Diagnostic Value of Plasma MicroRNAs for Lung Cancer Using Support Vector Machine Model

MiR-223-3p in Cancer Development and Cancer Drug Resistance: Same Coin, Different Faces

MicroRNAs (miRNAs) are mighty post-transcriptional regulators in cell physiology and pathophysiology. In this review, we focus on the role of miR-223-3p (henceforth miR-223) in various cancer types. MiR-223 has established roles in hematopoiesis, inflammation, and most cancers, where it can act as either an oncogenic or oncosuppressive miRNA, depending on specific molecular landscapes. MiR-223 has also been linked to either the sensitivity or resistance of cancer cells to treatments in a context-dependent way. Through this detailed review, we highlight that for some cancers (i.e., breast, non-small cell lung carcinoma, and glioblastoma), the oncosuppressive role of miR-223 is consistently reported in the literature, while for others (i.e., colorectal, ovarian, and pancreatic cancers, and acute lymphocytic leukemia), an oncogenic role prevails. In prostate cancer and other hematological malignancies, although an oncosuppressive role is frequently described, there is less of a consensus. Intriguingly, NLRP3 and FBXW7 are consistently identified as miR-223 targets when the miRNA acts as an oncosuppressor or an oncogene, respectively, in different cancers. Our review also describes that miR-223 was increased in biological fluids or their extracellular vesicles in most of the cancers analyzed, as compared to healthy or lower-risk conditions, confirming the potential application of this miRNA as a diagnostic and prognostic biomarker in the clinic.

Multiarmed DNA jumper and metal-organic frameworks-functionalized paper-based bioplatform for small extracellular vesicle-derived miRNAs assay

Small extracellular vesicle-derived microRNAs (sEV-miRNAs) have emerged as promising noninvasive biomarkers for early cancer diagnosis. Herein, we developed a molecular probe based on three-dimensional (3D) multiarmed DNA tetrahedral jumpers (mDNA-Js)-assisted DNAzyme activated by Na+, combined with a disposable paper-based electrode modified with a Zr-MOF-rGO-Au NP nanocomplex (ZrGA) to fabricate a novel biosensor for sEV-miRNAs Assay. Zr-MOF tightly wrapped by rGO was prepared via a one-step method, and it effectively aids electron transfer and maximizes the effective reaction area. In addition, the mechanically rigid, and nanoscale-addressable mDNA-Js assembled from the bottom up ensure the distance and orientation between fixed biological probes as well as avoid probe entanglement, considerably improving the efficiency of molecular hybridization. The fabricated bioplatform achieved the sensitive detection of sEV-miR-21 with a detection limit of 34.6 aM and a dynamic range from100 aM to 0.2 µM. In clinical blood sample tests, the proposed bioplatform showed results highly consistent with those of qRT-PCRs and the signal increased proportionally with the NSCLC staging. The proposed biosensor with a portable wireless USB-type analyzer is promising for the fast, easy, low-cost, and highly sensitive detection of various nucleic acids and their mutation derivatives, making it ideal for POC biosensing.

Prognostic value of plasma microRNAs for non-small cell lung cancer based on data mining models

BACKGROUND

As biomarkers, microRNAs (miRNAs) are closely associated with the occurrence, progression, and prognosis of non-small cell lung cancer (NSCLC). However, the prognostic predictive value of miRNAs in NSCLC has rarely been explored. In this study, the value in prognosis prediction of NSCLC was mined based on data mining models using clinical data and plasma miRNAs biomarkers.

METHODS

A total of 69 patients were included in this prospective cohort study. After informed consent, they filled out questionnaires and had their peripheral blood collected. The expressions of plasma miRNAs were examined by quantitative polymerase chain reaction (qPCR). The Whitney U test was used to analyze non-normally distributed data. Kaplan-Meier was used to plot the survival curve, the log-rank test was used to compare with the overall survival curve, and the Cox proportional hazards model was used to screen the factors related to the prognosis of lung cancer. Data mining techniques were utilized to predict the prognostic status of patients.

RESULTS

We identified that smoking (HR = 2.406, 95% CI = 1.256-4.611), clinical stage III + IV (HR = 5.389, 95% CI = 2.290-12.684), the high expression group of miR-20a (HR = 4.420, 95% CI = 1.760-11.100), the high expression group of miR-197 (HR = 3.828, 95% CI = 1.778-8.245), the low expression group of miR-145 ( HR = 0.286, 95% CI = 0.116-0.709), and the low expression group of miR-30a (HR = 0.307, 95% CI = 0.133-0.706) was associated with worse prognosis. Among the five data mining models, the decision trees (DT) C5.0 model performs the best, with accuracy and Area Under Curve (AUC) of 93.75% and 0.929 (0.685, 0.997), respectively.

