Eight hub genes as potential biomarkers for breast cancer diagnosis and prognosis: A TCGA-based study

Identifying the key hub genes linked with lung squamous cell carcinoma by examining the differentially expressed and survival genes

Lung Squamous Cell Carcinoma is characterised by significant alterations in RNA expression patterns, and a lack of early symptoms and diagnosis results in poor survival rates. Our study aimed to identify the hub genes involved in LUSC by differential expression analysis and their influence on overall survival rates in patients. Thus, identifying genes with the potential to serve as biomarkers and therapeutic targets. RNA sequence data for LUSC was obtained from TCGA and analysed using R Studio. Survival analysis was performed on DE genes. PPI network and hub gene analysis was performed on survival-relevant genes. Enrichment analysis was conducted on the PPI network to elucidate the functional roles of hub genes. Our analysis identified 2774 DEGs in LUSC patient datasets. Survival analysis revealed 511 genes with a significant impact on patient survival. Among these, 20 hub genes-FN1, ACTB, HGF, PDGFRB, PTEN, SNAI1, TGFBR1, ESR1, SERPINE1, THBS1, PDGFRA, VWF, BMP2, LEP, VTN, PXN, ABL1, ITGA3 and ANXA5-were found to have lower expression levels associated with better patient survival, whereas high expression of SOX2 correlated with longer survival. Enrichment analysis indicated that these hub genes are involved in critical cellular and cancer-related pathways. Our study has identified six key hub genes that are differentially expressed and exhibit significant influence over LUSC patient survival outcomes. Further, in vitro and in vivo studies must be conducted on the key genes for their utilisation as therapeutic targets and biomarkers in LUSC.

Transcriptomic analysis revealed potential regulatory biomarkers and repurposable drugs for breast cancer treatment

Breast cancer (BC) is the most widespread cancer worldwide. Over 2 million new cases of BC were identified in 2020 alone. Despite previous studies, the lack of specific biomarkers and signaling pathways implicated in BC impedes the development of potential therapeutic strategies. We employed several RNAseq datasets to extract differentially expressed genes (DEGs) based on the intersection of all datasets, followed by protein-protein interaction network construction. Using the shared DEGs, we also identified significant gene ontology (GO) and KEGG pathways to understand the signaling pathways involved in BC development. A molecular docking simulation was performed to explore potential interactions between proteins and drugs. The intersection of the four datasets resulted in 146 DEGs common, including AURKB, PLK1, TTK, UBE2C, CDCA8, KIF15, and CDC45 that are significant hub-proteins associated with breastcancer development. These genes are crucial in complement activation, mitotic cytokinesis, aging, and cancer development. We identified key microRNAs (i.e., hsa-miR-16-5p, hsa-miR-1-3p, hsa-miR-147a, hsa-miR-195-5p, and hsa-miR-155-5p) that are associated with aggressive tumor behavior and poor clinical outcomes in BC. Notable transcription factors (TFs) were FOXC1, GATA2, FOXL1, ZNF24 and NR2F6. These biomarkers are involved in regulating cancer cell proliferation, invasion, and migration. Finally, molecular docking suggested Hesperidin, 2-amino-isoxazolopyridines, and NMS-P715 as potential lead compounds against BC progression. We believe that these findings will provide important insight into the BC progression as well as potential biomarkers and drug candidates for therapeutic development.

Development of a breast cancer prognostic model based on vesicle-mediated transport-related genes to predict immune landscape and clinical drug therapy

BACKGROUND

Vesicle-mediated transport, vital for substance exchange and intercellular communication, is linked to tumor initiation and progression. This work was designed to study the role of vesicle-mediated transport-related genes (VMTRGs) in breast cancer (BC)prognosis.

METHODS

Univariate Cox analysis was utilized to screen prognosis-related VMTRGs. BC samples underwent unsupervised clustering based on VMTRGs to analyze survival, clinical factors, and immune cell abundance across different subtypes. We constructed a risk model using univariate Cox and LASSO regression analysis, with validation conducted using GEO datasets. Subsequently, we performed tumor mutational burden analysis, and immune landscape analysis on both groups. Ultimately, we conducted immunophenoscore (IPS) scoring to forecast immunotherapy and performed drug sensitivity analysis.

