Identification of key genes in lung adenocarcinoma based on a competing endogenous RNA network

Long non-coding RNA MIDEAS-AS1 inhibits growth and metastasis of triple-negative breast cancer via transcriptionally activating NCALD

BACKGROUND

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with higher aggressiveness and poorer outcomes. Recently, long non-coding RNAs (lncRNAs) have become the crucial gene regulators in the progression of human cancers. However, the function and underlying mechanisms of lncRNAs in TNBC remains unclear.

METHODS

Based on public databases and bioinformatics analyses, the low expression of lncRNA MIDEAS-AS1 in breast cancer tissues was detected and further validated in a cohort of TNBC tissues. The effects of MIDEAS-AS1 on proliferation, migration, invasion were determined by in vitro and in vivo experiments. RNA pull-down assay and RNA immunoprecipitation (RIP) assay were carried out to reveal the interaction between MIDEAS-AS1 and MATR3. Luciferase reporter assay, Chromatin immunoprecipitation (ChIP) and qRT-PCR were used to evaluate the regulatory effect of MIDEAS-AS1/MATR3 complex on NCALD.

RESULTS

LncRNA MIDEAS-AS1 was significantly downregulated in TNBC, which was correlated with poor overall survival (OS) and progression-free survival (PFS) in TNBC patients. MIDEAS-AS1 overexpression remarkably inhibited tumor growth and metastasis in vitro and in vivo. Mechanistically, MIDEAS-AS1 mainly located in the nucleus and interacted with the nuclear protein MATR3. Meanwhile, NCALD was selected as the downstream target, which was transcriptionally regulated by MIDEAS-AS1/MATR3 complex and further inactivated NF-κB signaling pathway. Furthermore, rescue experiment showed that the suppression of cell malignant phenotype caused by MIDEAS-AS1 overexpression could be reversed by inhibition of NCALD.

CONCLUSIONS

Collectively, our results demonstrate that MIDEAS-AS1 serves as a tumor-suppressor in TNBC through modulating MATR3/NCALD axis, and MIDEAS-AS1 may function as a prognostic biomarker for TNBC.

A hybrid deep forest-based method for predicting synergistic drug combinations

Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.

Elucidating the Interactome of G Protein-Coupled Receptors and Receptor Activity-Modifying Proteins

G protein-coupled receptors (GPCRs) are known to interact with several other classes of integral membrane proteins that modulate their biology and pharmacology. However, the extent of these interactions and the mechanisms of their effects are not well understood. For example, one class of GPCR-interacting proteins, receptor activity-modifying proteins (RAMPs), comprise three related and ubiquitously expressed single-transmembrane span proteins. The RAMP family was discovered more than two decades ago, and since then GPCR-RAMP interactions and their functional consequences on receptor trafficking and ligand selectivity have been documented for several secretin (class B) GPCRs, most notably the calcitonin receptor-like receptor. Recent bioinformatics and multiplexed experimental studies suggest that GPCR-RAMP interactions might be much more widespread than previously anticipated. Recently, cryo-electron microscopy has provided high-resolution structures of GPCR-RAMP-ligand complexes, and drugs have been developed that target GPCR-RAMP complexes. In this review, we provide a summary of recent advances in techniques that allow the discovery of GPCR-RAMP interactions and their functional consequences and highlight prospects for future advances. We also provide an up-to-date list of reported GPCR-RAMP interactions based on a review of the current literature. SIGNIFICANCE STATEMENT: Receptor activity-modifying proteins (RAMPs) have emerged as modulators of many aspects of G protein-coupled receptor (GPCR)biology and pharmacology. The application of new methodologies to study membrane protein-protein interactions suggests that RAMPs interact with many more GPCRs than had been previously known. These findings, especially when combined with structural studies of membrane protein complexes, have significant implications for advancing GPCR-targeted drug discovery and the understanding of GPCR pharmacology, biology, and regulation.

RAMP2-AS1 inhibits CXCL11 expression to suppress malignant phenotype of breast cancer by recruiting DNMT1 and DNMT3B

BACKGROUND

Breast cancer is the most common malignancy in women populations.

METHODS

RAMP2-AS1 and CXCL11 expression in breast cancer tissues and cells were determined using RT-qPCR or Western blot. RIP analysis confirmed the interaction between DNMT1, DNMT3B and RAMP2-AS1. ChIP assay verified that RAMP2-AS1 recruited DNMT1 and DNMT3B to the promoter region of CXCL11. FISH detected the sub-localization of RAMP2-AS1 in breast cancer cells. Bisulfite sequencing PCR (BSP) tested the methylation level of CXCL11. The cell viability, proliferation, migration and apoptosis were assessed by CCK-8, colony formation, transwell and flow cytometry assays, respectively. IHC was performed to evaluate the expression of Ki67, CXCL11, MMP2 in tumor tissues.

RESULTS

The level of RAMP2-AS1 was decreased in breast cancer tissues and cells, whereas CXCL11 was highly expressed. Patients with decreased RAMP2-AS1 had a poor prognosis. RAMP2-AS1 inhibited breast cancer cell malignant phenotype. Besides, RAMP2-AS1 regulated the methylation of CXCL11 by recruiting DNMT1 and DNMT3B to the promoter region of CXCL11. RAMP2-AS1 overexpression suppressed the malignant phenotype through CXCL11 and inhibited tumor growth in vivo.

CONCLUSION

RAMP2-AS1 suppresses breast cancer malignant phenotype via DNMT1 and DNMT3B mediated inhibition of CXCL11.

Prognostic significance of AP-2α/γ targets as cancer therapeutics

Identifying genes with prognostic importance could improve cancer treatment. An increasing number of reports suggest the existence of successful strategies based on seemingly "untargetable" transcription factors. In addition to embryogenesis, AP-2 transcription factors are known to play crucial roles in cancer development. Members of this family can be used as prognostic factors in oncological patients, and AP-2α/γ transcription factors were previously investigated in our pan-cancer comparative study using their target genes. The present study investigates tumors that were previously found similar with an emphasis on the possible role of AP-2 factors in specific cancer types. The RData workspace was loaded back to R environment and 3D trajectories were built via Monocle3. The genes that met the requirement of specificity were listed using top_markers(), separately for mutual and unique targets. Furthermore, the candidate genes had to meet the following requirements: correlation with AP-2 factor (through Correlation AnalyzeR) and validated prognostic importance (using GEPIA2 and subsequently KM-plotter or LOGpc). Eventually, the ROC analysis was applied to confirm their predictive value; co-dependence of expression was visualized via BoxPlotR. Some similar tumors were differentiated by AP-2α/γ targets with prognostic value. Requirements were met by only fifteen genes (EMX2, COL7A1, GRIA1, KRT1, KRT14, SLC12A5, SEZ6L, PTPRN, SCG5, DPP6, NTSR1, ARX, COL4A3, PPEF1 and TMEM59L); of these, the last four were excluded based on ROC curves. All the above genes were confronted with the literature, with an emphasis on the possible role played by AP-2 factors in specific cancers. Following ROC analysis, the genes were verified using immunohistochemistry data and progression-related signatures. Staining differences were observed, as well as co-dependence on the expression of e.g. CTNNB1, ERBB2, KRAS, SMAD4, EGFR or MKI67. In conclusion, prognostic value of targets suggested AP-2α/γ as candidates for novel cancer treatment. It was also revealed that AP-2 targets are related to tumor progression and that some mutual target genes could be inversely regulated.