Current trends and emerging diagnostic techniques for lung cancer

Controlling glycolysis to generate characteristic volatile organic compounds of lung cancer cells

Characteristic volatile organic compounds (VOCs) are anticipated to be used for the identification of lung cancer cells. However, to date, consistent biomarkers of VOCs in lung cancer cells have not been obtained through direct comparison between cancer and healthy groups. In this study, we regulated the glycolysis, a common metabolic process in cancer cells, and employed solid phase microextraction gas chromatography mass spectrometry (SPME-GC-MS) combined with untargeted analysis to identify the characteristic VOCs shared by cancer cells. The VOCs released by three types of lung cancer cells (A549, PC-9, NCI-H460) and one normal lung epithelial cell (BEAS-2B) were detected using SPME-GC-MS, both in their resting state and after treatment with glycolysis inhibitors (2-Deoxy-D-glucose, 2-DG/3-Bromopyruvic acid, 3-BrPA). Untargeted analysis methods were employed to compare the VOC profiles between each type of cancer cell and normal cells before and after glycolysis regulation. Our findings revealed that compared to normal cells, the three types of lung cancer cells exhibited three common differential VOCs in their resting state: ethyl propionate, acetoin, and 3-decen-5-one. Furthermore, under glycolysis control, a single common differential VOC-acetoin was identified. Notably, acetoin levels increased by 2.60-3.29-fold in all three lung cancer cell lines upon the application of glycolysis inhibitors while remaining relatively stable in normal cells. To further elucidate the formation mechanism of acetoin, we investigated its production by blocking glutaminolysis. This interdisciplinary approach combining metabolic biochemistry with MS analysis through interventional synthetic VOCs holds great potential for revolutionizing the identification of lung cancer cells and paving the way for novel cytological examination techniques.

Classification of lung cancer subtypes on CT images with synthetic pathological priors

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case's CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), positive predictive value (PPV) and F1-score.

Evaluation of miR-155 silencing using a molecular beacon in human lung adenocarcinoma cell line

Lung cancer (LC) is a leading cause of global cancer-related deaths, highlighting the development of innovative methods for biomarker detection improving the early diagnostics. microRNAs (miRs) alterations are known to be involved in the initiation and progression of human cancers and can act as biomarkers for diagnostics and treatment. Herein, we develop the application of molecular beacon (MB) technology to monitor miR-155-3p expression in human lung adenocarcinoma A549 cells without complementary DNA synthesis, amplification, or expensive reagents. Furthermore, we produced gold nanoparticles (AuNPs) for delivering antisense oligonucleotides into A549 cells to reduce miR-155-3p expression, which was subsequently detectable using the MB. The MB was designed and structural characterized by Förster Resonance Energy Transfer (FRET)-melting, Circular Dichroism (CD), Nuclear magnetic resonance (NMR), and fluorometric experiments, and then the hybridization conditions were optimized for an in vitro approach involving the detection of miR-155-3p in total RNA extracted from A549 cell line. The expression profile of miR-155-3p was obtained by RT-qPCR. The results demonstrated that MB was properly designed and showed efficacy in targeting miR-155-3p. Furthermore, a limit of detection down to nanomolar concentration was achieved and the specificity of the biosensor was proved. Moreover, the self-assembly of ASOs with AuNPs exhibited exceptional target specificity, effectively silencing miR-155-3p. Notably, compared to lipid-based transfection agent, AuNPs displayed superior silencing efficiency. We highlighted the ability of MB to detect changes in the target gene expression after gene silencing. Overall, this innovative approach represents a promising tool for detecting various biomarkers at the same time, with potential applications in clinical settings.

Development and validation of a preoperative CT‑based radiomics nomogram to differentiate tuberculosis granulomas from lung adenocarcinomas: an external validation study

BACKGROUND

An accurate and non-invasive approach is urgently needed to distinguish tuberculosis granulomas from lung adenocarcinomas. This study aimed to develop and validate a nomogram based on contrast enhanced-compute tomography (CE-CT) to preoperatively differentiate tuberculosis granuloma from lung adenocarcinoma appearing as solitary pulmonary solid nodules (SPSN).

METHODS

This retrospective study analyzed 143 patients with lung adenocarcinoma (mean age: 62.4 ± 6.5 years; 54.5% female) and 137 patients with tuberculosis granulomas (mean age: 54.7 ± 8.2 years; 29.2% female) from two centers between March 2015 and June 2020. The training and internal validation cohorts included 161 and 69 patients (7:3 ratio) from center No.1, respectively. The external testing cohort included 50 patients from center No.2. Clinical factors and conventional radiological characteristics were analyzed to build independent predictors. Radiomics features were extracted from each CT-volume of interest (VOI). Feature selection was performed using univariate and multivariate logistic regression analysis, as well as the least absolute shrinkage and selection operator (LASSO) method. A clinical model was constructed with clinical factors and radiological findings. Individualized radiomics nomograms incorporating clinical data and radiomics signature were established to validate the clinical usefulness. The diagnostic performance was assessed using the receiver operating characteristic (ROC) curve analysis with the area under the receiver operating characteristic curve (AUC).

RESULTS

One clinical factor (CA125), one radiological characteristic (enhanced-CT value) and nine radiomics features were found to be independent predictors, which were used to establish the radiomics nomogram. The nomogram demonstrated better diagnostic efficacy than any single model, with respective AUC, accuracy, sensitivity, and specificity of 0.903, 0.857, 0.901, and 0.807 in the training cohort; 0.933, 0.884, 0.893, and 0.892 in the internal validation cohort; 0.914, 0.800, 0.937, and 0.735 in the external test cohort. The calibration curve showed a good agreement between prediction probability and actual clinical findings.

CONCLUSION

The nomogram incorporating clinical factors, radiological characteristics and radiomics signature provides additional value in distinguishing tuberculosis granuloma from lung adenocarcinoma in patients with a SPSN, potentially serving as a robust diagnostic strategy in clinical practice.

