CRISPR-Enhanced Photocurrent Polarity Switching for Dual-lncRNA Detection Combining Deep Learning for Cancer Diagnosis.

Abnormal expression in long noncoding RNAs (lncRNAs) is closely associated with cancers. Herein, a novel CRISPR/Cas13a-enhanced photocurrent-polarity-switching photoelectrochemical (PEC) biosensor was engineered for the joint detection of dual lncRNAs, using deep learning (DL) to assist in cancer diagnosis. After target lncRNA-activated CRISPR/Cas13a cleaves to induce DNAzyme bidirectional walkers with the help of cofactor Mg2+, nitrogen-doped carbon-Cu/Cu2O octahedra are introduced into the biosensor, producing a photocurrent in the opposite direction of CdS quantum dots (QDs). The developed PEC biosensor shows high specificity and sensitivity with limits of detection down to 25.5 aM for lncRNA HOTAIR and 53.1 aM for lncRNA MALAT1. More importantly, this platform for the lncRNA joint assay in whole blood can successfully differentiate cancers from healthy people. Furthermore, the DL model is applied to explore the potential pattern hidden in data of the established technology, and the accuracy of DL cancer diagnosis can acquire 93.3%. Consequently, the developed platform offers a new avenue for lncRNA joint detection and early intelligent diagnosis of cancer.

Attention pyramid pooling network for artificial diagnosis on pulmonary nodules.

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914.

Ratiometric CRISPR/Cas12a-Triggered CHA System Coupling with the MSRE to Detect Site-Specific DNA Methylation.

The precise determination of DNA methylation at specific sites is critical for the timely detection of cancer, as DNA methylation is closely associated with the initiation and progression of cancer. Herein, a novel ratiometric fluorescence method based on the methylation-sensitive restriction enzyme (MSRE), CRISPR/Cas12a, and catalytic hairpin assembly (CHA) amplification were developed to detect site-specific methylation with high sensitivity and specificity. In detail, AciI, one of the commonly used MSREs, was employed to distinguish the methylated target from nonmethylated targets. The CRISPR/Cas12a system was utilized to recognize the site-specific target. In this process, the protospacer adjacent motif and crRNA-dependent identification, the single-base resolution of Cas12a, can effectively ensure detection specificity. The trans-cleavage ability of Cas12a can convert one target into abundant activators and can then trigger the CHA reaction, leading to the accomplishment of cascaded signal amplification. Moreover, with the structural change of the hairpin probe during CHA, two labeled dyes can be spatially separated, generating a change of the Förster resonance energy transfer signal. In general, the proposed strategy of tandem CHA after the CRISPR/Cas12a reaction not only avoids the generation of false-positive signals caused by the target-similar nucleic acid but can also improve the sensitivity. The use of ratiometric fluorescence can eradicate environmental effects by self-calibration. Consequently, the proposed approach had a detection limit of 2.02 fM. This approach could distinguish between colorectal cancer and precancerous tissue, as well as between colorectal patients and healthy people. Therefore, the developed method can serve as an excellent site-specific methylation detection tool, which is promising for biological and disease studies.

One-step and label-free ratiometric fluorescence assay for the detection of plasma exosome towards cancer diagnosis.

Exosomes are closely associated with tumor development and are regarded as viable biomarkers for cancer. Here, a ratiometric fluorescence method was proposed for the one-step and label-free detection of plasma exosomes. A bicolor streptavidin magnetic beads were specifically created with an immobilized Cy5-labeled hairpin aptamer for CD63 (Cy5-Apt) on its surface to identify exosome, and a green color SYBR Green I (SGI) embedded in the stem of Cy5-Apt to respond to exosomes. After exosome capture, the Cy5-Apt could undergo a conformational shift and release the encapsulated SGI, allowing exosome measurement based on the fluorescence ratio of Cy5 and SGI. The enrichment, separation and detection of exosomes in proposed method could be completed in one step (30 min), which is a significant improvement over previous method. Furthermore, the use of ratiometric fluorescence and magnetic separation allows for exosome enrichment and interference elimination from complex matrices, improving accuracy and sensitivity. Particularly, the assay could detect exosomes in plasma and has potential to distinguish lung cancer patients from healthy volunteers with an area under the receiver operator characteristic curve of 0.85. Besides, the study provided an efficient method for analyzing the various divisions of exosomes by merely modifying the aptamer, which holds great promise for point-of-care applications.

