Gene signature based on degradome-related genes can predict distal metastasis in cervical cancer patients

PRIM2 promotes proliferation and metastasis of pancreatic ductal adenocarcinoma through interactions with FAM111B

BACKGROUND

Pancreatic ductal adenocarcinomas (PDAC) are huge threat to human for the extreme malignancy. PRIM2 was reported as tumor marker, while the functions and regulatory mechanisms in PDAC are still unclear. The study aimed to investigate the function of PRIM2 in PDAC.

METHODS

Expression was detected using immunohistochemistry (IHC), Western blot, and real-time quantitative PCR (RT-qPCR) methods. Cell assays and xenograft model confirmed the phenotypes. Co-Immunoprecipitation (Co-IP) and protein stability assays were used for protein interactions.

RESULTS

Inhibiting PRIM2 resulted in decreased proliferation and migration both in vitro and in vivo. PRIM2 upregulated FAM111B at increased RNA levels and protein stability.

CONCLUSION

PRIM2/FAM111B axis promoted proliferation and migration by modulating the PI3K/AKT and epithelial-mesenchymal transition (EMT) markers. The axis has the potential to be targeted for PDAC treatment.

Family with sequence similarity 111 member B contributes to tumor growth and metastasis by mediating cell proliferation, invasion, and EMT via transforming acidic coiled-coil protein 3/PI3K/AKT signaling pathway in hepatocellular carcinoma

As a complex systemic disease, primary liver cancer ranks third in death rate for solid tumors worldwide. Family with sequence similarity 111 member B (FAM111B), which was found to be aberrantly mutated in multiple cancers, is a candidate oncogene. We aimed to determine the function and mechanism of FAM111B in hepatocellular carcinoma (HCC). The expression of FAM111B was evaluated in HCC tissues, adjacent tissues, HCC cell lines. The impact of FAM111B on proliferation, invasion, apoptosis and EMT of HCC cells were detected by CCK-8, Transwell, flow cytometry and Western blot assays. The relationship between FAM111B and transforming acidic coiled-coil protein 3 (TACC3) was assessed by CoIP and Immunofluorescence (IF) staining assays. The effect of FAM111B on tumor growth was detected by using xenograft model of nude mice. The expression of FAM111B was upregulated in HCC tissues and cell lines, and the prognosis of HCC patients was worse in the high FAM111B expression group, and its expression level was associated with the TNM stage of HCC. FAM111B silencing inhibited HCC cell proliferation and invasion, EMT and induced apoptosis. Besides, TACC3 served as an interactor for FAM111B, which could enhance TACC3 expression, thus activing PI3K/AKT pathway. Rescue experiments revealed that elevated of TACC3 restored the inhibitory effect of FAM111B overexpression on the cell functions via PI3K/AKT pathway. In vivo, FAM111B inhibition hampered tumor growth and metastasis of HCC. This study highlighted a key player of FAM111B in modulating the malignant biological progression of HCC via TACC3/PI3K/AKT signaling pathway, displaying a potential therapeutic target for HCC.

Silencing of FAM111B inhibited proliferation, migration and invasion of hepatoma cells through activating p53 pathway

BACKGROUND

The function of Family with sequence similarity 111 member B (FAM111B) has been reported in multiple malignancies, but its involvement in occurrence and development of hepatocellular carcinoma (HCC) is still unclear.

PURPOSE

To investigate the role of FAM111B in HCC and explore the potential molecular mechanism.

METHODS

We examined the mRNA level of FAM111B via qPCR and protein level via immunohistochemistry in human HCC tissues. siRNA was used to construct a FAM111B-knockdown model in HCC cell lines. CCK-8, colony formation, transwell, and wound healing assays were performed to investigate the effect of FAM111B on proliferation, migration and invasion of HCC cell. Gene Set Enrichment Analysis, western blotting, and flow cytometry were carried out to find the related molecular mechanism.

