Hierarchical Sample clustering (HSC) is widely performed to examine associations within expression data obtained from microarrays and RNA sequencing (RNA-seq). Researchers have investigated the HSC results with several possible criteria for grouping . However, the evaluation of arbitrary defined groups still counts in subjective visual inspection.Silhouette scores. Silhouettes was originally developed as a graphical aid for the validation of data clusters. It provides a measure of how well a sample is classified when it was assigned to a cluster by according to both the tightness of the clusters and the separation between them. It ranges from 1.0 to − 1.0, and a larger value for the average silhouette (AS) over all samples to be analyzed indicates a higher degree of cluster separation. The basic idea to use an AS is to replace the term cluster by group when calculating the scores. We investigated the validity of this score using simulated and real data designed for differential expression (DE) analysis. We found that larger  AS values agreed well with both higher (or lower) degrees of separation between different groups and higher percentages of differentially expressed genes (PDEG). We also found that the AS values were generally independent on the number of replicates (Nrep). Although the PDEG values depended on Nrep, we confirmed that both AS and PDEG values were close to zero when samples in the data showed an intermingled nature between the groups in the HSC dendrogram.To objectively evaluate the degree of separation between groups of interest in the HSC dendrogram, we propose to use Silhouettes is useful for exploring data with predefined group labels. It would help provide both an objective evaluation of HSC dendrograms and insights into the DE results with regard to the compared groups.The online version of this article (10.1186/s12575-018-0067-8) contains supplementary material, which is available to authorized users. High-throughput technologies, including microarrays and RNA-seq, are widely used to monitor genome-wide expression levels in samples of interest and to compare expression patterns in different groups or conditions  –6. The lAnother common approach for expression analyses is sample clustering (SC) based on similarity in expression patterns –21. UtilPDEG) remains lacking [Some researchers empirically know that an SC result of data designed for DE analysis  roughly corresponds to the DE result when the groups for the DE analysis are evaluated with respect to the SC result . If indi lacking .Silhouettes is a graphical aid for the interpretation and validation of cluster analysis [analysis . In SC, analysis , 29–31.Silhouette for the objective evaluation of gene expression data based on arbitrary grouping criteria. Although they are independent of SC, silhouette scores measuring the degrees of separation between groups of interest would enable a more objective discussion about the SC result in terms of the groups. We here focus on single-factor DE data where only one grouping criterion is primarily of interest in relation to the DE result. We evaluated the relationship among SC results, DE results, and AS values, using both simulated and real expression data (RNA-seq and microarrays). We found silhouettes  to provide a relevant measure for the degrees of separation between groups of interest in SC results. We also found a positive correlation between AS values and DE results.In this paper, we propose to use Blekhman, for short) consisting of 20,689 genes × 18 samples ) [iDEGES/edgeR-edgeR in [EEE-E in [clusterSample with default options ("1 – Spearman’s correlation coefficient (r)" as a distance estimate and average-linkage agglomeration) in TCC.In DE analyses, a gene expression matrix is typically generated, where each row indicates the gene (or derivatives), each column indicates the sample, and each cell indicates (i) counts for RNA-seq data or (ii) the signal intensity for microarray data. Our previous observation of the positive correlation between SC and DE results  was obtas (BRs)) . The anas (BRs)) –35. TCC s (BRs)) ) that uss (BRs)) , edgeR [s (BRs)) , 39, DESs (BRs)) , and DESs (BRs)) . For sims (BRs)) , 42, we edgeR in  and EEE-unique expression patterns were used for calculating distance defined as “1 – Spearman’s r.” This filtering procedure was intended to reduce the negative impact of genes with low expression levels when calculating the distance between samples. For example, the Blekhman data yielded 17,886 genes after the zero-count filtering and DE analyses were performed. After unique filtering, 16,560 genes were obtained, and HSC was performed using these genes. For simplicity, we focus on two-group comparisons with three replicates for each group, i.e.,  vs. , in most cases. In this study, we use the terms samples and replicates interchangeably. Our primary interest was to investigate the applicability of Silhouette for the objective evaluation of gene expression data based on arbitrary grouping criteria. By using silhouettes  as a relevant measure for the group differentiation in the HSC results, we re-evaluated our previous observations  [Throughout this study, we filtered out genes with zero counts  in all samples. For HSC analyses, an additional filtering was performed where genes having identical expression patterns were collapsed. Expression data having those r) metric and average-linkage agglomeration. There were three major clusters, each of which represented a particular species  and the RM cluster was relatively distant from the other clusters. Different from the clear interspecific discrimination , we observed a very low degree of separation between sexes (F vs. M) within each of the three major clusters. That is, samples labelled female (F) and male (M) were intermingled within each species, except for the PTF sub-cluster comprising three female samples .We first demonstrate the relationship between HSC and DE results using a representative dataset, the Blekhman data obtained for three species : humans (HS), chimpanzees (PT), and rhesus macaques (RM) . BrieflyPDEG) satisfying the 10% false discovery rate (FDR) threshold were obtained using TCC with default settings. The four PDEG values for the HS vs. PT comparison (7.56–9.58%) were much smaller than those for either the HS vs. RM (16.82–22.92%) or the PT vs. RM comparison (14.69–20.85%). These results are consistent with those of the original study [PDEG values (0.07–0.17%). This is consistent with the lack of separation between female and male samples within each species in the HSC analysis . Let vi be the average distance between i and the other group , of which i is not a sample member. The silhouette index si for sample i is calculated as (vi - ui)/max. The index si ranges from − 1 to 1; it is positive if ui < vi, zero if ui = vi, and negative if ui > vi. A larger si value indicates increased group separation and vice versa. By taking the mean si over all samples, the average silhouette (AS) value for each comparison can be obtained  using gene expression data.Silhouette  has beenPDEG and AS values obtained from four RMF-related comparisons were consistently larger than those from the four RMM-related comparisons. For example, for the HSF vs. RMF comparison, PDEG = 21.74% and AS = 0.611, while for the HSF vs. RMM comparison, PDEG = 16.85% and AS = 0.548. This difference is primarily explained by the smaller average distance of samples in RMF (0.0475) than in RMM (0.0722). Small PDEG values (0.07–0.17%) obtained for the sex  comparisons can be explained by the similarity between inter-group distances and intra-group distances. In other words, two-group comparisons showing AS ≈ 0 would result in few, if any, DEGs. The numbers of DEGs  can, of course, vary with FDR thresholds and generally increase when the threshold is less restrictive.It is noteworthy that, in the eight RM-related inter-group comparisons, both PDEG value by loosening the FDR threshold does not necessarily indicate an increase in the true number of DEGs. For example, PDEG = 0.78% at a 40% FDR for the PTF vs. PTM comparison indicates that 0.78 × 0.4 = 0.31% are non-DEGs, and the remaining 0.78 × (1.0–0.4) = 0.47% are, at least statistically, true DEGs. In our experience, the percentage of true DEGs (say PtrueDEG) generally approaches a constant value at a non-stringent FDR threshold, such as 30% or 40%. In this case, the maximum PtrueDEG value for any sex comparison was ~ 0.5%  and AS is not practical. This is simply because the PDEG value tends to increase as the number of replicates (Nrep) increases [Nrep.Based on our visual evaluation, the AS values effectively represented the overall relationship between groups of interest in the HSC analysis  with 40 replicates per group (Nrep = 40), 10,000 total genes, of which 20% were DEGs , the levels of DE were four-fold in individual groups, and the proportions of DEGs up-regulated in individual groups were the same . For a total of 80 samples , we obtained PDEG = 21.0% at a 10% FDR threshold, AS = 0.2409, and area under the ROC curve (AUC) = 0.9986. The AUC is a widely used measure of both the sensitivity and specificity