A dual-model immobilization-free photoelectrochemical/visual colorimetric bioanalysis based on microemulsion self-assemblies mediated multifunctional signal amplification strategy.

Herein, a novel silver ion-loaded gold microemulsion assemblies (Au/Ag+ MAs) mediated multifunctional signal amplification strategy was proposed to construct a sensitive immobilization-free photoelectrochemical (PEC)/colorimetric biosensor for carcinoembryonic antigen (CEA) detection. Through the sandwiched reaction among CEA, the CEA aptamer (DNA1) loaded on the Au nanoparticles (NPs) functionalized iron oxide (Fe3O4) nanospheres and another CEA aptamer (DNA2) immobilized on Au/Ag+ MAs, a complex is formed and acquired by magnetic separation. Then, Au/Ag+ MAs of the complex are disassembled into Au NPs and Ag+ ions driven by an acetone response, and the obtained demulsification solution is transferred to the cadmium sulfide/cadmium telluride (CdS/CdTe) photoactive composites modified electrode. Based on the multiple inhibition functions (blocking effect of oleylamine; energy transfer effect of Au NPs; and electron snatching effect of Ag+), the photocurrent of the electrode decreases obviously, resulting in the ultrasensitive detection of CEA (a detection limit of 16 fg mL-1). Interestingly, the ion-exchange reactions between CdS/CdTe composites and Ag+ ions generate silver sulfide/silver telluride (Ag2S/Ag2Te) composites, and a color change of composites can be distinguished directly, leading to a quick visual detection of CEA. Compared with the traditional single-modal assay for CEA, such dual-modal PEC/colorimetric assay is a more accurate and reliable due to different mechanisms and independent signal conversion. This work will offer a new perspective for the applications of various self-assemblies in PEC bioanalysis.

A diagnostic classification of lung nodules using multiple-scale residual network.

Computed tomography (CT) scans have been shown to be an effective way of improving diagnostic efficacy and reducing lung cancer mortality. However, distinguishing benign from malignant nodules in CT imaging remains challenging. This study aims to develop a multiple-scale residual network (MResNet) to automatically and precisely extract the general feature of lung nodules, and classify lung nodules based on deep learning. The MResNet aggregates the advantages of residual units and pyramid pooling module (PPM) to learn key features and extract the general feature for lung nodule classification. Specially, the MResNet uses the ResNet as a backbone network to learn contextual information and discriminate feature representation. Meanwhile, the PPM is used to fuse features under four different scales, including the coarse scale and the fine-grained scale to obtain more general lung features of the CT image. MResNet had an accuracy of 99.12%, a sensitivity of 98.64%, a specificity of 97.87%, a positive predictive value (PPV) of 99.92%, and a negative predictive value (NPV) of 97.87% in the training set. Additionally, its area under the receiver operating characteristic curve (AUC) was 0.9998 (0.99976-0.99991). MResNet's accuracy, sensitivity, specificity, PPV, NPV, and AUC in the testing set were 85.23%, 92.79%, 72.89%, 84.56%, 86.34%, and 0.9275 (0.91662-0.93833), respectively. The developed MResNet performed exceptionally well in estimating the malignancy risk of pulmonary nodules found on CT. The model has the potential to provide reliable and reproducible malignancy risk scores for clinicians and radiologists, thereby optimizing lung cancer screening management.

HPIP is an essential scaffolding protein running through the EGFR-RAS-ERK pathway and drives tumorigenesis.

The EGFR-RAS-ERK pathway plays a key role in cancer development and progression. However, the integral assembly of EGFR-RAS-ERK signaling complexes from the upstream component EGFR to the downstream component ERK is largely unknown. Here, we show that hematopoietic PBX-interacting protein (HPIP) interacts with all classical components of the EGFR-RAS-ERK pathway and forms at least two complexes with overlapping components. Experiments of HPIP knockout or knockdown and chemical inhibition of HPIP expression showed that HPIP is required for EGFR-RAS-ERK signaling complex formation, EGFR-RAS-ERK signaling activation, and EGFR-RAS-ERK signaling-mediated promotion of aerobic glycolysis as well as cancer cell growth in vitro and in vivo. HPIP expression is correlated with EGFR-RAS-ERK signaling activation and predicts worse clinical outcomes in patients with lung cancer. These results provide insights into EGFR-RAS-ERK signaling complex formation and EGFR-RAS-ERK signaling regulation and suggest that HPIP may be a promising therapeutic target for cancer with dysregulated EGFR-RAS-ERK signaling.

