Biomarkers are deemed to be potential tools in early diagnosis, therapeutic monitoring, and prognosis evaluation for cancer, with simplicity as well as economic advantages compared with computed tomography and biopsy. However, most of the current cancer biomarkers present insufficient sensitivity as well as specificity. Therefore, there is urgent requirement for the discovery of biomarkers for cancer. As one of the most exciting emerging technologies, protein array provides a versatile and robust platform in cancer proteomics research because it shows tremendous advantages of miniaturized features, high throughput, and sensitive detections in last decades. Here, we will present a relatively complete picture on the characteristics and advance of different types of protein arrays in application for biomarker discovery in cancer, and give the future perspectives in this area of research.

Purpose: Circulating microRNAs (miRNAs) have shown the potential for non-invasive diagnosis of various types of malignancies at an early stage. The aim of the study was to explore the feasibility of a combination of 8 serum miRNAs related to non-small-cell lung cancer (NSCLC) with the corresponding serum exosomal miRNAs in early diagnosis for the patients with NSCLC. Methods: We measured 8 serum miRNAs and the corresponding serum exosomal miRNAs including miR-21-5p, miR-126-3p, miR-141-3p, miR-146a-5p, miR-155-5p, miR-222-3p, miR-223-3p, and miR-486-5p in 48 patients with early NSCLC at stages I/II, 32 patients with lung benign lesion (LBL), and 48 healthy control (HC) by quantitative real-time polymerase chain reaction (qRT-PCR). Results: The expression levels of 4 serum miRNAs including miR-21-5p, miR-141-3p, miR-222-3p, and miR-486-5p, and 2 serum exosomal miRNAs including miR-146a-5p and miR-486-5p in the early NSCLC group were significantly different from that in the LBL group and the HC group (P < 0.01). The areas under the receiver operating characteristic curves (AUC) of the 4 serum miRNAs and 2 serum exosomal miRNAs in the early NSCLC group were ≥ 0.697, of which serum exosomal miR-146a-5p and miR-486-5p were 0.813 and 0.886, respectively, and higher than that of the 4 serum miRNAs. Additionally, a combination of 4 serum miRNAs with 2 serum exosomal miRNAs improved the AUC to 0.960 for the patients with NSCLC at early stages, with a sensitivity of 85.42% and a specificity of 92.50%. Conclusion: This study suggests that serum exosomal miRNAs other than serum miRNAs might be preferable biomarkers for the patients with NSCLC at early stages, and a combination of serum miRNAs with serum exosomal miRNAs contributes to the further improvement of early diagnosis for NSCLC.

Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. Currently, a lack of serological biomarkers for early LC diagnosis is a major roadblock for early intervention and prevention of LC. To undertake this challenge, we employed a two-phase strategy to discover and validate a biomarker panel using a protein array-based approach. In Phase I, we obtained serological autoimmune profiles of 80 LC patients and 20 healthy subjects on HuProt arrays, and identified 170 candidate proteins significantly associated with LC. In Phase II, we constructed a LC focused array with the 170 proteins, and profiled a large cohort, comprised of 352 LC patients, 93 healthy individuals, and 101 patients with lung benign lesions (LBL). The comparison of autoimmune profiles between the early stage LC and the combined group of healthy and LBL allowed us to identify and validate a biomarker panel of p53, HRas, and ETHE1 for diagnosis of early stage LC with 50% sensitivity at >90% specificity. Finally, the performance of this biomarker panel was confirmed in ELISA tests. In summary, this study represents one of the most comprehensive proteome-wide surveys with one of the largest (i.e. 1,101 unique samples) and most diverse (i.e. nine disease groups) cohorts, resulting in a biomarker panel with good performance.

Global pandemics such as COVID-19 have resulted in significant global social and economic disruption. Although polymerase chain reaction (PCR) is recommended as the standard test for identifying the SARS-CoV-2, conventional assays are time-consuming. In parallel, although artificial intelligence (AI) has been employed to contain the disease, the implementation of AI in PCR analytics, which may enhance the cognition of diagnostics, is quite rare. The information that the amplification curve reveals can reflect the dynamics of reactions. Here, we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices (µPADs) to detect synthetic RNA templates of the SARS-CoV-2 ORF1ab gene. The µPADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments. During analysis, real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks, including RNN, LSTM, and GRU. Of these, the GRU is found to be most effective and accurate. Based on the experimentally obtained datasets, qualitative forecasting can be made as early as 13 cycles, which significantly enhances the efficiency of the PCR tests by 67.5% (∼40 min). Also, an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles. To further improve PCR testing efficiency, we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers, which enables real-time quantitative analysis of PCR tests. The presented approach is the first to integrate AI for on-chip PCR data analysis. It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation. It is also capable of fully exploring the dynamics and intrinsic features of each reaction. This work leverages methodologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field. It is universally applicable and can be extended to multiple areas of fundamental research.

