Signature Gene Identification of Cancer Occurrence and Pattern Recognition

Multi-omics pan-cancer study of SPTBN2 and its value as a potential therapeutic target in pancreatic cancer

SPTBN2 is a protein-coding gene that is closely related to the development of malignant tumors. However, its prognostic value and biological function in pan-cancer, especially pancreatic cancer (PAAD), have not been reported. In the present study, a novel exploration of the value and potential mechanism of SPTBN2 in PAAD was conducted using multi-omics in the background of pan-cancer. Via various database analysis, up-regulated expression of SPTBN2 was detected in most of the tumor tissues examined. Overexpression of SPTBN2 in PAAD and kidney renal clear cell cancer patients potentially affected overall survival, disease-specific survival, and progression-free interval. In PAAD, SPTBN2 can be used as an independent factor affecting prognosis. Mutations and amplification of SPTBN2 were detected, with abnormal methylation of SPTBN2 affecting its expression and the survival outcome of PAAD patients. Immunoassay results demonstrate that SPTBN2 was a potential biomarker for predicting therapeutic response in PAAD, and may influence the immunotherapy efficacy of PAAD by regulating levels of CD8 + T cells and neutrophil infiltration. Results from an enrichment analysis indicated that SPTBN2 may regulate the development of PAAD via immune pathways. Thus, SPTBN2 is a potential prognostic biomarker and immunotherapy target based on its crucial role in the development of PAAD.

Decreased SPTBN2 expression regulated by the ceRNA network is associated with poor prognosis and immune infiltration in low‑grade glioma

The majority of low-grade gliomas (LGGs) in adults invariably progress to glioblastoma over time. Spectrin β non-erythrocytic 2 (SPTBN2) is detected in numerous tumors and is involved in tumor occurrence and metastasis. However, the specific roles and detailed mechanisms of SPTBN2 in LGG are largely unknown. The present study performed pan-cancer analysis for the expression and prognosis of SPTBN2 in LGG using The Cancer Genome Atlas and The Genotype-Tissue Expression. Western blotting was used to detect the amount of SPTBN2 between glioma tissues and normal brain tissues. Subsequently, based on expression, prognosis, correlation and immune infiltration, non-coding RNAs (ncRNAs) were identified that regulated SPTBN2 expression. Finally, tumor immune infiltrates associated with SPTBN2 and prognosis were performed. Lower expression of SPTBN2 was correlated with an unfavorable outcome in LGG. A significant correlation between the low SPTBN2 mRNA expression and poor clinicopathological features was observed, including wild-type isocitrate dehydrogenase status (P<0.001), 1p/19q non-codeletion (P<0.001) and elders (P=0.019). The western blotting results revealed that, compared with normal brain tissues, the amount of SPTBN2 was significantly lower in LGG tissues (P=0.0266). Higher expression of five microRNAs (miRs/miRNAs), including hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-16-5p, hsa-miR-34c-5p and hsa-miR-424-5p, correlated with poor prognosis by targeting SPTBN2 in LGG. Subsequently, four long ncRNAs (lncRNAs) [ARMCX5-GPRASP2, BASP1-antisense RNA 1 (AS1), EPB41L4A-AS1 and LINC00641] were observed in the regulation of SPTBN2 via five miRNAs. Moreover, the expression of SPTBN2 was significantly correlated with tumor immune infiltration, immune checkpoint expression and biomarkers of immune cells. In conclusion, SPTBN2 was lowly expressed and correlated with an unfavorable prognosis in LGG. A total of six miRNAs and four lncRNAs were identified as being able to modulate SPTBN2 in a lncRNA-miRNA-mRNA network of LGG. Furthermore, the current findings also indicated that SPTBN2 possessed anti-tumor roles by regulating tumor immune infiltration and immune checkpoint expression.

