Large-Scale Whole-Genome Sequencing of Three Diverse Asian Populations in Singapore.

Underrepresentation of Asian genomes has hindered population and medical genetics research on Asians, leading to population disparities in precision medicine. By whole-genome sequencing of 4,810 Singapore Chinese, Malays, and Indians, we found 98.3 million SNPs and small insertions or deletions, over half of which are novel. Population structure analysis demonstrated great representation of Asian genetic diversity by three ethnicities in Singapore and revealed a Malay-related novel ancestry component. Furthermore, demographic inference suggested that Malays split from Chinese ∼24,800 years ago and experienced significant admixture with East Asians ∼1,700 years ago, coinciding with the Austronesian expansion. Additionally, we identified 20 candidate loci for natural selection, 14 of which harbored robust associations with complex traits and diseases. Finally, we show that our data can substantially improve genotype imputation in diverse Asian and Oceanian populations. These results highlight the value of our data as a resource to empower human genetics discovery across broad geographic regions.

PQBP1 promotes translational elongation and regulates hippocampal mGluR-LTD by suppressing eEF2 phosphorylation.

Eukaryotic elongation factor 2 (eEF2) mediates translocation of peptidyl-tRNA from the ribosomal A site to the P site to promote translational elongation. Its phosphorylation on Thr56 by its single known kinase eEF2K inactivates it and inhibits translational elongation. Extensive studies have revealed that different signal cascades modulate eEF2K activity, but whether additional factors regulate phosphorylation of eEF2 remains unclear. Here, we find that the X chromosome-linked intellectual disability protein polyglutamine-binding protein 1 (PQBP1) specifically binds to non-phosphorylated eEF2 and suppresses eEF2K-mediated phosphorylation at Thr56. Loss of PQBP1 significantly reduces general protein synthesis by suppressing translational elongation. Moreover, we show that PQBP1 regulates hippocampal metabotropic glutamate receptor-dependent long-term depression (mGluR-LTD) and mGluR-LTD-associated behaviors by suppressing eEF2K-mediated phosphorylation. Our results identify PQBP1 as a novel regulator in translational elongation and mGluR-LTD, and this newly revealed regulator in the eEF2K/eEF2 pathway is also an excellent therapeutic target for various disease conditions, such as neural diseases, virus infection, and cancer.

Reconstruction of the full transmission dynamics of COVID-19 in Wuhan.

As countries in the world review interventions for containing the pandemic of coronavirus disease 2019 (COVID-19), important lessons can be drawn from the study of the full transmission dynamics of its causative agent-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)- in Wuhan (China), where vigorous non-pharmaceutical interventions have suppressed the local outbreak of this disease1. Here we use a modelling approach to reconstruct the full-spectrum dynamics of COVID-19 in Wuhan between 1 January and 8 March 2020 across 5 periods defined by events and interventions, on the basis of 32,583 laboratory-confirmed cases1. Accounting for presymptomatic infectiousness2, time-varying ascertainment rates, transmission rates and population movements3, we identify two key features of the outbreak: high covertness and high transmissibility. We estimate 87% (lower bound, 53%) of the infections before 8 March 2020 were unascertained (potentially including asymptomatic and mildly symptomatic individuals); and a basic reproduction number (R0) of 3.54 (95% credible interval 3.40-3.67) in the early outbreak, much higher than that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS)4,5. We observe that multipronged interventions had considerable positive effects on controlling the outbreak, decreasing the reproduction number to 0.28 (95% credible interval 0.23-0.33) and-by projection-reducing the total infections in Wuhan by 96.0% as of 8 March 2020. We also explore the probability of resurgence following the lifting of all interventions after 14 consecutive days of no ascertained infections; we estimate this probability at 0.32 and 0.06 on the basis of models with 87% and 53% unascertained cases, respectively-highlighting the risk posed by substantial covert infections when changing control measures. These results have important implications when considering strategies of continuing surveillance and interventions to eventually contain outbreaks of COVID-19.

Subtype-Specific Association of Mitochondrial DNA Copy Number With Poststroke/TIA Outcomes in 10†241 Patients in China.

