A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma

Utilizing MiSeq Sequencing to Detect Circulating microRNAs in Plasma for Improved Lung Cancer Diagnosis

Non-small cell lung cancer (NSCLC) is a major contributor to cancer-related deaths, but early detection can reduce mortality. NSCLC comprises mainly adenocarcinoma (AC) and squamous cell carcinoma (SCC). Circulating microRNAs (miRNAs) in plasma have emerged as promising biomarkers for NSCLC. However, existing techniques for analyzing miRNAs have limitations, such as restricted target detection and time-consuming procedures. The MiSeqDx System has been shown to overcome these limitations, making it a promising tool for routine clinical settings. We investigated whether the MiSeqDx could profile cell-free circulating miRNAs in plasma and diagnose NSCLC. We sequenced RNA from the plasma of patients with AC and SCC and from cancer-free smokers using the MiSeqDx to profile and compare miRNA expressions. The MiSeqDx exhibits high speed and accuracy when globally analyzing plasma miRNAs. The entire workflow, encompassing RNA to data analysis, was completed in under three days. We also identified panels of plasma miRNA biomarkers that can diagnose NSCLC with 67% sensitivity and 68% specificity, and detect SCC with 90% sensitivity and 94% specificity, respectively. This study is the first to demonstrate that rapid profiling of plasma miRNAs using the MiSeqDx has the potential to offer a straightforward and effective method for the early detection and classification of NSCLC.

Streptococcus pneumoniae promotes lung cancer development and progression

Streptococcus pneumoniae (SP) is associated with lung cancer, yet its role in the tumorigenesis remains uncertain. Herein we find that SP attaches to lung cancer cells via binding pneumococcal surface protein C (PspC) to platelet-activating factor receptor (PAFR). Interaction between PspC and PAFR stimulates cell proliferation and activates PI3K/AKT and nuclear factor kB (NF-kB) signaling pathways, which trigger a pro-inflammatory response. Lung cancer cells infected with SP form larger tumors in BALB/C mice compared to untreated cells. Mice treated with tobacco carcinogen and SP develop more lung tumors and had shorter survival period than mice treated with the carcinogen alone. Mutating PspC or PAFR abolishes tumor-promoting effects of SP. Overabundance of SP is associated with the survival. SP may play a driving role in lung tumorigenesis by activating PI3K/AKT and NF-kB pathways via binding PspC to PAFR and provide a microbial target for diagnosis and treatment of the disease.

Bronchoalveolar Lavage Fluid-Isolated Biomarkers for the Diagnostic and Prognostic Assessment of Lung Cancer

Lung cancer is considered one of the most fatal malignant neoplasms because of its late detection. Detecting molecular markers in samples from routine bronchoscopy, including many liquid-based cytology procedures, such as bronchoalveolar lavage fluid (BALF), could serve as a favorable technique to enhance the efficiency of a lung cancer diagnosis. BALF analysis is a promising approach to evaluating the tumor progression microenvironment. BALF's cellular and non-cellular components dictate the inflammatory response in a cancer-proliferating microenvironment. Furthermore, it is an essential material for detecting clinically significant predictive and prognostic biomarkers that may aid in guiding treatment choices and evaluating therapy-induced toxicities in lung cancer. In the present article, we have reviewed recent literature about the utility of BALF analysis for detecting markers in different stages of tumor cell metabolism, employing either specific biomarker assays or broader omics approaches.