SPTBN2 Promotes the Progression of Thyroid Cancer by Accelerating G1/S Transition and Inhibiting Apoptosis

Background. Thyroid carcinoma (TC) is an increasingly common malignancy of endocrine organs, and its most frequently encountered histotype is papillary thyroid cancer (PTC). Identifying new potential gene alterations is important for completely elucidating the mechanism of PTC initiation and progression. Thus, we performed whole transcriptome sequence analysis (RNA-seq) on 79 PTC tissue samples and paired adjacent nontumor tissue samples to study the molecular mechanism of TC tumorigenesis and progression further. The results of RNA-seq analysis showed that spectrin beta, nonerythrocytic 2 (SPTBN2), was markedly overexpressed in PTC tissues relative to that in the paired nontumor tissues. Additionally, the analysis results for 502 PTC samples and 58 nontumor thyroid samples from The Cancer Genome Atlas dataset were consistent with our RNA-seq results. However, the molecular mechanisms and function of SPTBN2 in TC progression remain unknown. Methods. We examined SPTBN2 gene expression in 48 papillary thyroid tumor tissues and paired adjacent normal thyroid tissues by using qRT-PCR. SPTBN2 expression in the TC cell lines was silenced by small interfering RNA. Then, the transfected TC cells were used to investigate the in vitro function of SPTBN2. Result. The expression of SPTBN2 was significantly upregulated in our RNA-seq cohort, our local validated cohort, and TCGA RNA-seq cohort. The results of the in vitro experiment revealed that in TC cell lines, SPTBN2 downregulation considerably suppressed tumor cell proliferation, the cell cycle, migration, colony formation, and invasion and induced cell apoptosis. Furthermore, the protein levels of CCNE2, CDK2, CDK4, and Bcl-2 were downregulated, and those of P21, Bax, cleaved caspase-8, and cleaved caspase-3 had increased in transfected TC cells relative to in control TC cells. Conclusion. The downregulation of SPTBN2 caused apoptosis and retarded G1/S cell cycle transition in TC cells. Thus, SPTBN2 may be a good candidate gene for TC diagnosis and therapy.

Identification of Recurrent Chromosome Breaks Underlying Structural Rearrangements in Mammary Cancer Cell Lines

Cancer genomes are characterized by the accumulation of small-scale somatic mutations as well as large-scale chromosomal deletions, amplifications, and complex structural rearrangements. This characteristic is at least partially dependent on the ability of cancer cells to undergo recurrent chromosome breakage. In order to address the extent to which chromosomal structural rearrangement breakpoints correlate with recurrent DNA double-strand breaks (DSBs), we simultaneously mapped chromosome structural variation breakpoints (using whole-genome DNA-seq) and spontaneous DSB formation (using Break-seq) in the estrogen receptor (ER)-positive breast cancer cell line MCF-7 and a non-cancer control breast epithelium cell line MCF-10A. We identified concurrent DSBs and structural variation breakpoints almost exclusively in the pericentromeric region of chromosome 16q in MCF-7 cells. We fine-tuned the identification of copy number variation breakpoints on 16q. In addition, we detected recurrent DSBs that occurred in both MCF-7 and MCF-10A. We propose a model for DSB-driven chromosome rearrangements that lead to the translocation of 16q, likely with 10q, and the eventual 16q loss that does not involve the pericentromere of 16q. We present evidence from RNA-seq data that select genes, including SHCBP1, ORC6, and MYLK3, which are immediately downstream from the 16q pericentromere, show heightened expression in MCF-7 cell line compared to the control. Data published by The Cancer Genome Atlas show that all three genes have increased expression in breast tumor samples. We found that SHCBP1 and ORC6 are both strong poor prognosis and treatment outcome markers in the ER-positive breast cancer cohort. We suggest that these genes are potential oncogenes for breast cancer progression. The search for tumor suppressor loss that accompanies the 16q loss ought to be augmented by the identification of potential oncogenes that gained expression during chromosomal rearrangements.