BACKGROUND: Mitochondrial DNA copy number (mtDNA-CN) is associated with the severity and mortality in patients with stroke, but the associations in different stroke subtypes remain unexplored. METHODS: We conducted an observational prospective cohort analysis on patients with ischemic stroke or transient ischemic attack enrolled in the Third China National Stroke Registry. We applied logistic models to assess the association of mtDNA-CN with functional outcome (modified Rankin Scale score, 3-6 versus 0-2) and Cox proportional hazard models to assess the association with stroke recurrence (treating mortality as a competing risk) and mortality during a 12-month follow-up, adjusting for sex, age, physical activity, National Institutes of Health Stroke Scale at admission, history of stroke and peripheral artery disease, small artery occlusion, and interleukin-6. Subgroup analyses stratified by age and stroke subtypes were conducted. RESULTS: The Third China National Stroke Registry enrolled 15 166 patients, of which 10 241 with whole-genome sequencing data were retained (mean age, 62.2 [SD, 11.2] years; 68.8% men). The associations between mtDNA-CN and poststroke/transient ischemic attack outcomes were specific to patients aged ≤65 years, with lower mtDNA-CN significantly associated with stroke recurrence in 12 months (subdistribution hazard ratio, 1.15 per SD lower mtDNA-CN [95% CI, 1.04-1.27]; P=5.2×10-3) and higher all-cause mortality in 3 months (hazard ratio, 2.19 [95% CI, 1.41-3.39]; P=5.0×10-4). Across subtypes, the associations of mtDNA-CN with stroke recurrence were specific to stroke of undetermined cause (subdistribution hazard ratio, 1.28 [95% CI, 1.11-1.48]; P=6.6×10-4). In particular, lower mtDNA-CN was associated with poorer functional outcomes in stroke of undetermined cause patients diagnosed with embolic stroke of undetermined source (odds ratio, 1.53 [95% CI, 1.20-1.94]; P=5.4×10-4), which remained significant after excluding patients with recurrent stroke (odds ratio, 1.49 [95% CI, 1.14-1.94]; P=3.0×10-3). CONCLUSIONS: Lower mtDNA-CN is associated with higher stroke recurrence rate and all-cause mortality, as well as poorer functional outcome at follow-up, among stroke of undetermined cause, embolic stroke of undetermined source, and younger patients.

Rare variant association tests for ancestry-matched case-control data based on conditional logistic regression.

With the increasing volume of human sequencing data available, analysis incorporating external controls becomes a popular and cost-effective approach to boost statistical power in disease association studies. To prevent spurious association due to population stratification, it is important to match the ancestry backgrounds of cases and controls. However, rare variant association tests based on a standard logistic regression model are conservative when all ancestry-matched strata have the same case-control ratio and might become anti-conservative when case-control ratio varies across strata. Under the conditional logistic regression (CLR) model, we propose a weighted burden test (CLR-Burden), a variance component test (CLR-SKAT) and a hybrid test (CLR-MiST). We show that the CLR model coupled with ancestry matching is a general approach to control for population stratification, regardless of the spatial distribution of disease risks. Through extensive simulation studies, we demonstrate that the CLR-based tests robustly control type 1 errors under different matching schemes and are more powerful than the standard Burden, SKAT and MiST tests. Furthermore, because CLR-based tests allow for different case-control ratios across strata, a full-matching scheme can be employed to efficiently utilize all available cases and controls to accelerate the discovery of disease associated genes.

Anoikis-related signature predicts prognosis and characterizes immune landscape of ovarian cancer.

Ovarian cancer (OV) is the most lethal gynecological malignancy worldwide, with high recurrence rates. Anoikis, a newly-acknowledged form of programmed cell death, plays an essential role in cancer progression, though studies focused on prognostic patterns of anoikis in OV are still lacking. We filtered 32 potential anoikis-related genes (ARGs) among the 6406 differentially expressed genes (DEGs) between the 180 normal controls and 376 TCGA-OV samples. Through the LASSO-Cox analysis, a 2-gene prognostic signature, namely AKT2, and DAPK1, was finally distinguished. We then demonstrated the promising prognostic value of the signature through the K-M survival analysis and time-dependent ROC curves (p-value < 0.05). Moreover, based on the signature and clinical features, we constructed and validated a nomogram model for 1-year, 3-year, and 5-year overall survival, with reliable prognostic values in both TCGA-OV training cohort (p-value < 0.001) and ICGC-OV validation cohort (p-value = 0.030). We evaluated the tumor immune landscape through the CIBERSORT algorithm, which indicated the upregulation of resting Myeloid Dendritic Cells (DCs), memory B cells, and naïve B cells and high expression of key immune checkpoint molecules (CD274 and PDCD1LG2) in the high-risk group. Interestingly, the high-risk group exhibited better sensitivity toward immunotherapy and less sensitivity toward chemotherapies, including Cisplatin and Bleomycin. Especially, based on the IHC of tissue microarrays among 125 OV patients at our institution, we reported that aberrant upregulation of DAPK1 was related to poor prognosis. Conclusively, the anoikis-related signature was a promising tool to evaluate prognosis and predict therapy responses, thus assisting decision-making in the realm of OV precision medicine.

Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer.

PURPOSE: Significant advancements in improving ovarian cancer (OC) outcomes have been limited over the past decade. To predict prognosis and improve outcomes of OC, we plan to develop and validate a robust prognosis signature based on blood features. METHODS: We screened age and 33 blood features from 331 OC patients. Using ten machine learning algorithms, 88 combinations were generated, from which one was selected to construct a blood risk score (BRS) according to the highest C-index in the test dataset. RESULTS: Stepcox (both) and Enet (alpha = 0.7) performed the best in the test dataset with a C-index of 0.711. Meanwhile, the low RBS group possessed observably prolonged survival in this model. Compared to traditional prognostic-related features such as age, stage, grade, and CA125, our combined model had the highest AUC values at 3, 5, and 7 years. According to the results of the model, BRS can provide accurate predictions of OC prognosis. BRS was also capable of identifying various prognostic stratifications in different stages and grades. Importantly, developing the nomogram may improve performance by combining BRS and stage. CONCLUSION: This study provides a valuable combined machine-learning model that can be used for predicting the individualized prognosis of OC patients.

Using off-target data from whole-exome sequencing to improve genotyping accuracy, association analysis and polygenic risk prediction.

Whole-exome sequencing (WES) has been widely used to study the role of protein-coding variants in genetic diseases. Non-coding regions, typically covered by sparse off-target data, are often discarded by conventional WES analyses. Here, we develop a genotype calling pipeline named WEScall to analyse both target and off-target data. We leverage linkage disequilibrium shared within study samples and from an external reference panel to improve genotyping accuracy. In an application to WES of 2527 Chinese and Malays, WEScall can reduce the genotype discordance rate from 0.26% (SE= 6.4 × 10-6) to 0.08% (SE = 3.6 × 10-6) across 1.1 million single nucleotide polymorphisms (SNPs) in the deeply sequenced target regions. Furthermore, we obtain genotypes at 0.70% (SE = 3.0 × 10-6) discordance rate across 5.2 million off-target SNPs, which had ~1.2× mean sequencing depth. Using this dataset, we perform genome-wide association studies of 10 metabolic traits. Despite of our small sample size, we identify 10 loci at genome-wide significance (P < 5 × 10-8), including eight well-established loci. The two novel loci, both associated with glycated haemoglobin levels, are GPATCH8-SLC4A1 (rs369762319, P = 2.56 × 10-12) and ROR2 (rs1201042, P = 3.24 × 10-8). Finally, using summary statistics from UK Biobank and Biobank Japan, we show that polygenic risk prediction can be significantly improved for six out of nine traits by incorporating off-target data (P < 0.01). These results demonstrate WEScall as a useful tool to facilitate WES studies with decent amounts of off-target data.

The role of PQBP1 in neural development and function.

Mutations in the polyglutamine tract-binding protein 1 (PQBP1) gene are associated with Renpenning syndrome, which is characterized by microcephaly, intellectual deficiency, short stature, small testes, and distinct facial dysmorphism. Studies using different models have revealed that PQBP1 plays essential roles in neural development and function. In this mini-review, we summarize recent findings relating to the roles of PQBP1 in these processes, including in the regulation of neural progenitor proliferation, neural projection, synaptic growth, neuronal survival, and cognitive function via mRNA transcription and splicing-dependent or -independent processes. The novel findings provide insights into the mechanisms underlying the pathogenesis of Renpenning syndrome and may advance drug discovery and treatment for this condition.

Clinical significance and immune infiltration analyses of a novel coagulation-related signature in ovarian cancer.

