Bioinformatic analysis revealing mitotic spindle assembly regulated NDC80 and MAD2L1 as prognostic biomarkers in non-small cell lung cancer development

siRNA-based therapy for gastric adenocarcinoma: what's next step?

Gastric cancer continues to have a high death rate despite advancements in their diagnosis and treatment. Novel treatment techniques are thus desperately needed. This is where double-stranded RNA molecules known as small interfering RNA (siRNA), which may selectively target the mRNA of disease-causing genes, may find use in medicine. For siRNAs to function properly in the human body, they must be shielded from deterioration. Furthermore, in order to maintain organ function, they must only target the tumor and spare normal tissue. siRNAs have been designed using clever delivery mechanisms including polymers and lipids to achieve these objectives. Although siRNA protection is not hard to acquire, it is still challenging to target cancer cells with them. Here, we first discuss the basic characteristics of gastric cancer before describing the properties of siRNA and typical delivery methods created specifically for gastric tumors. Lastly, we provide a succinct overview of research using siRNAs to treat gastric tumors.

NDC80 Kinetochore Complex Serves as a Potential Prognostic Predictor and Correlates with Immune Infiltrates in Epithelial Ovarian Cancer Patients

Purpose

This study focuses on evaluating the prognostic value of the NDC80 kinetochore complex in ovarian cancer (OC) using the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database and reveals the relationship between the NDC80 complex and immune infiltrates in OC.

Methods

We collected data on NDC80 complex expression levels in both OC tissues and non-OC ovarian tissues from the University of California Santa Cruz Xena and GEO databases. The clinicopathological characteristics correlated with overall survival were analyzed using Cox regression and the Kaplan-Meier method. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis and CIBERSORT were performed using data from TCGA database. Immunohistochemical staining was used to verify the higher expression level of NUF2 protein in OC in vitro. Meanwhile, we utilized the Tumor Immune Estimation Resource to analyze the correlation between the NDC80 complex and immunocyte infiltration.

Results

The NDC80 complex expression level was prominently higher in OC tissues than in non-OC ovarian tissues and correlated with advanced histologic grade characteristics. Gene expression profiling interactive analysis and the Kaplan-Meier survival curve uncovered a close relationship between high expression of the NDC80 complex and poor overall survival in OC patients. The univariate Cox regression hazard model produced age, pathologic stage, tumor status, primary therapy outcome, SPC24 expression level, and Karnofsky performance score as prognostic factors for OC patients. NDC80 complex expression levels were highly associated with immune cell infiltration, showing NK CD56 bright cells and NK cells with a negative correlation and T helper 2 cells with a positive correlation (P<0.05).

Conclusion

These findings provide evidence that an increased expression level of the NDC80 complex is closely associated with the progression of OC and could also serve as a novel target of immunotherapy in OC.

The m6A reader IGF2BP1 manipulates BUB1B expression to affect malignant behaviors, stem cell properties, and immune resistance of non-small-cell lung cancer stem cells

N6-methyladenosine (m6A) modification is the most common internal modification in eukaryotic mRNA and an important mechanism for post-transcriptional regulation of genes. This study focuses on the role of the m6A reader insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1) in the malignant behaviors of non-small-cell lung cancer (NSCLC) cells and especially the cancer stem cells (CSCs). We obtained IGF2BP1 as an aberrantly upregulated gene linking to poor survival of patients with NSCLC by bioinformatics, and then confirmed increased IGF2BP1 expression in NSCLC tissues and cells, especially in the enriched CSCs. Knockdown of IGF2BP1 suppressed proliferation, mobility and epithelial-mesenchymal transition activity of NSCLC cells and CSCs, and it reduced stemness, self-renewal ability, xenograft tumorigenesis and immune resistance of the CSCs. IGF2BP1 was predicted to have a positive correlation with BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and it upregulated BUB1B expression through m6A modification. Further overexpression of BUB1B in CSCs counteracted the effects of IGF2BP1 silencing and restored the malignant phenotype, self-renewal, and immune resistance of CSCs in vitro and in vivo. Taken together, this work demonstrates that IGF2BP1 manipulates BUB1B expression to affect malignant behaviors, stem cell properties and immune resistance of NSCLC stem cells.

