Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series

Abstract

Most of the research studies developed applying microarray technology to the characterization of different pathological states of any disease may fail in reaching statistically significant results. This is largely due to the small repertoire of analysed samples, and to the limitation in the number of states or pathologies usually addressed. Moreover, the influence of potential deviations on the gene expression quantification is usually disregarded. In spite of the continuous changes in omic sciences, reflected for instance in the emergence of new Next-Generation Sequencing-related technologies, the existing availability of a vast amount of gene expression microarray datasets should be properly exploited. Therefore, this work proposes a novel methodological approach involving the integration of several heterogeneous skin cancer series, and a later multiclass classifier design. This approach is thus a way to provide the clinicians with an intelligent diagnosis support tool based on the use of a robust set of selected biomarkers, which simultaneously distinguishes among different cancer-related skin states. To achieve this, a multi-platform combination of microarray datasets from Affymetrix and Illumina manufacturers was carried out. This integration is expected to strengthen the statistical robustness of the study as well as the finding of highly-reliable skin cancer biomarkers. Specifically, the designed operation pipeline has allowed the identification of a small subset of 17 differentially expressed genes (DEGs) from which to distinguish among 7 involved skin states. These genes were obtained from the assessment of a number of potential batch effects on the gene expression data. The biological interpretation of these genes was inspected in the specific literature to understand their underlying information in relation to skin cancer. Finally, in order to assess their possible effectiveness in cancer diagnosis, a cross-validation Support Vector Machines (SVM)-based classification including feature ranking was performed. The accuracy attained exceeded the 92% in overall recognition of the 7 different cancer-related skin states. The proposed integration scheme is expected to allow the co-integration with other state-of-the-art technologies such as RNA-seq.

Anti-Melanoma Activities of Artemisone and Prenylated Amino-Artemisinins in Combination With Known Anticancer Drugs

“…Accounting for approximately 40% of all new cancer diagnoses, skin cancer represents a leading public health problem ( Bray et al, 2018 ). Skin cancers are classified into two main groups based on the cell of origin and clinical behavior, firstly nonmelanoma skin cancers (NMSC) mainly originating in the keratinocytes, and secondly cutaneous malignant melanoma skin cancers, originating in the melanocytes ( Simões et al, 2015 ; Gálvez et al, 2018 ; Que et al, 2018 ). Malignant melanoma is the most severe form of skin cancer, and are notably intractable to current treatment modalities; the average survival rate is 6–10 months after diagnosis.…”

“…Malignant melanoma is the most severe form of skin cancer, and are notably intractable to current treatment modalities; the average survival rate is 6–10 months after diagnosis. Furthermore, metastatic melanoma cells tend to disseminate to multiple organs including brain, bone, liver and lungs, rendering treatment strategies decidedly more challenging than in the case of NMSCs that often remain at the site of origin ( Gálvez et al, 2018 ; Keung and Gershenwald, 2018 ). Surgery remains the standard treatment for melanoma where the bulk of the tumor must be excised.…”

Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level

“…However, this work implements a pipeline with the objective of carrying out the technology integration for the extraction of the DEGs. The pipeline is an evolution of our previous research that integrates different Microarrays platform with data from skin cancer in order to be able to discern among the proposed skin cancer states [23]. Furthermore, this research also builds a set of smart leukemia classifiers to perform a differentiation among the different types of leukemia addressed when unlabeled samples are presented.…”

Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets

“…In the case of favorably dealing with all these limitations, a new experimental challenge takes place: how to discern multiple SPSs by using changes in gene expression. Although hierarchical clustering highly helps in graphically showing such changes [30], methodological approaches based on multiclass classification are postulated as an innovative alternative when assessing the validity of DEGs for simultaneously diagnosing multiple SPSs [31]. Finally, the use of feature selection algorithms must be explored with the objective of selecting only informative DEGs, that in many cases can dramatically reduce the search space.…”