Background: Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy worldwide. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been identified as effective markers for the detection of multiple cancers. This study aimed to illuminate the mechanism of prostate androgen regulated transcript 1 (PART1) in HCC. Materials and Methods: The levels of PART1, miR-149-5p and mitogen-activated protein kinase 1 (MAP2K1) mRNA were detected by quantitative real-time polymerase chain reaction (qRT-PCR) assay. Cell proliferation was assessed by Cell Counting Kit-8 (CCK-8) assay, and cell migration and invasion were evaluated by transwell assay. Dual-luciferase reporter assay was carried out to examine the relationship among PART1, miR-149-5p and MAP2K1. Western blot assay was conducted to measure the protein expression of MAP2K1. Results: PART1 and MAP2K1 expression were greatly increased and miR-149-5p level was decreased in HCC tissues. Functional analysis revealed that the si-PART1 inhibited proliferation, migration and invasion of HCC cells. PART1 directly bound to miR-149-5p and miR-149-5p level was down-regulated by PART1. Moreover, restoration experiment demonstrated that the effect of PART1 knockdown on HCC cell progression could be partially rescued by miR-149-5p depletion. MiR-149-5p was predicted to target MAP2K1 and MAP2K1 expression was negatively modulated by miR-149-5p. Also, MAP2K1 rescued the inhibitory effects of miR-149-5p overexpression on proliferation, migration and invasion in HCC cells. Besides, the inhibition of miR-149-5p weakened the impact on MAP2K1 expression mediated by PART1 repression. Conclusion: PART1 promoted proliferation, migration and invasion of HCC cells by regulating miR-149-5p/MAP2K1 axis.

Background: Aberrant fucosylation observed in cancer cells contributes to an augmented release of fucosylated exosomes into the bloodstream, where miRNAs including miR-4732-3p hold promise as potential tumor biomarkers in our pilot study. However, the mechanisms underlying the sorting of miR-4732-3p into fucosylated exosomes during lung cancer progression remain poorly understood. Methods: A fucose-captured strategy based on lentil lectin-magnetic beads was utilized to isolate fucosylated exosomes and evaluate the efficiency for capturing tumor-derived exosomes using nanoparticle tracking analysis (NTA). Fluorescence in situ hybridization (FISH) and qRT-PCR were performed to determine the levels of miR-4732-3p in non-small cell lung cancer (NSCLC) tissue samples. A co-culture system was established to assess the release of miRNA via exosomes from NSCLC cells. RNA immunoprecipitation (RIP) and miRNA pull-down were applied to validate the interaction between miR-4732-3p and heterogeneous nuclear ribonucleoprotein K (hnRNPK) protein. Cell functional assays, cell derived xenograft, dual-luciferase reporter experiments, and western blot were applied to examine the effects of miR-4732-3p on MFSD12 and its downstream signaling pathways, and the impact of hnRNPK in NSCLC. Results: We enriched exosomes derived from NSCLC cells using the fucose-captured strategy and detected a significant upregulation of miR-4732-3p in fucosylated exosomes present in the serum, while its expression declined in NSCLC tissues. miR-4732-3p functioned as a tumor suppressor in NSCLC by targeting 3'UTR of MFSD12, thereby inhibiting AKT/p21 signaling pathway to induce cell cycle arrest in G2/M phase. NSCLC cells preferentially released miR-4732-3p via exosomes instead of retaining them intracellularly, which was facilitated by the interaction of miR-4732-3p with hnRNPK protein for selective sorting into fucosylated exosomes. Moreover, knockdown of hnRNPK suppressed NSCLC cell proliferation, with the elevated levels of miR-4732-3p in NSCLC tissues but the decreased expression in serum fucosylated exosomes. Conclusions: NSCLC cells escape suppressive effects of miR-4732-3p through hnRNPK-mediated sorting of them into fucosylated exosomes, thus supporting cell malignant properties and promoting NSCLC progression. Our study provides a promising biomarker for NSCLC and opens a novel avenue for NSCLC therapy by targeting hnRNPK to prevent the "exosome escape" of tumor-suppressive miR-4732-3p from NSCLC cells.