RESULTS

Human HCC tumor tissues exhibited higher expression of FAM111B, and high FAM111B expression was associated with poor prognosis. Vitro assays demonstrated that knockdown of FAM111B greatly repressed proliferation, migration and invasion of HCC cells. Furthermore, silencing of FAM111B significantly resulted in cell cycle arrest at G0/G1 and downregulation of epithelial-mesenchymal transition (EMT)-related proteins MMP7 and MMP9 via activation of p53 pathway.

CONCLUSION

FAM111B played an essential role in promoting HCC development by regulation of p53 pathway.

FAM111B Acts as an Oncogene in Bladder Cancer

Bladder cancer (BLCA) is a prevalent malignancy of the urinary system, associated with a high recurrence rate and poor prognosis. FAM111B, which encodes a protein containing a trypsin-like cysteine/serine peptidase domain, has been implicated in the progression of various human cancers; however, its involvement in BLCA remains unclear. In this study, we investigated the expression of FAM111B gene in tumor tissues compared to para-tumor tissues using immunohistochemistry and observed a significantly higher FAM111B gene expression in tumor tissues. Furthermore, analysis of clinical characteristics indicated that the increased FAM111B gene expression correlated with lymphatic metastasis and reduced overall survival. To investigate its functional role, we employed FAM111B-knockdown BLCA cell models and performed cell proliferation, wound-healing, transwell, and flow cytometry assays. The results showed that decreased FAM111B gene expression inhibited proliferation and migration but induced apoptosis in BLCA cells. In vivo experiments further validated that FAM111B knockdown suppressed tumor growth. Overall, our findings suggest that FAM111B acts as an oncogene in BLCA, playing a critical role in tumorigenesis, progression, and metastasis of BLCA. In conclusion, we have demonstrated a strong correlation between the expression of FAM111B gene and the development, progression, and metastasis of bladder cancer (BLCA). Thus, FAM111B is an oncogene associated with BLCA and holds promise as a molecular target for future treatment of this cancer.

N6 -methyladenosine-modified FAM111A-DT promotes hepatocellular carcinoma growth via epigenetically activating FAM111A

As an epitranscriptomic modulation manner, N6 -methyladenosine (m6 A) modification plays important roles in various diseases, including hepatocellular carcinoma (HCC). m6 A modification affects the fate of RNAs. The potential contributions of m6 A to the functions of RNA still need further investigation. In this study, we identified long noncoding RNA FAM111A-DT as an m6 A-modified RNA and confirmed three m6 A sites on FAM111A-DT. The m6 A modification level of FAM111A-DT was increased in HCC tissues and cell lines, and increased m6 A level was correlated with poor survival of HCC patients. m6 A modification increased the stability of FAM111A-DT transcript, whose expression level showed similar clinical relevance to that of the m6 A level of FAM111A-DT. Functional assays found that only m6 A-modified FAM111A-DT promoted HCC cellular proliferation, DNA replication, and HCC tumor growth. Mutation of m6 A sites on FAM111A-DT abolished the roles of FAM111A-DT. Mechanistic investigations found that m6 A-modified FAM111A-DT bound to FAM111A promoter and also interacted with m6 A reader YTHDC1, which further bound and recruited histone demethylase KDM3B to FAM111A promoter, leading to the reduction of the repressive histone mark H3K9me2 and transcriptional activation of FAM111A. The expression of FAM111A was positively correlated with the m6 A level of FAM111A-DT, and the expression of methyltransferase complex, YTHDC1, and KDM3B in HCC tissues. Depletion of FAM111A largely attenuated the roles of m6 A-modified FAM111A-DT in HCC. In summary, the m6 A-modified FAM111A-DT/YTHDC1/KDM3B/FAM111A regulatory axis promoted HCC growth and represented a candidate therapeutic target for HCC.