CONCLUSION

The results showed that the high expression level of miR-20a and miR-197, the low expression level of miR-145 and miR-30a were strongly associated with poorer prognosis in NSCLC patients, and the DT C5.0 model may serve as a novel, accurate, method for predicting prognosis of NSCLC.

A Machine Learning Model Based on microRNAs for the Diagnosis of Essential Hypertension

INTRODUCTION

Hypertension is a major and modifiable risk factor for cardiovascular diseases. Essential, primary, or idiopathic hypertension accounts for 90-95% of all cases. Identifying novel biomarkers specific to essential hypertension may help in understanding pathophysiological pathways and developing personalized treatments. We tested whether the integration of circulating microRNAs (miRNAs) and clinical risk factors via machine learning modeling may provide useful information and novel tools for essential hypertension diagnosis and management.

MATERIALS AND METHODS

In total, 174 participants were enrolled in the present observational case-control study, among which, there were 89 patients with essential hypertension and 85 controls. A discovery phase was conducted using small RNA sequencing in whole blood samples obtained from age- and sex-matched hypertension patients (n = 30) and controls (n = 30). A validation phase using RT-qPCR involved the remaining 114 participants. For machine learning, 170 participants with complete data were used to generate and evaluate the classification model.

RESULTS

Small RNA sequencing identified seven miRNAs downregulated in hypertensive patients as compared with controls in the discovery group, of which six were confirmed with RT-qPCR. In the validation group, miR-210-3p/361-3p/362-5p/378a-5p/501-5p were also downregulated in hypertensive patients. A machine learning support vector machine (SVM) model including clinical risk factors (sex, BMI, alcohol use, current smoker, and hypertension family history), miR-361-3p, and miR-501-5p was able to classify hypertension patients in a test dataset with an AUC of 0.90, a balanced accuracy of 0.87, a sensitivity of 0.83, and a specificity of 0.91. While five miRNAs exhibited substantial downregulation in hypertension patients, only miR-361-3p and miR-501-5p, alongside clinical risk factors, were consistently chosen in at least eight out of ten sub-training sets within the SVM model.

CONCLUSIONS

This study highlights the potential significance of miRNA-based biomarkers in deepening our understanding of hypertension's pathophysiology and in personalizing treatment strategies. The strong performance of the SVM model highlights its potential as a valuable asset for diagnosing and managing essential hypertension. The model remains to be extensively validated in independent patient cohorts before evaluating its added value in a clinical setting.

Lung cancer treatment potential and limits associated with the STAT family of transcription factors

Lung cancer is one of the mortal cancers and the leading cause of cancer-related mortality, with a cancer survival rate of fewer than 5% in developing nations. This low survival rate can be linked to things like late-stage detection, quick postoperative recurrences in patients receiving therapy, and chemoresistance developing against various lung cancer treatments. Signal transducer and activator of transcription (STAT) family of transcription factors are involved in lung cancer cell proliferation, metastasis, immunological control, and treatment resistance. By interacting with specific DNA sequences, STAT proteins trigger the production of particular genes, which in turn result in adaptive and incredibly specific biological responses. In the human genome, seven STAT proteins have been discovered (STAT1 to STAT6, including STAT5a and STAT5b). Many external signaling proteins can activate unphosphorylated STATs (uSTATs), which are found inactively in the cytoplasm. When STAT proteins are activated, they can increase the transcription of several target genes, which leads to unchecked cellular proliferation, anti-apoptotic reactions, and angiogenesis. The effects of STAT transcription factors on lung cancer are variable; some are either pro- or anti-tumorigenic, while others maintain dual, context-dependent activities. Here, we give a succinct summary of the various functions that each member of the STAT family plays in lung cancer and go into more detail about the advantages and disadvantages of pharmacologically targeting STAT proteins and their upstream activators in the context of lung cancer treatment.

LncRNA H19 promotes tumor angiogenesis in smokers by targeting anti-angiogenic miRNAs

A key concept in drug discovery is the identification of candidate therapeutic targets such as long noncoding RNAs (lncRNAs) because of their extensive involvement in neoplasms, and impressionability by smoking. Induced by exposure to cigarette smoke, lncRNA H19 targets and inactivates miR-29, miR-30a, miR-107, miR-140, miR-148b, miR-199a and miR-200, which control the rate of angiogenesis by inhibiting BiP, DLL4, FGF7, HIF1A, HIF1B, HIF2A, PDGFB, PDGFRA, VEGFA, VEGFB, VEGFC, VEGFR1, VEGFR2 and VEGFR3. Nevertheless, these miRNAs are often dysregulated in bladder cancer, breast cancer, colorectal cancer, glioma, gastric adenocarcinoma, hepatocellular carcinoma, meningioma, non-small-cell lung carcinoma, oral squamous cell carcinoma, ovarian cancer, prostate adenocarcinoma and renal cell carcinoma. As such, the present perspective article seeks to establish an evidence-based hypothetical model of how a smoking-related lncRNA known as H19 might aggravate angiogenesis by interfering with miRNAs that would otherwise regulate angiogenesis in a nonsmoking individual.