SPTBN2, a New Biomarker of Lung Adenocarcinoma

Objectives

The roles played by β-III-spectrin, also known as spectrin beta, non-erythrocytic 2 (SPTBN2), in the occurrence and development of lung adenocarcinoma (LUAD) have not been previously examined. Our study aimed to reveal the relationship between the SPTBN2 expression and LUAD.

Materials and Methods

Twenty pairs of LUAD tissues and adjacent tissues were collected from patients diagnosed and treated at the Thoracic Surgery Department of The First Affiliated Hospital of Zhengzhou University from July 2019 to September 2020. RNA sequencing (RNA-seq) analysis determined that the expression of SPTBN2 was higher in LUAD samples than in adjacent normal tissues. The expression levels of SPTBN2 were examined in various databases, including the Cancer Cell Line Encyclopedia (CCLE), Gene Expression Omnibus (GEO), and Human Protein Atlas (HPA). The Search Tool for the Retrieval of Interacting Genes (STRING) online website was used to examine protein–protein interactions involving SPTBN2, and the results were visualized by Cytoscape software. The Molecular Complex Detection (MCODE) plug-in for Cytoscape software was used to identify functional modules of the obtained protein–protein interaction (PPI) network. Gene enrichment analysis was performed, and survival analysis was conducted using the Kaplan–Meier plotter. The online prediction website TargetScan was used to predict SPTBN2-targeted miRNA sequences by searching for SPTBN2 sequences. Finally, we verified the expression of SPTBN2 in the obtained tissue samples using real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). The human lung cancer cell lines A549 and H1299 were selected for the transfection of small interfering RNA (siRNA) targeting SPTBN2 (si-SPTBN2), and the knockdown efficiency was evaluated by RT-qPCR. The cellular proliferation, migration, and invasion capacities of A549 and H1299 cells were determined using the cell counting kit-8 (CCK-8) proliferation assay; the wound-healing assay and the Transwell migration assay; and the Matrigel invasion assay, respectively.

Results

The expression of SPTBN2 in non–small cell lung cancer (NSCLC) ranked 13th among cancer cell lines based on the CCLE database. At the mRNA and protein levels, the expression levels of SPTBN2 were higher in LUAD tissues than in normal lung tissues. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that proteins related to SPTBN2 were enriched in apoptotic and phagosomal pathways. Kaplan–Meier survival analysis revealed that SPTBN2 expression was significantly related to the prognosis of patients with LUAD. The TargetScan database verified that miR-16 was a negative regulator of SPTBN2 mRNA expression. The results of the CCK-8 cell proliferation assay revealed that SPTBN2 knockdown significantly inhibited the cell proliferation abilities of A549 and H1299 cells. The wound-healing assay indicated that SPTBN2 knockdown resulted in reduced migration after 48 h compared with the control group. The Transwell migration and invasion test revealed that the migration and invasion abilities were greatly decreased by SPTBN2 knockdown compared with control conditions.

Conclusion

We uncovered a novel gene, SPTBN2, that was significantly upregulated in LUAD tissues relative to normal tissue expression. SPTBN2 is highly expressed in LUAD, positively correlated with poor prognosis, and can promote the proliferation, migration, and invasion of LUAD cells.