Insulin‑like growth factor axis: A potential nanotherapy target for resistant cervical cancer tumors (Review)

Cervical cancer is among the most frequently occurring neoplasms worldwide, and it particularly affects individuals in developing countries. Factors such as the low quality of screening tests, the high incidence of locally advanced cancer stages and the intrinsic resistance of certain tumors are the main causes of failure in the treatment of this neoplasm. Due to advances in the understanding of carcinogenic mechanisms and bioengineering research, advanced biological nanomaterials have been manufactured. The insulin-like growth factor (IGF) system comprises multiple growth factor receptors, including IGF receptor 1. These receptors are activated by binding to their respective growth factor ligands, IGF-1 and IGF-2, and insulin, and play an important role in the development, maintenance, progression, survival and treatment resistance of cervical cancer. In the present review, the role of the IGF system in cervical cancer and three nanotechnological applications that use elements of this system are described, namely Trap decoys, magnetic iron oxide nanoparticles and protein nanotubes. Their use in the treatment of resistant cervical cancer tumors is also discussed.

Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy

Introduction

Cellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-related genes in thyroid cancer (THCA) and their relationship with the TME.

Methods

Senescence-related genes were identified from the Molecular Signatures Database and used to conduct consensus clustering across TCGA-THCA. Differentially expressed genes (DEGs) were identified between the clusters used to perform multivariate Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a senescence-related signature. TCGA dataset was randomly divided into training and test datasets to verify the prognostic ability of the signature. Subsequently, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. Finally, the expression of signature genes was detected across TCGA-THCA and GSE33630 datasets, and further validated by RT-qPCR.

Results

Three senescence clusters were identified based on the expression of 432 senescence-related genes. Then, 23 prognostic DEGs were identified in TCGA dataset. The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-risk THCA shows a better prognosis and higher immunotherapy response than high-risk THCA. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. The validation part demonstrated that ADAMTSL4, DOCK6, FAM111B, and SEMA6B were expressed at higher levels in the tumor tissue, whereas lower expression of MRPS10 and PSMB7 was observed.

Discussion

In conclusion, the senescence-related signature is a promising biomarker for predicting the outcome of THCA and has the potential to guide immunotherapy.

Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis

Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.

Identification of circRNA-lncRNA-miRNA-mRNA competitive endogenous RNA networks as prognostic bio-markers in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent neoplasms of malignancy. The pathogenesis of HNSCC is involved in the change of gene expression and the abnormal interaction between genes.

OBJECTIVE

The purpose of our research is to find genes that affect the survival time of patients and construct circRNA-lncRNA-miRNA-mRNA network. This paper aims to reveal the development mechanism of HNSCC and provide new ideas for clinical prognosis prediction.

METHODS

Transcriptome data were obtained from TCGA and GEO databases. A total of 556 samples were included. We screened the differentially expressed lncRNAs, circRNAs and mRNAs to predict interactions between lncRNA-miRNA, circRNA-miRNA and miRNA-mRNA. Then, we selected the hub mRNAs to predict the prognosis of patients.

RESULTS

We identified 8 hub prognostic mRNAs (FAM111A, IFIT2, CAV1, KLF9, OCIAD2, SLCO3A1, S100A16 and APOL3) by a series of bioinformatics analyses and established the prognostic ceRNA network of 8 mRNAs, 3 miRNAs, 2 circRNAs and 1 lncRNAs according to the targeting relationship by using databases.

CONCLUSION

We established the circRNA-lncRNA-miRNA-mRNA gene interaction network in HNSCC. We illuminated the molecular mechanism underlying the gene regulation associated with the pathogenesis of HNSCC and predicted the biomarkers related to prognosis.

Molecular Differences between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers

The most frequently diagnosed histological types of cervical cancer (CC) are squamous cell carcinoma (SCC) and adenocarcinoma (ADC). Clinically, the prognosis of both types is controversial. A molecular profile that distinguishes each histological subtype and predicts the prognosis would be of great benefit to CC patients. Methods: The transcriptome of CC patients from The Cancer Genome Atlas (TCGA) was analyzed using the DESeq2 package to obtain the differentially expressed genes (DEGs) between ADC and SCC. The DEGs were validated on a publicly available Mexican-Mestizo patient transcriptome dataset (GSE56303). The global biological pathways involving the DEGs were obtained using the Webgestalt platform. The associations of the DEGs with Overall Survival (OS) were assessed. Finally, three DEGs were validated by RT-qPCR in an independent cohort of Mexican patients. Results. The molecular profiles of ADC and SCC of the CC patients of the TCGA database and the Mexican-Mestizo cohort (GSE56303) were determined obtaining 1768 and 88 DEGs, respectively. Strikingly, 70 genes were concordant—with similar Log2FoldChange values—in both cohorts. The 70 DEGs were involved in IL-17, JAK/STAT, and Ras signaling. Kaplan-Meier OS analysis from the Mexican-Mestizo cohort showed that higher GABRB2 and TSPAN8 and lower TMEM40 expression were associated with better OS. Similar results were found in an independent Mexican cohort. Conclusions: Molecular differences were detected between the ADC and SCC subtypes; however, further studies are required to define the appropriate prognostic biomarker for each histological type.