A Direct-Contact Photocurrent-Direction-Switching Biosensing Platform Based on In Situ Formation of CN QDs/TiO2 Nanodiscs and Double-Supported 3D DNA Walking Amplification.

Herein, a direct-contact photocurrent-direction-switching photoelectrochemical (PEC) biosensing platform for the ultrasensitive and selective detection of soluble CD146 (sCD146) is reported for the first time via in situ formation of carbon nitride quantum dots (CN QDs)/titanium dioxide (TiO2 ) nanodiscs with the double-supported 3D DNA walking amplification. In this platform, metal organic frameworks (MOFs)-derived porous TiO2 nanodiscs exhibit excellent anodic photocurrent, whereas a single-stranded auxiliary DNA (ssDNA) as biogate is absorbed onto the TiO2 nanodiscs to block active sites. Subsequently, with the help of intermediate DNAs from target sCD146-induced double-supported 3D DNA walking signal amplification, the ssDNA can leave away from the surface of TiO2 nanodiscs due to the specific hybridization with intermediate DNAs. Afterward, the successful direct contact of CN QDs on TiO2 nanodiscs by porosity and electrostatic adsorption, leads to the effective photocurrent-direction switching from anodic to cathodic photocurrent. Based on direct-contact photocurrent-direction-switching CN QDs/TiO2 nanodiscs system and double-supported 3D DNA walking signal amplification, sCD146 is detected sensitively with a wide linear range (10 fg mL-1 to 5 ng mL-1 ) and a low limit of detection (2.1 fg mL-1 ). Also, the environmentally friendly and direct-contact photocurrent-direction-switching PEC biosensor has an application prospect for cancer biomarker detection.

HUNK inhibits epithelial-mesenchymal transition of CRC via direct phosphorylation of GEF-H1 and activating RhoA/LIMK-1/CFL-1.

Epithelial-mesenchymal transition (EMT) is associated with the invasive and metastatic phenotypes in colorectal cancer (CRC). However, the mechanisms underlying EMT in CRC are not completely understood. In this study, we find that HUNK inhibits EMT and metastasis of CRC cells via its substrate GEF-H1 in a kinase-dependent manner. Mechanistically, HUNK directly phosphorylates GEF-H1 at serine 645 (S645) site, which activates RhoA and consequently leads to a cascade of phosphorylation of LIMK-1/CFL-1, thereby stabilizing F-actin and inhibiting EMT. Clinically, the levels of both HUNK expression and phosphorylation S645 of GEH-H1 are not only downregulated in CRC tissues with metastasis compared with that without metastasis, but also positively correlated among these tissues. Our findings highlight the importance of HUNK kinase direct phosphorylation of GEF-H1 in regulation of EMT and metastasis of CRC.

MiR-3180 inhibits hepatocellular carcinoma growth and metastasis by targeting lipid synthesis and uptake.

