Screening a DNA Aptamer Specifically Targeting Integrin ß3 and Partially Inhibiting Tumor Cell Migration.

Due to its key roles in malignant tumor progression and reprograming of the tumor microenvironment, integrin ß3 has attracted great attention as a new target for tumor therapy. However, the structure-function relationship of integrins ß3 remains incompletely understood, leading to the shortage of specific and effective targeting probes. This work uses a purified extracellular domain of integrin ß3 and integrin ß3-positive cells to screen aptamers, specifically targeting integrin ß3 in the native conformation on live cells through the SELEX approach. Following meticulous truncation and characterization of the initial aptamer candidates, the optimized aptamer S10yh2 was produced, exhibiting a low equilibrium dissociation constant (Kd) in the nanomolar range. S10yh2 displays specific recognition of cancer cells with varying levels of integrin ß3 expression and demonstrates favorable stability in serum. Subsequent analysis of docking sites revealed that S10yh2 binds to the seven amino acid residues located in the core region of integrin ß3. The S10yh2 aptamer can downregulate the level of integrin heterodimer αvß3 on integrin ß3 overexpressed cancer cells and partially inhibit cell migration behavior. In summary, S10yh2 is a promising probe with a small size, simple synthesis, good stability, high binding affinity, and selectivity. It therefore holds great potential for investigating the structure-function relationship of integrins.

Logical Analysis of Multiple Single-Nucleotide-Polymorphisms with Programmable DNA Molecular Computation for Clinical Diagnostics.

Analyzing complex single-nucleotide-polymorphism (SNP) combinations in the genome is important for research and clinical applications, given that different SNP combinations can generate different phenotypic consequences. Recent works have shown that DNA-based molecular computing is powerful for simultaneously sensing and analyzing complex molecular information. Here, we designed a switching circuit-based DNA computational scheme that can integrate the sensing of multiple SNPs and simultaneously perform logical analysis of the detected SNP information to directly report clinical outcomes. As a demonstration, we successfully achieved automatic and accurate identification of 21 different blood group genotypes from 83 clinical blood samples with 100 % accuracy compared to sequencing data in a more rapid manner (3 hours). Our method enables a new mode of automatic and logical sensing and analyzing subtle molecular information for clinical diagnosis, as well as guiding personalized medication.

Establishment of a serological molecular model for the early diagnosis and progression monitoring of bone metastasis in lung cancer.

BACKGROUND: The prognosis is very poor for lung cancer patients with bone metastasis. Unfortunately, a suitable method has yet to become available for the early diagnosis of bone metastasis in lung cancer patients. The present work describes an attempt to develop a novel model for the early identification of lung cancer patients with bone metastasis risk. METHODS: As the test group, 205 primary lung cancer patients were recruited, of which 127 patients had bone metastasis; the other 78 patients without bone metastasis were set as the negative control. Additionally, 106 healthy volunteers were enrolled as the normal control. Serum levels of several cytokines in the bone microenvironment (CaN, OPG, PTHrP, and IL-6) and bone turnover markers (tP1NP, ß-CTx) were detected in all samples by ECLIA or ELISA assay. Receiver operating characteristic (ROC) curves and multivariate analyses were performed to evaluate diagnostic abilities and to assess the attributable risk of bone metastasis for each of these indicators; the diagnostic model was established via logistic regression analysis. The prospective validation group consisted of 44 patients with stage IV primary lung cancer on whom a follow-up of at least 2 years was conducted, during which serum bone biochemical marker concentrations were monitored. RESULTS: The serological molecular model for the diagnosis of bone metastasis was logit (p). ROC analysis showed that when logit (p) > 0.452, the area under curve of the model was 0.939 (sensitivity: 85.8%, specificity: 89.7%). Model validation demonstrated accuracy with a high degree of consistency (specificity: 85.7%, specificity: 87.5%, Kappa: 0.770). The average predictive time for bone metastasis occurrence of the model was 9.46 months earlier than that of the bone scan diagnosis. Serum OPG, PTHrP, tP1NP, ß-CTx, and the diagnostic model logit (p) were all positively correlated with bone metastasis progression (P < 0.05). CONCLUSIONS: This diagnostic model has the potential to be a simple, non-invasive, and sensitive tool for diagnosing the occurrence and monitoring the progression of bone metastasis in patients with lung cancer.

Click DNA cycling in combination with gold nanoparticles loaded with quadruplex DNA motifs enable sensitive electrochemical quantitation of the tuberculosis-associated biomarker CFP-10 in sputum.