Enhancing Lung Cancer Care in Portugal: Bridging Gaps for Improved Patient Outcomes

Lung cancer has the highest incidence and cancer-related mortality worldwide. In Portugal, it ranks as the fourth most common cancer, with nearly 6000 new cases being diagnosed every year. Lung cancer is the main cause of cancer-related death among males and the third cause of cancer-related death in females. Despite the globally accepted guidelines and recommendations for what would be the ideal path for a lung cancer patient, several challenges occur in real clinical management across the world. The recommendations emphasize the importance of adequate screening of high-risk individuals, a precise tumour biopsy, and an accurate final diagnosis to confirm the neoplastic nature of the nodule. A detailed histological classification of the lung tumour type and a comprehensive molecular characterization are of utmost importance for the selection of an efficacious and patient-directed therapeutic approach. However, in the context of the Portuguese clinical organization and the national healthcare system, there are still several gaps in the ideal pathway for a lung cancer patient, involving aspects ranging from the absence of a national lung cancer screening programme through difficulties in histological diagnosis and molecular characterization to challenges in therapeutic approaches. In this manuscript, we address the most relevant weaknesses, presenting several proposals for potential solutions to improve the management of lung cancer patients, helping to decisively improve their overall survival and quality of life.

The sensor applications for prostate and lung cancer biomarkers in terms of electrochemical analysis

Prostate and lung cancers are the most common types of cancer and affect a large part of the population around the world, causing deaths. Therefore, the rapid identification of cancer can profoundly impact reducing cancer-related death rates and protecting human lives. Significant resources have been dedicated to investigating new methods for early disease detection. Cancer biomarkers encompass various biochemical entities, including nucleic acids, proteins, sugars, small metabolites, cytogenetic and cytokinetic parameters, and whole tumor cells in bodily fluids. These tools can be utilized for various purposes, such as risk assessment, diagnosis, prognosis, treatment efficacy, toxicity evaluation, and predicting a return. Due to these versatile and critical purposes, there are widespread studies on the development of new, sensitive, and selective approaches for the determination of cancer biomarkers. This review illustrates the significant lung and prostate cancer biomarkers and their determination utilizing electrochemical sensors, which have the advantage of improved sensitivity, low cost, and simple analysis. Additionally, approaches such as improving sensitivity with nanomaterials and ensuring selectivity with MIPs are used to increase the performance of the sensor. This review aims to overview the most recent electrochemical biosensor applications for determining vital biomarkers of prostate and lung cancers in terms of nanobiosensors and molecularly imprinted polymer (MIP)-based biosensors.

Dual-targets fluorescent nanoprobe for precise subtyping of lung cancer

A Au-Se bond-based nanoprobe using 3',3-diselenopropionic acid to simultaneously link response chains for Pro-GRP protein and Cyfra21-1 was developed. Early diagnosis and subtyping of lung cancer can be achieved based on the nanoprobes' differential response of the probes to the two targets in lung cancer patients' serum.

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.

The inhibitory effect of apatinib on different small cell lung cancer cells and in lung cancer-bearing mice and patients

PURPOSE

To observe the inhibitory effect of the small molecule tyrosine kinase inhibitor apatinib on small cell lung cancer in vitro and vivo.

MATERIAL AND METHODS

Three small lung cancer cells were selected CCK-8 and monoclonal assay was used to determine the effect of apatinib on proliferation. The effects on cell cycle and apoptosis were detected by flow cytometry and TUNEL. We observed the inhibitory effect of different doses of apatinib on xenograft tumor. The efficacy and safety of apatinib in 20 patients with advanced small cell lung cancer were observed.

RESULTS

For small cell lung cancer with high expression of VEGFR2, apatinib has a significant inhibitory effect both in vitro and in vivo. It can play an inhibitory role by promoting apoptosis and cell cycle arrest pathways. For small cell lung cancer with low expression of VEGFR, the inhibitory effect on cells in vitro was not significant. It has certain inhibitory effect on nude mouse transplanted tumor and small cell lung cancer patients, but the effect is weaker than that of animal models and patients with small cell lung cancer cells with high expression of VEGFR2.

CONCLUSION

Apatinib has a significant inhibitory effect on small cell lung cancer with high expression of VEGFR2 and may be a treatment for small cell lung cancer patients.

Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model

BACKGROUND

In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combining radiomics and deep learning to predict OLNM preoperatively in SPILAC patients across multiple centers.

METHODS

In this study, 1325 cT1a-bN0M0 SPILAC patients from six hospitals were retrospectively analyzed and divided into pathological nodal positive (pN+) and negative (pN-) groups. Three predictive models for OLNM were developed: a radiomics model employing decision trees and support vector machines; a deep learning model using ResNet-18, ResNet-34, ResNet-50, DenseNet-121, and Swin Transformer, initialized randomly or pre-trained on large-scale medical data; and a fusion model integrating both approaches using addition and concatenation techniques. The model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

All patients were assigned to four groups: training set (n = 470), internal validation set (n = 202), and independent test set 1 (n = 227) and 2 (n = 426). Among the 1325 patients, 478 (36%) had OLNM (pN+). The fusion model, combining radiomics with pre-trained ResNet-18 features via concatenation, outperformed others with an average AUC (aAUC) of 0.754 across validation and test sets, compared to aAUCs of 0.715 for the radiomics model and 0.676 for the deep learning model.

CONCLUSION

The radiomics-deep learning fusion model showed promising ability to generalize in predicting OLNM from CT scans, potentially aiding personalized treatment for SPILAC patients across multiple centers.

Urinary VOCs as biomarkers of early stage lung tumour development in mice

BACKGROUND

Lung cancer is the primary cause of cancer-induced death. In addition to prevention and improved treatment, it has increasingly been established that early detection is critical to successful remission.

OBJECTIVE

The aim of this study was to identify volatile organic compounds (VOCs) in urine that could help diagnose mouse lung cancer at an early stage of its development.

METHODS

We analysed the VOC composition of urine in a genetically engineered lung adenocarcinoma mouse model with oncogenic EGFR doxycycline-inducible lung-specific expression. We compared the urinary VOCs of 10 cancerous mice and 10 healthy mice (controls) before and after doxycycline induction, every two weeks for 12 weeks, until full-blown carcinomas appeared. We used SPME fibres and gas chromatography - mass spectrometry to detect variations in cancer-related urinary VOCs over time.