RESULTS

We identified 102 VMTRGs associated with BC prognosis. Using these 102 VMTRGs, BC patients were classified into 3 subtypes, with Cluster3 patients showing significantly better survival rates. We constructed a prognostic model for BC based on 12 VMTRGs that effectively predicted patient survival. Riskscore was an independent prognostic factor for BC patients. According to median risk score, high-risk group (HRG) had higher TMB values. The immune landscape of the HRG exhibited characteristics of cold tumor, with higher immune checkpoint expression levels and lower IPS scores, whereas Gemcitabine, Nilotinib, and Oxaliplatin were more suitable for treating low-risk group.

CONCLUSION

We classified BC subtypes and built a prognostic model based on VMTRGs. The genes in the prognostic model may serve as potential targets for BC therapy.

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity-related biological features can effectively screen high-risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity-gene clinical prognostic index (LOG-CPI), combining a 7-gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5-year accuracy = 0.828, 0.760, 0.751, and 0.792, respectively). LOG-CPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.

Junctional adhesion molecule 3 is a potential therapeutic target for small cell lung carcinoma

There are few effective therapies for small cell lung carcinoma (SCLC); thus, we need to develop novel and efficacious treatments. We hypothesized that an antibody-drug conjugate (ADC) could be a promising option for SCLC. Several publicly available databases were used to demonstrate the extent to which junctional adhesion molecule 3 (JAM3) mRNA was expressed in SCLC and lung adenocarcinoma cell lines and tissues. Three SCLC cell lines, Lu-135, SBC-5, and Lu-134 A, were selected and examined for JAM3 protein expression by flow cytometry. Finally, we examined the response of the three SCLC cell lines to a conjugate between an anti-JAM3 monoclonal antibody HSL156 (developed in-house) and the recombinant protein DT3C, which consists of diphtheria toxin lacking the receptor-binding domain but containing the C1, C2, and C3 domains of streptococcal protein G. In silico analyses revealed that JAM3 mRNA was expressed higher in SCLC cell lines and tissues than in those of lung adenocarcinoma. As expected, all the three SCLC cell lines examined were positive for JAM3 at the mRNA and protein levels. Consequently, control SCLC cells, but not JAM3-silenced ones, were highly sensitive to HSL156-DT3C conjugates, resulting in dose- and time-dependent decreased viability. Finally, silencing JAM3 alone suppressed the growth of all SCLC cell lines examined. Taken together, these findings suggest that an ADC targeting JAM3 could represent a new approach to treating SCLC patients.

Fibronectin 1 as a Key Gene in the Genesis and Progression of Cadmium-Related Bladder Cancer

Exposure to cadmium (Cd), a non-essential heavy metal, leads to the malignant transformation of urothelial cells and promotes bladder cancer (BC) development, but the mechanisms are unclear. Therefore, we aimed to explore the possible molecules associated with Cd-related BC. By analyzing and mining biological big data in public databases, we screened genes associated with the malignant transformation of uroepithelial cells caused by Cd and further screened the key gene associated with BC prognosis from these genes. The possible roles of the key gene in BC progression were then further explored through biological big data analysis and cellular experiments. Data mining yielded a total of 387 malignant transformation-related genes, which were enriched in multiple cancer-related signaling pathways, such as cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, and Jak-STAT signaling pathway. Further screening identified Fibronectin 1 (FN1) as the key gene. High expression of FN1 was associated with the advanced pathologic stage, T stage, N stage, and M stage and predicted an unfavorable outcome in BC patients. FN1 expression was positively associated with the infiltration of several types of immune cells, particularly tumor-associated macrophages and cancer-associated fibroblasts. Overexpression of FN1 could be detected in Cd-treated urothelial cells and BC cell lines. Interestingly, silencing of FN1 impaired the proliferation and invasive capacity of BC cells. In conclusion, the present study provides new insight into the mechanism of Cd-related BC. FN1 might be a prognostic marker and therapeutic target for BC. Future studies are needed to confirm these results.