An electrochemical aptamer-based assay is described for the determination of CFP-10 which is an early secretary biomarker of Mycobacterium tuberculosis. CFP-10 is specifically captured by its aptamer and then induces a DNA cross-linking click reaction, the release of CFP-10, and an amplification cycle of repeated CFP-10 release. This mechanism (with dual amplification via DNA click and target release cycle) causes more and more CFP-10 Apt strands on the electrode surface to expose their 5' overhang and to hybridize with the DNA complexes linked to the gold nanoparticles (AuNPs). Consequently, large amounts of AuNPs, each loaded with a number of quadruplex DNA motifs, can be bound on the electrode surface and remarkably enhance the signal. Under optimal conditions, the method has a detection limit as low as 10 pg.mL-1 of CFP-10. The method was successfully applied to the diagnosis of M. tuberculosis in sputum. Graphical abstract Schematic representation of an electrochemical CFP-10 (10-kDa culture filtrate protein) assay using click DNA cycling in combination with gold nanoparticles loaded with quadruplex DNA motifs. Click chemistry reaction between Dibenzocyclooctyne (DBCO)-DNA and azido-DNA can liberate the CFP-10 antigen for the next cycle, which can be viewed as the first amplification step. G-quadruplex-based DNAzyme is formed due to the guanine-rich sequences of DNA S1, which can be viewed as the second amplification step.

NF-κB and enhancer-binding CREB protein scaffolded by CREB-binding protein (CBP)/p300 proteins regulate CD59 protein expression to protect cells from complement attack.

The complement system can be activated spontaneously for immune surveillance or induced to clear invading pathogens, in which the membrane attack complex (MAC, C5b-9) plays a critical role. CD59 is the sole membrane complement regulatory protein (mCRP) that restricts MAC assembly. CD59, therefore, protects innocent host cells from attacks by the complement system, and host cells require the constitutive and inducible expression of CD59 to protect themselves from deleterious destruction by complement. However, the mechanisms that underlie CD59 regulation remain largely unknown. In this study we demonstrate that the widely expressed transcription factor Sp1 may regulate the constitutive expression of CD59, whereas CREB-binding protein (CBP)/p300 bridge NF-κB and CREB, which surprisingly functions as an enhancer-binding protein to induce the up-regulation of CD59 during in lipopolysaccharide (LPS)-triggered complement activation, thus conferring host defense against further MAC-mediated destruction. Moreover, individual treatment with LPS, TNF-α, and the complement activation products (sublytic MAC (SC5b-9) and C5a) could increase the expression of CD59 mainly by activating NF-κB and CREB signaling pathways. Together, our findings identify a novel gene regulation mechanism involving CBP/p300, NF-κB, and CREB; this mechanism suggests potential drug targets for controlling various complement-related human diseases.

The Association Between VEGF +936C/T and -634G/C Polymorphisms and Breast Cancer Susceptibility, Tumor Growth, and Metastases: Evidence From 20,728 Subjects.

The relation between the polymorphisms of vascular endothelial growth factor (VEGF) and breast cancer remains inconclusive. In our meta-analysis based on 10,340 breast cancer patients and 10,388 controls, we found breast cancer susceptibility was elevated in individuals carrying the VEGF +936C allele, especially in Asians, and the +936CC increases tumor growth. The G allele of -634G/C polymorphism reduces breast cancer susceptibility in Asians, and breast cancer patients of -634GG genotype has decreased tumor growth. These results suggest that both the VEGF +936C/T and -634G/C polymorphisms influence breast cancer susceptibility and tumor growth, instead of metastasis.

A novel CRIg-targeted complement inhibitor protects cells from complement damage.

The inappropriate activation of complement may contribute to various immune diseases. The alternative pathway (AP) predominates during complement activation regardless of the initiating pathways. Hence, the main AP regulator factor H (FH) holds great potential as an attractive therapeutic intervention. In addition, complement receptor of the immunoglobulin superfamily (CRIg) has been demonstrated to inhibit AP and, more notably, still specifically binds to C3b/iC3b. We thus developed novel CRIg-targeted complement inhibitors by connecting the functional domains of CRIg and FH, which we termed CRIg-FH and CRIg-L-FH. CRIg-L-FH, slightly more potent than CRIg-FH, considerably inhibited both AP- and also classical pathway (CP)-mediated hemolysis and successfully eliminated the deposition of C3b/iC3b. Kinetic analysis further revealed that the binding affinity constant (KD) of CRIg/FH was in the micromolar range, consistent with its long-lasting binding to complement-attacked cells. CRIg-L-FH efficiently protected aberrant erythrocytes of patients with paroxysmal nocturnal hemoglobinuria (PNH) from AP- and CP-mediated complement damage (IC50 was 22.43 and 64.69 nM, respectively). Moreover, CRIg-L-FH was found to inhibit complement activation induced by the anti-Thy1 antibody in a mesangioproliferative glomerulonephritis (MPGN) rat model. Hence, CRIg-L-FH protects glomerular mesangial cells (GMCs) from complement-mediated injury and proliferative lesions. These findings strongly suggest that CRIg/FH is a potential therapeutic drug candidate for a range of complement-mediated diseases.

