Identification of RFC5 as a novel potential prognostic biomarker in lung cancer through bioinformatics analysis

Identification of expression profiles and prognostic value of RFCs in colorectal cancer

Colorectal cancer (CRC) ranks among the most prevalent cancers globally, with its incidence closely tied to DNA damage. The Replication Factor C (RFC) complexes comprises five protein subunits: RFC1, RFC2, RFC3, RFC4, and RFC5. These RFC complexes play crucial roles in DNA replication, repair pathways, activities post DNA damage, and ATP-dependent processes during DNA synthesis. However, the impact of RFC complexes proteins on CRC prognosis remains unclear. To explore this, we employed a computational analysis approach, utilizing platforms such as the DepMap portal, GEPIA, DAVID Bioinformatics for KEGG pathway analysis, Human Protein Atlas (HPA), STRING, and TIMER. Our results indicate that the mRNA levels of RFC1 and RFC5 were the least expressed among CRC cell lines compared to other RFC complex subunits. Notably, low RFC1 and RFC5 expression was correlated with poor prognosis in terms of CRC patients' overall survival (OS). Immunohistochemical results from the Human Protein Atlas demonstrated medium staining for RFC1, RFC2, and RFC5 in CRC tissues. Furthermore, the low expression of RFC1 and RFC5 showed a significant correlation with high expression levels of miR-26a-5p and miR-636, impacting cell proliferation through mismatch repair, DNA replication, and the nucleotide excision repair pathway. Although the precise functions of RFC1 in cancer are still unknown, our findings suggest that the small-molecule single target, CHEMBL430483, and multiple target molecules could be potential treatments for CRC. In conclusion, the elevated expression of miR-26a-5p and miR-636 targeting RFC1 and RFC5 expression holds promise as a potential biomarker for early-stage CRC detection. These insights provide novel directions and strategies for CRC therapies.

RFC2 promotes aerobic glycolysis and progression of colorectal cancer

BACKGROUND

Replication factor C subunit 2 (RFC2) participates in the growth and metastasis of various malignancies. Our study investigated the roles of RFC2 in colorectal cancer (CRC).

RESULTS

RFC2 expression was upregulated in CRC tissues and cells. High RFC2 expression was associated with poor prognosis. Knockdown RFC2 inhibited proliferation, induced apoptosis, and suppressed migration and invasion of CRC cells. CREB5 was a transcription factor of RFC2, and CREB5 knockdown suppressed RFC2 expression. Furthermore, RFC2 promoted aerobic glycolysis and MET/PI3K/AKT/mTOR pathway.

CONCLUSION

RFC2 promoted the progression of CRC cells via activating aerobic glycolysis and the MET/PI3K/AKT/mTOR pathway.

Identification of Potential Biomarkers Using Integrative Approach: A Case Study of ESCC

This paper presents a consensus-based approach that incorporates three microarray and three RNA-Seq methods for unbiased and integrative identification of differentially expressed genes (DEGs) as potential biomarkers for critical disease(s). The proposed method performs satisfactorily on two microarray datasets (GSE20347 and GSE23400) and one RNA-Seq dataset (GSE130078) for esophageal squamous cell carcinoma (ESCC). Based on the input dataset, our framework employs specific DE methods to detect DEGs independently. A consensus based function that first considers DEGs common to all three methods for further downstream analysis has been introduced. The consensus function employs other parameters to overcome information loss. Differential co-expression (DCE) and preservation analysis of DEGs facilitates the study of behavioral changes in interactions among DEGs under normal and diseased circumstances. Considering hub genes in biologically relevant modules and most GO and pathway enriched DEGs as candidates for potential biomarkers of ESCC, we perform further validation through biological analysis as well as literature evidence. We have identified 25 DEGs that have strong biological relevance to their respective datasets and have previous literature establishing them as potential biomarkers for ESCC. We have further identified 8 additional DEGs as probable potential biomarkers for ESCC, but recommend further in-depth analysis.

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

Objective. N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods. In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results. Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.

Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes

Purpose

To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping.

Procedures

In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard.

Results

For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM).