Proposed Cellular Function of the Human FAM111B Protein and Dysregulation in Fibrosis and Cancer

FAM111B gene mutations are associated with a hereditary fibrosing poikiloderma known to cause poikiloderma, tendon contracture, myopathy, and pulmonary fibrosis (POIKTMP). In addition, the overexpression of FAM111B has been associated with cancer progression and poor prognosis. This review inferred the molecular function of this gene's protein product and mutational dysfunction in fibrosis and cancer based on recent findings from studies on this gene. In conclusion, FAM111B represents an uncharacterized protease involved in DNA repair, cell cycle regulation, and apoptosis. The dysregulation of this protein ultimately leads to fibrotic diseases like POIKTMP and cancers via the disruption of these cellular processes by the mutation of the FAM111B gene. Hence, it should be studied in the context of these diseases as a possible therapeutic target.

Complement factor B regulates cellular senescence and is associated with poor prognosis in pancreatic cancer

BACKGROUND

The interplay between cancer cells and stromal components, including soluble mediators released from cancer cells, contributes to the progression of pancreatic ductal adenocarcinoma (PDAC). Here, we set out to identify key secreted proteins involved in PDAC progression.

METHODS

We performed secretome analyses of culture media of mouse pancreatic intraepithelial neoplasia (PanIN) and PDAC cells using Stable Isotope Labeling by Amino acid in Cell culture (SILAC) with click chemistry and liquid chromatography-mass spectrometry (LC-MS/MS). The results obtained were verified in primary PDAC tissue samples and cell line models.

RESULTS

Complement factor B (CFB) was identified as one of the robustly upregulated proteins, and found to exhibit elevated expression in PDAC cells compared to PanIN cells. Endogenous CFB knockdown by a specific siRNA dramatically decreased the proliferation of PDAC cells, PANC-1 and MIA PaCa-II. CFB knockdown induced increases in the number of senescence-associated-β-galactosidase (SA-β-gal) positive cells exhibiting p21 expression upregulation, which promotes cellular senescence with cyclinD1 accumulation. Furthermore, CFB knockdown facilitated downregulation of proliferating cell nuclear antigen and led to cell cycle arrest in the G1 phase in PDAC cells. Using immunohistochemistry, we found that high stromal CFB expression was associated with unfavorable clinical outcomes with hematogenous dissemination after surgery in human PDAC patients. Despite the presence of enriched CD8+ tumor infiltrating lymphocytes in the PDAC tumor microenvironments, patients with a high stromal CFB expression exhibited a significantly poorer prognosis compared to those with a low stromal CFB expression. Immunofluorescence staining revealed a correlation between stromal CFB expression in the tumor microenvironment and an enrichment of immunosuppressive regulatory T-cells (Tregs), myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs). We also found that high stromal CFB expression showed a positive correlation with high CD8+/Foxp3+ Tregs populations in PDAC tissues.

CONCLUSIONS

Our data indicate that CFB, a key secreted protein, promotes proliferation by preventing cellular senescence and is associated with immunological tumor promotion in PDAC. These findings suggest that CFB may be a potential target for the treatment of PDAC.

