Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Abstract

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

snoRNAs Offer Novel Insight and Promising Perspectives for Lung Cancer Understanding and Management

“…Meanwhile, analysis of aberrant snoRNAs that distinguished female smokers and non-smokers within normal and cancerous tissues from lung adenocarcinoma, identified 28 snoRNAs the expression of which was significantly altered between normal and tumoral tissues, irrespective of the smoking status, reinforcing the finding that alterations in snoRNA expression occur in lung cancer [50] (Table 1). In parallel, using machine learning algorithms, Pan et al [51] investigated the expression pattern of more than 1000 snoRNAs in 8 cancers including NSCLC (LUAD n = 559 and LUSC n = 521). They found a specific signature encompassing only a few snoRNAs compared to other types of cancers both for LUAD (SNORD7, SNORD81 and SNORD99) and LUSC (SNORA31A, SNORA47 and SNORD83B).…”

Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma

“…After the irrelevant features were removed, the relevant methylation and expression features were ranked based on their importance evaluated with MCFS (Monte Carlo Feature Selection) (Draminski et al, 2008). The MCFS was a widely used method to rank features based on classification trees (Chen et al, , 2019Pan et al, 2018Pan et al, , 2019aLi et al, 2019). First, for the d features, we selected s subsets and each subset included m features (m was much smaller than d).…”

“…The MCFS can find the significant top-ranking features by comparing with permutations. To objectively evaluate the significant top-ranking features' prediction performance, we performed LOOCV (Leave One Out Cross Validation) using SVM (Support Vector Machine) classifier Sun et al, 2018;Pan et al, 2019a). Each time, one sample was chosen as test samples and all other samples were used to train the SVM predictor.…”

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

“…The second stage was to determine the number of selected genes using the IFS method (Chen et al, 2018b;Chen et al, 2019b;Chen et al, 2019c;Chen et al, 2019d;Chen et al, 2019f;Li et al, 2019a;Pan et al, 2019a;Pan et al, 2019b;). To do so, 200 classifiers were constructed using top 1, top 2, top 200 genes.…”

“…We tried several different classifiers: (1) SVM (Support Vector Machine) (Jiang et al, 2019;Yan et al, 2019;Chen et al, 2019a;Li et al, 2019a;Pan et al, 2019a;Wang and Huang, 2019b;Chen et al, 2019d), (2) 1NN (1 Nearest Neighbor) (Lei et al, 2013;Chen et al, 2016;Wang et al, 2017a), (3) 3NN (3 Nearest Neighbors), (4) 5NN (5 Nearest Neighbors), (5) Decision Tree (DT) (Huang et al, 2008;Huang et al, 2011;Chen et al, 2015), (6) Neural Network (NN) (Liu et al, 2017;Pan et al, 2018;Chen et al, 2019e). The function svm from R package e1071, function knn from R package class, function rpart from R package rpart, function nnet from R package nnet were used to apply these classification algorithms.…”