Ovarian cancer (OV) is the most lethal gynecological malignancies worldwide. The coagulation cascade could induce tumor cell infiltration and contribute to OV progression. However, coagulation-related gene (CRG) signature for OV prognosis hasn't been determined yet. In this study, we evaluated the prognostic value of coagulation scores through receiver operating characteristics (ROC) analysis and K-M curves, among OV patients at our institution. Based on the transcriptome data of TCGA-OV cohort, we stratified two coagulation-related subtypes with distinct differences in prognosis and tumor immune microenvironment (p < 0.05). Moreover, from the 6406 differentially-expressed genes (DEGs) between the GTEx (n = 180) and TCGA-OV cohorts (n = 376), we identified 138 potential CRGs. Through LASSO-Cox algorithm, we finally distinguished a 3-gene signature (SERPINA10, CD38, and ZBTB16), with promising prognostic ability in both TCGA (p < 0.001) and ICGC cohorts (p = 0.040). Stepwise, we constructed a nomogram based on the clinical features and coagulation-related signature for overall survival prediction, with the C-index of 0.6761, which was evaluated by calibration curves. Especially, based on tissue microarrays analysis, Quantitative real-time fluorescence PCR (qRT-PCR), and Western Blot, we found that aberrant upregulation of CRGs was related to poor prognosis in OV at both mRNA and protein level (p < 0.05). Collectively, the coagulation-related signature was a robust prognostic biomarker, which could provide therapeutic benefits for chemotherapy/immunotherapy and assist clinical decision in OV patients.

Genetic and observational associations of lung function with gastrointestinal tract diseases: pleiotropic and mendelian randomization analysis.

BACKGROUND: The two-way communications along the gut-lung axis influence the immune function in both gut and lung. However, the shared genetic characteristics of lung function with gastrointestinal tract (GIT) diseases remain to be investigated. METHODS: We first investigated the genetic correlations between three lung function traits and four GIT diseases. Second, we illustrated the genetic overlap by genome-wide pleiotropic analysis (PLACO) and further pinpointed the relevant tissue and cell types by partitioning heritability. Furthermore, we proposed pleiotropic genes as potential drug targets by drug database mining. Finally, we evaluated the causal relationships by epidemiologic observational study and Mendelian randomization (MR) analysis. RESULTS: We found lung function and GIT diseases were genetically correlated. We identified 258 pleiotropic loci, which were enriched in gut- and lung-specific regions marked by H3K4me1. Among these, 16 pleiotropic genes were targets of drugs, such as tofacitinib and baricitinib targeting TYK2 for the treatment of ulcer colitis and COVID-19, respectively. We identified a missense variant in TYK2, exhibiting a shared causal effect on FEV1/FVC and inflammatory bowel disease (rs12720356, PPLACO=1.38 × 10- 8). These findings suggested TYK2 as a promising drug target. Although the epidemiologic observational study suggested the protective role of lung function in the development of GIT diseases, no causalities were found by MR analysis. CONCLUSIONS: Our study suggested the shared genetic characteristics between lung function and GIT diseases. The pleiotropic variants could exert their effects by modulating gene expression marked by histone modifications. Finally, we highlighted the potential of pleiotropic analyses in drug repurposing.

A tumor-associated splice-isoform of MAP2K7 drives dedifferentiation in MBNL1-low cancers via JNK activation.

Master splicing regulator MBNL1 shapes large transcriptomic changes that drive cellular differentiation during development. Here we demonstrate that MBNL1 is a suppressor of tumor dedifferentiation. We surveyed MBNL1 expression in matched tumor/normal pairs across The Cancer Genome Atlas and found that MBNL1 was down-regulated in several common cancers. Down-regulation of MBNL1 predicted poor overall survival in breast, lung, and stomach adenocarcinomas and increased relapse and distant metastasis in triple-negative breast cancer. Down-regulation of MBNL1 led to increased tumorigenic and stem/progenitor-like properties in vitro and in vivo. A discrete set of alternative splicing events (ASEs) are shared between MBNL1-low cancers and embryonic stem cells including a MAP2K7∆exon2 splice variant that leads to increased stem/progenitor-like properties via JNK activation. Accordingly, JNK inhibition is capable of reversing MAP2K7∆exon2-driven tumor dedifferentiation in MBNL1-low cancer cells. Our work elucidates an alternative-splicing mechanism that drives tumor dedifferentiation and identifies biomarkers that predict enhanced susceptibility to JNK inhibition.

Integrative analysis of the expression and prognosis for CENPs in ovarian cancer.

Centromere proteins (CENPs) are nuclear proteins that are involved in centromere formation and chromosome segregation during mitosis. Some members of CENPs have been extensively studied in the initiation and development of cancers. However, the expression patterns and exact roles of CENPs in ovarian cancer (OC) have not been fully elucidated. In this study, we comprehensively assessed the genetic variation, expression patterns and prognostic value of CENPs in OC by several databases. The mRNA expression levels of CENPA/F/H/L/N/U/W were found to be significantly upregulated in OC and related to worse prognosis. Additionally, function enrichment analysis showed that CENPs were involved in DNA repair and cell division. Meanwhile, immune infiltration analysis elucidated that CENPs were associated with myeloid-derived suppressor cells (MDSCs) and major histocompatibility complex (MHC). These results suggested that CENPs might serve as potential diagnostic and prognostic markers and provide new insights for the development of CENPs-targeted therapeutics for ovarian cancer.