Construction and Validation of Prognostic Regulation Network Based on RNA-Binding Protein Genes in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSC) is a common histologic subtype of non-small cell lung cancer with a poor prognosis. RNA-binding proteins (RBPs) are key modulators in the posttranscriptional regulation and RBP alterations are commonly found in various cancer types. However, its roles in predicting the tumorigenesis and prognosis have not been identified in LUSC. To identify the roles of RBPs in the tumorigenesis and prognosis of LUSC, the RNA sequencing data of patients with LUSC were retrieved from The Cancer Genome Atlas (TCGA) databases. The differential expressed genes (DEGs) were evaluated and identified. The intersection of manually curated RBPs and tumorigenesis-related DEGs was filtered to the univariate Cox regression analysis. The intersection genes with prognostic value were defined as prognostic RNA-binding protein genes (PRBPGs). Based on them, the predicted model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic curve and the risk score. In addition, to explore the key regulatory network, the relationship among PRBPGs, target RNA, and absolute quantification of 50 hallmarks of cancer was also identified by Pearson correlation analysis. A total of 311 genes were filtered as the intersection of 1542 manually curated RBPs and tumorigenesis-related DEGs and the results revealed 17 PRBPGs. Based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.739). The Kaplan-Meier survival curve showed the significant prognostic value of risk score (p < 0.001). Moreover, we uncovered the regulatory networks of PHF5A-TOMM22-oxidative phosphorylation, TLR3-CTSO inflammation-related pathway, SECISBP2L-targeted RNA (ADGRF5, TGFBR2, CD302, AC096921.2, AHCYL2, RPS6KA2, SLC34A2, and SFTPB) angiogenesis, and SECISBP2L-AKAP13 signaling (DNA repair, MTORC1 signaling, and MYC targets). The regulation mechanisms and cellular location of key PRBPGs were validated by assay for targeting accessible chromatin with high-throughput sequencing and single-cell RNA sequencing. Our study identifies PRBPGs as reliable indexes in predicting the tumorigenesis and prognosis of patients with LUSC and provides a well-applied model for predicting the overall survival for patients with LUSC. Besides, we also identified the regulatory network among PRBPGs, target RNA, and cancer gene sets in mediating the LUSC tumorigenesis.

Current and Emerging Applications of Droplet Digital PCR in Oncology: An Updated Review

In the era of personalized medicine and targeted therapies for the management of patients with cancer, ultrasensitive detection methods for tumor genotyping, such as next-generation sequencing or droplet digital polymerase chain reaction (ddPCR), play a significant role. In the search for less invasive strategies for diagnosis, prognosis and disease monitoring, the number of publications regarding liquid biopsy approaches using ddPCR has increased substantially in recent years. There is a long list of malignancies in which ddPCR provides a reliable and accurate tool for detection of nucleic acid-based markers derived from cell-free DNA, cell-free RNA, circulating tumor cells, extracellular vesicles or exosomes when isolated from whole blood, plasma and serum, helping to anticipate tumor relapse or unveil intratumor heterogeneity and clonal evolution in response to treatment. This updated review describes recent developments in ddPCR platforms and provides a general overview about the major applications of liquid biopsy in blood, including its utility for molecular response and minimal residual disease monitoring in hematological malignancies or the therapeutic management of patients with colorectal or lung cancer, particularly for the selection and monitoring of treatment with tyrosine kinase inhibitors. Although plasma is the main source of genetic material for tumor genomic profiling, liquid biopsy by ddPCR is being investigated in a wide variety of biologic fluids, such as cerebrospinal fluid, urine, stool, ocular fluids, sputum, saliva, bronchoalveolar lavage, pleural effusion, mucin, peritoneal fluid, fine needle aspirate, bile or pancreatic juice. The present review focuses on these "alternative" sources of genetic material and their analysis by ddPCR in different kinds of cancers.

High-Throughput Detection of Multiple miRNAs and Methylated DNA by Droplet Digital PCR

Altered miRNA expression and DNA methylation have highly active and diverse roles in carcinogenesis. Simultaneous detection of the molecular aberrations may have a synergistic effect on the diagnosis of malignancies. Herein, we develop a high-throughput assay for detecting multiple miRNAs and DNA methylation using droplet digital PCR (ddPCR) coupled with a 96-microwell plate. The microplate-based ddPCR could absolutely and reproducibly quantify 15 miRNAs and 14 DNA methylation sites with a high sensitivity (one copy/µL and 0.1%, respectively). Analyzing sputum and plasma of 40 lung cancer patients and 36 cancer-free smokers by this approach identified an integrated biomarker panel consisting of two sputum miRNAs (miRs-31-5p and 210-3p), one sputum DNA methylation (RASSF1A), and two plasma miRNAs (miR-21-5p and 126) for the diagnosis of lung cancer with higher sensitivity and specificity compared with a single type of biomarker. The diagnostic value of the integrated biomarker panel for the early detection of lung cancer was confirmed in a different cohort of 36 lung cancer patients and 39 cancer-free smokers. The high-throughput assay for quantification of multiple molecular aberrations across sputum and plasma could improve the early detection of lung cancer.