Serum Circulating mRNA Panel for the Early Detection of Gastric Cancer: A Potential Biomarker Test.

Circulating free messenger RNAs (cfmRNAs) in serum have emerged as potential noninvasive biomarkers for cancer diagnosis, including gastric cancer (GC). This study utilized RNA-sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify a training set of 100 differentially expressed genes (DEGs) specific to GC patients. Employing a support vector machine (SVM) classification, we narrowed down the candidate gene set to 23, which was further refined to 4 genes-DMBX1, EVX1, MAL, and PIWIL1-after validation through reverse transcription quantitative polymerase chain reaction (RT-qPCR). The diagnostic performance of mRNA panels, particularly the combinations of DMBX1 with EVX1 and EVX1 with PIWIL1, was exceptional, achieving area under the curve (AUC) values of 0.800, sensitivities of 90.0%, and specificities of 80.0%. The accuracy of these biomarkers was corroborated through various machine learning algorithms, underscoring their robust diagnostic potential. The findings of this study are poised to significantly influence clinical practice by providing robust tools for early GC detection. As these biomarkers undergo further investigation and validation, they hold promise to become integral to the diagnostic for GC.

Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism.

Background: Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients. Methods: The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves. Findings: Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients. Interpretation: This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies. Funding: This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

Plasma SARS-CoV-2 N antigen is a powerful molecular marker for early detection of severe COVID-19 in patients and monitoring disease progression.

BACKGROUND AND AIMS: Clear and effective indicators for early detection of severe coronavirus disease 2019 (COVID-19) are insufficient. We investigated the clinical value of the plasma SARS-CoV-2 N antigen (plasma N antigen) for severe COVID-19 early identification and disease progression monitoring. MATERIALS AND METHODS: A cross-sectional study compared the diagnostic value of plasma N antigen levels detected within two days after hospital admission in 957 patients with COVID-19 during the BA2.2 outbreak in Shanghai (April 6-June 15, 2022). A follow-up study analyzed the plasma N antigen prognostic value in 274 non-severe patients, and a longitudinal study evaluated its continuous monitoring value in 16 patients with COVID-19 grade changes. RESULTS: Plasma N antigen concentrations were significantly higher in severely ill than in non-severely ill patients. The plasma N antigen was superior to nasopharyngeal nucleic acid CT values and established COVID-19 blood biomarkers in identifying severe COVID-19. Patients with high plasma N-antigen concentrations at initial admission were more prone to developing severe COVID-19. The changes in plasma N antigen concentrations were consistent with disease progression. Two logistic regression models, including and excluding plasma N antigen, were established, with model 1 (including plasma N antigen) (AUC = 0.971, 0.958-0.980) yielding a better diagnostic value for severe COVID-19 than Model 2 (plasma N antigen excluded). CONCLUSION: The plasma N antigen is superior to nasopharyngeal nucleic acids and established COVID-19 blood biomarkers for severe COVID-19 early recognition and progression monitoring, enabling the most accurate patient triaging and efficient utilization of medical resources.

Time series analysis revealed prognostic value of continuous nasopharyngeal SARS-CoV-2 nucleic acid quantification for COVID-19: A retrospective study of >3000 COVID-19 patients from 2 centers.