of the DE pipeline [PDEG value (21.0% at FDR = 0.1) is in good agreement with the true value .We next investigated the effects of pipeline , 36. Thebaseline matrix with 40 replicates per group (Nrep = 40). Bootstrap resampling was performed 100 times at Nrep = 3, 6, …, and 30 (without replacement). Consistent the previous observations [PDEG values increased as a function of Nrep  as Nrep increased  were investigated. We confirmed that PDEG, but not on AS, is dependent on Nrep , however, we observed a transition in the distribution of PDEG values at around PsimDEG = 1%. Although the PDEG value monotonously increased as Nrep increases when PsimDEG was 20% or more, the PDEG value switched to a monotonously decreasing trend when PsimDEG was 0.1% or less. Overall, the PDEG values approached the true values  as Nrep increased. These results indicate that more accurate DE results can be obtained as Nrep increases, irrespective of the true percentages of DEGs in the data.Next, the effects of unique filtering did not have any effect for this dataset). For the data  comparing two groups (42 wild-type samples vs. 44 Δsnf2 mutant samples), we obtained PDEG = 78.1% and AS = 0.7289. Note that the AUC value could not be calculated for the data because, different from simulated data, we do not know which genes are true DEGs. We investigated the effects of Nrep on parameter estimates. The results were quite similar to those obtained using simulated data  were completely mixed. In statistical terms, this situation is essentially the same as the null hypothesis (H0: A = B). The acceptance of H0 (AS = 0) indicates there are no or few DEGs in the two-group data (PDEG = 0). In this sense, AS could be used as helpful information for the interpretation of DE results, especially when only a few statistically significant DEGs are obtained.Next, we investigated the relationships between ion Fig. . Howevershapes of the distributions were similar. For example, the PDEG value at AS = 0.6 was approximately 0.6 for the simulated data, while PDEG for real data was approximately 0.2. Since the AS value for the simulated data at PDEG = 0.2 was approximately 0.3, the difference for AS at PDEG = 0.2 was 0.3. Similarly, the difference for PDEG at AS = 0.6 was 0.4. It should also be noted that the distribution of values for Blekhman (magenta) and Schurch (black with AS > 0.5) was different  were obtained for the Blekhman data, high AS values (0.5585–0.8998) and high PDEG values (13.03–56.34%) were obtained for the Schurch data. The difference can be explained by the intra-group distances. For the Schurch data, including 42 wild-type samples (group A) and 44 Δsnf2 mutant samples (group B), the distances for groups A and B were 0.0144 and 0.0084, respectively. The values obtained for the Schurch data were clearly smaller than those obtained for the Blekhman data  were performed using 13,932 genes after zero-count filtering. AS calculations and HSC were performed using 13,133 genes after unique filtering. The results for this dataset comparing 10 vs. 11 samples were PDEG = 11.0% at a 10% FDR threshold and AS = 0.1872. Regarding the effects of Nrep, we observed similar trends to those obtained using the Schurch data  and AS = 0.1874 (0.7306) at Nrep = 3  and AS (= 0.3524) among 100 trials at Nrep = 3 were obtained when comparing  vs. . This is reasonable because five of the six samples (except for A3) were members of either the A2 or B1 cluster. Relatively high values can be obtained by comparing two groups in which all members belong to either the A2 or B1 clusters. Indeed, we observed PDEG = 34.8% and AS = 0.4623 when comparing  vs. , though this comparison was not included in the 100 original trials shown in Additional file In general, high (or low) AS values indicate clear (or unclear) separation of groups. The HSC dendrogram for the Bottomly data showed relatively unclear separation of two groups  and AS (= 0.2699) among 100 trials at Nrep = 6 were obtained when comparing  vs. . All eight samples in the A2 and B1 clusters was included in the comparison. Additionally, a comparison between the two clusters, i.e.,  vs. , yielded PDEG = 31.8% and AS = 0.3701. Accordingly, the decreases in PDEG (=31.8% to 23.5%) and AS (0.3701 to 0.2699) values by the addition of four samples  not included in the two clusters are reasonable. We observed that the impact of sampling effects tends to shrink as Nrep (> 7) increases. This is probably because the maximum number of samples in the four clusters is seven for the B4 cluster; the addition of samples not included in the cluster can contribute to decreases in the PDEG and AS values.Different from the Schurch data where the impact of sampling effects shrunk as PDEG values) were performed using 12,410 genes after zero-count filtering. AS calculation and HSC were performed using 11,738 genes after unique filtering. The results for this dataset comparing 17 vs. 24 samples were PDEG = 0.169%, SNR = 1.023, and AS = 0.0118. The values were considerably lower than those obtained for both the Schurch and Bottomly data and were similar to those for the three sex comparisons  consistons Fig. . This rePDEG values as Nrep increased  would be obtained when AS < 0.1 for count data  × 24 samples (= 3 tissues × 2 conditions × 4 BRs). Briefly, Nakai et al. studied the expression levels of genes in brown adipose tissues (BAT), white adipose tissues (WAT), and liver tissues (LIV). They compared two conditions (fed vs. fasted for 24 h) for each tissue type. We here denoted the fed BAT samples BAT_fed, the 24 h–fasted LIV samples LIV_fas, and so on. To quantify expression from the probe-level data , we applied three algorithms . The first dataset  consisthms . Differhms . The HSC dendrogram for the Nakai data displayed three major clusters corresponding to the three tissue types  for all quantification algorithms  were smaller than those in the BAT vs. LIV comparison (41.79–44.63%) or WAT vs. LIV comparison (39.74–44.05%). Different from the clear inter-tissue differentiation , we detected a relatively low degree of separation between conditions (fed vs. fasted) within each of the three major clusters. The PDEG values for the fed vs. fasted comparison were 4.5–8.79%. Of these three comparisons, the intra-BAT comparison  showed the highest PDEG (8.79%) and AS (0.207) values.We performed 15 two-group comparisons with four BRs for each group, i.e.,  vs. . Overall, we observed highly similar trends for the Nakai data and the Blekhman data . Briefly, Kamei et al. compared gene expression in livers for rats fed a low-iron diet (approximately 3 ppm iron) for 3 days and a normal diet (48 ppm iron) as a control. The PDEG and AS values obtained (Iron_def vs. Control) were close to zero and the HSC dendrogram showed an intermingled structure  consistIron_def1) was a clear outlier in the HSC dendrogram for the RMA-quantified data, but not in the other dendrograms  are within the error range.We should note that one sample ; this definition is widely used [r) and AS may be useful.HSC dendrograms of the merged data provided several insights  there is a positive correlation between AS and PDEG values under a fixed Nrep. It is not necessary to estimate PDEG from AS values because DE results (including PDEG) can directly be obtained via the DE pipeline. The AS provides helpful information for interpreting DE results as well as HSC results.In this study, we proposed to use silhouettes  as an objective measure for the degrees of separation between groups of interest based on expression data. To our knowledge, the use of AS independent from HSC is the first practical application in the field of expression analysis. Our main findings are (i) AS is an effective indicator of the overall relationship in the HSC dendrogram based on arbitrary grouping criteria; (ii) AS values are independent of r). Considering this finding in addition to other data, some samples could be misidentified as outliers  filtering genes with low expression, 2) calculating distances among samples, and 3) calculating the AS values based on distance estimates. The high similarity among samples in the Kamei data could be detected by investigating the distances defined as  were clearly higher than those with outliers (PDEG = 74.7% and AS = 0.6530), indicating the importance of developing methods for the automatic detection of outliers [true outliers as well as three false positives. We obtained a promising result (PDEG = 77.6% and AS = 0.7301) using the remaining 84 samples. Rational removal of outlying samples would yield better DE results. We expect that AS would help objective evaluation of the changes in the DE results accompanying outlier removal.Of course, there are true outliers, e.g., ten outlying samples in the original Schurch data , 44. We set Fig. . The valoutliers , 56. Ouroutliers  successfIn practice, Silhouettes can be utilized as supporting information to interpret DE results, especially when no or few DEGs are obtained. As demonstrated by several examples   and BiocV) of the NB distribution can generally be modeled as V = μ + φμ2. The empirical distribution of read counts to obtain the mean (μ) and