cancer subtypes
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2024 ◽  
Vol 84 ◽  
M. Ahmad ◽  
Y. Hameed ◽  
M. Khan ◽  
M Usman ◽  
A. Rehman ◽  

Abstract Cancer is a fatal malignancy and its increasing worldwide prevalence demands the discovery of more sensitive and reliable molecular biomarkers. To investigate the GINS1 expression level and its prognostic value in distinct human cancers using a series of multi-layered in silico approach may help to establish it as a potential shared diagnostic and prognostic biomarker of different cancer subtypes. The GINS1 mRNA, protein expression, and promoter methylation were analyzed using UALCAN and Human Protein Atlas (HPA), while mRNA expression was further validated via GENT2. The potential prognostic values of GINS1 were evaluated through KM plotter. Then, cBioPortal was utilized to examine the GINS1-related genetic mutations and copy number variations (CNVs), while pathway enrichment analysis was performed using DAVID. Moreover, a correlational analysis between GINS1 expression and CD8+ T immune cells and a the construction of gene-drug interaction network was performed using TIMER, CDT, and Cytoscape. The GINS1 was found down-regulated in a single subtypes of human cancer while commonly up-regulated in 23 different other subtypes. The up-regulation of GINS1 was significantly correlated with the poor overall survival (OS) of Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), and Kidney renal clear cell carcinoma (KIRC). The GINS1 was also found up-regulated in LIHC, LUAD, and KIRC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of GINS1 in two diverse pathways, while few interesting correlations were also documented between GINS1 expression and its promoter methylation level, CD8+ T immune cells level, and CNVs. Moreover, we also predicted few drugs that could be used in the treatment of LIHC, LUAD, and KIRC by regulating the GINS1 expression. The expression profiling of GINS1 in the current study has suggested it a novel shared diagnostic and prognostic biomarker of LIHC, LUAD, and KIRC.

Ana Carolina Pavanelli ◽  
Flavia Rotea Mangone ◽  
Piriya Yoganathan ◽  
Simone Aparecida Bessa ◽  
Suely Nonogaki ◽  

2022 ◽  
Vol 12 ◽  
Yiran Zhou ◽  
Qinghua Cui ◽  
Yuan Zhou

tRNA-derived fragments (tRFs) constitute a novel class of small non-coding RNA cleaved from tRNAs. In recent years, researches have shown the regulatory roles of a few tRFs in cancers, illuminating a new direction for tRF-centric cancer researches. Nonetheless, more specific screening of tRFs related to oncogenesis pathways, cancer progression stages and cancer prognosis is continuously demanded to reveal the landscape of the cancer-associated tRFs. In this work, by combining the clinical information recorded in The Cancer Genome Atlas (TCGA) and the tRF expression profiles curated by MINTbase v2.0, we systematically screened 1,516 cancer-associated tRFs (ca-tRFs) across seven cancer types. The ca-tRF set collectively combined the differentially expressed tRFs between cancer samples and control samples, the tRFs significantly correlated with tumor stage and the tRFs significantly correlated with patient survival. By incorporating our previous tRF-target dataset, we found the ca-tRFs tend to target cancer-associated genes and onco-pathways like ATF6-mediated unfolded protein response, angiogenesis, cell cycle process regulation, focal adhesion, PI3K-Akt signaling pathway, cellular senescence and FoxO signaling pathway across multiple cancer types. And cell composition analysis implies that the expressions of ca-tRFs are more likely to be correlated with T-cell infiltration. We also found the ca-tRF expression pattern is informative to prognosis, suggesting plausible tRF-based cancer subtypes. Together, our systematic analysis demonstrates the potentially extensive involvements of tRFs in cancers, and provides a reasonable list of cancer-associated tRFs for further investigations.