Machine learning analysis of TCGA cancer data

In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order to study the state of the art, this review is provided to cover the main works that have used ML with TCGA data. Firstly, the principal discoveries made by the TCGA consortium are presented. Once these bases have been established, we begin with the main objective of this study, the identification and discussion of those works that have used the TCGA data for the training of different ML approaches. After a review of more than 100 different papers, it has been possible to make a classification according to following three pillars: the type of tumour, the type of algorithm and the predicted biological problem. One of the conclusions drawn in this work shows a high density of studies based on two major algorithms: Random Forest and Support Vector Machines. We also observe the rise in the use of deep artificial neural networks. It is worth emphasizing, the increase of integrative models of multi-omic data analysis. The different biological conditions are a consequence of molecular homeostasis, driven by both protein coding regions, regulatory elements and the surrounding environment. It is notable that a large number of works make use of genetic expression data, which has been found to be the preferred method by researchers when training the different models. The biological problems addressed have been classified into five types: prognosis prediction, tumour subtypes, microsatellite instability (MSI), immunological aspects and certain pathways of interest. A clear trend was detected in the prediction of these conditions according to the type of tumour. That is the reason for which a greater number of works have focused on the BRCA cohort, while specific works for survival, for example, were centred on the GBM cohort, due to its large number of events. Throughout this review, it will be possible to go in depth into the works and the methodologies used to study TCGA cancer data. Finally, it is intended that this work will serve as a basis for future research in this field of study.

A Comprehensive Analysis of the Downregulation of miRNA-1827 and Its Prognostic Significance by Targeting SPTBN2 and BCL2L1 in Ovarian Cancer

Background: Previous studies demonstrated that miRNA-1827 could repress various cancers on proliferation, angiogenesis, and metastasis. However, little attention has been paid to its role in ovarian cancer as a novel biomarker or intervention target, especially its clinical significance and underlying regulatory network.

Methods: A meta-analysis of six microarrays was adopted here to determine the expression trend of miRNA-1827, and was further validated by gene expression profile data and cellular experiments. We explored the functional annotations through enrichment analysis for the differentially expressed genes targeted by miRNA-1827. Subsequently, we identified two hub genes, SPTBN2 and BCL2L1, based on interaction analysis using two online archive tools, miRWALK (it consolidates the resources of 12 miRNA-focused servers) and Gene Expression Profiling Interactive Analysis (GEPIA). Finally, we validated their characteristics and clinical significance in ovarian cancer.

Results: The comprehensive meta-analysis revealed that miRNA-1827 was markedly downregulated in clinical and cellular specimens. Transfection of the miRNA-1827 mimic could significantly inhibit cellular proliferation. Concerning its target genes, they were involved in diverse biological processes related to tumorigenesis, such as cell proliferation, migration, and the apoptosis signaling pathway. Moreover, interaction analysis proved that two hub genes, SPTBN2 and BCL2L1, were highly associated with poor prognosis in ovarian cancer.

Conclusion: These integrated bioinformatic analyses indicated that miRNA-1827 was dramatically downregulated in ovarian cancer as a tumor suppressor. The upregulation of its downstream modulators, SPTBN2 and BCL2L1, was associated with an unfavorable prognosis. Thus, the present study has identified miRNA-1827 as a potential intervention target for ovarian cancer based on our bioinformatic analysis processes.

Molecular docking and machine learning analysis of Abemaciclib in colon cancer

BACKGROUND

The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design and discovery of new specific treatments for each patient. In this context, this work offers new ways to reuse existing databases and work to create added value in research. Three published signatures with significante prognostic value in Colon Adenocarcinoma (COAD) were indentified. These signatures were combined in a new meta-signature and validated with main Machine Learning (ML) and conventional statistical techniques. In addition, a drug repurposing experiment was carried out through Molecular Docking (MD) methodology in order to identify new potential treatments in COAD.

RESULTS

The prognostic potential of the signature was validated by means of ML algorithms and differential gene expression analysis. The results obtained supported the possibility that this meta-signature could harbor genes of interest for the prognosis and treatment of COAD. We studied drug repurposing following a molecular docking (MD) analysis, where the different protein data bank (PDB) structures of the genes of the meta-signature (in total 155) were confronted with 81 anti-cancer drugs approved by the FDA. We observed four interactions of interest: GLTP - Nilotinib, PTPRN - Venetoclax, VEGFA - Venetoclax and FABP6 - Abemaciclib. The FABP6 gene and its role within different metabolic pathways were studied in tumour and normal tissue and we observed the capability of the FABP6 gene to be a therapeutic target. Our in silico results showed a significant specificity of the union of the protein products of the FABP6 gene as well as the known action of Abemaciclib as an inhibitor of the CDK4/6 protein and therefore, of the cell cycle.