PURPOSE: Reprogrammed lipid metabolism is a hallmark of cancer that provides energy, materials, and signaling molecules for rapid cancer cell growth. Cancer cells acquire fatty acids primarily through de novo synthesis and uptake. Targeting altered lipid metabolic pathways is a promising anticancer strategy. However, their regulators have not been fully investigated, especially those targeting both synthesis and uptake. METHODS: Immunohistochemistry was performed on samples from patients with hepatocellular carcinoma (HCC) to establish the correlation between miR-3180, stearoyl-CoA desaturase-1 (SCD1), and CD36 expression, quantified via qRT-PCR and western blotting. The correlation was analyzed using a luciferase reporter assay. Cell proliferation, migration, and invasion were analyzed using CCK-8, wound healing, and transwell assays, respectively. Oil Red O staining and flow cytometry were used to detect lipids. Triglycerides and cholesterol levels were analyzed using a reagent test kit. CY3-labeled oleic acid transport was analyzed using an oleic acid transport assay. Tumor growth and metastasis were detected in vivo in a xenograft mouse model. RESULTS: MiR-3180 suppressed de novo fatty acid synthesis and uptake by targeting the key lipid synthesis enzyme SCD1 and key lipid transporter CD36. MiR-3180 suppressed HCC cell proliferation, migration, and invasion in an SCD1- and CD36-dependent manner in vitro. The mouse model demonstrated that miR-3180 inhibits HCC tumor growth and metastasis by inhibiting SCD1- and CD36-mediated de novo fatty acid synthesis and uptake. MiR-3180 expression was downregulated in HCC tissues and negatively correlated with SCD1 and CD36 levels. Patients with high miR-3180 levels showed better prognosis than those with low levels. CONCLUSIONS: Our investigation indicates that miR-3180 is a critical regulator involved in de novo fatty acid synthesis and uptake, which inhibits HCC tumor growth and metastasis by suppressing SCD1 and CD36. Therefore, miR-3180 is a novel therapeutic target and prognostic indicator for patients with HCC.

A review of sensors for classification and subtype discrimination of cancer: Insights into circulating tumor cells and tumor-derived extracellular vesicles.

Liquid biopsy can reflect the state of tumors in vivo non-invasively, thus providing a strong basis for the early diagnosis, individualized treatment monitoring and prognosis of tumors. Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs) contain information-rich components, such as nucleic acids and proteins, and they are essential markers for liquid biopsies. Their capture and analysis are of great importance for the study of disease occurrence and development and, consequently, have been the subject of many reviews. However, both CTCs and tdEVs carry the biological characteristics of their original tissue, and few reviews have focused on their function in the staging and classification of cancer. In this review, we focus on state-of-the-art sensors based on the simultaneous detection of multiple biomarkers within CTCs and tdEVs, with clinical applications centered on cancer classification and subtyping. We also provide a thorough discussion of the current challenges and prospects for novel sensors with the ultimate goal of cancer classification and staging. It is hoped that these most advanced technologies will bring new insights into the clinical practice of cancer screening and diagnosis.

Magnetic-nanowaxberry-based microfluidic ExoSIC for affinity and continuous separation of circulating exosomes towards cancer diagnosis.

Exosomes are seen as promising biomarkers for minimally invasive liquid biopsies and disease surveillance. However, the complexity of body fluids, inherent heterogeneity, and tiny size of exosomes impede their extraction, consequently restricting their clinical application. In this study, in order to efficiently isolate exosomes from clinical samples, an irregular serpentine channel microfluidic chip (ExoSIC) was designed to continuously separate exosomes from plasma based on a magnetic-nanowaxberry (MNWB). In the ExoSIC, irregular serpentine microchannels are utilized to increase fluid chaotic mixing, hence improving exosome capture efficiency. In comparison to commonly used spherical magnetic particles, the designed MNWB can not only enhance the capture efficiency of exosomes, but also possess a size-exclusion effect to improve exosome purity. Consequently, the ExoSIC exhibited a large yield (24 times higher than differential centrifugation), optimum purity (greater than precipitation and similar to differential centrifugation), and high specificity. Furthermore, the ExoSIC was utilized for plasma-based cancer diagnosis by multiplex monitoring of five exosomal biomarkers (exosomal concentration, EGFR, EpCAM, SAA1 and FV), and the AUC reached 0.791. This work provides a comprehensive framework for exosome-based cancer diagnostics in order to meet clinical requirements for exosome isolation and downstream analysis.

Universal DNAzyme walkers-triggered CRISPR-Cas12a/Cas13a bioassay for the synchronous detection of two exosomal proteins and its application in intelligent diagnosis of cancer.