BACKGROUND: Early stratification of disease progression remains one of the major challenges towards the post-coronavirus disease 2019 (COVID-19) era. The clinical relevance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid load is debated due to the heterogeneity in patients' underlying health conditions. We determined the prognostic value of nasopharyngeal viral load dynamic conversion for COVID-19. METHODS: The cycling threshold (Ct) values of 28,937 nasopharyngeal SARS-CoV-2 RT-PCRs were retrospectively collected from 3,364 COVID-19 patients during hospitalization and coordinated to the onset of disease progression. The ROC curve was utilized to determine the predictive performance of the rate of Ct value alteration between two consecutive RT-PCR runs within 48 h (ΔCt%) for disease transformation across patients with different COVID-19 severity and immune backgrounds, and further validated with 1,860 SARS-CoV-2 RT-PCR results from an independent validation cohort of 262 patients. For the 67 patients with severe COVID-19, Kaplan-Meier analysis was performed to evaluate the difference in survival between patients stratified by the magnitude of Ct value alteration between the late and early stages of hospitalization. RESULTS: The kinetics of viral nucleic acid conversion diversified across COVID-19 patients with different clinical characteristics and disease severities. The ΔCt% is a clinical characteristic- and host immune status-independent indicator for COVID-19 progression prediction (AUC = 0.79, 95 % CI = 0.76 to 0.81), which outperformed the canonical blood test markers, including c-reactive protein (AUC = 0.57, 95 % CI = 0.53 to 0.61), serum amyloid A (AUC = 0.61, 95 % CI = 0.54 to 0.68), lactate dehydrogenase (AUC = 0.61, 95 % CI = 0.56 to 0.67), d-dimer (AUC = 0.56, 95 % CI = 0.46 to 0.66), and lymphocyte count (AUC = 0.62, 95 % CI = 0.58 to 0.66). Patients with persistent high SARS-CoV-2 viral load (an increase of mean Ct value < 50 %) during the first 3 days of hospitalization demonstrated a significantly unfavorable survival (HR = 0.16, 95 % CI = 0.04 to 0.65, P = 2.41 × 10-3). CONCLUSIONS: Viral nucleic acid dynamics of SARS-CoV-2 eliminates the inter-patient variance of basic health conditions and therefore, can serve as a prognostic marker for COVID-19.

An automated DNA computing platform for rapid etiological diagnostics.

Rapid and accurate classification of the etiology for acute respiratory illness not only helps establish timely therapeutic plans but also prevents inappropriate use of antibiotics. Host gene expression patterns in peripheral blood can discriminate bacterial from viral causes of acute respiratory infection (ARI) but suffer from long turnaround time, as well as high cost resulting from the measurement methods of microarrays and next-generation sequencing. Here, we developed an automated DNA computing-based platform that can implement an in silico trained classification model at the molecular level with seven different mRNA expression patterns for accurate diagnosis of ARI etiology in 4 hours. By integrating sample loading, marker amplification, classifier implementation, and results reporting into one platform, we obtained a diagnostic accuracy of 87% in 80 clinical samples without the aid of computer and laboratory technicians. This platform creates opportunities toward an accurate, rapid, low-cost, and automated diagnosis of disease etiology in emergency departments or point-of-care clinics.

Cancer diagnosis with DNA molecular computation.

Early and precise cancer diagnosis substantially improves patient survival. Recent work has revealed that the levels of multiple microRNAs in serum are informative as biomarkers for the diagnosis of cancers. Here, we designed a DNA molecular computation platform for the analysis of miRNA profiles in clinical serum samples. A computational classifier is first trained in silico using miRNA profiles from The Cancer Genome Atlas. This is followed by a computationally powerful but simple molecular implementation scheme using DNA, as well as an effective in situ amplification and transformation method for miRNA enrichment in serum without perturbing the original variety and quantity information. We successfully achieved rapid and accurate cancer diagnosis using clinical serum samples from 22 healthy people (8) and people with lung cancer (14) with an accuracy of 86.4%. We envision that this DNA computational platform will inspire more clinical applications towards inexpensive, non-invasive and rapid disease screening, classification and progress monitoring.

A Functional Polymorphism (rs2494752) in the AKT1 Promoter Region and Gastric Adenocarcinoma Risk in an Eastern Chinese Population.

AKT is an important signal transduction protein that plays a crucial role in cancer development. Therefore, we evaluated associations between single nucleotide polymorphisms (SNPs) in the AKT promoter region and gastric cancer (GCa) risk in a case-control study of 1,110 GCa patients and 1,114 matched cancer-free controls. We genotyped five SNPs (AKT1 rs2494750G >C, AKT1 rs2494752A >G, AKT1 rs10138227C >T, AKT2 rs7254617G>A and AKT2 rs2304186G >T) located in the 5' upstream regulatory, first intron or promoter regions. In the logistic regression analysis, a significantly elevated GCa risk was associated with the rs2494752 AG/GG variant genotypes (adjusted odds ratio [OR] = 1.20, 95% confidence interval [CI] = 1.02-1.42) under a dominant genetic model, and this risk was more evident in subgroups of ever drinkers. The luciferase reporter assay showed that the rs2494752 G allele significantly increased luciferase activity. Our results suggest that the potentially functional AKT1 rs2494752 SNP may affect GCa susceptibility, likely by modulating the AKT1 promoter transcriptional activity. Larger, independent studies are warranted to validate our findings.