dispersion (φ) parameters of the NB model was obtained from Arabidopsis data (three BRs for both treated and non-treated samples) in [simulateReadCounts function is stored in the TCC class object with information about the simulated conditions and is therefore ready-to-analyze for both the DE analysis and HSC. These data were used to obtain Fig. The two-group simulated data were produced using the “simulateReadCounts” function in TCC . The varples) in . The outhttp://genome.cshlp.org/content/suppl/2009/12/16/gr.099226.109.DC1/suppTable1.xls) [Blekhman’s mammalian data were obtained from the supplementary website le1.xls) . The raw.xls . Thhttps://github.com/bartongroup/profDGE48/tree/master/Preprocessed_data) [Schurch’s yeast data were obtained from the GitHub website (ed_data) . After mhttp://bowtie-bio.sourceforge.net/recount/countTables/bottomly_count_table.txt) [Bottomly’s mouse data were  obtainedble.txt)  and conshttp://bowtie-bio.sourceforge.net/recount/countTables/cheung_count_table.txt) [Cheung’s human data  were obtble.txt)  and consmas5 function in the R/Bioconductor package affy [2-transformed. The RMA-quantified data were obtained using the rma function in the same package, i.e., affy. The output of the function was already log2-transformed. The RobLoxBioC-quantified data were obtained using the robloxbioc function in the R package RobLoxBioC [2-transformed. These data were used to obtain Additional files Nakai’s probe-level data (.CEL files) were obtained from the ArrayExpress website  through age affy . ExpressbLoxBioC . The expKamei’s probe-level data (.CEL files) were obtained from the ArrayExpress website  using thmas5 and robloxbioc in R ver. 3.3.2 (affy ver. 1.52.0).Note that the quantification procedure was performed using R ver. 3.1.3 (affy ver. 1.44.0) because we encountered an error when executing the functions clusterSample function with default options (“1 – Spearman’s r” as the distance and unique expression patterns as an objective low-count filtering method) in TCC [p-values. The ranks were used to calculate AUC values when analyzing simulated data  were used to obtain the numbers of DEGs satisfying an arbitrarily defined FDR threshold (mainly 10%).The HSC was performed using the ) in TCC . The DE ) in TCC . The genata Fig. . The AUCsilhouette function in the package cluster. Examples of the procedure to estimate AS values are given in Additional file The AS values were calculated using the Additional file 1:si) for each sample i and the average (AS). The sample names  for i correspond to those shown in Fig. PDEG values at various FDR thresholds . The values at 10% FDR were the same as those shown in Fig. PtrueDEG), defined as PDEG × (1 − FDR threshold), at corresponding FDR thresholds shown in (b). (XLSX 19 kb)Detailed results for Blekhman’s RNA-seq count data. (a) Silhouette indices . Bootstrapping results for simulated data under different PsimDEG values are shown: PsimDEG = 10% (Page 1), 5% (Page 2), 2% (Page 3), 1% (Page 4), 0.5% (Page 5), 0.1% (Page 6), and 0.02% (Page 7). Other legends are the same as those in Fig. Effects of  5% Page , 2% , 6 (blue), and 9 (sky blue). (PPTX 65 kb)Results for Schurch’s RNA-seq count data. For (a–b), Bootstrapping results for Schurch data comparing 42 wild-type samples and 44 Δsnf2 mutant samples are shown. Legends are the same as those in Fig. Additional file 4:B1 cluster , A8 cluster , A2 cluster , and B4 cluster . (d) Scatter plots of PDEG vs. AS at Nrep = 3 (black), 6 (blue), and 9 (sky blue). (PPTX 55 kb)Results for Bottomly’s RNA-seq count data. For (a–b), Bootstrapping results for Bottomly data comparing 10 C57BL/6J strains  vs. 11 DBA/2 J strains  are shown. (c) HSC dendrogram. For explanation, four clusters are defined in (d) the HSC dendrogram: the Additional file 5:PDEG vs. AS at Nrep = 3 (black), 6 (blue), and 9 (sky blue). (PPTX 58 kb)Results for Cheung’s RNA-seq count data. For (a–b), Bootstrapping results for Cheung data comparing 17 females  vs. 24 males  are shown. (c) HSC dendrogram. (d) Scatter plots of Additional file 6:genes × 24 samples and (b) PDEG and AS values from a total of 15 two-group comparisons with Nrep = 4 are shown: MAS-quantified data (Page 1), RMA-quantified data (Page 2), and RobLoxBioC-quantified data (Page 3). (PPTX 76 kb)Results for Nakai’s microarray data. (a) HSC dendrogram for Nakai data consisting of 31,099 Additional file 7:genes × 10 samples and compares two conditions (five Iron_def samples vs. five Control samples). The PDEG and AS values are also shown on the right side of the dendrogram. (PPTX 49 kb)Results for Kamei’s microarray data. HSC dendrograms for (a) MAS-, (b) RMA-, and (c) RobLoxBioC-quantified data are shown. These data consist of 31,099 Additional file 8:genes × 34 samples (24 from Nakai and 10 from Kamei data). (PPTX 62 kb)HSC dendrograms for merged microarray data (Nakai + Kamei). HSC dendrograms for (a) MAS-, (b) RMA-, and (c) RobLoxBioC-quantified data are shown. These data consist of 31,099 Additional file 9:BAT_fed samples vs. 4 WAT_fed samples vs. 4 LIV_fed samples, with AS = 0.460. Example 2 compares three-group data with two BRs, “BAT_fed1 and 2” vs. “WAT_fed1 and 2” vs. “LIV_fed1 and 2,” with AS = 0.438. Example 3 compares three-group data with two BRs, “BAT_fed1 and BAT_fas1” vs. “BAT_fed2 and BAT_fas2” vs. “BAT_fed3 and BAT_fas3,” with AS = − 0.185. Example 4 compares two-group data with four BRs, 4 BAT_fed samples vs. 4 WAT_fed samples, with AS = 0.374. Example 5 compares two-group data with four BRs, 4 BAT_fed samples vs. 4 LIV_fed samples, with AS = 0.657. (R 3 kb)Examples of AS estimates for two- and three-group data. The procedures for analyzing Nakai’s MAS-quantified data consisting of 31,099 probesets × 24 samples are provided. Example 1 compares three-group data with four BRs, 4 Additional file 10:XX in the filename “rcode_XX_...” Note that two files (“rcode_08_Add6_pre.R” and “rcode_10_Add7_pre.R”) must be executed using R ver. 3.1.3 (affy ver. 1.44.0) instead of R ver. 3.3.2 (affy ver. 1.52.0). (ZIP 33 kb)R-codes for analyses. This zipped file includes a total of 23 R-code files. Results can be obtained by executing scripts in the order of the serial numbers


The widespread applications in microarray technology have produced the vast quantity of publicly available gene expression datasets. However, analysis of gene expression data using biostatistics and machine learning approaches is a challenging task due to (1) high noise; (2) small sample size with high dimensionality; (3) batch effects and (4) low reproducibility of significant biomarkers. These issues reveal the complexity of gene expression data, thus significantly obstructing microarray technology in clinical applications. The integrative analysis offers an opportunity to address these issues and provides a more comprehensive understanding of the biological systems, but current methods have several limitations. This work leverages state of the art machine learning development for multiple gene expression datasets integration, classification and identification of significant biomarkers. We design a novel integrative framework, MVIAm - Multi-View based Integrative Analysis of microarray data for identifying biomarkers. It applies multiple cross-platform normalization methods to aggregate multiple datasets into a multi-view dataset and utilizes a robust learning mechanism Multi-View Self-Paced Learning (MVSPL) for gene selection in cancer classification problems. We demonstrate the capabilities of MVIAm using simulated data and studies of breast cancer and lung cancer, it can be applied flexibly and is an effective tool for facing the four challenges of gene expression data analysis. Our proposed model makes microarray integrative analysis more systematic and expands its range of applications. The use of gene expression datasets to discover highly reliable biomarkers is an important goal in clinical applications. The significant biomarkers can help researchers to detect the disease in individuals, classify the type of disease, predict the response of therapy and so on2.Microarray technology is one of the most recent advances being used for cancer research, which can measure the expression levels of many thousands or tens of thousands of genes simultaneously. With the rapid development of microarray technology, many database repositories of high throughput gene expression data have been created and published for researchers to use, Gene Expression Omnibus (GEO), for example, currently have stored more than 2.76 million samples over 105,000 studies4. (2) Small sample size with high dimensionality: The gene expression dataset generally contains a large number of genes and small size of samples, which called large p & small n problem5. Only a small fraction of genes are closely relevant to the target disease, and most genes are irrelevant6. From a machine learning perspective, numerous irrelevant genes may introduce noise and reduce the performance of the classifier8. (3) Batch effects: It occurs because measurements are affected by many factors including experiments principle, data collection standards, and personnel differences. The