RESULTS

This study allowed us to identify eight diagnostic biomarkers that help discriminate early stages of cancer tumour development (i.e., before MRI imaging techniques could identify it).

CONCLUSION

The analysis of mice urinary VOCs have shown that cancer can induce changes in odour profiles at an early stage of cancer development, opening a promising avenue for early diagnosis of lung cancer in other models.

microRNAs in exhaled breath condensate for diagnosis of lung cancer in a resource-limited setting: a concise review

Lung cancer is one of the common cancers globally with high mortality and poor prognosis. Most cases of lung cancer are diagnosed at an advanced stage due to limited diagnostic resources. Screening modalities, such as sputum cytology and annual chest radiographs, have not proved sensitive enough to impact mortality. In recent years, annual low-dose computed tomography has emerged as a potential screening tool for early lung cancer detection, but it may not be a feasible option for developing countries. In this context, exhaled breath condensate (EBC) analysis has been evaluated recently as a noninvasive tool for lung cancer diagnosis. The breath biomarkers also have the advantage of differentiating various types and stages of lung cancer. Recent studies have focused more on microRNAs (miRNAs) as they play a key role in tumourigenesis by regulating the cell cycle, metastasis and angiogenesis. In this review, we have consolidated the current published literature suggesting the utility of miRNAs in EBC for the detection of lung cancer.

Automatic Lung Cancer Subtypes Classification on CT Images with Self-generated Multi-modality Hybrid Features

Lung cancer is a malignant tumor with rapid progression and high fatality rate. According to histological morphology and cell behaviours of cancerous tissues, lung cancer can be classified into a variety of subtypes. Since different cancer subtype corresponds to distinct therapies, the early and accurate diagnosis is critical for following treatments and prognostic managements. In clinical practice, the pathological examination is regarded as the gold standard for cancer subtypes diagnosis, while the disadvantage of invasiveness limits its extensive use, leading the non-invasive and fast-imaging computed tomography (CT) test a more commonly used modality in early cancer diagnosis. However, the diagnostic results of CT test are less accurate due to the relatively low image resolution and the atypical manifestations of cancer subtypes. In this work, we propose a novel automatic classification model to offer the assistance in accurately diagnosing the lung cancer subtypes on CT images. Inspired by the findings of cross-modality associations between CT images and their corresponding pathological images, our proposed model is developed to incorporate general histopathological information into CT imagery-based lung cancer subtypes diagnostic by omitting the invasive tissue sample collection or biopsy, and thereby augmenting the diagnostic accuracy. Experimental results on both internal evaluation datasets and external evaluation datasets demonstrate that our proposed model outputs more accurate lung cancer subtypes diagnostic predictions compared to existing CT-based state-of-the-art (SOTA) classification models, by achieving significant improvements in both accuracy (ACC) and area under the receiver operating characteristic curve (AUC).Clinical Relevance- This work provides a method for automatically classifying the lung cancer subtypes on CT images.

Metal Nanoparticles as Novel Agents for Lung Cancer Diagnosis and Therapy

Lung cancer is one of the most common malignancies worldwide and contributes to most cancer-related morbidity and mortality cases. During the past decades, the rapid development of nanotechnology has provided opportunities and challenges for lung cancer diagnosis and therapeutics. As one of the most extensively studied nanostructures, metal nanoparticles obtain higher satisfaction in biomedical applications associated with lung cancer. Metal nanoparticles have enhanced almost all major imaging strategies and proved great potential as sensor for detecting cancer-specific biomarkers. Moreover, metal nanoparticles could also improve therapeutic efficiency via better drug delivery, improved radiotherapy, enhanced gene silencing, and facilitated photo-driven treatment. Herein, the recently advanced metal nanoparticles applied in lung cancer therapy and diagnosis are summarized. Future perspective on the direction of metal-based nanomedicine is also discussed. Stimulating more research interests to promote the development of metal nanoparticles in lung cancer is devoted.

Recent Trends in Biosensing and Diagnostic Methods for Novel Cancer Biomarkers

Cancer is one of the major public health issues in the world. It has become the second leading cause of death, with approximately 75% of cancer deaths transpiring in low- or middle-income countries. It causes a heavy global economic cost estimated at more than a trillion dollars per year. The most common cancers are breast, colon, rectum, prostate, and lung cancers. Many of these cancers can be treated effectively and cured if detected at the primary stage. Nowadays, around 50% of cancers are detected at late stages, leading to serious health complications and death. Early diagnosis of cancer diseases substantially increases the efficient treatment and high chances of survival. Biosensors are one of the potential screening methodologies useful in the early screening of cancer biomarkers. This review summarizes the recent findings about novel cancer biomarkers and their advantages over traditional biomarkers, and novel biosensing and diagnostic methods for them; thus, this review may be helpful in the early recognition and monitoring of treatment response of various human cancers.

Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model

OBJECTIVE

To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma.

METHODS

A total of 612 patients with pathologically-confirmed lung adenocarcinoma were retrospectively enrolled and were randomly divided into training cohort (n = 489) and internal validation cohort (n = 123). Besides, 108 patients were enrolled and constituted an independent test cohort (n = 108). Patients' clinical characteristics and CT semantic features were collected. The radiomics features were derived from contrast-enhanced CT images. The clinical-semantic model and radiomics signature were built to predict LN metastasis. Furthermore, Swin Transformer was adopted to develop a DL signature predictive of LN metastasis. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. The comparisons of AUC were conducted by the DeLong test.

RESULTS

The proposed DL signature yielded an AUC of 0.948-0.961 across all three cohorts, significantly superior to both clinical-semantic model and radiomics signature (all p < 0.05). The calibration curves show that DL signature predicted probabilities fit well the actual observed probabilities of LN metastasis. DL signature gained a higher net benefit than both clinical-semantic model and radiomics signature. The incorporation of radiomics signature or clinical-semantic risk predictors failed to reveal an incremental value over the DL signature.

