Aberrant insulin-like growth factor I receptor (IGF1R) signaling pathway serves as a well-established target for cancer drug therapy. The intragenic antisense long noncoding RNA (lncRNA) IRAIN, a putative tumor suppressor, is downregulated in breast cancer cells, while IGF1R is overexpressed, leading to an abnormal IGF1R/IRAIN ratio that promotes tumor growth. To precisely target this pathway, we developed an “antisense lncRNA-mediated intragenic cis competition” (ALIC) approach to therapeutically correct the elevated IGF1R/IRAIN bias in breast cancer cells. We used CRISPR-Cas9 gene editing to target the weak promoter of IRAIN antisense lncRNA and showed that in targeted clones, intragenic activation of the antisense lncRNA potently competed in cis with the promoter of the IGF1R sense mRNA. Notably, the normalization of IGF1R/IRAIN transcription inhibited the IGF1R signaling pathway in breast cancer cells, decreasing cell proliferation, tumor sphere formation, migration, and invasion. Using “nuclear RNA reverse transcription-associated trap” sequencing, we uncovered an IRAIN lncRNA-specific interactome containing gene targets involved in cell metastasis, signaling pathways, and cell immortalization. These data suggest that aberrantly upregulated IGF1R in breast cancer cells can be precisely targeted by cis transcription competition, thus providing a useful strategy to target disease genes in the development of novel precision medicine therapies.

Colon cancer is the third most common cancer worldwide, and lymphatic metastasis is one of the principal factors affecting patient prognosis. Recent studies have revealed that long non-coding RNAs (lncRNAs) serve as important regulators in the pathogenesis of colon cancer, therefore affecting patient survival rates. In the present study, colon cancer-associated lncRNAs were screened based on their influence on patient survival. A number of survival-associated lncRNAs (and their potential mechanisms of action) were identified, with the strongest candidate being MIR210HG. Gene expression correlation and protein-protein interaction (PPI) network analyses were performed to identify MIR210HG-associated genes. Various bioinformatics analyses (including gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses) were conducted to investigate the prognostic role of MIR210HG and its associated genes, in colon cancer. Higher expression levels of MIR210HG were associated with shorter overall survival in patients with colon cancer, which was significant in 373 candidates. Multiple findings indicated that MIR210HG may exert its effects in colon cancer through the modulation of energy metabolism and cell adhesion. Further predictions suggested that MIR210HG may affect colon cancer via transcription and post-transcriptional processing. Collectively, these results provided evidence of a transcriptional regulatory network of MIR210HG in colon cancer, and suggested its potential role as a novel biomarker and therapeutic target for colon cancer.

Germline variants of PIP4K2A impact susceptibility of acute lymphoblastic leukemia (ALL) through inducing its overexpression. Although limited reports suggested the oncogenic role of PIP4K2A in cancers, regulatory network and prognostic effect of this gene remains poorly understood in tumorigenesis and leukemogenesis. In this study, we conducted genome-wide gene expression association analyses in pediatric B-ALL cohorts to discover expression associated genes and pathways, which is followed by the bioinformatics analyses to investigate the prognostic role of PIP4K2A and its related genes in multiple cancer types. 214 candidates were identified to be significantly associated with PIP4K2A expression in ALL patients, with known cancer-related genes rankings the top (e.g., RAC2, RBL2, and TFDP1). These candidates do not only tend to be clustered in the same types of leukemia, but can also separate the patients into novel molecular subtypes. PIP4K2A is noticed to be frequently overexpressed in multiple other types of leukemia and solid cancers from cancer cohorts including TCGA, and associated with its candidates in subtype-specific and cancer-specific manners. Interestingly, the association status varied in tumors compared to their matched normal tissues. Moreover, PIP4K2A and its related candidates exhibit stage-independent prognostic effects in multiple cancers, mostly with its lower expression significantly associated with longer overall survival (p < 0.05). Our findings reveal the transcriptional regulatory network of PIP4K2A in leukemia, and suggest its potentially important role on molecular subtypes of multiple cancers and subsequent treatment outcomes.

GATA3 polymorphisms were reported to be significantly associated with susceptibility of pediatric B-lineage acute lymphoblastic leukemia (ALL), by impacting on GATA3 expression. We noticed that ALL-related GATA3 polymorphism located around in the tissue-specific enhancer, and significantly associated with GATA3 expression. Although the regulatory network of GATA3 has been well reported in T cells, the functional status of GATA3 is poorly understood in B-ALL. We thus conducted genome-wide gene expression association analyses to reveal expression associated genes and pathways in nine independent B-ALL patient cohorts. In B-ALL patients, 173 candidates were identified to be significantly associated with GATA3 expression, including some reported GATA3-related genes (e.g., ITM2A) and well-known tumor-related genes (e.g., STAT4). Some of the candidates exhibit tissue-specific and subtype-specific association with GATA3. Through overexpression and down-regulation of GATA3 in leukemia cell lines, several reported and novel GATA3 regulated genes were validated. Moreover, association of GATA3 expression and its targets can be impacted by SNPs (e.g., rs4894953), which locate in the potential GATA3 binding motif. Our findings suggest that GATA3 may be involved in multiple tumor-related pathways (e.g., STAT/JAK pathway) in B-ALL to impact leukemogenesis through epigenetic regulation.

Breast cancer has been reported as one of the most frequently diagnosed malignant diseases and the leading cause of cancer death in women all around the world. Furthermore, this complicated cancer is divided into multiple subtypes which present different clinical symptoms and need correspondingly directed therapy. We took BECN1, a core gene in autophagy performing a tumor inhibitory effect, as a starting point. The study in this paper aims to identify genes related to breast cancer and its multiple subtypes by integrating multiple omics data using the least absolute shrinkage and selection operator (LASSO), which is a statistical method that can integrate more than two types of omics data. All the data is obtained from The Cancer Genome Atlas (TCGA) platform which stores clinical and molecular tumor data. The model constructed is based on three kinds of data including mRNA-gene expression with a dependent variable level, DNA methylation and copy number alterations as independent variables. Finally, we propose four subnets of four subtypes of breast cancer, and consider as a result of microarray analysis that AFF3 is associated with BECN1 in breast cancer, and may be a potential therapeutic target. This finding may provide some potential targeted therapeutics for the four different subtypes of breast cancer at the genetic level. In conclusion, finding out the major role Beclin-1 plays in breast cancer subtypes is of great value. The results obtained are instructive for further research and may provide excellent results in clinical applications, as well as testing in animal experiments, and may also indicate a new method to perform bioinformatics analysis.