Nicotine-induced miR-21-3p promotes chemoresistance in lung cancer by negatively regulating FOXO3a

Lung cancer is the leading cause of cancer-related mortality worldwide and cigarette smoking is reported to contribute to the lung cancer-related mortality. The present study aimed to investigate the molecular mechanism underlying nicotine-induced chemoresistance in lung cancer. The expression of microRNA (miR)-21-3p and its predicted target FOXO3a in lung cancer cells was detected via reverse transcription-quantitative PCR, in the presence or absence of nicotine. The regulatory effect of miR-21-3p and FOXO3a on lung cancer cell proliferation and apoptosis induced by docetaxel or cisplatin treatment was evaluated by performing Cell Counting Kit-8 and Annexin V/PI staining assays, respectively. The interaction between miR-21-3p and FOXO3a was analyzed by performing luciferase reporter assays and western blotting. FOXO3a overexpression rescue experiments were conducted in vitro and in vivo using a xenograft mouse model to assess the function of miR-21-3p/FOXO3a in lung cancer. Nicotine induced miR-21-3p expression in lung cancer cells in a dose-dependent manner. miR-21-3p downregulated FOXO3a expression by directly binding to the 3′-untranslated region of FOXO3a. Moreover, miR-21-3p knockdown sensitized lung cancer cells to docetaxel or cisplatin treatment. Mechanistically, FOXO3a was predicted as a direct target of miR-21-3p. FOXO3a overexpression promoted the chemosensitivity of lung cancer cells to docetaxel or cisplatin treatment. Furthermore, FOXO3a overexpression antagonized the regulatory function of miR-21-3p on docetaxel- or cisplatin-treated lung cancer cells. In the docetaxel- or cisplatin-treated lung cancer xenograft mouse model, miR-21-3p promoted chemoresistance via negatively regulating FOXO3a. Therefore, the present study demonstrated that nicotine-induced miR-21-3p promoted chemoresistance to docetaxel or cisplatin treatment via negatively regulating FOXO3a, which may serve as a novel therapeutic strategy for the treatment of patients with chemoresistant lung cancer.

Cellular and Molecular Profiling of Tumor Microenvironment and Early-Stage Lung Cancer

Lung cancers are broadly divided into two categories: non-small-cell lung carcinoma (NSCLC), which accounts for 80-85% of all cancer cases, and small-cell lung carcinoma (SCLC), which covers the remaining 10-15%. Recent advances in cancer biology and genomics research have allowed an in-depth characterization of lung cancers that have revealed new therapy targets (EGFR, ALK, ROS, and KRAS mutations) and have the potential of revealing even more biomarkers for diagnostic, prognostic, and targeted therapies. A new source of biomarkers is represented by non-coding RNAs, especially microRNAs (miRNAs). MiRNAs are short non-coding RNA sequences that have essential regulatory roles in multiple cancers. Therefore, we aim to investigate the tumor microenvironment (TME) and miRNA tumor profile in a subset of 51 early-stage lung cancer samples (T1 and T2) to better understand early tumor and TME organization and molecular dysregulation. We analyzed the immunohistochemistry expression of CD4 and CD8 as markers of the main TME immune populations, E-cadherin to evaluate early-stage epithelial-to-mesenchymal transition (EMT), and p53, the main altered tumor suppressor gene in lung cancer. Starting from these 4 markers, we identified and validated 4 miRNAs that target TP53 and regulate EMT that can be further investigated as potential early-stage lung cancer biomarkers.

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

Pulmonary hypertension (PH) is a common disease that affects the normal functioning of the human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an ideal source for a minimally invasive disease diagnosis. This study hypothesized that the transcriptional fluctuations in the PMBCs exposed to the PH arteries may stably reflect the disease. However, the dimension of a human transcriptome is much higher than the number of samples in all the existing datasets. So, an ensemble feature selection algorithm, EnRank, was proposed to integrate the ranking information of four popular feature selection algorithms, i.e., T-test (Ttest), Chi-squared test (Chi2), ridge regression (Ridge), and Least Absolute Shrinkage and Selection Operator (Lasso). Our results suggested that the EnRank-detected biomarkers provided useful information from these four feature selection algorithms and achieved very good prediction accuracy in predicting the PH patients. Many of the EnRank-detected biomarkers were also supported by the literature.