CONCLUSIONS

The proposed DL signature based on Swin Transformer achieved a promising performance in predicting LN metastasis and could confer important information in noninvasive mediastinal LN staging and individualized therapeutic options.

KEY POINTS

• Accurate prediction for lymph node metastasis is crucial to formulate individualized therapeutic options for patients with lung adenocarcinoma. • The deep learning signature yielded an AUC of 0.948-0.961 across all three cohorts in predicting lymph node metastasis, superior to both radiomics signature and clinical-semantic model. • The incorporation of radiomics signature or clinical-semantic risk predictors into deep learning signature failed to reveal an incremental value over deep learning signature.

Aerosolised micro and nanoparticle: formulation and delivery method for lung imaging

Purpose

The application of contrast and tracing agents is essential for lung imaging, as indicated by the wide use in recent decades and the discovery of various new contrast and tracing agents. Different aerosol production and pulmonary administration methods have been developed to improve lung imaging quality. This review details and discusses the ideal characteristics of aerosol administered via pulmonary delivery for lung imaging and the methods for the production and pulmonary administration of dry or liquid aerosol.

Methods

We explored several databases, including PubMed, Scopus, and Google Scholar, while preparing this review to discover and obtain the abstracts, reports, review articles, and research papers related to aerosol delivery for lung imaging and the formulation and pulmonary delivery method of dry and liquid aerosol. The search terms used were "dry aerosol delivery", "liquid aerosol delivery", "MRI for lung imaging", "CT scan for lung imaging", "SPECT for lung imaging", "PET for lung imaging", "magnetic particle imaging", "dry powder inhalation", "nebuliser", and "pressurised metered-dose inhaler".

Results

Through the literature review, we found that the critical considerations in aerosol delivery for lung imaging are appropriate lung deposition of inhaled aerosol and avoiding toxicity. The important tracing agent was also found to be Technetium-99m (^99mTc), Gallium-68 (⁶⁸Ga) and superparamagnetic iron oxide nanoparticle (SPION), while the essential contrast agents are gold, iodine, silver gadolinium, iron and manganese-based particles. The pulmonary delivery of such tracing and contrast agents can be performed using dry formulation (graphite ablation, spark ignition and spray dried powder) and liquid aerosol (nebulisation, pressurised metered-dose inhalation and air spray).

Conclusion

A dual-imaging modality with the combination of different tracing or contrast agents is a future development of aerosolised micro and nanoparticles for lung imaging to improve diagnosis success.

Graphical abstract

A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome-Interactome Signature for Predicting Non-Small Cell Lung Cancer

The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting NSCLC. The differentially expressed genes (DEGs) were identified in NSCLC compared with normal tissues using TCGA and GTEx data. A biological network was constructed using DEGs, and the top 20 upregulated and 20 downregulated hub genes were identified. These hub genes were used to identify signature genes with penalized logistic regression using the LASSO to predict NSCLC. Our model’s development involved the following steps: (i) the dataset was divided into 80% for training (TR) and 20% for testing (TD1); (ii) a LASSO logistic regression analysis was performed on the TR with 10-fold cross-validation and identified a combination of 17 genes as NSCLC predictors, which were used further for development of the LASSO model. The model’s performance was assessed on the TD1 dataset and achieved an accuracy and an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.986 and 0.998, respectively. Furthermore, the performance of the LASSO model was evaluated using three independent NSCLC test datasets (GSE18842, GSE27262, GSE19804) and achieved high accuracy, with an AUC-ROC of >0.99, >0.99, and 0.95, respectively. Based on this study, a web application called NSCLCpred was developed to predict NSCLC.

Simple diagnosis of cancer by detecting CEA and CYFRA 21-1 in saliva using electronic sensors

One way of early diagnosis of cancer is by detecting the biomarkers that get introduced into easily accessible body fluids. We report the development of portable and rapid electronic biosensors for quantitative detection of two secretive cancer biomarkers-Carcinoembryonic antigen (CEA) and Cytokeratin fragment 19 (CYFRA 21-1). The reduced graphene oxide (rGO)/ melamine (MEL)/antibodies/ bovine serum albumin (BSA) based devices were tested for 1 pg/mL to 800 ng/mL of CEA and CYFRA 21-1. The responses of the sensors ranged from 7.14 to 59.1% and from 6.18 to 64% for 1 pg/mL to 800 ng/mL CEA and CYFRA 21-1 respectively. A read-out circuit was assembled to develop a portable prototype which was used to assess the concentrations of the two antigens present in saliva samples of 14 subjects. The prototype could accurately discriminate between 9 oral squamous cell carcinoma patients and 5 healthy controls.

How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules

Simple Summary

Pulmonary nodules are considered a sign of bronchogenic carcinoma, detecting them early will reduce their progression and can save lives. Lung cancer is the second most common type of cancer in both men and women. This manuscript discusses the current applications of artificial intelligence (AI) in lung segmentation as well as pulmonary nodule segmentation and classification using computed tomography (CT) scans, published in the last two decades, in addition to the limitations and future prospects in the field of AI.

Abstract

Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification.

A high-performance electrochemical aptasensor based on graphene-decorated rhodium nanoparticles to detect HER2-ECD oncomarker in liquid biopsy

Evaluation of extracellular domain of human epidermal growth factor receptor-2 (HER2-ECD) oncomarker status is an impressive factor in screening, diagnosing and monitoring early-stage breast cancer (BC). Electrochemical aptamer-based nanobiosensor with high sensitivity and selectivity for quantitative and qualitative measurement of HER2-ECD oncomarker was developed. In this study, the nanocomposite made by distinct materials included reduced graphene oxide nano-sheets (rGONs) and rhodium nanoparticles (Rh-NPs) on the graphite electrode (GE) surface. This structure resulted in amplified electrochemical activity, high surface area, stability, and bio-compatibility. Each of the steps of preparing nanomaterials and setting up biosensor were carefully examined by analytical and electrochemical techniques. Various modified electrodes were constructed and analyzed in terms of electrochemical performance, morphology, size, and shape of nanomaterials. The GE-based aptasensor had a noteworthy and conducive results against HER2-ECD with a wide dynamic range of 10.0-500.0 ng/mL, a low limit of detection (LOD) of 0.667 ng/mL (significantly less than the clinical cut-off), and a low limit of quantification (LOQ) of 2.01 ng/mL. The benefits provided by this aptasensor such as broad dynamic range, high sensitivity, selectivity, stability, reproducibility, and low cost suggest tremendous potential for non-invasive detection and monitoring of the HER2-ECD levels of BC care and clinical diagnosis.

