A systematic method was proposed to infer differential gene regulatory networks (GRNs) among lung adenocarcinoma (LUAD) samples at different stages by integrating multi‐omics data to uncover significant network features and to identify tumor progression (TP) biomarker genes. The mRNA expressions, copy number variations, and DNA methylations of two independent LUAD cohorts (TCGA and SPORE) at stages I, II, and III were used, respectively. As results, the transition from normal to early onset was showed to be critical to reveal the pathogenesis of LUAD; 61 genes were identified as TP‐related biomarkers, including two types of microRNAs of ABLIM2 and ZFAS1. These identified biomarkers may set light on the underlying mechanism of LUAD TP and may serve as potential drug targets for new treatments. Moreover, our study provides a general framework for TP biomarker identification for other types of cancer, which may improve the mechanism research for cancer development.

Aberrant methylation is one of the early detectable events in many tumors, which is very promising for pan-cancer early-stage diagnosis and prognosis. To efficiently analyze the big pan-cancer methylation data and to overcome the co-methylation phenomenon, a MapReduce-based distributed and parallel-designed partial least squares approach was proposed. The large-scale high-dimensional methylation data were first decomposed into distributed blocks according to their genome locations. A distributed and parallel data processing strategy was proposed based on the framework of MapReduce, and then latent variables were further extracted for each distributed block. A set of pan-cancer signatures through a differential co-expression network followed by statistical tests was further identified based on their gene expression profiles. In total, 15 TCGA and 3 GEO datasets were used as the training and testing data, respectively, to verify our method. As a result, 22,000 potential methylation loci were selected as highly related loci with early-stage pan-cancer diagnosis. Of these, 67 methylation loci were further identified as pan-cancer signatures considering their gene expression as well. The survival analysis as well as pathway enrichment analysis on them shows that not only these loci may serve as potential drug targets, but also the proposed method may serve as a uniform framework for signature identification with big data.

Structural aberrations (SA) have been shown to play an essential role in the occurrence and development of cancer. SAs are typically characterized by copy number alteration (CNA) dose and distortion length. Although sequencing techniques and analytical methods have facilitated the identification and cataloging of somatic CNAs, there are no effective methods to quantify SA considering the amplitude, location, and neighborhood of each nucleotide in each fragment. Therefore, a new SA index based on dynamic time warping is proposed. The SA index analysed 22448 samples of 35 types/subtypes of cancers. Most types had significant differences in SA levels ranging between 12p and 20q. This suggests that genes or inter-gene regions may warrant greater attention, as they can be used to distinguish between different types of cancers and become targets for specific treatments. SA indexes were then used to quantify the differences between cancers. Additionally, SA fingerprints were identified for every cancer type. Kidney chromophobe, adrenocortical carcinoma, and ovarian serous cystadenocarcinoma are the three severest types with structural aberrations caused by cancer, while thyroid carcinoma is the least. Our research provides new possibilities for the better utilization of chromosomal instability for further exploiting cancer aneuploidy, thus improving cancer therapy.

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