Microbiota Biomarkers for Lung Cancer

Non-small cell lung cancer (NSCLC) is the number one cancer killer and its early detection can reduce mortality. Accumulating evidences suggest an etiopathogenic role of microorganisms in lung tumorigenesis. Certain bacteria are found to be associated with NSCLC. Herein we evaluated the potential use of microbiome as biomarkers for the early detection of NSCLC. We used droplet digital PCR to analyze 25 NSCLC-associated bacterial genera in 31 lung tumor and the paired noncancerous lung tissues and sputum of 17 NSCLC patients and ten cancer-free smokers. Of the bacterial genera, four had altered abundances in lung tumor tissues, while five were aberrantly abundant in sputum of NSCLC patients compared with their normal counterparts (all p < 0.05). Acidovorax and Veillonella were further developed as a panel of sputum biomarkers that could diagnose lung squamous cell carcinoma (SCC) with 80% sensitivity and 89% specificity. The use of Capnocytophaga as a sputum biomarker identified lung adenocarcinoma (AC) with 72% sensitivity and 85% specificity. The use of Acidovorax as a sputum biomarker had 63% sensitivity and 96% specificity for distinguishing between SCC and AC, the two major types of NSCLC. The sputum biomarkers were further validated for the diagnostic values in a different cohort of 69 NSCLC cases and 79 cancer-free controls. Sputum microbiome might provide noninvasive biomarkers for the early detection and classification of NSCLC.

miRNA Expression Signatures of Therapy Response in Squamous Cell Carcinomas

Simple Summary

miRNAs play role in various diseases and can also modulate therapy response. Our aim was to identify predictive miRNAs in platinum treated squamous cell carcinomas (SCC). Using a set of 266 squamous cancer samples we uncovered 16, 103, and 9 miRNAs correlated to chemotherapy response in the cervical, head and neck, and lung squamous cell carcinomas, respectively. By employing a logistic regression model, a signature comprising a set of six miRNAs was established capable to predict chemotherapy response with an AUC of 0.897. Our results show common molecular features of SCC tumors and pinpoint the most important miRNAs related to treatment outcome.

Abstract

Introduction: Squamous cell carcinomas (SCC) are a major subgroup of malignant tumors with a platinum-based first-line systematic chemotherapy. miRNAs play a role in various diseases and modulate therapy response as well. The aim of this study was to identify predictive miRNAs in platinum-treated SCCs. Methods: miRNA expression data of platinum-treated head and neck (HNSC), cervical (CESC) and lung (LUSC) cancer were collected from the TCGA repositories. Treatment response was defined based on presence or absence of disease progression at 18 months. Responder and nonresponder cohorts were compared using Mann–Whitney and Receiver Operating Characteristic tests. Logistic regression was developed to establish a predictive miRNA signature. Significance was set at FDR < 5%. Results: The integrated database includes 266 SCC patient samples with platinum-based therapy and available follow-up. We uncovered 16, 103, and 9 miRNAs correlated to chemotherapy response in the CESC, HNSC, and LUSC cohorts, respectively. Eight miRNAs overlapped between the CESC and HNSC subgroups, and three miRNAs overlapped between the LUSC and HNSC subgroups. We established a logistic regression model in HNSC and CESC which included six miRNAs: hsa-miR-5586 (Exp (B): 2.94, p = 0.001), hsa-miR-632 (Exp (B): 10.75, p = 0.002), hsa-miR-2355 (Exp (B): 0.48, p = 0.004), hsa-miR-642a (Exp (B): 2.22, p = 0.01), hsa-miR-101-2 (Exp (B): 0.39, p = 0.013) and hsa-miR-6728 (Exp (B): 0.21, p = 0.016). The model using these miRNAs was able to predict chemotherapy resistance with an AUC of 0.897. Conclusions: We performed an analysis of RNA-seq data of squamous cell carcinomas samples and identified significant miRNAs correlated to the response against platinum-based therapy in cervical, head and neck, and lung tumors.