An electronic biosensor based on semiconducting tetrazine polymer immobilizing matrix coated on rGO for carcinoembryonic antigen

Point-of-care devices are expected to play very critical roles in early diagnosis and better treatment of cancer. Here, we report the end-to-end development of novel and portable biosensors for detecting carcinoembryonic antigen (CEA), a cancer biomarker, almost instantly at room temperature. The device uses reduced graphene oxide (rGO) as the base conducting layer and a novel poly[(1,4-phenylene)-alt-(3,6-(1,2,4,5-tetrazine)/3,6-(1,2,4,5-dihydrotetrazine))] (PhPTz) as an immobilizing matrix for the CEA antibodies. Judiciously introduced nitrogen-rich semiconducting PhPTz brings multiple advantages to the device—(1) efficiently immobilizes anti-CEA via synergistic H-bonding with peptide and N-glycal units and (2) transports the charge density variations, originated upon antibody-antigen interactions, to the rGO layer. The CEA was dropped onto the anti-CEA/PhPTz/rGO devices at ambient conditions, to facilitate binding and the change in current flowing through the sensors was measured. A response of 2.75–33.7 μA was observed when the devices were tested for a broad range of concentrations (0.25 pg/mL to 800 ng/mL) of CEA. A portable read-out circuit was assembled using Arduino UNO and a voltage divider circuit, and a simple algorithm was developed for the classification of the CEA concentrations. The prediction accuracy of the interfacing electronics along with the algorithm was found to be 100%.

A Retrospectively Study: Diagnosis of Pathological Types of Malignant Lung Tumors by Dual-layer Detector Spectral Computed Tomography

Object: By retrospectively analyzing the energy spectrum of squamous cell carcinoma, adenocarcinoma, small cell lung cancer (SCLC), and pulmonary metastases that underwent dual-layer detector spectral computed tomography (DLCT) 3-phase scan of the chest, we explored the value of a multiparameter energy spectrum in the assessment of pathological types of lung tumors. Methods: Cases of squamous cell carcinoma (n = 20), adenocarcinoma (n = 24), SCLC (n = 26), and metastases (n = 14) were collected. Then the largest cross-sectional area (LCA) of the lesion, computed tomography (CT) values in the plain scan phase, arterial and venous phases (HU, HUa, and HUv), iodine concentration, and effective atomic number in the arterial and venous phases (ICa, ICv, Zeff[a], and Zeff[v]) were measured and compared among the nonsmall cell lung cancer (NSCLC), SCLC and metastases, and other 3 groups of SCLC, squamous cell carcinoma, and adenocarcinoma. Results: Only the LCA is statistically different among SCLC, NSCLC, and metastases (P < .05). And the treated subgroup analysis did not show significant differences among the groups. However, the untreated subgroup analysis showed that there was a significant difference between NSCLC and metastases in LCA, SCLC and metastases in ICa, NSCLC and SCLC in HUv, NSCLC and SCLC in Zeff(v) (P < .05). Conclusion: The energy spectrum parameters of DLCT have a certain clinical value in distinguishing NSCLC from SCLC in the Zeff(v) and distinguishing SCLC from metastases in the ICa.

The combination of computed tomography features and circulating tumor cells increases the surgical prediction of visceral pleural invasion in clinical T1N0M0 lung adenocarcinoma

Background

Visceral pleural invasion (VPI) is a clinical manifestation associated with a poor prognosis, and diagnosing it preoperatively is highly imperative for successful sublobar resection of these peripheral tumors. We evaluated the roles of computed tomography (CT) features and circulating tumor cells (CTCs) for improving VPI detection in patients with clinical T1N0M0 invasive lung adenocarcinoma.

Methods

Three hundred and ninety-one patients were reviewed retrospectively in this study, of which 234 presented with a pleural tag or pleural contact on CT images. CTCs positive for the foliate receptors were enriched and analyzed prior to surgery. Logistic regression analyses were performed to assess the association of CT features and CTCs with VPI, and the receiver operating characteristic (ROC) curve was generated to compare the predictive power of these variables.

Results

Patients mostly underwent either segmentectomies (18.9%) or lobectomies (79.0%). Only 49 of the 234 patients with pleural involvement on CT showed pathologically confirmed VPI. Multivariate logistic regression analysis revealed that CTC level ≥10.42 FU/3 mL was a significant VPI risk factor for invasive adenocarcinoma cases ≤30 mm [adjusted odds ratio (OR) =4.62, 95% confidence interval (CI): 2.05–10.44, P<0.001]. Based on CT features, subgroup analyses showed that the solid portion size was a statistically significant independent predictor of VPI for these peripheral nodules with pleural tag, while the solid portion length of the interface was an independent predictor of pleural contact. The receiver operating curve analyses showed that the combination of CTC and CT features were highly predictive of VPI [area under the curve (AUC) =0.921 for pleural contact and 0.862 for the pleural tag, respectively].

Conclusions

CTC, combined with CT features of pleural tag or pleural contact, could significantly improve VPI detection in invasive lung adenocarcinomas at clinical T1N0M0 stage prior to the patient’s surgery.

Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

A systematic review and meta-analysis of diagnostic performance and physicians' perceptions of artificial intelligence (AI)-assisted CT diagnostic technology for the classification of pulmonary nodules

Background

Lung cancer was the second most commonly diagnosed cancer and the leading cause of cancer death in 2020. Although artificial intelligence (AI)-assisted diagnostic technologies have shown promise and has been used in clinical practice in recent years, no products related to AI-assisted CT diagnostic technologies for the classification of pulmonary nodules have been approved by the National Medical Products Administration in China. The objective of this article was to systematically review the diagnostic performance of AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant and to analyze physicians’ perceptions of this technology in China.