Molecular and Clinical Characterization of a Novel Prognostic and Immunologic Biomarker FAM111A in Diffuse Lower-Grade Glioma

Background

Family with sequence similarity 111 member A (FAM111A), as a replication factor required for proliferating cell nuclear antigen (PCNA) loading, has been demonstrated a possible association with carcinogenesis. However, the role of FAM111A in lower-grade glioma (LGG) remains unclear. We aim at investigating the expression and function of FAM111A in lower-grade glioma at the molecular and clinical levels.

Methods

In total, 711 lower-grade glioma samples were analyzed in our research, including 182 RNA-seq data from the Chinese Glioma Genome Atlas (CGGA) dataset and 529 RNA-seq data from The cancer Genome Atlas (TCGA) dataset. R language and the GraphPad software were used for the majority of statistical analysis and graphical work.

Results

FAM111A expression was overexpressed in WHO grade III and IDH-wildtype lower-grade glioma. FAM111A was significantly downregulated in the IDHmut-Codel molecular subtype. Univariate and multivariate Cox analysis demonstrated that FAM111A was an independent prognostic factor in LGG patients. Functional characterization of FAM111A revealed that it was associated with inflammatory response and immune response to tumor cells. FAM111A could also act as an indicator of the stromal and immune population, especially for monocytic lineage, myeloid dendritic cells and fibroblasts. It was positively correlated with macrophages, especially the M2 macrophage cells. Furthermore, FAM111A revealed predictive value for the immune subtypes and immune checkpoint blockade therapy.

Conclusion

FAM111A expression was closely related to the malignant phenotype, molecular pathology and immune response of lower-grade glioma. It might be a promising target for LGG immunotherapeutic strategies.

A 70-Gene Signature for Predicting Treatment Outcome in Advanced-Stage Cervical Cancer

Cervical cancer is the fourth most common cancer in women worldwide. The current approaches still have limitations in predicting the therapy outcome of each individual because of cancer heterogeneity. The goal of this study was to establish a gene expression signature that could help when choosing the right therapeutic method for the treatment of advanced-stage cervical cancer. The 666 patients were collected from four independent datasets. The 70-gene expression signature was established using univariate Cox proportional hazard regression analysis. The 70-gene signature was significantly different between low- and high-risk groups in the training dataset (p = 4.24e-6) and in the combined three validation datasets (p = 4.37e-3). Treatment of advanced-stage cancer patients in the high-risk group with molecular-targeted therapy combined with chemoradiotherapy yielded a better survival rate than with only chemoradiotherapy (p = 0.0746). However, treatment of the patients in the low-risk group with the combined therapy resulted in significantly lower survival (p = 0.00283). Functional classification of 70 genes revealed involvement of the angiogenesis pathway, specifically phosphatidylinositol 3-kinase signaling (p = 0.040), extracellular matrix organization (p = 0.0452), and cell adhesion (p = 0.011). The 70-gene signature could predict the prognosis and indicate an optimal therapeutic modality in molecular-targeted therapy or chemotherapy for advanced-stage cervical cancer.

Elastic Net-Based Identification of a Multigene Combination Predicting the Survival of Patients with Cervical Cancer

Background

The objective of the present study was to identify prognostication biomarkers in patients with cervical cancer.

Material/Methods

Survival related genes were identified in The Cancer Genome Atlas (TCGA) cervical cancer study, and they were included into an elastic net regularized Cox proportional hazards regression model (CoxPH). The genes that their coefficients that were not zero were combined to build a prognostication combination. The prognostication performance of the multigene combination was evaluated and validated using Kaplan-Meier curve and univariate and multivariable CoxPH model. Meanwhile, a nomogram was built to translate the multigene combination into clinical application.

Results

There were 37 survival related genes identified, 9 of which were integrated to build a multigene combination. The area under the curve (AUC) of receiver operating characteristic (ROC) curve at 1-year, 3-year, 5-year, and 7-year in the training set were 0.757, 0.744, 0.799, and 0.854, respectively, and the multigene combination could stratify patients into significantly different prognostic groups (hazard ratio [HR]=0.2223, log-rank P<0.0001). Meanwhile, the corresponding AUCs in the test set was 0.767, 0.721, 0.735, and 0.703, respectively, and the multigene combination could classify patients into different risk groups (HR=0.3793, log-rank P=0.0021). The multigene combination could stratify patients with early stage and advanced stage into significantly different survival groups in the training set and test set. The prognostication performance of the multigene combination was better compared with 3 existing prognostic signatures. Finally, a multigene containing nomogram was developed.