Conclusions

Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

Poly(1-vinylimidazole) polyplexes as novel therapeutic gene carriers for lung cancer therapy

The present work explores the ability of poly(1-vinylimidazole) (PVI) to complex small interfering RNA (siRNA) silencing vascular endothelial growth factor (VEGF) and the in vitro efficiency of the formed complexes in A549 lung cancer cells. The polyplex formed was found to exhibit 66% complexation efficiency. The complexation was confirmed by gel retardation assays, FTIR and thermal analysis. The blank PVI polymer was not toxic to cells. The polyplex was found to exhibit excellent internalization and escaped the endosome effectively. The polyplex was more effective than free siRNA in silencing VEGF in lung cancer cells. The silencing of VEGF was quantified using Western blot and was also reflected in the depletion of HIF-1α levels in the cells treated with the polyplex. VEGF silencing by the polyplex was found to augment the cytotoxic effects of the chemotherapeutic agent 5-fluorouracil. Microarray analysis of the mRNA isolated from cells treated with free siRNA and the polyplex reveal that the VEGF silencing by the polyplex also altered the expression levels of several other genes that have been connected to the proliferation and invasion of lung cancer cells. These results indicate that the PVI complexes can be an effective agent to counter lung cancer.

Exploring the Molecular Mechanism of the Drug-Treated Breast Cancer Based on Gene Expression Microarray

: Breast cancer (BRCA) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers expressed during estradiol and tamoxifen treatment of BRCA. The microarray dataset of E-MTAB-4975 from Array Express database was downloaded, and the differential expressed genes (DEGs) between estradiol-treated BRCA sample and tamoxifen-treated BRCA sample were identified by limma package. The pathway and gene ontology (GO) enrichment analysis, construction of protein-protein interaction (PPI) network, module analysis, construction of target genes-miRNA interaction network and target genes-transcription factor (TF) interaction network were performed using bioinformatics tools. The expression, prognostic values, and mutation of hub genes were validated by SurvExpress database, cBioPortal, and human protein atlas (HPA) database. A total of 856 genes (421 up-regulated genes and 435 down-regulated genes) were identified in T47D (overexpressing Split Ends (SPEN) + estradiol) samples compared to T47D (overexpressing Split Ends (SPEN) + tamoxifen) samples. Pathway and GO enrichment analysis revealed that the DEGs were mainly enriched in response to lysine degradation II (pipecolate pathway), cholesterol biosynthesis pathway, cell cycle pathway, and response to cytokine pathway. DEGs (MCM2, TCF4, OLR1, HSPA5, MAP1LC3B, SQSTM1, NEU1, HIST1H1B, RAD51, RFC3, MCM10, ISG15, TNFRSF10B, GBP2, IGFBP5, SOD2, DHF and MT1H) , which were significantly up- and down-regulated in estradiol and tamoxifen-treated BRCA samples, were selected as hub genes according to the results of protein-protein interaction (PPI) network, module analysis, target genes-miRNA interaction network and target genes-TF interaction network analysis. The SurvExpress database, cBioPortal, and Human Protein Atlas (HPA) database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. A comprehensive bioinformatics analysis was performed, and potential therapeutic applications of estradiol and tamoxifen were predicted in BRCA samples. The data may unravel the future molecular mechanisms of BRCA.

Identification of EGFR as a Novel Key Gene in Clear Cell Renal Cell Carcinoma (ccRCC) through Bioinformatics Analysis and Meta-Analysis

Clear cell renal cell carcinoma (ccRCC) was the most aggressive histological type of renal cell carcinoma (RCC) and accounted for 70-80% of cases of all RCC. The aim of this study was to identify the potential biomarker in ccRCC and explore their underlying mechanisms. Four profile datasets were downloaded from the GEO database to identify DEGs. GO and KEGG analysis of DEGs were performed by DAVID. A protein-protein interaction (PPI) network was constructed to predict hub genes. The hub gene expression within ccRCC across multiple datasets and the overall survival analysis were investigated utilizing the Oncomine Platform and UALCAN dataset, separately. A meta-analysis was performed to explore the relationship between the hub genes: EGFR and ccRCC. 127 DEGs (55 upregulated genes and 72 downregulated genes) were identified from four profile datasets. Integrating the result from PPI network, Oncomine Platform, and survival analysis, EGFR, FLT1, and EDN1 were screened as key factors in the prognosis of ccRCC. GO and KEGG analysis revealed that 127 DEGs were mainly enriched in 21 terms and 4 pathways. The meta-analysis showed that there was a significant difference of EGFR expression between ccRCC tissues and normal tissues, and the expression of EGFR in patients with metastasis was higher. This study identified 3 importance genes (EGFR, FLT1, and EDN1) in ccRCC, and EGFR may be a potential prognostic biomarker and novel therapeutic target for ccRCC, especially patients with metastasis.