Integrated analysis of miRNAs and DNA methylation identifies miR-132-3p as a tumor suppressor in lung adenocarcinoma

BACKGROUND

Aberrant miRNA expression and DNA methylation are two major epigenetic events in lung adenocarcinoma (LUAD). We conducted a combined analysis of the molecular changes in LUAD.

METHODS

We analyzed differentially expressed miRNAs and methylated CpG loci in 489 LUAD tissues versus 49 normal lung tissues of the Cancer Genome Atlas (TCGA). The results were validated in cell lines and xenograft mouse models and additional pairs of 36 LUAD and 36 normal lung tissues.

RESULTS

A total of 125 differentially expressed miRNAs and 145 differentially methylated CpG loci were identified in the LUAD versus normal lung tissues of TCGA data. Expression of the 22 miRNAs was inversely correlated with the 47 differentially methylated sites located in the miRNAs. Molecular and cellular function analysis showed that the abnormally methylated miRNAs were mainly involved in cell-to-cell signaling and interaction in airway cells. The DNA methylation status and altered expressions of miRNAs and their target genes were confirmed in 36 pairs of lung tumor and noncancerous lung tissues. Furthermore, aberrant miRNA expressions or DNA methylations alone could be involved in tumorigenesis of LUAD via different pathways. In addition, elevated miR-132-3p expression, reduced expression of its targeted gene (ZEB2), and decreased cell proliferation was observed in lung cancer cells treated with DNA methyltransferase inhibitor. Moreover, in vitro and in vivo analyses showed that miR-132-3p-3p downregulation via DNA methylation promoted tumorigenicity of lung cancer by directly regulating ZEB2.

CONCLUSIONS

The interaction between two epigenetic aberrations could have important functions in LUAD. miR-132-3p might act as a tumor suppressor in the tumorigenicity of LUAD.

KEY POINTS

SIGNIFICANT FINDINGS OF THE STUDY: Systemically investigating relationship between aberrant miRNA expression and DNA methylation in lung cancer could improve understanding of lung tumorigenesis and develop diagnostic and therapeutic targets.

WHAT THIS STUDY ADDS

Three forms of relationships between the two epigenetic changes are defined. miR-132-3p is further identified as a tumor suppressor in lung cancer.

Identification Six Metabolic Genes as Potential Biomarkers for Lung Adenocarcinoma

Metabolic genes have been reported to act as crucial roles in tumor progression. Lung adenocarcinoma (LUAD) is one of the most common cancers worldwide. This study aimed to predict the potential mechanism and novel markers of metabolic signature in LUAD. The gene expression profiles and the clinical parameters were obtained from The Cancer Genome Atlas-Lung adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus data set (GSE72094). A total of 105 differentially expressed metabolic genes of intersect expression in TCGA-LUAD and GSE72094 were screened by R language. Univariate Cox regression model found 18 survival-related genes and then the least absolute shrinkage and selection operator model was successfully constructed. Six significant genes prognostic model was validated though independent prognosis analysis. The model revealed high values for prognostic biomarkers by time-dependent receiver operating characteristic (ROC) analysis, risk score, Heatmap, and nomogram. In addition, Gene Set Enrichment Analysis showed that multiplex metabolism pathways correlated with LUAD. Furthermore, we found the six genes aberrantly expressed in LUAD samples. Our study showed that metabolism pathways play important roles in LUAD progression. The six metabolic genes could predict potential prognostic and diagnostic biomarkers in LUAD.

A Strong Decrease in TIMP3 Expression Mediated by the Presence of miR-17 and 20a Enables Extracellular Matrix Remodeling in the NSCLC Lesion Surroundings

Background: Lung cancer is one of the most common causes of death worldwide with a relatively high fatality rate and a mean 5-years survival of about 18%. One of the hallmarks of cancer is the extracellular matrix (ECM) remodeling, which is crucial for metastasis. This process may be regulated by miRs targeting metalloproteinases (MMPs) associated with the ECM breakdown and metastatic process or blocking the action of tissue inhibitors of metalloproteinases (TIMPs). Search for early biomarkers is essential in detecting non-small cell lung cancer (NSCLC) and distinguishing its subtypes: Adenocarcinoma (AC) from Squamous Cell Carcinoma (SCC), enabling targeted chemotherapy.