Methods

All relevant studies from 6 literature databases were searched and screened according to the inclusion and exclusion criteria. Data were extracted and the study quality was assessed by two reviewers. The study heterogeneity and publication bias were estimated. A questionnaire survey on the perceptions of physicians was conducted in 9 public tertiary hospitals in China. A meta-analysis, meta-regression and univariate logistic model were used in the systematic review and to explore the association of physicians’ perceptions with their rate of support for the clinical application of the technology.

Results

Twenty-seven studies with 5,727 pulmonary nodules were finally included in the meta-analysis. We found that the quality of the included studies was generally acceptable and that the pooled sensitivity and specificity of AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant were 0.90 and 0.89, respectively. The pooled diagnostic odds ratio (DOR) was 70.33. The majority of the surveyed physicians in China perceived “reduced workload for radiologists” and “improved diagnostic efficiency” as the important benefits of this technology. In addition, diagnostic accuracy (including misdiagnosis) and practical experience were significantly associated with whether physicians supported its clinical application.

Conclusions

In the context of lung cancer diagnosis, AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant has good diagnostic performance, but its specificity needs to be improved.

Lateral flow assay-based detection of long non-coding RNAs: A point-of-care platform for cancer diagnosis

Lateral flow assay (LFA) is a flexible, simple, low-costpoint-of-care platform for rapid detection of disease-specific biomarkers. Importantly, the ability of the assay to capture the circulating bio-molecules has gained significant attention, as it offers a potential minimal invasive system for early disease diagnosis and prognosis. In the present article, we review an innovative concept of LFA-based detection of circulating long non-coding RNAs (lncRNAs), one of the key regulators of fundamental biological processes. In addition, their disease-specific expression pattern and presence in biological fluids at differential levels make them excellent biomarker candidates for cancer detection. Our article also provides an update on the requirements for developing and improving such systems and discusses the key aspects of material selection, operational concepts, principles and conceptual design. We assume that the reviewed points will be helpful to improve the diagnostic applicability of LFA based lncRNA detection in cancer diagnosis.

Adsorption of cisplatin on oxidized graphene nanoribbons for improving the uptake in non-small cell lung carcinoma cell line A549

BACKGROUND

Graphene nanoribbons are nanosized strips of graphene with unique physicochemical properties like higher drug loading capacity and affinity for tumor cells.

OBJECTIVE

The principal objective of this research was to develop oxidized graphene nanoribbons (O-GNRs)-a based delivery system for cisplatin against non-small cell lung carcinoma cell line A549 by selective endocytosis.

METHOD

The O-GNRs prepared using various synthetic steps like oxidative unzipping were evaluated for various parameters like morphology, Fourier Transform Infrared (FTIR) study, % adsorption efficacy, Differential scanning colometric (DSC) study, and in-vitro efficacy studies.

RESULTS

Graphene nanoribbons with the length of 200-250 nm and width of 20-40 nm were obtained. The FTIR spectrum of drug-loaded O-GNRs exhibited a characteristic peak at 1550 cm-1 (-N-H group) of cisplatin. The DSC indicated the presence of sharp endothermic peaks at 59 ºC (PEG), 254 ºC (-C-NH3) and 308.6 ºC (-C-Pt). The % adsorption efficiency was found to be 74.56 ± 0.798% with in-vitro release in controlled manner (63.36 % ± 0.489 %) for 24 h.

CONCLUSION

The nanoformulation showed an average inhibition of 22.72% at a lower dose of cisplatin (> 25%) by passive targeting cell line A549 by DNA alkylation. In the near future, graphene-based systems will establish potential nanosystems in cancer treatment due to the additive effect of graphene with various therapeutic agents.

Predictive Value of Folate Receptor-Positive Circulating Tumor Cells for the Preoperative Diagnosis of Lymph Node Metastasis in Patients with Lung Adenocarcinoma

Noninvasive assessments of the risk of lymph node metastasis (LNM) in patients with lung adenocarcinoma (LAD) are of great value for selecting individualized treatment options. However, the diagnostic accuracies of different preoperative LN evaluation methods in routine clinical practice are not satisfactory. Here, an assessment to detect folate receptor (FR)-positive circulating tumor cells (CTCs) based on ligand-targeted enzyme-linked polymerization is established. FR-positive CTCs have the potential to improve the specificity and sensitivity of diagnosing LNM in lung cancer patients. The addition of CTC level improved the diagnostic efficiency of the initial prediction model that comprises other clinical parameters. A nomogram for predicting preoperative LNM is established, which showed good prediction and calibration capacities and achieved an average area under the curve of 0.786. Good correlations are observed between the CTC level and nodal classifications, such as the number of positive LNs and the ratio of the number of positive LNs to removed LNs (LN ratio or LNR). The ligand-targeted enzyme-linked polymerization-assisted assessment of CTCs enables noninvasive detection and has a useful predictive value for the preoperative diagnosis of LNM in patients with LAD.

A Miniature Bio-Photonics Companion Diagnostics Platform for Reliable Cancer Treatment Monitoring in Blood Fluids

In this paper, we present the development of a photonic biosensor device for cancer treatment monitoring as a complementary diagnostics tool. The proposed device combines multidisciplinary concepts from the photonic, nano-biochemical, micro-fluidic and reader/packaging platforms aiming to overcome limitations related to detection reliability, sensitivity, specificity, compactness and cost issues. The photonic sensor is based on an array of six asymmetric Mach Zender Interferometer (aMZI) waveguides on silicon nitride substrates and the sensing is performed by measuring the phase shift of the output signal, caused by the binding of the analyte on the functionalized aMZI surface. According to the morphological design of the waveguides, an improved sensitivity is achieved in comparison to the current technologies (<5000 nm/RIU). This platform is combined with a novel biofunctionalization methodology that involves material-selective surface chemistries and the high-resolution laser printing of biomaterials resulting in the development of an integrated photonics biosensor device that employs disposable microfluidics cartridges. The device is tested with cancer patient blood serum samples. The detection of periostin (POSTN) and transforming growth factor beta-induced protein (TGFBI), two circulating biomarkers overexpressed by cancer stem cells, is achieved in cancer patient serum with the use of the device.

Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

The incidence and mortality rates of lung cancers are different between females and males. Therefore, sex information should be an important part of how to train and optimize a diagnostic model. However, most of the existing studies do not fully utilize this information. This study carried out a comparative investigation between sex-specific models and sex-independent models. Three feature selection algorithms and five classifiers were utilized to evaluate the contribution of the sex information to the detection of early-stage lung cancers. Both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) showed that the sex-specific models outperformed the sex-independent detection of early-stage lung cancers. The Venn plots suggested that females and males shared only a few transcriptomic biomarkers of early-stage lung cancers. Our experimental data suggested that sex information should be included in optimizing disease diagnosis models.

Neutral Desorption Extractive Electrospray Ionization Mass Spectrometry Analysis Sputum for Non-Invasive Lung Adenocarcinoma Detection

Purpose

Increased use of low-dose spiral computed tomography (LDCT: low-dose computed tomography) screening has contributed to more frequent incidental detection of peripheral lung nodules, part of them were adenocarcinoma, which need to be further evaluated to establish a definitive diagnosis. Here, our primary objective was to evaluate the ambient mass spectrometry (AMS) sputum analysis as a non-invasive lung adenocarcinoma (LAC) diagnosis solution.

Patients and Methods

Neutral desorption extractive electrospray ionization mass spectrometry (ND-EESI-MS) and collision induced dissociation (CID) were used to detect sputum metabolites from 143 spontaneous sputum samples. Partial least squares-discriminant analysis (PLS-DA) was used to refine the biomarker panel, whereas orthogonal PLS-DA (OPLS-DA) was used to operationalize the enhanced biomarker panel for diagnosis.

Results

In this approach, 19 altered metabolites were detected by ND-EESI-MS from 76 cases of LAC and 67 cases of control. Significance testing and receiver operating characteristic (ROC) analysis identified 5 metabolites [hydroxyphenyllactic acid, phytosphingosine, N-nonanoylglycine, sphinganine, S-carboxymethyl-L-cysteine] with p <0.05 and AUC >0.75, respectively. Evaluation of model performance for prediction of LAC resulted in a cross-validation classification accuracy of 87.9%. Metabolic pathway analysis showed that sphingolipid metabolism, fatty acid metabolism, carnitine synthesis and Warburg effect were most impacted in response to disease.

Conclusion

This study indicates that the application of ND-EESI-MS to sputum analysis can be used as a non-invasive detection of peripheral lung nodules. The use of sputum metabolite biomarkers may aid in the development of a further evaluation program for lung adenocarcinoma.

Recent advances in aptamer-based sensors for breast cancer diagnosis: special cases for nanomaterial-based VEGF, HER2, and MUC1 aptasensors

Cancer is one of the most common and important diseases with a high mortality rate. Breast cancer is among the three most common types of cancer in women, and the mortality rate has reached 0.024% in some countries. For early-stage preclinical diagnosis of breast cancer, sensitive and reliable tools are needed. Today, there are many types of biomarkers that have been identified for cancer diagnosis. A wide variety of detection strategies have also been developed for the detection of these biomarkers from serum or other body fluids at physiological concentrations. Aptamers are single-stranded DNA or RNA oligonucleotides and promising in the production of more sensitive and reliable biosensor platforms in combination with a wide range of nanomaterials. Conformational changes triggered by the target analyte have been successfully applied in fluorometric, colorimetric, plasmonic, and electrochemical-based detection strategies. This review article presents aptasensor approaches used in the detection of vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (HER2), and mucin-1 glycoprotein (MUC1) biomarkers, which are frequently studied in the diagnosis of breast cancer. The focus of this review article is on developments of the last decade for detecting these biomarkers using various sensitivity enhancement techniques and nanomaterials.

Support Vector Machine for Lung Adenocarcinoma Staging Through Variant Pathways

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors. How to effectively diagnose LUAD at an early stage and make an accurate judgement of the occurrence and progression of LUAD are still the focus of current research. Support vector machine (SVM) is one of the most effective methods for diagnosing LUAD of different stages. The study aimed to explore the dynamic change of differentially expressed genes (DEGs) in different stages of LUAD, and to assess the risk of LUAD through DEGs enriched pathways and establish a diagnostic model based on SVM method. Based on TMN stages and gene expression profiles of 517 samples in TCGA-LUAD database, coefficient of variation (CV) combined with one-way analysis of variance (ANOVA) were used to screen out feature genes in different TMN stages after data standardization. Unsupervised clustering analysis was conducted on samples and feature genes. The feature genes were analyzed by Pearson correlation coefficient to construct a co-expression network. Fisher exact test was conducted to verify the most enriched pathways, and the variation of each pathway in different stages was analyzed. SVM networks were trained and ROC curves were drawn based on the predicted results so as to evaluate the predictive effectiveness of the SVM model. Unsupervised hierarchical clustering analysis results showed that almost all the samples in stage III/IV were clustered together, while samples in stage I/II were clustered together. The correlation of feature genes in different stages was different. In addition, with the increase of malignant degree of lung cancer, the average shortest path of the network gradually increased, while the closeness centrality gradually decreased. Finally, four feature pathways that could distinguish different stages of LUAD were obtained and the ability was tested by the SVM model with an accuracy of 91%. Functional level differences were quantified based on the expression of feature genes in lung cancer patients of different stages, so as to help the diagnosis and prediction of lung cancer. The accuracy of our model in differentiating between stage I/II and stage III/IV could reach 91%.