2022 ◽  
Vol 12 (1) ◽  
Claudia Cava ◽  
Alexandros Armaos ◽  
Benjamin Lang ◽  
Gian G. Tartaglia ◽  
Isabella Castiglioni

AbstractBreast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

Agnieszka Irena Jagiełło-Gruszfeld ◽  
Magdalena Rosinska ◽  
Malgorzata Meluch ◽  
Katarzyna Pogoda ◽  
Anna Niwińska ◽  

Neoadjuvant systemic therapy has now become the the standard in early breast cancer management. Chemotherapy in combination with trastuzumab +/- pertuzumab targeted therapy can improve rates of pathologic complete response (pCR) in patients with HER2-positive breast cancer. Achieving a pCR is considered a good prognostic factor, in particular in patients with more aggressive breast cancer subtypes such as TNBC or HER2 positive cancers. Furthermore, most studies demonstrate that chemotherapy in combination with trastuzumab and pertuzumab is well tolerated. The retrospective analysis presented here concentrates on neoadjuvant therapy with the TCbH-P regimen, with a particular emphasis on patients over 60 years of age. We analysed the factors affecting the achievement of pCR and presented adverse effects of the applied therapies, which opened a discussion about optimizing the therapy of older patients with HER-2 positive breast cancer.

2022 ◽  
Jaime Iranzo ◽  
George Gruenhagen ◽  
Jorge Calle-Espinosa ◽  
Eugene V. Koonin

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistatic interactions and their quantitative effect on tumor evolution remains a challenge. We developed a method to quantify COnditional SELection on the Excess of Nonsynonymous Substitutions (Coselens) in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identified 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection accounts for 25-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario, where gene-specific across-pathway interactions shape differentiated cancer subtypes.

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 306
Samyuktha Suresh ◽  
Solène Huard ◽  
Amélie Brisson ◽  
Fariba Némati ◽  
Rayan Dakroub ◽  

Identifying new therapeutic strategies for triple-negative breast cancer (TNBC) patients is a priority as these patients are highly prone to relapse after chemotherapy. Here, we found that protein arginine methyltransferase 1 (PRMT1) is highly expressed in all breast cancer subtypes. PRMT1 depletion decreases cell survival by inducing DNA damage and apoptosis in various breast cancer cell lines. Transcriptomic analysis and chromatin immunoprecipitation revealed that PRMT1 regulates the epidermal growth factor receptor (EGFR) and the Wnt signaling pathways, reported to be activated in TNBC. PRMT1 enzymatic activity is also required to stimulate the canonical Wnt pathway. Type I PRMT inhibitors decrease breast cancer cell proliferation and show anti-tumor activity in a TNBC xenograft model. These inhibitors display synergistic interactions with some chemotherapies used to treat TNBC patients as well as erlotinib, an EGFR inhibitor. Therefore, targeting PRMT1 in combination with these chemotherapies may improve existing treatments for TNBC patients.

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261062
Pelin Ozfiliz Kilbas ◽  
Nisan Denizce Can ◽  
Tugba Kizilboga ◽  
Fikret Ezberci ◽  
Hamdi Levent Doganay ◽  

Bag-1 protein is a crucial target in cancer to increase the survival and proliferation of cells. The Bag-1 expression is significantly upregulated in primary and metastatic cancer patients compared to normal breast tissue. Overexpression of Bag-1 decreases the efficiency of conventional chemotherapeutic drugs, whereas Bag-1 silencing enhances the apoptotic efficiency of therapeutics, mostly in hormone-positive breast cancer subtypes. In this study, we generated stable Bag-1 knockout (KO) MCF-7 breast cancer cells to monitor stress-mediated cellular alterations in comparison to wild type (wt) and Bag-1 overexpressing (Bag-1 OE) MCF-7 cells. Validation and characterization studies of Bag-1 KO cells showed different cellular morphology with hyperactive Akt signaling, which caused stress-mediated actin reorganization, focal adhesion decrease and led to mesenchymal characteristics in MCF-7 cells. A potent Akt inhibitor, MK-2206, suppressed mesenchymal transition in Bag-1 KO cells. Similar results were obtained following the recovery of Bag-1 isoforms (Bag-1S, M, or L) in Bag-1 KO cells. The findings of this study emphasized that Bag-1 is a mediator of actin-mediated cytoskeleton organization through regulating Akt activation.

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