systematic noise introduced when samples are processed in multiple batches have a detrimental effect on data derived from microarrays10. (4) Low reproducibility of significant biomarkers: The published significant biomarkers from internal validation rarely overlap with other research groups11. These four issues reveal the complexity of gene expression data, which constrains the development of microarray technology in clinical applications.Analysis of gene expression data using biostatistics and machine learning approaches is facing four major challenges: (1) High noise: Random noise and systematic biases exist in gene expression data not only impact the scientific validity and costs of studies but also disrupts accurate prediction of phenotype that may ultimately impact patients13. Current microarray integrative analysis was first proposed by Hamid et al.14, basically classified into “late stage” data integration and “early stage” data integration. However, current methods for microarray integrative analysis have several limitations. Most “late stage” data integration methods identify genes based on combining univariate summary statistics, such as p-value15, effect size16 and rank aggregation17. As a result, it is difficult to identify non-redundant significant genes and systematically determine  how many genes to include in the subset, such as GeneMeta18 and metaArray19. Moreover, such methods neglect correlations among genes and do not eliminate the batch effects between different datasets. Current “early stage” data integration methods usually apply one cross-platform normalization method to aggregate multiple datasets into a single unified large dataset. After that, classification and variable selection for the merged dataset can be achieved by the machine learning methods. For example, Ma et al.20 proposed the meta threshold gradient descent regularization (MTGDR) for gene selection in the integrative analysis of gene expression data. Meta-lasso method was published by Li et al.21, which not only boosts the statistic power to identify significant genes but also keeps the flexibility of gene selection. Recently, Hughey et al.22 developed integrative analysis using elastic net penalized with logistic regression model (LEN), a powerful and versatile method for variable selection in classification. Special emphasis, cross-platform normalization is an essential part of the “early stage” data integration, because it can eliminate the differences between datasets from different microarray platforms while preserving underlying the differences in biology23. A number of cross-platform normalization methods have been developed and provide effective batch adjustment for microarray data, such as ComBat24, cross-platform normalization (XPN) method25, and batch effects removal (ber)26. However, different cross-platform normalization methods are based on different statistical models with different accuracy, precision and overall effectiveness27. Current “early stage” data integration methods usually apply one cross-platform normalization method, which cannot ensure maximum elimination of the batch effects. Beyond that, none of these integrative analysis methods have a robust learning mechanism to minimize the influence of the noise. Therefore, there is a crucial need for a novel integrative analysis method for robust analysis of the microarray data, prediction of cancer types and identification of significant biomarkers.To face these challenges and take advantage of multiple published gene expression datasets, the integrative analysis of gene expression data has become an effective tool by aggregating multiple datasets and increasing the statistical power in identifying a small subset of genes to effectively predict the type of the disease28. The novel integrative framework MVIAm extends the traditional “early” stage data integration to multi-view data integration. Generally, multi-view data contains complementary information and has more comprehensive information than those of single-view data29. In recent years, several multi-view machine learning methods for integrating multi-view data have been developed30. The supervised multi-view data integration methods generally include concatenation-based and ensemble-based integration31. MVIAm enables more multi-view machine learning methods for supervised homogeneous data integration. The multi-view gene expression data generated by MVIAm has the following characteristics:We design a novel integrative framework called MVIAm . MVIAm can be divided into three phases: pre-processing each dataset, aggregation and generate multi-view data, and analysis of multi-view data. MVIAm aggregates multiple microarray gene expression datasets through different cross-platform normalization methods and generates multiple aggregated gene expression datasets. Each aggregated dataset has the same set of samples and features but is generated by the different statistical models, which belongs to one type of multi-view datap & n problem and increase the statistical power in identifying biomarkers.Multi-view data typically contains complementary information and has more comprehensive understanding of the biological systems.The batch effects cannot be completely eliminated, meaning that each view of the data still has different types of bias.Multi-view data generated by MVIAm can significantly increase the sample size, which greatly alleviates large 32. Self-paced learning (SPL) is a typical sample reweighting method, especially used in high noise situations33. It was proposed based on the core idea of curriculum learning34. Curriculum learning (CL) is inspired by human learning and is learned by gradually including samples from easy to complex into the training process. SPL embeds curriculum design as a regularization term into the learning objective, automatically select samples into training from easy to complex in a purely self-paced way. Due to its generality and generalization, SPL has been widely used in various tasks38. Moreover, Meng et al.39 have provided some new theoretical understanding of the SPL scheme, which helps us have a deep insight into it. To analysis multi-view gene expression data, we propose Multi-View Self-Paced Learning (MVSPL), a robust supervised multi-view data integration method. The main idea of MVSPL is to interactively recommend high-confidence samples with smaller loss values and automatically select samples from easy to complex to train the model for each view.Although quality control and different cross-platform normalization methods are used to process gene expression data, it is inevitable that the data has noises and biases. In the phase of analyzing gene expression data, in order to alleviate the impact of the noise on the learning process and take advantage of significantly increased data, we introduce a robust learning mechanism called self-paced learningWe design a novel framework of gene expression data integration called MVIAm, which can generate multi-view gene expression data based on different cross-platform normalization methods. Moreover, we propose a robust learning method MVSPL to analyze multi-view gene expression data for gene selection and cancer classification problem. It is an effective tool to address the challenges of microarray data analysis.1), a sparse logistic regression model with elastic net (LEN), ensemble-based elastic net (Ensemble_EN) and SPL.Experimental results on both simulation and real experiments substantiate the superiority of MVSPL as compared to a sparse logistic regression model with Lasso  packages according to the type of microarray platform. The CDF package is necessary for probe annotation for Affymetrix data. The probes of the normalized data can be successfully mapped to Entrez Gene IDs by annotation packages in Bioconductor41. If multiple probes match a single Entrez ID, we calculated the median of values of those probes as the expression value for this gene.The original Affymetrix data was first normalized and log-transformed by a robust multi-array average (RMA)24 and ber26. ComBat is an Empirical Bayes method, includes two methods, a parametric prior method (ComBat_p) and a non-parametric method (ComBat_n), based on the prior distributions of the estimated parameters. Ber, removes batch effects by using a two-stage regression approach, includes two methods, with bagging method (ber_bg) and without bagging method (ber).One challenge of microarray integrative analysis is that each gene expression dataset may have gene expression values for slightly different sets of genes. Commonly method, the common genes from all gene expression datasets are extracted as the merged set of genes. After that, MVIAm utilizes different cross-platform normalization methods to process the gene expression dataset to eliminate the batch effects. In this work, we use two cross-platform normalization methods to eliminate the batch effects, ComBat35 model to multi-view scenarios. The fundamental concept of SPL please see the part of related work. Suppose given a dataset with multiple views i-th input sample with p features under the j-th view and yi is the