Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.
Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide. The HCC diagnosis is usually achieved by biomarkers, which can also help in prognosis prediction. Furthermore, it might represent certain therapeutic interventions through some combinations of biomarkers. Here, we review on our current understanding of HCC biomarkers.
Current
metabolic analysis is far from ideal to engage clinics and needs rationally
designed materials and device. Here we developed a novel plasmonic
chip for clinical metabolic fingerprinting. We first constructed a
series of chips with gold nanoshells on the surface through controlled
particle synthesis, dip-coating, and gold sputtering for mass production.
We integrated the optimized chip with microarrays for laboratory
automation and micro-/nanoscaled experiments, which afforded direct
high-performance metabolic fingerprinting by laser desorption/ionization
mass spectrometry using 500 nL of various biofluids and exosomes.
Further we for the first time demonstrated on-chip in vitro metabolic diagnosis of early stage lung cancer patients using serum
and exosomes. This work initiates a new bionanotechnology based platform
for advanced metabolic analysis toward large-scale diagnostic use.
BackgroundQuantification of circulating tumor cells (CTC) is valuable for evaluation of non-small cell lung cancer (NSCLC). The sensitivity of current methods constrains their use to detect rare CTCs in early stage. Here we evaluate a novel method, ligand-targeted polymerase chain reaction (LT-PCR), that can detect rare CTCs in NSCLC patients.MethodsCTCs were enriched by immunomagnetic depletion of leukocytes and then labeled by a conjugate of a tumor-specific ligand and an oligonucleotide. After washing off free conjugates, the bound conjugates were stripped from CTCs and then analyzed by qPCR. To evaluate the clinical utility, blood samples were obtained from 72 NSCLC patients (33 initially diagnosed and 39 on chemotherapy), 20 benign patients, and 24 healthy donors.ResultsExperiments with healthy blood spiked with tumor cells indicated the LT-PCR allows specific detection of CTC. The clinical study showed that the initially diagnosed patients have an average of 20.8 CTC units with metastatic diseases, 11.8 CTC units with localized diseases, and 6.0 CTC units with benign diseases. With the threshold of 8.5 CTC units, the assay can detect 80% of stage I/II, 67% of stage III, and 93% of stage IV cancer. With the benign patients and healthy donors as control group, the method can detect cancer with a sensitivity of 81.8% and a specificity of 93.2%.ConclusionThe LT-PCR would allow quantification of CTC in NSCLC patients at a more sensitive level, providing a potential tool for stratifying malignant lung diseases, especially at early stage.
Diagnosis of lung cancer is performed using a plasmonic gold (pGOLD) chip through multiplexed near‐infrared (NIR) detection of carcino‐embryonic antigen (CEA), Cyfra21‐1, and neuron‐specific enolase (NSE) in the serum samples of patients. With ≈50‐fold enhancement of NIR fluorescence, multiplexed microarray analysis of CEA, Cyfra21‐1, and NSE in 10 μL of human serum or whole blood samples on pGOLD chip leads to markedly improved limit‐of‐quantification, limit‐of‐detection, reproducibility, and higher diagnostic sensitivity and specificity compared to traditional biochips and Luminex technology currently in use in hospitals.
Previous studies have indicated that miR-146a-5p acts as an oncogene in several types of cancer, yet a tumor suppressor gene in others. In non-small cell lung cancer (NSCLC), one report showed that it was downregulated and played the role of tumor suppressor. However, another study showed that miR-146a-5p was overexpressed in the serum of NSCLC patients compared to healthy controls. Therefore, it is obvious that further study of the function of miR-146a-5p in NSCLC is necessary to fully understand its importance. Herein, we have verified that miR- 146a- 5p acts as a tumor suppressor in NSCLC. Our data revealed that the expression level of miR-146a-5p was significantly decreased in several human NSCLC cell lines, and also less abundant in human NSCLC tissues, when compared with controls. Moreover, we observed that miR-146a-5p could suppress cell proliferation, both in vitro and in vivo. Our results also showed that miR-146a-5p directly targeted the 3′-UTR of CCND1 and CCND2 mRNAs as well as decreased their expression at both mRNA and protein levels, causing cell cycle arrest at the G0/G1 phase. Furthermore, siRNA-mediated downregulation of CCND1 or CCND2 yielded the same effects on proliferation and cell cycle arrest as miR-146a-5p upregulation did in the NSCLC cell lines. We confirmed that the expression of miR-146a-5p had negative relationship with CCND1 or CCND2. Besides, we also found that miR-146a-5p could inhibit tumor growth in xengroft mouse models, and CCND1 and CCND2 were downregulated in miR-146a-5p overexpressed xengroft tumor tissues. In summary, our results demonstrated that miR-146a-5p could suppress the proliferation and cell cycle progression in NSCLC cells by inhibiting the expression of CCND1 and CCND2.
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