Exosomal proteins are considered to be promising indicators of cancer. Herein, a novel DNAzyme walkers-triggered CRISPR-Cas12a/Cas13a strategy was proposed for the synchronous determination of exosomal proteins: serum amyloid A-1 protein (SAA1) and coagulation factor V (FV). In this design, the paired antibodies were used to recognize targets, thereby ensuring the specificity. DNAzyme walkers were employed to convert the contents of SAA1 and FV into activators (P1 and P2), and one target can produce abundant activators, thus achieving an initial amplification of signal. Furthermore, the P1 and P2 can activate CRISPR-Cas12a/Cas13a system, which in turn trans-cleaves the reporters, enabling a second amplification and generating two fluorescent signals. The assay is highly sensitive (limits of detection as low as 30.00 pg/mL for SAA1 and 200.00 pg/mL for FV), highly specific and ideally accurate. More importantly, it is universal and can be used to detect both non-membrane and membrane proteins in exosome. Besides, the method can be successfully applied to detect SAA1 and FV in plasma exosomes to differentiate between lung cancer patients and healthy individuals. To explore the application of the developed method in tumor diagnosis, a deep learning model based on the expressions of SAA1 and FV was developed. The accuracy of this model can achieve 86.96%, which proves that it has a promising practical application capacity. Thus, this study does not only provide a new tool for the detection of exosomal proteins and cancer diagnosis, but also propose a new strategy to detect non-nucleic acid analytes for CRISPR-Cas system.

Accurate classification of lung nodules on CT images using the TransUnet.

Background: Computed tomography (CT) is an effective way to scan for lung cancer. The classification of lung nodules in CT screening is completely doctor dependent, which has drawbacks, including difficulty classifying tiny nodules, subjectivity, and high false-positive rates. In recent years, deep convolutional neural networks, a deep learning technology, have been shown to be effective in medical imaging diagnosis. Herein, we propose a deep convolutional neural network technique (TransUnet) to automatically classify lung nodules accurately. Methods: TransUnet consists of three parts: the transformer, the Unet, and global average pooling (GAP). The transformer encodes discriminative features via global self-attention modeling on CT image patches. The Unet, which collects context by constricting route, enables exact lunge nodule localization. The GAP categorizes CT images, assigning each sample a score. Python was employed to pre-process all CT images in the LIDI-IDRI, and the obtained 8,474 images (3,259 benign and 5,215 lung nodules) were used to evaluate the method's performance. Results: The accuracies of TransUnet in the training and testing sets were 87.90 and 84.62%. The sensitivity, specificity, and AUC of the proposed TransUnet on the testing dataset were 70.92, 93.17, and 0.862%, respectively (0.844-0.879). We also compared TransUnet to three well-known methods, which outperformed these methods. Conclusion: The experimental results on LIDI-IDRI demonstrated that the proposed TransUnet has a great performance in classifying lung nodules and has a great potential application in diagnosing lung cancer.

Transcriptional Repression of Aerobic Glycolysis by OVOL2 in Breast Cancer.

Aerobic glycolysis (Warburg effect), a hallmark of cancer, plays a critical role in cancer cell growth and metastasis; however, direct inhibition of the Warburg effect remains largely unknown. Herein, the transcription factor OVO-like zinc finger 2 (OVOL2) is demonstrated to directly repress the expression of several glycolytic genes, blocking the Warburg effect and breast tumor growth and metastasis in vitro and in vivo. OVOL2 inhibits glycolysis by recruiting the nuclear receptor co-repressor (NCoR) and histone deacetylase 3 (HDAC3). The tumor suppressor p53, a key regulator of cancer metabolism, activates OVOL2 by binding to the oncoprotein mouse double minute 2 homolog (MDM2) and inhibiting MDM2-mediated ubiquitination and degradation of OVOL2. OVOL2 expression is negatively correlated with glycolytic gene expression and can be a good predictor of prognosis in patients with breast cancer. Therefore, targeting the p53/MDM2/OVOL2 axis provides a potential avenue for cancer treatment, especially breast cancer.

New horizons in the identification of circulating tumor cells (CTCs): An emerging paradigm shift in cytosensors.

Circulating tumor cells (CTCs) are cancer cells that are shed from a primary tumor into the bloodstream and function as seeds for cancer metastasis at distant locations. Enrichment and identification methods of CTCs in the blood of patients plays an important role in diagnostic assessments and personalized treatments of cancer. However, the current traditional identification methods not only impact the viability of cells, but also cannot determine the type of cancer cells when the disease is unknown. Hence, new methods to identify CTCs are urgently needed. In this context, many advanced and safe technologies have emerged to distinguish between cancer cells and blood cells, and to distinguish specific types of cancer cells. In this review, at first we have briefly discussed recent advances in technologies related to the enrichment of CTCs, which lay a good foundation for the identification of CTCs. Next, we have summarized state-of-the-art technologies to confirm whether a given cell is indeed a tumor cell and determine the type of tumor cell. Finally, the challenges for application and potential directions of the current identification methods in clinical analysis of CTCs have been discussed.