i-th sample with the value 0 or 1 for every view in the classification model. Let yi and the estimated value j-th view. The βj) model, which extends the self-paced learningm denotes the total number of views. i-th input sample  under the j-th view, and yi is the corresponding label of j. λj) can be used to solve the MVSPL model. The optimization process is as follows:v(1), v(2), …, vm)( are zero vectors in Rm. γ(1), γ(2), …, γm) is increased to allow more samples with larger loss values into training in the next iteration. When γj)( and the latent weight variables vj). With the latent weight variables fixed, Step 12 computes the optimal solution based on the generalized linear model with lasso penalty by using Coordinate Descent algorithm42 with the time complexity of O(n2 × p), where p represents the number of features and n ≪ p. This step computes the optimal solution in multiple views, so the time complexity is O(n2 × p × m). Due to m ≪ n, therefore, the time complexity of Algorithm 1 is O(n2 × p × m).According to Algorithm 1, the MVSPL model can obtain the optimal solution for each view. Algorithm 1 jointly learns the modal parameter D′ = {X1, X2, …, Xu} with multiple views  are coming, where u is the number of test samples. We first fix β(1), β(2), …, βm), , …, }, where Xi =  is the i-th input sample with p features and yi is class of the i-th sample . Let L) denotes the loss function, which calculates the loss between the real label yi and the estimated value f. The β represents the model parameter inside the decision function f. The goal of the SPL is to jointly learn the model parameter β and the latent weight variable v =  by minimizing:The self-paced learning model combines a weighted loss term for all samples and a general self-paced regularizer imposed on the samples weight. Suppose given a dataset γ is the age parameter for controlling the learning pace and λ is a tuning parameter. The alternative optimization strategy algorithm can effectively solve the SPL problem. When β is fixed, the optimum weight variable where β and the latent weight variable v, we can conclude that: (1) When updating v with a fixed β, if the loss value of a sample is smaller than the age parameter γ, then the sample is treated as an easy sample with β with a fixed v, using the selected samples 43, Sparse logistic regression with the elastic net penalty (LEN)44, Ensemble-based elastic net (Ensemble_EN)45 and SPL32. When MVIAm generates single-view data, it degenerates into traditional “early stage” data integration, and data analysis can be performed by L1, LEN and SPL. Ensemble_EN constructs a prediction model on each view of data before combing the model predictions and obtains the final prediction result based on Eq.  with n samples and each samples with p features, for the i-th sample, Xi = . After that, the correlation parameter ρ can be added to the simulated data46.zij~i.i.d.N. The simulated dataset is generated from the logistic regression model, which can be given as:ε = T is the independent random errors from N, σ is the noise control parameter.We generate three independent simulated datasets for integration and each dataset with the character of small sample size and high dimensionality. Using the normal distribution to generate p = 2000). The coefficient β is set as follows:We generated simulated data by the above procedure. Three independent simulated datasets were generated with the same number of variables .We curated data from eight publicly available microarray studies, four breast cancer datasets (same platform) and four lung cancer datasets (disparate platform) Tables  and 3. A1, LEN and SPL achieve the best performance in the view of data by using ComBat_p to eliminate the batch effects. Therefore, these three methods use this view of the aggregated dataset for data analysis in real data analysis. MVSPL and Ensemble_EN analyze two views of data in the real data experiments, which use ComBat_p and ComBat_n to eliminate the batch effects.For the real microarray data, two types of experimental designs are used in this work. One type evaluates the performance using a random partition. The other type validates the prediction performance on the independent datasets. All publicly available cancer datasets are processed and aggregated in the manner described above (Supplementary Tables λ of the training dataset is obtained by 10-fold cross-validation. We repeat this procedure 30 times and report the average measurement and standard error.For the part of evaluating the performance using a random partition, we randomly divide the datasets such that 70% of the datasets become the training samples and the remaining samples become the test samples. The estimation of the optimal regularization parameter 1, LEN, SPL and Ensemble_EN, respectively. Our method achieves the best generalization ability than the competing methods. For lung cancer dataset, as shown in Fig. Figures 22. After pre-processing each dataset individually, all the training datasets and the independent validation dataset are merged in the manner described above. The classifier is trained on the samples from the aggregated training dataset and the optimal regularization parameter λ is obtained by 10-fold cross-validation. After that, the classifier is tested on the samples from the independent validation dataset.For the part of validating the classifier on independent dataset, the design of the validation process is the same as that of metAnalyzeAll1, LEN, SPL, Ensemble_EN and MVSPL in the validation datasets of breast cancer and lung cancer studies. Validating classifiers on the validation dataset, MVSPL consistently outperforms other competing methods in cancer classification problem. As shown in the left hand of Fig. 1 and LEN. Beyond that, Ensemble_EN with the suboptimal performance. In breast cancer study, multi-view analysis method performs better validation prediction performance than single-view analysis method. For lung cancer study, as shown in the right hand of Fig. 1, LEN, SPL and Ensemble_EN, respectively. The validation prediction performance of SPL is inferior to MVSPL but is obviously superior to L1, LEN and Ensemble_EN. Moreover, the validation results of Ensemble_EN is outperformed than L1 and LEN. To summary, by learning from easy to complex samples and interact with multiple views, MVSPL with the best generalization ability than other competing methods. Generally speaking, MVSPL can be successfully applied to the microarray integrative analysis in cancer classification. The average number of selected genes for all methods is summarized in Supplementary Table Figure 51. Figure For a brief biological analysis of selected genes, we summaries of the 20 top-ranked genes selected by the five integrative analysis methods in two cancer studies, which are shown in Tables 53. The upregulated expression of CDK14 promotes tumor cell proliferation, migration and invasion through Wnt/β— catenin signaling pathway in breast cancer54. UPK3A is highly expressed in breast cancer55, which is selected only by MVSPL and SPL. Beyond that, MVSPL selects some other unique genes compared with other methods. Phuong et al.56 confirmed that MAT2A expression in TAM-resistant human breast cancer tissues was higher than that in TAM-responsive cases. Nass et al.57 proposed that NNAT expression determined by immunohistochemistry might therefore become a helpful additional biomarker to identify high-risk breast cancer patients. For lung cancer, Greenman et al.58 reported in 2005 that the role of TTN as a cancer gene is currently a mathematically based prediction and will require direct biological evaluation. And after a few years, Tan H et al.59 said TTN and/or MUC16 were retained in the top 10 for lung cancer, suggesting their tumorigenic relevance to these cancers. MASP1 is over expressed in lung cancer60. In this part, we analysis the 20 top-ranked genes selected by the five methods in two cancer studies in gene level. According to the network of interactions among the genes, we find a few numbers of genes are connected to other frequently altered genes from the publicly available datasets and some genes are targeted by certain cancer drugs.In addition, a number of genes selected by the five methods have been reported in the literature. For example, in breast cancer, downregulation of ALOX15 expression has been reported inp & small n problem, batch effects and low reproducibility of significant biomarkers. In this work, we design a novel framework called MVIAm to strive to tackle these issues. MVIAm utilizes different cross-platform normalization methods to minimize the impact of batch effects, keeps as much useful information as possible in the microarray gene expression data. In addition, the aggregated gene expression datasets generated by MVIAm belong to multi-view data. It implies that MVIAm can significantly alleviate the large p & small n problem compared to the existing integrative analysis methods. Therefore, MVIAm can increase the statistical power in identifying the significant biomarkers. To analysis of multi-view gene expression data, we propose a robust learning mechanism called