Integrative analysis of scRNA-seq and GWAS data pinpoints periportal hepatocytes as the relevant liver cell types for blood lipids.

Liver, a heterogeneous tissue consisting of various cell types, is known to be relevant for blood lipid traits. By integrating summary statistics from genome-wide association studies (GWAS) of lipid traits and single-cell transcriptome data of the liver, we sought to identify specific cell types in the liver that were most relevant for blood lipid levels. We conducted differential expression analyses for 40 cell types from human and mouse livers in order to construct the cell-type specifically expressed gene sets, which we refer to as construction of the liver cell-type specifically expressed gene sets (CT-SEGS). Under the assumption that CT-SEGS represented specific functions of each cell type, we applied stratified linkage disequilibrium score regression to determine cell types that were most relevant for complex traits and diseases. We first confirmed the validity of this method (of delineating functionally relevant cell types) by identifying the immune cell types as relevant for autoimmune diseases. We further showed that lipid GWAS signals were enriched in the human and mouse periportal hepatocytes. Our results provide important information to facilitate future cellular studies of the metabolic mechanism affecting blood lipid levels.

Single Molecule Level and Label-Free Determination of Multibiomarkers with an Organic Field-Effect Transistor Platform in Early Cancer Diagnosis.

The single molecule level determination with a transistor (SiMoT) platform has attracted considerable attention in the recognition of various ultralow abundance biomolecules, while complicated labeling and testing processes limit its further applications. Recently, organic field-effect transistor (OFET)-based biosensors are good candidates for constructing an advanced label-free SiMoT platform due to their facile fabrication process, rapid response time, and low sample volume with a wide range of detection. However, the sensitivity of most OFET-based biosensors is in the order of nM and pM, which cannot meet the detection requirements of ultralow abundance protein. Herein, a label-free SiMoT platform is demonstrated by integrating pillar[n]arene as a signal amplifier, and the detection limit can reach 4.75 aM. Besides, by simultaneous determination of α-fetoprotein, carcinoembryonic antigen, and prostate antigen, the proposed multiplexed OFET-based SiMoT platform provides a key step in reliable early cancer diagnosis.

Dietary Inflammatory Index and Ovarian Cancer Risk: A Meta-Analysis.

Existing evidence suggests diet and chronic inflammation as risk factors in ovarian cancer (OC) development. We aim to conduct a meta-analysis exploring possible associations between dietary inflammatory potential and OC. A systematic search was conducted through PubMed, Scopus, Embase, and PMC databases for studies reporting relationships between dietary inflammatory potential and OC risk published up to September 2020. We included six studies for stepwise analysis, of which 5,468 among 197,086 individuals developed OC. Pooled odds ratios (ORs) were calculated by fixed-effects models, while heterogeneity was assessed by Q test and I2 statistic. The results revealed a positive association between dietary inflammatory potential measured by the Dietary Inflammatory Index (DII) and OC(P < 0.05). Individuals with higher DII scores had a 42% increased risk of OC incidence [OR = 1.42, 95% confidence interval (CI): 1.19-1.65]. The analysis considering DII as a continuous variable showed an increased risk of 10% for 1-point increase of DII(OR = 1.10, 95% CI: 1.06-1.14). Subgroup analysis revealed that increased risk of OC in individuals with higher DII scores vs. those with lower DII was only significant among post-menopausal women(OR = 1.72, 95% CI: 1.26-2.21) rather than those pre/peri-menopausal(OR = 1.21, 95% CI: 0.63-1.79). Pro-inflammatory diets with higher DII score were significantly related to increased OC risk among post-menopausal women.

Gαq splice variants mediate phototransduction, rhodopsin synthesis, and retinal integrity in Drosophila.