Conclusions

We developed a multigene combination which could be treated as an independent prognostic factor in cervical cancer and be translated into clinical application.

Identification of Long Non-Coding RNA Expression Profiles and Co-Expression Genes in Thyroid Carcinoma Based on The Cancer Genome Atlas (TCGA) Database

Background

Thyroid carcinoma is a malignancy with high morbidity and mortality. Genetic alterations play pivot roles in the pathogenesis of thyroid carcinoma, where long noncoding RNA (lncRNA) have been identified to be crucial. This study sought to investigate the biological functions of lncRNA expression profiles in thyroid carcinoma.

Material/Methods

The lncRNAs expression profiles were acquired from The Cancer Genome Atlas (TCGA) database according to 510 thyroid cancer tissues and 58 normal thyroid tissues. By using R package edgeR, differentially expressed RNAs were obtained. Also, an overall survival model was established based on Cox regression and clinical data then testified by Kaplan-Meier plot, receiver operating characteristic (ROC)-curve and C-index analysis. We investigated the co-expressed genes with lncRNAs involved in the prognostic model, as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted R package clusterProfile.

Results

A total of 352 lncRNAs were identified as differentially expressed in thyroid carcinoma, and an overall survival model consisting of 8 signature lncRNAs was proposed (ROC=0.862, C-index=0.893, P<0.05), 3 of which (DOCK9-DT, FAM111A-DT, and LINC01736) represent co-expressed mRNAs. However, as an oncogene, only FAM111A-DT increased the prognostic risk in thyroid carcinoma. Furthermore, we found differential genes LINC01016, LHX1-DT, IGF2-AS, ND MIR1-1HG-AS1, significantly related to lymph node metastasis (P<0.05).

Conclusions

In this study, we clarified the differential lncRNA expression profiles which were related to the tumorigenesis and prognosis in thyroid carcinoma. Our results provide new rationale and understandings to the pathogenesis and regulatory mechanisms of thyroid carcinoma.

OmicsARules: a R package for integration of multi-omics datasets via association rules mining

BACKGROUND

The improvements of high throughput technologies have produced large amounts of multi-omics experiments datasets. Initial analysis of these data has revealed many concurrent gene alterations within single dataset or/and among multiple omics datasets. Although powerful bioinformatics pipelines have been developed to store, manipulate and analyze these data, few explicitly find and assess the recurrent co-occurring aberrations across multiple regulation levels.

RESULTS

Here, we introduced a novel R-package (called OmicsARules) to identify the concerted changes among genes under association rules mining framework. OmicsARules embedded a new rule-interestingness measure, Lamda3, to evaluate the associated pattern and prioritize the most biologically meaningful gene associations. As demonstrated with DNA methlylation and RNA-seq datasets from breast invasive carcinoma (BRCA), esophageal carcinoma (ESCA) and lung adenocarcinoma (LUAD), Lamda3 achieved better biological significance over other rule-ranking measures. Furthermore, OmicsARules can illustrate the mechanistic connections between methlylation and transcription, based on combined omics dataset. OmicsARules is available as a free and open-source R package.

CONCLUSIONS

OmicsARules searches for concurrent patterns among frequently altered genes, thus provides a new dimension for exploring single or multiple omics data across sequencing platforms.