MVSPL to minimize high noise interference. The MVSPL method can improve the generalization performance by learning multi-view data in a meaningful order and improve the prediction performance by the interaction between multiple views. MVSPL actually corresponds to the sum of SPL model under multiple views plus a regularization term. This method implements robust learning regimes in multiple views under the regularization that the robust loss forms in multiple views are closely related. According to the results of simulation and real data experiments, MVSPL has the superior performance compared with L1, LEN, SPL and Ensemble_EN. Especially in the test and validation dataset, MVSPL shows prominent generalization performance. In a word, MVSPL is a feasible and effective method for variable selection and classification in high dimensional data.Due to the complexity of gene expression data, there are four major issues constrain the development of microarray technology in clinical applications: high noise, large There are some ongoing challenges and promising directions that motivate future work. First, our proposed method conducts variable selection with aggregated microarray data in an “all-in-or-all-out” fashion, that is, a gene identified in all of studies or not identified in any study. However, due to data heterogeneity, there may be some genes are important in some studies while unimportant in others. In the future, we will take this situation into account to improve our model. Second, rapid advances in technology have led to a vast quantity of large-scale molecular omics datasets, it provides a distinct view of the complex biological system. Multi-omics dataset with the same set of samples but several distinct feature sets, which naturally belongs to multi-view data. In the future, we will apply our method to the analysis of multi-omics data. We think the computational analysis of the multi-omics data provides an unprecedented opportunity to deepen our understanding of complex cancer mechanisms. Our proposed method makes integrative analysis more systematic and expands its range of applications.Supplementary


In recent years, the introduction of new molecular techniques in experimental and clinical settings has allowed researchers and clinicians to propose circulating-tumor DNA (ctDNA) analysis and liquid biopsy as novel promising strategies for the early diagnosis of cancer and for the definition of patients’ prognosis. It was widely demonstrated that through the non-invasive analysis of ctDNA, it is possible to identify and characterize the mutational status of tumors while avoiding invasive diagnostic strategies. Although a number of studies on ctDNA in patients’ samples significantly contributed to the improvement of oncology practice, some investigations generated conflicting data about the diagnostic and prognostic significance of ctDNA. Hence, to highlight the relevant achievements obtained so far in this field, a clearer description of the current methodologies used, as well as the obtained results, are strongly needed. On these bases, this review discusses the most relevant studies on ctDNA analysis in cancer, as well as the future directions and applications of liquid biopsy. In particular, special attention was paid to the early diagnosis of primary cancer, to the diagnosis of tumors with an unknown primary location, and finally to the prognosis of cancer patients. Furthermore, the current limitations of ctDNA-based approaches and possible strategies to overcome these limitations are presented. It has been widely demonstrated that several physiological and pathological conditions induce cells and tissues remodeling and, in turn, the rearrangement of stroma and tissue microenvironment. These events are generally sustained by necrotic or apoptotic processes, leading to the disaggregation of tissues and the consequent dissemination of cells and cellular debris in the intercellular space and in the bloodstream. Through these processes, circulating cells and DNA fragments reach the biological fluids and can be easily detected using different molecular techniques ,2,3. OthCirculating tumor DNA is a tumor-derived fragmented DNA found in the bloodstream and other biological fluids, not associated with cfDNA which, on the contrary, is released by normal cells . In the Several mechanisms have been described to explain the mechanism of ctDNA release in the bloodstream or into other body fluids . The mosRecent studies on circulating extracellular DNA in human biological fluids  were performed with the final goal of improving oncology practice. Circulating tumor DNA, as well as genomic and epigenetic alterations, such as single nucleotide polymorphisms (SNPs) , chromos7 tumor cells are necessary to obtain detectable amounts of ctDNA, and 109 cells are necessary to obtain an image using high–resolution computer tomography [Recently, liquid biopsy, a new analytical approach based on the analysis of a peripheral blood sample for the identification of tumor-specific alterations directly in the bloodstream, was introduced in the clinical practice for diagnostic and prognostic purposes ,26. The Consequently, ctDNA levels in plasma or serum may offer early diagnosis in the very few months of disease progression, even before any detectable change in the tumor images obtained through X-Ray, MRI, PET, CT, or PET/CT or changes in blood protein markers levels ,38. In tIt is clear that ctDNA levels correlate well with tumor size (tumor load)  and sharThis review discusses the most advanced applications of ctDNA analysis which have been developed in the last decade, listed in It is well-accepted the notion that early diagnosis of tumor is a key factor in the management of cancer patients, which is essential to enhance the overall survival and progression-free survival of patients . The maiThe concept of “early diagnosis” should not be confused with “screening,” the former consisting in the identification of a disease in healthy and asymptomatic patients through the use of tests and/or medical procedures applied to specific population at risk for such disease .Compared to early diagnosis, cancer screening consists of a specific investigative strategy encompassing a wider geographical area and is directed toward a target population. In this context, several screening programs have been developed for the early identification of different types of cancers . Both eaIn the last decade, greatest attention has been paid to the use of liquid biopsy as a good screening method for both hematological and solid tumors, as well as its role as a new diagnostic test available for the early diagnosis of cancer .In this context, the development of novel next generation sequencing (NGS) methods has made a significant contribution to the early diagnosis of cancer based on the detection of low-frequency mutations in ctDNA . As knowKRAS, TP53, APC, FBXW7, SMAD4) in ctDNA from 26 patients with pancreatobiliary carcinoma was carried out with a diagnostic accuracy of 97.7%, sensitivity of 92.3%, and specificity of 100% [NGS allows the detection of ctDNA changes occurring in the whole genome ,71,72, t of 100% . Recently, an exceptionally promising method (CancerSEEK) was developed for the primary screening of eight types of cancer , which focuses on a small panel of markers (16 genes and eight proteins) and is based on massively parallel sequencing . The autThe GRAIL study, launched by Illumina to develop its preclinical cancer detection technology in blood by high-intensity sequencing assays, population-scale clinical studies, and machine learning such as neural networks, started in 2016 and will last five years. The GRAIL Clinical Research Program set up is presented in Next-generation sequencing (NGS) platforms are not the only tools available for the efficient analysis of ctDNA samples. In the last years, the digital PCR, a new type of polymerase chain reaction, was developed and efficiently used for the detection of small amount of ctDNA . Among tMcEvoy and coworkers (2018) analyzed the circulating-free DNA samples of 32 melanoma patients and showed that ddPCR was able to detect BRAF, NRAS, or KIT mutations in all the patients with a mutational tumor burden >10 (23 out of 32) . SimilarOverall, both the NGS and the ddPCR methodology may be used for the effective detection of ctDNA. The choice of the best platform depends on the molecular target examined. The NGS is used to carry out a wide screening of the possible circulating mutations associated with the tumor, whereas the ddPCR is generally used for the identification of specific tumor mutations. It was widely demonstrated that a wide range of substances, including pesticides, pollutants, fibers, and heavy metals, might induce genetic and epigenetic damages to the cells predisposing for a plethora of diseases, including cancer ,90,91,92In this context, the recent theory of real life risk simulation (RLRS) demonstrated that all the aforementioned risk factors interact with each other, highlighting a real hazard for human health ,98,99. AIt is clear that all these environmental factors are able to induce the development