Meta-analytical biomarker search of EST expression data reveals three differentially expressed candidates

The prediction of pCR and chemosensitivity for breast cancer patients using DLG3, RADL and Pathomics signatures based on machine learning and deep learning

BACKGROUND

Limited studies have investigated the predictive value of multiomics signatures (radiomics, deep learning features, pathological features and DLG3) in breast cancer patients who underwent neoadjuvant chemotherapy (NAC). However, no study has explored the relationships among radiomic, pathomic signatures and chemosensitivity. This study aimed to predict pathological complete response (pCR) using multiomics signatures, and to evaluate the predictive utility of radiomic and pathomic signatures for guiding chemotherapy selection.

METHODS

The oncogenic function of DLG3 was explored in breast cancer cells via DLG3 knockdown. Immunohistochemistry (IHC) was used to evaluate the relationship between DLG3 expression and docetaxel/epirubin sensitivity. Machine learning (ML) and deep learning (DL) algorithms were used to develop multiomics signatures. Survival analysis was conducted by K-M curves and log-rank. Multivariate logistic regression analysis was used to develop nomograms.

RESULTS

A total of 311 patients with malignant breast tumours who underwent NAC were retrospectively included in this multicentre study. Multiomics (DLG3, RADL and PATHO) signatures could accurately predict pCR (AUC: training: 0.900; testing: 0.814; external validation: 0.792). Its performance is also superior to that of clinical TNM staging and the single RADL signature in different cohorts. Patients in the low DLG3 group more easily achieved pCR, and those in the high RADL Signature_pCR and PATHO_Signature_pCR (OR = 7.93, 95 % CI: 3.49-18, P < 0.001) groups more easily achieved pCR. In the TEC regimen NAC group, patients who achieved pCR had a lower DLG3 score (4.00 ± 2.33 vs. 6.43 ± 3.01, P < 0.05). Patients in the low RADL_Signature_DLG3 and PATHO_Signature_DLG3 groups had lower DLG3 IHC scores (P < 0.05). Patients in the high RADL signature, PATHO signature and DLG3 signature groups had worse DFS and OS.

CONCLUSIONS

Multiomics signatures (RADL, PATHO and DLG3) demonstrated great potential in predicting the pCR of breast cancer patients who underwent NAC. The RADL and PATHO signatures are associated with DLG3 status and could help doctors or patients choose proper neoadjuvant chemotherapy regimens (TEC regimens). This simple, structured, convenient and inexpensive multiomics model could help clinicians and patients make treatment decisions.

Hypermethylation of DLG3 Promoter Upregulates RAC1 and Activates the PI3K/AKT Signaling Pathway to Promote Breast Cancer Progression

Objective

This investigation aimed to figure out the relation between discs large homolog 3 (DLG3) expression and the progression and prognosis of breast cancer (BC).

Methods

qRT-PCR was utilized for confirming DLG3 expression and RAC1 mRNA expression in BC tissues and cells. Subsequently, after overexpression or interference of DLG3, the changes of the biological activities of BC cells, including cell proliferation, migration, invasion, and apoptosis, were detected through CCK-8, colony formation assay, wound healing assay, transwell assay, and flow cytometry, respectively. Furthermore, western blotting was utilized to measure the protein expression of DLG3 and RAC1, as well as related proteins of epithelial-mesenchymal transition (EMT) and the PI3K/AKT signaling pathway.

Results

At both cellular and tissue level in BC, DLG3 was downregulated and methylation level was upregulated; RAC1 showed an opposite change and was of a negative correlation with DLG3. In MCF-7 and HCC1937, we found that the upregulation of DLG3 could inhibit RAC1 expression as well as cell proliferation, invasion, migration, and EMT, while promoting apoptosis. Also, DLG3 inhibited the activation of the P13K/AKT pathway.

Conclusion

Hypermethylation of DLG3 promoter upregulates RAC1 and activates the PI3K/AKT pathway, thus promoting BC progression. This conclusion provides ideas and experimental basis for improving and treating BC patients.