Experimental study of the vascular normalization window for tumors treated with apatinib and the efficacy of sequential chemotherapy with apatinib in lung cancer-bearing mice and patients

In the tumor vascular system, the vascular structure is disordered, the morphology is abnormal, and the structure of the blood vessel walls is incomplete, leading to leakage of the blood vessel wall, elevated interstitial fluid pressure, and elevated blood flow resistance. These alterations lead to local microenvironmental changes, which mainly manifest as a lack of oxygen and acidosis, further affecting the efficacy of chemotherapy drugs. Antiangiogenic drugs can normalize the abnormalities caused by tumor angiogenesis, thereby transferring oxygen and drugs to tumor cells more efficiently through normalized blood vessels and enhancing the efficacy of chemotherapy drugs. Apatinib is a specific VEGFR‐2 inhibitor that blocks the transmission of the VEGF/VEGFR‐2 signaling pathway. In this study, we constructed a nude mouse xenograft model of lung cancer and administered apatinib at different doses and times to detect the normalization of reactive blood vessels through VEGF, α‐SMA, college‐IV, HIF‐1α, and MMP. The ultrastructure of tumor blood vessels was observed by electron microscopy, and the dose and timing of apatinib‐induced normalization of lung cancer in nude mice were confirmed. Then, we observed the inhibitory effect of apatinib combined with pemetrexed on transplanted tumors of lung cancer cells in nude mice at different time points and observed whether combination pemetrexed chemotherapy showed more significant effects in the time window of vascular normalization induced by apatinib. The inhibition of the growth of transplanted tumors was examined. Then 20 patients with advanced non–small cell lung cancer were enrolled, and apatinib sequential chemotherapy drugs were applied as a third‐line chemotherapy regimen to observe its clinical efficacy.

Non-invasive Biodiversified Sensors: A Modernized Screening Technology for Cancer

Background:

Biological sensors revolutionize the method of diagnoses of diseases from early to final stages using the biomarkers present in the body. Biosensors are advantageous due to the involvement of minimal sample collection with improved specificity and sensitivity for the detection of biomarkers.

Methods:

Conventional biopsies restrict problems like patient non-compliance, cross-infection and high cost and to overcome these issues biological samples like saliva, sweat, urine, tears and sputum progress into clinical and diagnostic research for the development of non-invasive biosensors. This article covers various non-invasive measurements of biological samples, optical-based, mass-based, wearable and smartphone-based biosensors for the detection of cancer.

Results:

The demand for non-invasive, rapid and economic analysis techniques escalated due to the modernization of the introduction of self-diagnostics and miniature forms of devices. Biosensors have high sensitivity and specificity for whole cells, microorganisms, enzymes, antibodies, and genetic materials.

Conclusion:

Biosensors provide a reliable early diagnosis of cancer, which results in faster therapeutic outcomes with in-depth fundamental understanding of the disease progression.

Long non-coding RNA LOC554202 promotes acquired gefitinib resistance in non-small cell lung cancer through upregulating miR-31 expression

Non-small-cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutation inevitably have a relapse due to the occurrence of acquired resistance, resulting in treatment failure. However, little is known about the mechanisms of acquired resistance of NSCLC patients. Here, we elucidated the expression pattern of LOC554202 and miR-31, and their biological functions and mechanisms in NSCLC with acquired EGFR TKI resistance to gefitinib. In the present study, we observed that LOC554202 and miR-31 promoted proliferation and clonogenic growth of gefitinib-resistant NSCLC cells in vitro. LOC554202 upregulated miR-31 expression and they both reduced sensitivity of NSCLC cells to gefitinib. In a xenograft mice model, we found that knockdown of miR-31 significantly repressed gefitinib-resistant NSCLC cells growth in vivo. Furthermore, both LOC554202 and miR-31 levels were significantly increased in NSCLC patients acquiring resistance to gefitinib, and the expression of LOC554202 was positively correlated with the expression of miR-31. By luciferase reporter assays, we identified RAS P21 Protein Activator 1 (RASA1) and Hypoxia Inducible Factor 1 Subunit Alpha Inhibitor (FIH-1) as direct targets of miR-31 in NSCLC cells. Mechanistically, miR-31 directly repressed RASA1 and FIH-1 expression, and thus, at least partially activated the RAF-MEK-ERK and PI3K-AKT signaling pathways in NSCLC with acquired resistance to gefitinib. In conclusion, these data will help us develop potential therapeutic targets for the diagnosis and treatment of acquired EGFR TKI resistance in EGFR-mutant NSCLC.

Oncogenicity of lncRNA FOXD2-AS1 and its molecular mechanisms in human cancers

OBJECTIVES

Long non-coding RNAs (lncRNAs) are a group of noncoding RNAs with length larger than 200 nucleotides. LncRNAs have limited or no protein-coding capacity because of lack of obvious open reading frame. An increasing number of researches have shown that lncRNAs participate in the complex regulation network of cancer and play an important role in tumourigenesis and progression such as proliferation, migration and invasion. LncRNA FOXD2 adjacent opposite strand RNA 1 (FOXD2-AS1), located on chromosome 1p33 and with a transcript length of 2527 nucleotides, is a novel cancer-related lncRNA. FOXD2-AS1 was recently found to exhibit aberrant expression in various malignancies, including gastric, lung, bladder, colorectal, nasopharyngeal, esophageal, hepatocellular, thyroid and skin cancer, and its deregulation might be related to survival and prognosis of cancer patients. Pertinent to clinical practice, FOXD2-AS1 might act as a feasible biomarker or therapeutic target in human cancers. In this paper, we made a summary on the current findings concerning the biological functions and molecular mechanisms of FOXD2-AS1 in tumor progression.

MATERIALS AND METHODS

In this paper, we summarized and figured out recent studies about the expression and molecular biological mechanisms of FOXD2-AS1 in tumor progression. Existing relevant studies were obtained through a systematic search from PubMed, Embase, BioMedNet, GEO database and Cochrane Library.

RESULTS

FOXD2-AS1 was a valuable tumor-associated lncRNA. Its expression level was up-regulation in various malignancies, including gastric, lung, bladder, colorectal, nasopharyngeal, esophageal, hepatocellular, thyroid and skin cancer. In addition, the aberrant expressions of FOXD2-AS1 have shown to contribute to proliferation, migration and invasion of cancer cells, and its deregulation is related to carcinogensis, overall survival, disease free survival, prognosis and tumor progression.

CONCLUSIONS

LncRNA FOXD2-AS1 is an oncogene and probably represents a feasible biomarker or therapeutic target in human cancers.