Heterotrimeric G proteins mediate a variety of signaling processes by coupling G protein-coupled receptors to intracellular effector molecules. In Drosophila, the Gαq gene encodes several Gαq splice variants, with the Gαq1 isoform protein playing a major role in fly phototransduction. However, Gαq1 null mutant flies still exhibit a residual light response, indicating that other Gαq splice variants or additional Gq α subunits are involved in phototransduction. Here, we isolated a mutant fly with no detectable light responses, decreased rhodopsin (Rh) levels, and rapid retinal degeneration. Using electrophysiological and genetic studies, biochemical assays, immunoblotting, real-time RT-PCR, and EM analysis, we found that mutations in the Gαq gene disrupt light responses and demonstrate that the Gαq3 isoform protein is responsible for the residual light response in Gαq1 null mutants. Moreover, we report that Gαq3 mediates rhodopsin synthesis. Depletion of all Gαq splice variants led to rapid light-dependent retinal degeneration, due to the formation stable Rh1-arrestin 2 (Arr2) complexes. Our findings clarify essential roles of several different Gαq splice variants in phototransduction and retinal integrity in Drosophila and reveal that Gαq3 functions in rhodopsin synthesis.

Facile Functionalization Strategy for Ultrasensitive Organic Protein Biochips in Multi-Biomarker Determination.

In recent years, organic field-effect transistors (OFETs) have shown great potential for advanced protein biochips due to their inherent biocompatibility and high-throughput detectability. However, the development of OFET-based protein biochips is still at an early stage. On the one hand, single-biomarker determination is not sufficient for the diagnosis of cancer; thus, simultaneous monitoring of electrical signals toward multi-biomarkers is widely concerned and explored. On the other hand, an optimized functionalization strategy for efficient protein immobilization is another key to make OFET-based protein biochips accessible with improved detection performance. Herein, a facile functionalization strategy is developed for excellent charge-transport thin films by suppressing the gelation of diketopyrrolopyrrole (DPP)-based polymer semiconductors with the addition of the glutaraldehyde cross-linking agent. Besides, functional groups are introduced on the device surface for efficient attachment of antibodies as receptors via a condensation reaction, enabling simultaneous determination of α-fetoprotein biomarker and carcinoembryonic antigen biomarker with improved sensitivity and reliability. Therefore, the proposed high-throughput OFET-based protein biochip has the potential to be widely utilized in early liver cancer diagnosis.

Organic Field-Effect Transistor-Based Biosensors with Enhanced Sensitivity and Reliability under Illumination for Carcinoembryonic Antigen Bioassay.

Achieving biosensors of high sensitivity and reliability is extremely significant for early diagnosis and treatment of tumor diseases. Herein, a novel organic field-effect transistor (OFET)-based biosensor was developed and applied for carcinoembryonic antigen (CEA) bioassay. This OFET-based biosensor can respond sensitively to the antigen-antibody immune-recognition reaction under illumination and darkness, respectively, thereby generating electrical signal changes of source-drain current (IDS) and threshold voltage (Vth). The OFET-based biosensor exhibits detection limits for CEA detection of 0.5 and 0.2 pM, respectively, using IDS and Vth as the response signals under darkness. When a specific intensity of light is applied, light will influence the charge-carrier transport process in the conductive channel, thus causing biosignals to turn into higher electrical signal changes of photocurrent and threshold voltage under illumination. Compared with the detection results in the dark, the biosensor exhibits higher sensitivity for CEA detection under illumination with detection limits of 13.5 and 16.9 fM. Also, multisignal outputs effectively improve the reliability of the biosensor for CEA detection. Consequently, with powerful detection functions, this OFET-based biosensor is expected to become a high-performance biosensing platform for the detection of various biological substances in the future.

Ultrasensitive and Reliable Organic Field-Effect Transistor-Based Biosensors in Early Liver Cancer Diagnosis.

Organic field-effect transistors (OFETs) are considered as one of the cost-effective biosensor devices with rapid detection capabilities and multiparameter responses. However, the functionalization processes on normal organic devices might impact the device performance for its further sensitive and reliable sensing applications. Herein, we develop a novel organic material, 2,6-bis(4-formylphenyl)anthracene (BFPA) for use as the protective and functional layer of OFET-based biosensors, enabling ultrasensitive determination of alpha-fetoprotein (AFP) with femtomolar accuracy in human serum. By monitoring changes of the source-drain current (Ids) and threshold voltage (Vth) electrical signals, the device exhibits improved reliability in detecting AFP biomarkers and is able to differentiate between liver cancer patients and healthy individuals. Featuring label-free determination, shorter analysis time, and lower sample volume, this ultrasensitive and reliable OFET-based biosensor displays numerous advantages over traditional strategies such as enzyme-linked immunosorbent assay and electrochemiluminescence immunoassay, demonstrating that the proposed OFET-based biosensors have broader analytical and clinical applications for early liver cancer diagnosis.