Methods:MiR-17 and miR-20a targeting MMP2 and TIMP3 were selected by TCGA data analysis with further validation using miRTarBase and literature. The study group comprised 47 patients with primary NSCLC (AC and SCC subtypes). RNA was isolated from the tumor and normal-looking neighboring tissue (NLNT) free of cancer cells. MiRs from peripheral blood exosomes were extracted on admission and 5–7 days after surgery. Gene and miRs expression were assessed in qPCR using TaqMan probes.

Results: The MMP2 has been expressed on a similar level in NLNT, as in cancer. While, TIMP3 expression was decreased both in cancer tissue and NLNT, with significantly lower expression in cancer. TIMP3 downregulation in NLNT and in SCC subtype correlated negatively with miR-20a. The preoperative miR-17 expression was significantly higher among patients with SCC compared to AC. Receiver operating characteristic (ROC) analysis of miR-17 as AC subtype classifier revealed 90% specificity and 48% sensitivity in optimal cut-off point with area under ROC curve (AUC): 0.71 (95%CI: 0.55–0.87). Within NSCLC subtypes: a strong negative correlation between pack-years (PY) and TIMP3 expression was observed for NLNT in the SCC group.

Conclusion: The TIMP3 silencing observed in the NLNT and its negative correlation with presurgical expression of miR-20a (from serum exosomes), suggest that miRs can influence ECM remodeling at a distance from the center of the lesion. The miRs expression pattern in serum obtained before surgery significantly differs between AC and SCC subtypes. Moreover, decreased TIMP3 expression in NLNT (in SCC group) negatively correlates with the amount of tobacco smoked in a lifetime in PY.

A Plasma Long Noncoding RNA Signature for Early Detection of Lung Cancer

The early detection of lung cancer is a major clinical challenge. Long noncoding RNAs (lncRNAs) have important functions in tumorigenesis. Plasma lncRNAs directly released from primary tumors or the circulating cancer cells might provide cell-free cancer biomarkers. The objective of this study was to investigate whether the lncRNAs could be used as plasma biomarkers for early-stage lung cancer. By using droplet digital polymerase chain reaction, we determined the diagnostic performance of 26 lung cancer–associated lncRNAs in plasma of a development cohort of 63 lung cancer patients and 33 cancer-free individuals, and a validation cohort of 39 lung cancer patients and 28 controls. In the development cohort, 7 of the 26 lncRNAs were reliably measured in plasma. Two (SNHG1 and RMRP) displayed a considerably high plasma level in lung cancer patients vs. cancer-free controls (all P < .001). Combined use of the plasma lncRNAs as a biomarker signature produced 84.13% sensitivity and 87.88% specificity for diagnosis of lung cancer, independent of stage and histological type of lung tumor, and patients' age and sex (all P > .05). The diagnostic value of the plasma lncRNA signature for lung cancer early detection was confirmed in the validation cohort. The plasma lncRNA signature may provide a potential blood-based assay for diagnosing lung cancer at the early stage. Nevertheless, a prospective study is warranted to validate its clinical value.

A Direct Plasma miRNA Assay for Early Detection and Histological Classification of Lung Cancer