of cancers through several molecular modifications directly detectable in ctDNA samples. A growing body of evidence reported that the epigenetic changes induced by all these mechanisms might predict the presence of diseases, as well as the aboriginal tissue. In this way, it is possible to determine the precancerous or cancerous tissue of origin, thus identifying cancers with unknown primary localization whose frequency is 4–5% of all registered invasive cancer types . Circulating DNA may contain genetic features of cells derived from tissue from other individuals, thus its analysis is widely used as a non-invasive prenatal diagnosis and for predicting the risk of host versus graft disease in transplant medicine . In bothBesides nucleosome modification, other epigenetic alterations are widely described in literature and might be responsible for cancer development and progression . All theBoth these epigenetic modulations, i.e., DNA methylation and alterations of the expression levels of ncRNA, can be effectively studied through liquid biopsy and the analysis of circulating nucleic acids ,116.Liang W. and colleagues developed recently a blood-based test for the detection of early-stage lung cancer using a non-invasive and high-sensitive high-throughput DNA bisulfite sequencing test. In particular, through the sequencing of more than 250 tissue samples, the authors identified nine methylation markers, achieving 79.5% (63.5%–90.7%) of sensitivity and 85.2% (66.3%–95.8%) of specificity when these markers were tested in a validation cohort of circulating DNA obtained from lung cancer and healthy patients .Similarly, Menschikowski M and coworkers developed an optimized bias-based pre-amplification-digital droplet PCR (OBBPA-ddPCR) for the detection of methylated tumor DNA fragments levels with the goal of developing a diagnostic tool for early prostate cancer diagnosis. In particular, the authors showed that as few as five copies of methylated ctDNA out of 700,000 copies of unmethylated ctDNA fragments were identified through the use of ddPCR pre-amplification. Using this technique for the analysis of serum samples derived from 22 prostate cancer patients and 18 normal individuals, the authors reported a specificity of 100% in the detection of specific methylated markers .Considering the analysis of ncRNA as indicator of epigenetics modification in cancer, several studies demonstrated the utility of liquid biopsy for the analysis of miRNAs and lncRNAs expression levels. In a recent review article, Pardini B. reported the state of the art of miRNAs analysis in several cancer types by using liquid biopsy. The authors showed that specific sets of miRNAs were successfully identified for almost all solid and hematological cancers . In the The prognostic properties of circulating DNA in oncology could help in choosing the most appropriate therapeutic approach , thus avoiding ineffective treatment methods, as well as unwanted side effects and associated costs . NeverthA 2015 study focusing on predictive value comparison of ctDNA pool levels and circulating tumor cells revealed that the ctDNA concentrations neither reflected disease prognosis nor survival, although the number of circulating cells slightly correlated with both overall survival (OS) and progression-free survival (PFS) time . The intThese data show that the analysis of ctDNA for prognostic purposes may be valid only for some tumors. In this context, concordant data were generated for some cancers, such as cutaneous melanoma, NSCLC, and colorectal cancer, where the evaluation of ctDNA before the beginning or during pharmacological treatments provided useful information to define patients’ OS and PFS ,128,129.Other studies demonstrated that the initial increase of ctDNA percentages, followed by a drastic reduction of the number of mutated DNA copies, is associated to a good clinical response and therefore to a better prognosis. On the contrary, the initial reduction of mutated DNA, followed by a new increase of ctDNA, is associated to therapeutic failure and the onset of drug resistance mechanisms ,133.Thus, in addition to several ongoing studies, these data demonstrate that the analysis of ctDNA and liquid biopsy samples obtained during the treatment may give important information capable of monitoring the prognosis of patients, as well as identifying specific therapeutic protocols pursuing the principles of personalized medicine.It is important to note that differences in both ctDNA amount and CTCs number could be linked to the pathological stage of tumor. In particular, a recent study performed on early and metastatic breast cancer patients showed significant differences both in CTCs and ctDNA detectable levels in early versus metastatic tumors for PIK3CA, E545K, and H1047R hotspot mutations . AdditioOther studies demonstrated that the concomitant evaluation of CTCs and cfDNA can be used for an effective cancer prognosis. Marie-Hélène Delfau-Larue and colleagues reported that CTCs and bcl2-JH mutation detected in cfDNA might predict the disease progression, reflecting the tumor burden . In partAlthough ctDNA analysis has been shown to be useful for both early diagnosis of tumors and to define cancer patients’ prognosis, the analysis of CTCs appears to be more suitable for monitoring patients with medium or advanced stage of the tumor in order to establish the overall survival and the progression free survival. Starting from these considerations, the research group coordinated by Rossi G. recently performed a study on metastatic breast cancer patients evaluating both CTCs and ctDNA . In thisOverall, these studies showed that the presence of ctDNA or CTCs is associated with the presence of tumors, even early-stage tumors, while higher amounts of ctDNA and/or CTCs are correlated with higher risk of disease progression and with a poorer prognosis for the patients, independently from their tumor stage.A personalized approach in cancer diagnosis implies integral tumor profiling for each individual patient, which might be possible by tracking plasma ctDNA tumor-related mutations. The purpose of studying biopsy specimens maybe the selection of a personalized anticancer therapy, relevant to the mutational profile of the specific tumor. Application of the plasma ctDNA analysis allows for the monitoring of disease dynamics and the prescribed therapy effectiveness in order to detect any residual tumor after resection, relapse, or even metastasis within a particular patient. Progress in this area would be impossible without the development of appropriate screening techniques. Since the frequency of mutant tumor alleles is often less than 0.01% of the total pool of circulating DNA ,137, theIn 2010, the Velculescu’s group developed personalized analysis of rearranged ends (PARE) as a method of identifying specific somatic rearrangements in the chromosomal DNA of solid tumors. This method is based on massively parallel sequencing, followed by PCR analysis in order to detect the specific biomarkers in the bloodstream ,57. In tNewman and coauthors developed the technology “cancer personalized profiling by deep sequencing” (CAPP-Seq) through the utilization of biotinylated oligonucleotides specific to the most recurrent mutating regions in order to monitor ctDNA in patients with NSCLC . Using tA personalized approach based on the genotyping of tumor tissue and the subsequent search of ctDNA in the bloodstream was successfully tested in small pilot studies in bladder urothelial carcinoma ,147,148.A valid support for the identification of new ctDNA biomarkers for the personalized research in cancer may be provided by the analysis of the molecular data already available for several cancers and collected in big consortia like The Cancer Genome Atlas, COSMIC, or GEO Datasets ,151,152.BRAF, PTEN, and KIT mutations in ctDNA from liquid biopsy samples obtained from melanoma patients was previously described. The analysis of the specific mutations is certainly not accidental. Indeed, these mutations are also analyzed in liquid biopsy samples as they are the most frequently detected mutation in melanoma tumor biopsies, as demonstrated by several studies [In this context, different experimental and computational studies allowed the identification of a specific set of genetic and epigenetics alterations potentially detectable in liquid biopsy samples ,158. The studies ,160. Oth studies ,126. Sim studies ,161.SEPT9 gene and the combination of SEPT9/ALX4 mutated genes. Interestingly, the analysis of gene panels compared with single gene mutation tests are best in terms of sensitivity and specificity. For instance, SEPT9/ALX4 panel exhibited a level of 71% sensitivity and 95% specificity for advanced adenomas, thus supporting SEPT9/ALX4 as a biomarker for precancerous lesions [HJC1, CYCD2, PAX5, RB1, SRBC, NPY, PENK, WIF1, ALX4, HLFT, HPP1, MLH1, APC, CDKN2A/P16h, TMEFF2, NGFR, FRP2, NEUROG1, and RUNX3 [ALU83, ALU244, OSMR, and SFRP1 has also demonstrated its diagnostic value [APC, KRAS, TP53, and PI3KCA, methylated WIF1 and NPY), predictive , and therapy tailoring  [KRAS mutation detected in liquid biopsy samples