Weighted Gene Correlation Network Meta-Analysis Reveals Functional Candidate Genes Associated with High- and Sub-Fertile Reproductive Performance in Beef Cattle

Improved reproductive efficiency could lead to economic benefits for the beef industry, once the intensive selection pressure has led to a decreased fertility. However, several factors limit our understanding of fertility traits, including genetic differences between populations and statistical limitations. In the present study, the RNA-sequencing data from uterine samples of high-fertile (HF) and sub-fertile (SF) animals was integrated using co-expression network meta-analysis, weighted gene correlation network analysis, identification of upstream regulators, variant calling, and network topology approaches. Using this pipeline, top hub-genes harboring fixed variants (HF × SF) were identified in differentially co-expressed gene modules (DcoExp). The functional prioritization analysis identified the genes with highest potential to be key-regulators of the DcoExp modules between HF and SF animals. Consequently, 32 functional candidate genes (10 upstream regulators and 22 top hub-genes of DcoExp modules) were identified. These genes were associated with the regulation of relevant biological processes for fertility, such as embryonic development, germ cell proliferation, and ovarian hormone regulation. Additionally, 100 candidate variants (single nucleotide polymorphisms (SNPs) and insertions and deletions (INDELs)) were identified within those genes. In the long-term, the results obtained here may help to reduce the frequency of subfertility in beef herds, reducing the associated economic losses caused by this condition.

High expression of DLG3 is associated with decreased survival from breast cancer

Abnormal expression or activity of proteins that regulate cell polarity can contribute to tumour progression. Discs large homolog (DLG) proteins play crucial roles in the maintenance of cell polarity and tissue morphogenesis. Previous studies of breast cancer patients showed that DLG3 had greater expression in the cancerous tissues than non‐cancerous tissues, but the relationship between DLG3 expression and breast cancer progression and prognosis is not clear. Here, we investigated the association of DLG3 expression with breast cancer progression and prognosis using data on clinicopathological parameters from The Cancer Genome Atlas (TCGA) database, with different clinicopathological parameters using ualcan and linkedomics, and with different stages and subtypes using immunohistochemical staining. The results indicated greater DLG3 expression in cancerous breast tissues than normal breast tissues and in luminal and Her2+ subtypes than in the triple‐negative subtype. DLG3 expression also had a positive correlation with pathologic stage and decreased survival rate. Our data suggest that DLG3 should be considered as a new diagnostic and prognostic biomarker for breast cancer.

Systematic screening and identification of novel psoriasis‑specific genes from the transcriptome of psoriasis‑like keratinocytes

Psoriasis is a chronic inflammatory skin disease. Keratinocytes (KCs), as skin‑specific cells, serve an important role in the immunopathogenesis of psoriasis. In the present study, transcriptome data derived from psoriasis‑like KCs were used together with the reported transcriptome data from the skin/epidermis of patient with psoriasis, excluding known psoriasis‑associated genes that have been well described in the previous studies according to GeneCards database, to screen for novel psoriasis‑associated genes. According to the human expressed sequence tag of UniGene dataset, six genes that are located near psoriasis‑associated loci were highly expressed in skin. Among these six genes, four genes (epiregulin, NIPA like domain containing 4, serpin family B member 7 and WAP four‑disulfide core domain 12) were highly expressed in normal mouse epidermis (mainly KCs) and mouse psoriatic epidermis cells, but not in psoriatic dermis cells, which further emphasized the specificity of these genes. Furthermore, in systemic inflammatory response syndrome (SIRS), SERPINB7 showed no difference in expression in immune‑activated tissues from SIRS and control mice. It was also found that the mRNA expression levels of SERPINB in lesional skin of patients with psoriasis were significantly higher than in non‑lesional psoriatic skin from the same patients. SERPINB7 may be a valuable candidate for further studies. In the present study, a method for identifying novel key pathogenic skin‑specific molecules is presented, which may be used for investigating and treating psoriasis.

Combining multi-OMICs information to identify key-regulator genes for pleiotropic effect on fertility and production traits in beef cattle

The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.

Advances in translational bioinformatics and population genomics in the Asia-Pacific

The theme of the 2012 International Conference on Bioinformatics (InCoB) in Bangkok, Thailand was "From Biological Data to Knowledge to Technological Breakthroughs." Besides providing a forum for life scientists and bioinformatics researchers in the Asia-Pacific region to meet and interact, the conference also hosted thematic sessions on the Pan-Asian Pacific Genome Initiative and immunoinformatics. Over the seven years of conference papers published in BMC Bioinformatics and four years in BMC Genomics, we note that there is increasing interest in the applications of -omics technologies to the understanding of diseases, as a forerunner to personalized genomic medicine.