MicroRNA-377-3p inhibits hepatocellular carcinoma growth and metastasis through negative regulation of CPT1C-mediated fatty acid oxidation.

BACKGROUND: Altered lipid metabolism is closely related to the occurrence and development of hepatocellular carcinoma (HCC). Carnitine palmitoyltransferase 1C (CPT1C) is a member of CPT1 family and plays a key role in cancer development and progression. However, how microRNAs (miRNAs) regulate CPT1C-mediated fatty acid transport and oxidation remains to be elucidated. METHODS: Oil Red O staining, mitochondrial, and lipid droplets immunofluorescence staining were used to detect the functions of miR-377-3p and CPT1C in fatty acid oxidation. Colocalization of palmitate and mitochondria was performed to investigate the function of miR-377-3p and CPT1C in fatty acid transport into mitochondria. Fatty acid oxidation (FAO) assay was used to detect the function of miR-377-3p and CPT1C in FAO. Cell proliferation, migration and invasion assays and animal experiments were used to evaluate the role of miR-377-3p/CPT1C axis in HCC progression in vitro and in vivo. Immunofluorescence staining was used to identify the clinical significance of miR-377-3p and CPT1C in HCC patients. RESULTS: MiR-377-3p inhibits CPT1C expression by targeting its 3'-untranslated region. Through repression of CPT1C, miR-377-3p suppresses fatty acid oxidation by preventing fatty acid from entering into mitochondria and decreasing ATP production in HCC cells. Inhibiting fatty acid oxidation abolishes the ability of miR-377-3p/CPT1C axis to regulate HCC proliferation, migration, invasion and metastasis in vitro and in vivo. In HCC patients, CPT1C is significantly upregulated, and miR-377-3p expression and lipid droplets are negatively correlated with CPT1C expression. High expression of miR-377-3p and CPT1C predict better and worse clinical outcomes, respectively. CONCLUSIONS: We uncover the key function and the relevant mechanisms of the miR-377-3p/CPT1C axis in HCC, which might provide a potential target for the treatment of HCC.

State-of-the-arts techniques and current evolving approaches in the separation and detection of circulating tumor cell.

Circulating tumor cells (CTCs) are cancer cells that shed from the primary tumor and then enter the circulatory system, a small part of which may evolve into metastatic cancer under appropriate microenvironment conditions. The detection of CTCs is a truly noninvasive, dynamic monitor for disease changes, which has considerable clinical implications in the selection of targeted drugs. However, their inherent rarity and heterogeneity pose significant challenges to their isolation and detection. Even the "gold standard", CellSearch™, suffers from high expenses, low capture efficiency, and the consumption of time. With the advancement of CTCs analysis technologies in recent years, the yield and efficiency of CTCs enrichment have gradually been improved, as well as detection sensitivity. In this review, the isolation and detection strategies of CTCs have been completely described and the potential directions for future research and development have also been highlighted through analyzing the challenges faced by current strategies.

Construction and bioapplications of aptamer-based dual recognition strategy.

Aptamer-based dual recognition strategy, using dual aptamers or the cooperation of aptamers with other recognition elements, can better utilize the advantages of each recognition molecule and increase the design flexibility to effectively overcome the limitations of a single molecule recognition strategy, thereby improving the sensitivity and selectivity and facilitating the regulation of biological process. Hence, this review systematically tracks the construction and application of dual aptamers recognition strategy in the versatile detection of protein biomarkers, pathogenic microorganisms, cancer cells, and the treatment of some diseases and, more importantly, in functional regulation and imaging of cell-surface protein receptors. Then, the cooperation of aptamers with other recognition elements are briefly introduced. Potential challenges facing this field have been highlighted, aiming to expand bioanalytical applications of aptamer-based dual or multiple recognition strategies and meet the growing demand for precision medicine.