can be proposed as a valid biomarker of diagnosis and prognosis of CRC [APC, mSEPT9, and BRAF alterations [Colorectal cancer (CRC) patients are often diagnosed when the disease is already advanced or metastatic due to the late onset of tumor symptoms and poor patient compliance to the current diagnostic methods . Hence,  lesions . Nonethend RUNX3 ,166. Moric value . It is n MAP2K1) ,169. Accs of CRC ,171. Otherations ,173.HER2, PIK3CA and PD-L1 expression in ctDNA or circulating tumor cells. However, the specificity and sensitivity values of the analyses were not convincing and the discordance of the data obtained among the molecular subtypes of mammary tumors and among the tumor stages limited the statistical significance of these circulating biomarkers evaluation [Regarding breast cancer, no conclusive studies have been generated so far. Several studies have tried to propose the evaluation of aluation ,175,176.In the following chapters, the analysis of ctDNA diagnostic and prognostic potential will be discussed for the glioma and urothelial cancers. Therefore, it is evident that there is no single circulating biomarker for the diagnosis and prognosis of all tumors, but different markers specific for different tumors might exist . On thisBrain tumors are quite difficult to diagnose and are the least treatable. Taking into account the fatal nature of gliomas, the importance of early diagnosis for the patient is of paramount importance . ImagingFaria et al. (2018)  determinAs previously mentioned regarding circulating Alu methylated sequences, some studies demonstrated heterogeneity in ctDNA amounts obtained from glioblastoma patients at different tumor stages. More generally, Chetan Bettegowda and coworkers evaluated the concentration of ctDNA in 640 patients with various cancer types, including glioblastoma, and at different stages . Overall, the PCR-based methods used showed that >75% of patients with advanced tumors harbor mutant DNA fragments, while ctDNA was found in less than 50% of patients with primary tumors, especially those patients with brain tumors, of which the majority of samples were early-stage tumors .Regarding studies performed exclusively on glioma patients at different stages, Piccioni D.E. and colleagues published a biggest study aimed at evaluating the clinical usefulness of ctDNA analysis. They analyzed 419 primary brain tumors, including 370 astrocytic and oligodendroglial tumors (of which 222 were glioblastoma) . Among tThese results confirmed that ctDNA can be more easily identified in higher-grade brain tumors compared to lower-grade tumors. Therefore, ctDNA analysis represents a useful tool for monitoring these patients. Concerning the low-grade tumors, alterations in the circulating DNA are found only in a low percentage of patients. Therefore, the analysis of ctDNA alone may not be sufficient to effectively diagnose tumors at their initial stage. In this case, this analysis must be coupled with the current diagnostics strategies based on imaging techniques.AR gene and the expression of truncated splice variants which display ligand-independent activity [It was shown that ctDNA was detected in 50% of plasma and in 70% of urine samples from patients with renal cell carcinoma (RCC), prostate cancer (PCa), and bladder carcinoma (BC) . Regardiactivity ,214,215.activity .POU4F2 and PCDH17 in urine ctDNA allows for the discrimination between patients with bladder cancer and patients with other urological conditions compared with healthy volunteers, with 90% sensitivity and 94% specificity [TWIST1 and NID2 in urine ctDNA allow the identification of patients with a primary tumor with a sensitivity of 90% and a specificity of 93% [In the case of BC, the total pool of circulating DNA in urine was significantly increased in patients compared with the control group . Furthercificity . Methylay of 93% ,220,221.TERT gene promoter , aneuploidy analysis, and copy number changes on 11 gene mutations localized on 39 chromosome arms. This study does not discuss ctDNA, and instead focuses on DNA derived from urothelial cells released in urine samples. However, this method could be easily translated to ctDNA detection. In fact, in a recent study, genomic profiles of urine cell DNA, urine circulating DNA, and DNA from paraffin-embedded samples of 23 well-characterized tumors of patients with BC were compared [P < 0.001). Consequently, ctDNA has a higher analytical sensitivity for the detection of clinically significant genomic aberrations (P < 0.04). It is assumed that the increased tumor genome in ctDNA might be the result of a higher rate of tumor cell necrosis in urine, relative to normal urothelium [Similar to CancerSEEK, a method for screening upper and lower urinary tract urothelial tumors has been developed for large-scale urothelial DNA analysis in urine  ,223. Thecompared . The datothelium .As described in the previous paragraph, the study of Bettegowda C. showed an increase of ctDNA levels in high-grade tumors compared to low-grade ones. However, this study contained a limited number of urological samples .PIK3CA and FGFR3 mutations in more than 800 bladder cancer patients with both non–muscle-invasive bladder cancer (NMIBC) and radical cystectomy following muscle-invasive bladder cancer (MIBC) [PIK3CA and FGFR3 circulating mutations had a higher incidence of tumor recurrence [Taking into account studies analyzing urological cancers only, Christensen E. and collaborators studied the plasma and urine samples presence of r (MIBC) . As oppor (MIBC) . These dcurrence . TherefoTP53 gene [EGFR, KRAS, BRAF, ALK, PIK3CA, ERBB2, DDR2, AKT, and MEK1 oncogenes in 92 NSCLC tumor samples and in 13 plasma samples. Also, cytological samples have been analyzed. Those samples are often a unique source of the original tumor material, but are of poorer quality for different molecular studies. It was observed that a mutated allele with a frequency higher than 10%, was positively detected even with a small amount of DNA (5 ng). With less than 10% of mutated allele, the detection must be performed with 10ng of DNA as starting material. The authors do not recommend the use of molecular diagnostics for the analysis of low-quality samples, with less than 20% of tumor within the whole sample. MS is recommended as an alternative high-performance method for detecting known mutations, even in the case of low-quality samples. It allows genotyping the deficient component of ctDNA in a tissue sample, which has an important impact on the clinical management of the patient [Although, at present, the leading approach for the characterization of ctDNA is the NGS approach, other methods based on mass spectrometry are being developed. Short oligonucleotide mass analysis (SOMA) was proposed in the early 2000s for the study of ctDNA in plasma and in urine . The useP53 gene . The metP53 gene ,224. TheP53 gene . For thaP53 gene . MS anal patient .With the progressive development of methods for studying extracellular tumor DNA, the wide clinical application of this technology will occur in a matter of time. The main drawback is genetic heterogeneity and high mutagenesis of tumors . During The majority of patients in the study had stage II–III of disease, which does not yet provide an opportunity to assess the applicability of the test for early diagnosis .As control, the authors analyzed the urine of only healthy individuals. Thus, the high specificity of the CancerSeek approach requires further validation with non-cancer control with associated diseases, such as inflammatory diseases of the genitourinary system, which are common in the elderly .The limitations of the method are linked with the same heterogeneity of tumors, such as lung cancer . These cThe limitations of the ctDNA approach are summarized in the comments of various authors concerning CancerSEEK methodology:The authors themselves state that the CancerSEEK does not work equally for all cancers. The panel achieved a total median sensitivity of 70% with a specificity of ≥99%, but significant differences in sensitivity were observed between the different types of tumors analyzed  . In general, most of the available studies cover narrow areas of the wide oncology field, they analyze only specific types of tumors and they need to be confirmed on larger patient cohorts.Used as primary oncologic screening, liquid biopsy represents a valid alternative to tissue biopsy which allows the clinicians to focus on the most common, early, and stable markers, coupled with a specific the oncologic process. Such liquid biopsy allows doctors to perform the therapeutic profiling of cancer patients to monitor the anticancer therapy efficacy, as well as to predict the progression of an already identified cancer. In this way, it is possible to identify fine alterations of markers specific for each patient . This review can be summarized with the words of Bert Vogelstein, MD, Clayton Professor of Oncology and Howard Hughes Medical Institute researcher: “If we are going to make progress in early cancer detection, we have to begin looking at it in a more realistic way, recognizing that no test will detect all cancers” .