanisms of Radiomics model, with experimental validation.

Results: Radiomics features of tumor and peritumor showed some complementarity in the prognostic prediction of EC patients. The best predictive efficacy was demonstrated by the combined Radiomics model based on XGboost, with AUCs of 0.862, 0.885, 0.870 (validation set) and 0.823, 0.869, 0.849 (test set 1) and 0.850, 0.731, 0.800 (test set 2). Radiomics models demonstrated high incremental value to existing clinical indicators and can effectively improve prognostic prediction. In addition, Radiomics models have been shown to have synergistic prognostic predictive potential with pathomics, transcriptomics, and proteomics. Finally, mechanical explorations suggest that Radiomics models may be associated with tumor angiogenesis-related pathways, of which FLT1 was highlighted.

Conclusions: Machine learning-based Radiomics model contributes to predicting postoperative OS in EC patients and suggests a correlation with tumor angiogenesis.

The oncogenic potential of SARS-CoV-2 has been hypothetically proposed, but real-world data on COVID-19 infection and vaccination are insufficient. Therefore, this large-scale population-based retrospective study in Seoul, South Korea, aimed to estimate the cumulative incidences and subsequent risks of overall cancers 1 year after COVID-19 vaccination. Data from 8,407,849 individuals between 2021 and 2023 were obtained from the Korean National Health Insurance database. The participants were categorized into two groups based on their COVID-19 vaccination status. The risks for overall cancer were assessed using multivariable Cox proportional hazards models, and data were expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). The HRs of thyroid (HR, 1.351; 95% CI, 1.206-1.514), gastric (HR, 1.335; 95% CI, 1.130-1.576), colorectal (HR, 1.283; 95% CI, 1.122-1.468), lung (HR, 1.533; 95% CI, 1.254-1.874), breast (HR, 1.197; 95% CI, 1.069-1.340), and prostate (HR, 1.687; 95% CI, 1.348-2.111) cancers significantly increased at 1 year post-vaccination. In terms of vaccine type, cDNA vaccines were associated with the increased risks of thyroid, gastric, colorectal, lung, and prostate cancers; mRNA vaccines were linked to the increased risks of thyroid, colorectal, lung, and breast cancers; and heterologous vaccination was related to the increased risks of thyroid and breast cancers. Given the observed associations between COVID-19 vaccination and cancer incidence by age, sex, and vaccine type, further research is needed to determine whether specific vaccination strategies may be optimal for populations in need of COVID-19 vaccination.

Alzheimer's disease (AD) is the most common cause of dementia and accounts for approximately 60-80% of total dementia patients. Currently, accurate diagnosis for AD relies on cerebrospinal fluid (CSF) sampling or a positron emission tomography (PET) scan, methods that cannot be done in primary care centers where most people go with cognitive complaints. This Limitation calls for the urgent need to develop blood-related diagnostic tests that could facilitate early detection and enable timely treatment. Recent CSF proteomic research categorized AD into five molecular subtypes with discrete Genetic risk profiles. Subtypes 1-3, namely neuronal hyperplasticity, innate immune activation, and RNA dysregulation, were characterized by more classical AD-related changes, like accumulation of amyloid/tau and synaptic and immune dysfunction, respectively. On the contrary, non-traditional AD mechanisms in subtypes 4-5 were choroid plexus (CP) dysfunction and blood-brain barrier (BBB) dysfunction, emphasizing clearance deficits in association with brain barrier dysfunction. The unchanged tau levels later may be explained by an alternate disease mechanism (clearance dysfunction). These subtypes included BBB and CP dysfunction. Biomarker identification based on the mechanism of disease progression would increase the precision of diagnoses, allowing for tailored interventions and aiding in the creation of novel therapies for subtypes that might not react favorably to conventional amyloid/tau-targeting strategies. Finding biomarkers specific to each subtype would aid in patient classification, resulting in more individualized therapy as opposed to a "one-size-fits-all" strategy. The present review emphasized the importance of identifying blood-based biomarkers (BBMs) related to brain barrier dysfunction from CSF studies and personalized treatment strategies to streamline the diagnostic workup, and may be utilized in standard clinical practice for the early detection of AD.