Cell-free microRNAs in plasma provide circulating biomarkers for lung cancer. Most techniques for analysis of miRNAs require a large plasma volume to purify a sufficient RNA yield followed by complicated downstream processing. Small differences in the multiple procedures often cause large analytical variations and poor diagnostic values of the plasma biomarkers. Here we investigate whether directly quantifying plasma miRNAs without RNA purification could diagnose lung cancer. FirePlex assay was directly applied to 20 μl plasma of 56 lung cancer patients and 28 cancer free controls for quantifying 11 lung tumor–associated miRNAs. FirePlex assay is easier, less expensive and time-consuming for quantification of plasma miRNAs compared with conventional reverse transcription PCR with an equivalent analytic performance. From the lung tumor–associated miRNAs, a prediction model based on two miRNAs (miRs-205-5p and -210-3p) was developed, producing 78.6% sensitivity and 89.3% specificity for identifying lung cancer. The diagnostic value was independent of stage of lung tumor, and patients’ age and sex (all P > 0.05). Furthermore, based on the same two miRNAs, additional prediction models were developed with 75.0% sensitivity and 89.3% specificity for diagnosis of lung squamous cell carcinoma, and 82.2% sensitivity and 89.3% specificity for lung adenocarcinoma. The direct plasma assay can improve the efficacy of miRNA assessment in a small plasma volume by reducing multiple procedure-associated analytical variables. The developed plasma miRNA biomarkers might be useful for the early detection and histological classification of lung cancer.

An epigenetic classifier for early stage lung cancer

Background

Methylated genes detected in sputum are promise biomarkers for lung cancer. Yet the current PCR technologies for quantification of DNA methylation and diagnostic value of the sputum biomarkers are not sufficient to be used for lung cancer early detection. The emerging droplet digital PCR (ddPCR) is a straightforward means for precise, direct, and absolute quantification of nucleic acids. Here, we investigate whether ddPCR can sensitively and robustly quantify DNA methylation in sputum for more precise diagnosis of lung cancer.

Results

First, the analytic performance of methylation-specific ddPCR (ddMSP) and quantitative methylation-specific PCR (qMSP) is determined in methylated and unmethylated DNA samples. Second, 29 genes, previously proposed as potential sputum biomarkers for lung cancer, are analyzed by using ddMSP in a training set of 127 lung cancer patients and 159 controls. ddMSP has higher sensitivity, precision, and reproducibility for quantification of methylation compared with qMSP (all p < 0.05). A classifier comprising four sputum methylation biomarkers for lung cancer is developed by using ddMSP, producing 86.6% sensitivity and 90.6% specificity, independent of stage and histology of lung cancer (all p > 0.05). The classifier has higher accuracy compared with sputum cytology (88.8 vs. 70.6%, p < 0.01). The diagnostic performance is confirmed in a testing set of 89 cases and 107 controls.

Conclusions

ddMSP is a robust tool for reliable quantification of DNA methylation in sputum, and the epigenetic classifier could help diagnose lung cancer at the early stage.

An integromic signature for lung cancer early detection

We previously developed three microRNAs (miRs-21, 210, and 486-5p), two long noncoding RNAs (lncRNAs) (SNHG1 and RMRP), and two fucosyltransferase (FUT) genes (FUT8 and POFUT1) as potential plasma biomarkers for lung cancer. However, the diagnostic performance of the individual panels is not sufficient to be used in the clinics. Given the heterogeneity of lung tumors developed from multifactorial molecular aberrations, we determine whether integrating the different classes of molecular biomarkers can improve diagnosis of lung cancer. By using droplet digital PCR, we analyze expression of the seven genes in plasma of a development cohort of 64 lung cancer patients and 33 cancer-free individuals. The panels of three miRNAs (miRs-21, 210, and 486-5p), two lncRNAs (SNHG1 and RMRP), and two FUTs (FUT8 and POFUT1) have a sensitivity of 81-86% and a specificity of 84-87% for diagnosis of lung cancer. From the seven genes, an integromic plasma signature comprising miR-210, SNHG1, and FUT8 is developed that produces higher sensitivity (95.45%) and specificity (96.97%) compared with the individual biomarker panels (all p<0.05). The diagnostic value of the signature was confirmed in a validation cohort of 40 lung cancer patients and 29 controls, independent of stage and histological type of lung tumor, and patients' age, sex, and smoking status (all p>0.05). The integration of the different categories of biomarkers might improve diagnosis of lung cancer.