MicroRNA-126: A new and promising player in lung cancer

Lung cancer is one of the most common malignant tumors associated with cancer death; however, the mechanisms involved in lung tumor development have not been completely elucidated, which impedes the advancement of clinical diagnosis and therapy. MicroRNA-126 (miR-126) is an important member of the microRNA family and is encoded by intron 7 of epidermal growth factor-like domain-containing gene 7. Increasing evidence has demonstrated that miR-126, as a distinct endothelial-enriched miRNA and new tumor suppressor gene, serves a promising role in the occurrence, development and metastasis of various types of cancer, including liver cancer, colorectal cancer, melanoma and lung cancer. In the present review, the current knowledge of the role of miR-126 in lung cancer growth, metastasis, diagnosis and prognosis as well as therapy was summarized, which may provide new insights on the biological roles of miRNAsin lung cancer and facilitate the ultimate development of miRNA-based therapies in clinical patients with non-small cell lung cancer.

MicroRNAs as Biomarkers in Canine Osteosarcoma: A New Future?

Sarcomas are frequent in dogs and canine species are excellent animal models for studying the human counterpart. However, osteosarcomas are a rare form of sarcoma with high death rates in humans and dogs. miRNAs are small endogenous RNAs that regulate gene expression post-transcriptionally. The discovery of miRNAs could give a contribute in the diagnosis and prognosis of different types of tumors in animal species, as already in humans. The differentiated expression of miRNAs is a frequent finding in cancers and is related to their pathogenesis in many cases. Most canine and human sarcomas show similar miRNA aberrations. Lower levels of miR-1 and miR-133b in canine osteosarcoma tissues were found to increase tumorigenesis through a higher expression of their target genes MET and MCL1. The overexpression of miR-9 promotes a metastatic phenotype in canine osteosarcomas and its capacity as a prognostic biomarker for the disease is currently being evaluated. MicroRNAs at the 14q32 locus could be used as prognostic biomarkers, since their decreased expression has been associated with poor prognosis in canine and human osteosarcomas. Furthermore, a decreased expression of miR-34a in osteosarcoma tumour cells has been associated with shorter disease-free survival times and its reintroduction as a synthetic prodrug shows good potential as a novel therapeutic target to fight the disease. Circulating miR-214 and miR-126 are significantly increased in a broad-spectrum cancer and have the ability to successfully predict the prognosis of dogs. However, further studies are needed to make the use of miRNAs as biomarkers a common practice.

MiRNAs and LncRNAs: Dual Roles in TGF-β Signaling-Regulated Metastasis in Lung Cancer

Lung cancer is one of the most malignant cancers around the world, with high morbidity and mortality. Metastasis is the leading cause of lung cancer deaths and treatment failure. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs), two groups of small non-coding RNAs (nc-RNAs), are confirmed to be lung cancer oncogenes or suppressors. Transforming growth factor-β (TGF-β) critically regulates lung cancer metastasis. In this review, we summarize the dual roles of miRNAs and lncRNAs in TGF-β signaling-regulated lung cancer epithelial-mesenchymal transition (EMT), invasion, migration, stemness, and metastasis. In addition, lncRNAs, competing endogenous RNAs (ceRNAs), and circular RNAs (circRNAs) can act as miRNA sponges to suppress miRNAs, thereby mediating TGF-β signaling-regulated lung cancer invasion, migration, and metastasis. Through this review, we hope to cast light on the regulatory mechanisms of miRNAs and lncRNAs in TGF-β signaling-regulated lung cancer metastasis and provide new insights for lung cancer treatment.

Identification of Three Differentially Expressed miRNAs as Potential Biomarkers for Lung Adenocarcinoma Prognosis

OBJECTIVE

The aim of this study areto screen MicroRNAs (miRNAs) related to the prognosis of lung adenocarcinoma (LUAD) and to explore the possible molecular mechanisms.

METHODS

The data for a total of 535 patients with LUAD data were downloaded from The Cancer Genome Atlas (TCGA) database. The miRNAs for LUAD prognosis were screened by both Cox risk proportional regression model and Last Absolute Shrinkage and Selection Operator (LASSO) regression model. The performances of the models were verified by time-dependent Receiver Operating Characteristic (ROC) curve. The possible biological processes linked to the miRNAs' target genes were analyzed by Gene Ontology (GO), Kyoto gene and genome encyclopedia (KEGG).

RESULTS

Among 127 differentially expressed miRNAs identified from the screening analysis, there are 111 up-regulated and 16 down-regulated miRNAs. Three of them, hsa-miR-1293, hsa-miR-490 and hsa-miR- 5571, were also significantly associated with the survival of the LUAD patients. The targets of the three miRNAs are significantly enriched in systemic lupus erythematosus pathways.

CONCLUSION

Hsa-miR-1293, hsa-miR-490 and hsa-miR-5571 can be potentially used as novel biomarkers for the prognosis prediction of LUAD.