Multiple cancer type classification by small RNA expression profiles with plasma samples from multiple facilities

Robust circulating microRNA signature for the diagnosis and early detection of pancreatobiliary cancer

BACKGROUND

A new circulating biomarker superior to carbohydrate antigen 19-9 (CA19-9) is needed for diagnosing pancreatobiliary cancer (PBca). The aim of this study was to identify serum microRNA (miRNA) signatures comprising reproducible and disease-related miRNAs.

METHODS

This multicenter study involved patients with treatment-naïve PBca and healthy participants. The optimized serum processing conditions were evaluated using t-distributed stochastic neighbor embedding (t-SNE) visualization. Serum miRNA candidates for disease association were selected using weighted gene coexpression network analysis (WGCNA). A miRNA signature combining multiple serum miRNAs was tested in exploratory, validation, and independent validation sets. The synthesis and secretion of diagnostic miRNAs were evaluated using human pancreatic cancer cells.

RESULTS

In total, 284 (150 healthy and 134 PBca) of 827 serum samples were processed within 2 h of blood collection before freezing, distributed in the same area as that in the t-SNE map, and assigned to an exploratory set. The 193 optimized samples were assigned to either the validation (50 healthy, 47 PBca) or independent validation (50 healthy, 46 PBca) set. Index-1, a combination of five serum miRNAs (hsa-miR-1343-5p, hsa-miR-4632-5p, hsa-miR-4665-5p, hsa-miR-665, and hsa-miR-6803-5p) with disease association in WGCNA, showed a sensitivity and specificity of > 80% and an AUC outperforming that of CA19-9 in the exploratory, validation, and independent validation sets. The AUC of Index-1 was superior to that of CA19-9 (0.856 vs. 0.649, p = 0.038) for detecting T1 tumors. miR-665, a component of Index-1, was expressed in human pancreatic cancer cells, and its transfection inhibited cell growth.

CONCLUSIONS

The serum miRNA signature Index-1 is useful for detecting PBca and could facilitate the early diagnosis of PBca. These findings can help improve clinical PBca detection by providing an optimized biomarker that overcomes the limitations of the current standard.

Early detection of pancreatic cancer by comprehensive serum miRNA sequencing with automated machine learning

BACKGROUND

Pancreatic cancer is often diagnosed at advanced stages, and early-stage diagnosis of pancreatic cancer is difficult because of nonspecific symptoms and lack of available biomarkers.

METHODS

We performed comprehensive serum miRNA sequencing of 212 pancreatic cancer patient samples from 14 hospitals and 213 non-cancerous healthy control samples. We randomly classified the pancreatic cancer and control samples into two cohorts: a training cohort (N = 185) and a validation cohort (N = 240). We created ensemble models that combined automated machine learning with 100 highly expressed miRNAs and their combination with CA19-9 and validated the performance of the models in the independent validation cohort.

RESULTS

The diagnostic model with the combination of the 100 highly expressed miRNAs and CA19-9 could discriminate pancreatic cancer from non-cancer healthy control with high accuracy (area under the curve (AUC), 0.99; sensitivity, 90%; specificity, 98%). We validated high diagnostic accuracy in an independent asymptomatic early-stage (stage 0-I) pancreatic cancer cohort (AUC:0.97; sensitivity, 67%; specificity, 98%).

CONCLUSIONS

We demonstrate that the 100 highly expressed miRNAs and their combination with CA19-9 could be biomarkers for the specific and early detection of pancreatic cancer.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Improved lung cancer classification by employing diverse molecular features of microRNAs

MiRNAs are edited or modified in multiple ways during their biogenesis pathways. It was reported that miRNA editing was deregulated in tumors, suggesting the potential value of miRNA editing in cancer classification. Here we extracted three types of miRNA features from 395 LUAD and control samples, including the abundances of original miRNAs, the abundances of edited miRNAs, and the editing levels of miRNA editing sites. Our results show that eight classification algorithms selected generally had better performances on combined features than on the abundances of miRNAs or editing features of miRNAs alone. One feature selection algorithm, i.e., the DFL algorithm, selected only three features, i.e., the frequencies of hsa-miR-135b-5p, hsa-miR-210-3p and hsa-mir-182_48u (an edited miRNA), from 316 training samples. Seven classification algorithms achieved 100% accuracies on these three features for 79 independent testing samples. These results indicate that the additional information of miRNA editing is useful in improving the classification of LUAD samples.

Comprehensive circulating microRNA profile as a supersensitive biomarker for early-stage lung cancer screening

PURPOSE

 Less-invasive early diagnosis of lung cancer is essential for improving patient survival rates. The purpose of this study is to demonstrate that serum comprehensive miRNA profile is high sensitive biomarker to early-stage lung cancer in direct comparison to the conventional blood biomarker using next-generation sequencing (NGS) technology combined with automated machine learning (AutoML).

METHODS

We first evaluated the reproducibility of our measurement system using Pearson's correlation coefficients between samples derived from a single pooled RNA sample. To generate comprehensive miRNA profile, we performed NGS analysis of miRNAs in 262 serum samples. Among the discovery set (57 patients with lung cancer and 57 healthy controls), 1123 miRNA-based diagnostic models for lung cancer detection were constructed and screened using AutoML technology. The diagnostic faculty of the best performance model was evaluated by inspecting the validation samples (74 patients with lung cancer and 74 healthy controls).

RESULTS

The Pearson's correlation coefficients between samples derived from the pooled RNA sample ≥ 0.98. In the validation analysis, the best model showed a high AUC score (0.98) and a high sensitivity for early stage lung cancer (85.7%, n = 28). Furthermore, in comparison to carcinoembryonic antigen (CEA), a conventional blood biomarker for adenocarcinoma, the miRNA-based model showed higher sensitivity for early-stage lung adenocarcinoma (CEA, 27.8%, n = 18; miRNA-based model, 77.8%, n = 18).

CONCLUSION

The miRNA-based diagnostic model showed a high sensitivity for lung cancer, including early-stage disease. Our study provides the experimental evidence that serum comprehensive miRNA profile can be a highly sensitive blood biomarker for early-stage lung cancer.

Platelet-Based Liquid Biopsies through the Lens of Machine Learning

Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics.

miRNAs in prostate cancer: Intercellular and extracellular communications

Prostate cancer is the most prevalent male cancer in Western Europe and North America. Although new drugs were recently approved, clinical challenges such as accurately predicting and screening drug-resistant prostate cancer remain. microRNAs are short noncoding RNA molecules that participate in gene regulation at the post-transcriptional level by targeting messenger RNAs. There is accumulating evidence that intracellular microRNAs play important roles as promoters or inhibitors of prostate cancer progression. Additionally, recent studies showed that microRNAs are encapsulated in extracellular vesicles and shuttled into the extracellular space. Transfer of extracellular microRNAs contributes to intercellular communication between prostate cancer cells and components of the tumor microenvironment, which can promote prostate cancer progression. Furthermore, due to their encapsulation in extracellular vesicles, extracellular microRNAs can be stably present in body fluids which contain high levels of RNase. Thus, circulating microRNAs have great potential as noninvasive diagnostic and prognostic biomarkers for prostate cancer. Here, we summarize the roles of intracellular and extracellular microRNAs in prostate cancer progression and discuss the potential of microRNA-based therapeutics as a novel treatment strategy for prostate cancer.