A plasma miRNA signature for lung cancer early detection

The early detection of lung cancer continues to be a major clinical challenge. Using whole-transcriptome next-generation sequencing to analyze lung tumor and the matched noncancerous tissues, we previously identified 54 lung cancer-associated microRNAs (miRNAs). The objective of this study was to investigate whether the miRNAs could be used as plasma biomarkers for lung cancer. We determined expressions of the lung tumor-miRNAs in plasma of a development cohort of 180 subjects by using reverse transcription PCR to develop biomarkers. The development cohort included 92 lung cancer patients and 88 cancer-free smokers. We validated the biomarkers in a validation cohort of 64 individuals comprising 34 lung cancer patients and 30 cancer-free smokers. Of the 54 miRNAs, 30 displayed a significant different expression level in plasma of the lung cancer patients vs. cancer-free controls (all P < 0.05). A plasma miRNA signature (miRs-126, 145, 210, and 205-5p) with the best prediction was developed, producing 91.5% sensitivity and 96.2% specificity for lung cancer detection. Diagnostic performance of the plasma miRNA signature had no association with stage and histological type of lung tumor, and patients' age, sex, and ethnicity (all p > 0.05). The plasma miRNA signature was reproducibly confirmed in the validation cohort. The plasma miRNA signature may provide a blood-based assay for diagnosing lung cancer at the early stage, and thereby reduce the associated mortality and cost.

A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules

Lung cancer is primarily caused by cigarette smoking and the leading cancer killer in the USA and across the world. Early detection of lung cancer by low-dose CT (LDCT) can reduce the mortality. However, LDCT dramatically increases the number of indeterminate pulmonary nodules (PNs), leading to overdiagnosis. Having a definitive preoperative diagnosis of malignant PNs is clinically important. Using microarray and droplet digital PCR to directly profile plasma miRNA expressions of 135 patients with PNs, we identified 11 plasma miRNAs that displayed a significant difference between patients with malignant versus benign PNs. Using multivariate logistic regression analysis of the molecular results and clinical/radiological characteristics, we developed an integrated classifier comprising two miRNA biomarkers and one radiological characteristic for distinguishing malignant from benign PNs. The classifier had 89.9% sensitivity and 90.9% specificity, being significantly higher compared with the biomarkers or clinical/radiological characteristics alone (all p < 0.05). The classifier was validated in two independent sets of patients. We have for the first time shown that the integration of plasma biomarkers and radiological characteristics could more accurately identify lung cancer among indeterminate PNs. Future use of the classifier could spare individuals with benign growths from the harmful diagnostic procedures, while allowing effective treatments to be immediately initiated for lung cancer, thereby reduces the mortality and cost. Nevertheless, further prospective validation of this classifier is warranted.

Plasma exosome miR-196a and miR-1246 are potential indicators of localized pancreatic cancer

Patients with localized pancreatic cancer (stage I and stage IIA) have a much higher survival rate than those presenting at later stages, yet early detection remains a challenge to this malignancy. The aim of this study was to evaluate whether exosome miRNA signatures are indicative of localized pancreatic cancer. Exosomes were collected from the conditioned media of pancreatic cancer cell lines and plasma samples of localized pancreatic cancer patients (Stage I-IIA, n=15), and healthy subjects (n=15). Cellular and exosome miRNAs from pancreatic cancer cell lines were profiled by next-generation small RNA sequencing. Plasma exosome miRNA expression was analyzed by qRT-PCR. We found that certain miRNAs, such as miR-196a and miR-1246, are highly enriched in pancreatic cancer exosomes. Consistently, plasma exosome miR-196a and miR-1246 levels were significantly elevated in pancreatic cancer patients as compared to healthy subjects. An analysis of the cancer subtypes indicated that plasma exosome miR-196a is a better indicator of pancreatic ductal adenocarcinoma (PDAC), whereas plasma exosome miR-1246 is significantly elevated in patients with intraductal papillary mucinous neoplasms (IPMN). In contrast, there were no differences in the plasma exosome miR-196a and miR-1246 levels between patients with pancreatic neuroendocrine tumors (NET) and healthy subjects. In conclusion, we demonstrate that certain miRNA species, such as miR-196a and miR-1246, are highly enriched in pancreatic cancer exosomes and elevated in plasma exosomes of patients with localized pancreatic cancer.