Novel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network Analysis

Cervical cancer is the second most common malignancy and the third reason for mortality among women in developing countries. Although infection by the oncogenic human papilloma viruses is a major cause, genomic contributors are still largely unknown. Network analyses, compared with candidate gene studies, offer greater promise to map the interactions among genomic loci contributing to cervical cancer risk. We report here a differential co-expression network analysis in five gene expression datasets (GSE7803, GSE9750, GSE39001, GSE52903, and GSE63514, from the Gene Expression Omnibus) in patients with cervical cancer and healthy controls. Kaplan-Meier Survival and principle component analyses were employed to evaluate prognostic and diagnostic performances of biomarker candidates, respectively. As a result, seven distinct co-expressed gene modules were identified. Among these, five modules (with sizes of 9-45 genes) presented high prognostic and diagnostic capabilities with hazard ratios of 2.28-11.3, and diagnostic odds ratios of 85.2-548.8. Moreover, these modules were associated with several key biological processes such as cell cycle regulation, keratinization, neutrophil degranulation, and the phospholipase D signaling pathway. In addition, transcription factors ETS1 and GATA2 were noted as common regulatory elements. These genomic biomarker candidates identified by differential co-expression network analysis offer new prospects for translational cancer research, not to mention personalized medicine to forecast cervical cancer susceptibility and prognosis. Looking into the future, we also suggest that the search for a molecular basis of common complex diseases should be complemented by differential co-expression analyses to obtain a systems-level understanding of disease phenotype variability.

FAM111B, a direct target of p53, promotes the malignant process of lung adenocarcinoma

Purpose: Lung adenocarcinoma (LUAD) is a main subtype of lung cancer, which is the leading cause of cancer-related deaths. The five-year survival rates of lung cancer patients are still comparatively low. Therefore, potential therapeutic targets are urgently needed to improve the survival of lung cancer patients. In this study, we identified FAM111B as an oncogene and potential therapeutic target for LUAD.

Methods: The TCGA database and tissue microarray analysis were used to compare the expression of FAM111B in tumor tissue and normal tissues and evaluate the relationship between FAM111B expression and clinical survival. FAM111B was knocked down and overexpressed to observe whether FAM111B could affect the proliferation, migration, cell cycle, and apoptosis of LUAD cells in vivo and in vitro.

Results: FAM111B was highly expressed in tumor tissues compared with normal tissues (P<0.01). LUAD patients with hyper-expression of FAM111B had a lower recurrence-free survival (P<0.01) and shorter overall survival (P<0.01). Knocking down FAM111B inhibited cell proliferation, migration and invasion in vitro and tumor growth in vivo. Silencing FAM111B could arrest LUAD cells at G2/M phase and increase apoptosis. Overexpression of FAM111B promoted the growth of lung cancer cells. FAM111B was identified as a direct target of p53 in existing researches by chip-seq analysis. Bioinformatics analysis predicted that FAM111B could directly bind to BAG3 (BCL2 associated athanogene 3). When FAM111B was down-regulated, both expression of BAG 3 and BCL2 were significantly reduced, whereas decreasing the expression of BAG3 had no effect on FAM111B.

Conclusions: Our study indicated that FAM111B might be an oncogene and potential therapeutic target in LUAD which could be involved in the regulation of tumor cells by p53 signaling pathway and play an important role in the process of cell cycle and apoptosis by influencing the expression of BAG3 and BCL2.

Thymus-specific serine protease, a protease that shapes the CD4 T cell repertoire

The lifespan of T cells is determined by continuous interactions of their T cell receptors (TCR) with self-peptide-MHC (self-pMHC) complexes presented by different subsets of antigen-presenting cells (APC). In the thymus, developing thymocytes are positively selected through recognition of self-pMHC presented by cortical thymic epithelial cells (cTEC). They are subsequently negatively selected by medullary thymic epithelial cells (mTEC) or thymic dendritic cells (DC) presenting self-pMHC complexes. In the periphery, the homeostasis of mature T cells is likewise controlled by the interaction of their TCR with self-pMHC complexes presented by lymph node stromal cells while they may be tolerized by DC presenting tissue-derived self-antigens. To perform these tasks, the different subsets of APC are equipped with distinct combination of antigen processing enzymes and consequently present specific repertoire of self-peptides. Here, we discuss one such antigen processing enzyme, the thymus-specific serine protease (TSSP), which is predominantly expressed by thymic stromal cells. In thymic DC and TEC, TSSP edits the repertoire of peptide presented by class II molecules and thus shapes the CD4 T cell repertoire.