Pan-cancer analyses have provided comprehensive landscapes of somatic mutations, somatic copy number alterations, mutations in chromatin regulatory factor genes, viral expression and host gene fusion in those tumors. Those large datasets provided a great opportunity to examine the global landscape of aberrations at DNA, RNA and protein levels. The Cancer Genome Atlas (TCGA) has generated comprehensive molecular profiles including somatic mutation, copy number variation, gene expression, DNA methylation, microRNA expression, and protein expression for more than 30 different human tumor types. Lastly, we identified a few genes that might play a role in sexual dimorphism in certain cancers. One third of the 50 most frequently appearing genes were pseudogenes the degree of enrichment may be indicative of their importance in tumor classification. We regard the frequency with which a gene appeared in those sets as measuring its importance for tumor classification. We achieved similar results when we analyzed 23 non-sex-specific tumor types separately for males and females. This accuracy is remarkable given the number of the tumor types and the total number of samples involved.

We were able to identify many sets of 20 genes that could correctly classify more than 90% of the samples from 31 different tumor types using TCGA RNA-seq data. The differentially discriminative genes we identified might be important for the gender differences in tumor incidence and survival. FOXA1 has been shown to play a role for sexual dimorphism in liver cancer. Genes that were differentially expressed between genders included BNC1, FAT2, FOXA1, and HOXA11.

Remarkably, more than 80% of the 100 most discriminative genes selected from each gender separately overlapped. Results from these gender-specific analyses largely recapitulated results when gender was ignored. We also carried out pan-cancer classification, separately for males and females, on 23 sex non-specific tumor types (those unrelated to reproductive organs). Accuracies were high for all but three of the 31 tumor types, in particular, for READ (rectum adenocarcinoma) which was largely indistinguishable from COAD (colon adenocarcinoma). We could correctly classify more than 90% of the test set samples. We randomly assigned 75% of samples into training and 25% into testing, proportionally allocating samples from each tumor type. Using RNA-seq expression data, we undertook a pan-cancer classification of 9,096 TCGA tumor samples representing 31 tumor types. Those features may serve as biomarkers for tumor diagnosis and drug development. We aim to identify a set of genes whose expression patterns can distinguish diverse tumor types. The Cancer Genome Atlas (TCGA) has generated comprehensive molecular profiles.

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