CONCLUSIONS

The results of our ML and differential expression experiments have first shown the FABP6 gene as a possible new cancer biomarker due to its specificity in colonic tumour tissue and no expression in healthy adjacent tissue. Next, the MD analysis showed that the drug Abemaciclib characteristic affinity for the different protein structures of the FABP6 gene. Therefore, in silico experiments have shown a new opportunity that should be validated experimentally, thus helping to reduce the cost and speed of drug screening. For these reasons, we propose the validation of the drug Abemaciclib for the treatment of colon cancer.

Identification of hub genes in papillary thyroid carcinoma: robust rank aggregation and weighted gene co-expression network analysis

BACKGROUND

Papillary thyroid carcinoma (PTC), which is the most common endocrine malignancy, has been steadily increasing worldwide in incidence over the years, while mechanisms underlying the pathogenesis and diagnostic for PTC are incomplete. The purpose of this study is to identify potential biomarkers for diagnosis of PTC, and provide new insights into pathogenesis of PTC.

METHODS

Based on weighted gene co-expression network analysis, Robust Rank Aggregation, functional annotation, GSEA and DNA methylation, were employed for investigating potential biomarkers for diagnosis of PTC.

RESULTS

Black and turquoise modules were identified in the gene co-expression network constructed by 1807 DEGs that from 6 eligible gene expression profiles of Gene Expression Omnibus database based on Robust Rank Aggregation and weighted gene co-expression network analysis. Hub genes were significantly down-regulated and the expression levels of the hub genes were different in different stages in hub gene verification. ROC curves indicated all hub genes had good diagnostic value for PTC (except for ABCA6 AUC = 89.5%, the 15 genes with AUC > 90%). Methylation analysis showed that hub gene verification ABCA6, ACACB, RMDN1 and TFPI were identified as differentially methylated genes, and the decreased expression level of these genes may relate to abnormal DNA methylation. Moreover, the expression levels of 8 top hub genes were correlated with tumor purity and tumor-infiltrating immune cells. These findings, including functional annotations and GSEA provide new insights into pathogenesis of PTC.

CONCLUSIONS

The hub genes and methylation of hub genes may as potential biomarkers provide new insights for diagnosis of PTC, and all these findings may be the direction to study the mechanisms underlying of PTC in the future.

Dysregulated lncRNA-miRNA-mRNA Network Reveals Patient Survival-Associated Modules and RNA Binding Proteins in Invasive Breast Carcinoma

Breast cancer is the most common cancer in women, but few biomarkers are effective in clinic. Previous studies have shown the important roles of non-coding RNAs in diagnosis, prognosis, and therapy selection for breast cancer and have suggested the significance of integrating molecules at different levels to interpret the mechanism of breast cancer. Here, we collected transcriptome data including long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA for ~1,200 samples, including 1079 invasive breast carcinoma samples and 104 normal samples, from The Cancer Genome Atlas (TCGA) project. We identified differentially expressed lncRNAs, miRNAs, and mRNAs that distinguished invasive carcinoma samples from normal samples. We further constructed an integrated dysregulated network consisting of differentially expressed lncRNAs, miRNAs, and mRNAs and found housekeeping and cancer-related functions. Moreover, 58 RNA binding proteins (RBPs) involved in biological processes that are essential to maintain cell survival were found in the dysregulated network, and 10 were correlated with overall survival. In addition, we identified two modules that stratify patients into high- and low-risk subgroups. The expression patterns of these two modules were significantly different in invasive carcinoma versus normal samples, and some molecules were high-confidence biomarkers of breast cancer. Together, these data demonstrated an important clinical application for improving outcome prediction for invasive breast cancers.