Differentially Expressed Cell Cycle Genes and STAT1/3-Driven Multiple Cancer Entanglement in Psoriasis, Coupled with Other Comorbidities

Psoriasis is a persistent T-cell-supported inflammatory cutaneous disorder, which is defined by a significant expansion of basal cells in the epidermis. Cell cycle and STAT genes that control cell cycle progression and viral infection have been revealed to be comorbid with the development of certain cancers and other disorders, due to their abnormal or scanty expression. The purpose of this study is to evaluate the expression of certain cell cycle and STAT1/3 genes in psoriasis patients and to determine the types of comorbidities associated with these genes. To do so, we opted to adopt the in silico methodology, since it is a quick and easy way to discover any potential comorbidity risks that may exist in psoriasis patients. With the genes collected from early research groups, protein networks were created in this work using the NetworkAnalyst program. The crucial hub genes were identified by setting the degree parameter, and they were then used in gene ontology and pathway assessments. The transcription factors that control the hub genes were detected by exploring TRRUST, and DGIdb was probed for remedies that target transcription factors and hubs. Using the degree filter, the first protein subnetwork produced seven hub genes, including STAT3, CCNB1, STAT1, CCND1, CDC20, HSPA4, and MAD2L1. The hub genes were shown to be implicated in cell cycle pathways by the gene ontology and Reactome annotations. The former four hubs were found in signaling pathways, including prolactin, FoxO, JAK/STAT, and p53, according to the KEGG annotation. Furthermore, they enhanced several malignancies, including pancreatic cancer, Kaposi's sarcoma, non-small cell lung cancer, and acute myeloid leukemia. Viral infections, including measles, hepatitis C, Epstein-Barr virus, and HTLV-1 and viral carcinogenesis were among the other susceptible diseases. Diabetes and inflammatory bowel disease were conjointly annotated. In total, 129 medicines were discovered in DGIdb to be effective against the transcription factors BRCA1, RELA, TP53, and MYC, as opposed to 10 medications against the hubs, STAT3 and CCND1, in tandem with 8 common medicines. The study suggests that the annotated medications should be tested in suitable psoriatic cell lines and animal models to optimize the drugs used based on the kind, severity, and related comorbidities of psoriasis. Furthermore, a personalized medicine protocol must be designed for each psoriasis patient that displays different comorbidities.

Multiple datasets to explore the tumor microenvironment of cutaneous squamous cell carcinoma

BACKGROUND

Cutaneous squamous cell carcinoma (cSCC) is one of the most frequent types of cutaneous cancer. The composition and heterogeneity of the tumor microenvironment significantly impact patient prognosis and the ability to practice precision therapy. However, no research has been conducted to examine the design of the tumor microenvironment and its interactions with cSCC.

MATERIAL AND METHODS

We retrieved the datasets GSE42677 and GSE45164 from the GEO public database, integrated them, and analyzed them using the SVA method. We then screened the core genes using the WGCNA network and LASSO regression and checked the model's stability using the ROC curve. Finally, we performed enrichment and correlation analyses on the core genes.

RESULTS

We identified four genes as core cSCC genes: DTYMK, CDCA8, PTTG1 and MAD2L1, and discovered that RORA, RORB and RORC were the primary regulators in the gene set. The GO semantic similarity analysis results indicated that CDCA8 and PTTG1 were the two most essential genes among the four core genes. The results of correlation analysis demonstrated that PTTG1 and HLA-DMA, CDCA8 and HLA-DQB2 were significantly correlated.

CONCLUSIONS

Examining the expression levels of four primary genes in cSCC aids in our understanding of the disease's pathophysiology. Additionally, the core genes were found to be highly related with immune regulatory genes, suggesting novel avenues for cSCC prevention and treatment.

Identification and Validation of Hub Genes with Poor Prognosis in Hepatocellular Carcinoma by Integrated Bioinformatical Analysis

Background

Hepatocellular carcinoma (HCC) is the reason for the world’s second largest cancer-related death. It is clinically valuable to study the molecular mechanisms of HCC occurrence and development for formulating more effective diagnosis and treatment strategies.

Methods

The five microarray data sets GSE45267, GSE101685, GSE84402, GSE62232 and GSE45267 were downloaded from Gene Expression Omnibus (GEO) database, including 165 HCC tissues and 73 normal tissues. Differential expressed genes (DEGs) between HCC tissues and normal tissues were determined by GEO2R. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and the protein–protein interaction network (PPI) network analysis were employed to identify DEGs and to evaluate the clinical significance in prognosis of HCC.

Results

A total of 152 genes differentially expressed in HCC tissues and normal tissues were identified. GO and KEGG functional enrichment analysis revealed that 39 up-regulated genes were mainly enriched in mitosis, cell cycle and oocyte meiosis, while those down-regulated genes (113) were concentrated in exogenous drug catabolism and the metabolism of cytochrome P450 on exogenous drugs. Totally, 19 hub genes were chosen by PPI network and module analysis and verified by The Cancer Genome Atlas (TCGA) database. Finally, 8 hub genes were selected, including CDK1, CYP2C8, CCNB1, AURKA, CYP2C9, BUB1B, MAD2L1 and TTK, which were associated with the overall survival rate of HCC patients.

Conclusion

This study presented eight target genes connected to the prognosis of HCC patients. Those mainly exists in cell cycle and drug catabolism, which may be latent targets for clinical treatment.

Identification of MAD2L1 as a Potential Biomarker in Hepatocellular Carcinoma via Comprehensive Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) is widely acknowledged as a malignant tumor with rapid progression, high recurrence rate, and poor prognosis. At present, there is a paucity of reliable biomarkers at the clinical level to guide the management of HCC and improve patient outcomes. Our research is aimed at assessing the prognostic value of MAD2L1 in HCC.

Methods

Four datasets, GSE121248, GSE101685, GSE85598, and GSE62232, were selected from the GEO database to analyze differentially expressed genes (DEGs) between HCC and normal liver tissues. After functional analysis, we constructed a protein-protein interaction network (PPI) for DEGs and identified core genes in this network with high connectivity with other genes. We assessed the relationship between core genes and the pathogenesis and prognosis of HCC. Finally, we explored the gene regulatory signaling mechanisms involved in HCC pathogenesis.

Results

145 DEGs were screened from the intersection of the four GEO datasets. MAD2L1 was associated with most genes according to the PPI network and was selected as a candidate gene for further study. Survival analysis suggested that high MAD2L1 expression in HCC correlated with a worse prognosis. In addition, real-time quantitative PCR (RT-qPCR), western blot (WB), and immunohistochemistry (IHC) findings suggested that the expression of MAD2L1 was abnormally increased in HCC tissues and cells compared to paraneoplastic tissues and normal hepatocytes.

Conclusion

We found that high MAD2L1 expression in HCC was significantly associated with overall patient survival and clinical features. We also explored the potential biological properties of this gene.

Bioinformatic analysis identifying FGF1 gene as a new prognostic indicator in clear cell Renal Cell Carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) has been the commonest renal cell carcinoma (RCC). Although the disease classification, diagnosis and targeted therapy of RCC has been increasingly evolving attributing to the rapid development of current molecular pathology, the current clinical treatment situation is still challenging considering the comprehensive and progressively developing nature of malignant cancer. The study is to identify more potential responsible genes during the development of ccRCC using bioinformatic analysis, thus aiding more precise interpretation of the disease METHODS: Firstly, different cDNA expression profiles from Gene Expression Omnibus (GEO) online database were used to screen the abnormal differently expressed genes (DEGs) between ccRCC and normal renal tissues. Then, based on the protein-protein interaction network (PPI) of all DEGs, the module analysis was performed to scale down the potential genes, and further survival analysis assisted our proceeding to the next step for selecting a credible key gene. Thirdly, immunohistochemistry (IHC) and quantitative real-time PCR (QPCR) were conducted to validate the expression change of the key gene in ccRCC comparing to normal tissues, meanwhile the prognostic value was verified using TCGA clinical data. Lastly, the potential biological function of the gene and signaling mechanism of gene regulating ccRCC development was preliminary explored.

RESULTS

Four cDNA expression profiles were picked from GEO database based on the number of containing sample cases, and a total of 192 DEGs, including 39 up-regulated and 153 down-regulated genes were shared in four profiles. Based on the DEGs PPI network, four function modules were identified highlighting a FGF1 gene involving PI3K-AKT signaling pathway which was shared in 3/4 modules. Further, both the IHC performed with ccRCC tissue microarray which contained 104 local samples and QPCR conducted using 30 different samples confirmed that FGF1 was aberrant lost in ccRCC. And Kaplan-Meier overall survival analysis revealed that FGF1 gene loss was related to worse ccRCC patients survival. Lastly, the pathological clinical features of FGF1 gene and the probable biological functions and signaling pathways it involved were analyzed using TCGA clinical data.

CONCLUSIONS

Using bioinformatic analysis, we revealed that FGF1 expression was aberrant lost in ccRCC which statistical significantly correlated with patients overall survival, and the gene's clinical features and potential biological functions were also explored. However, more detailed experiments and clinical trials are needed to support its potential drug-target role in clinical medical use.