Gemcitabine impacts differentially on bladder and kidney cancer cells: distinct modulations in the expression patterns of apoptosis-related microRNAs and BCL2 family genes

A novel Harris Hawks Optimization-based clustering method for elucidating genetic associations in osteoarthritis and Diverse Cancer Types

Considering the high incidence of osteoarthritis (OA), especially of the knee and hip, this study explores the possible genetic associations between OA and cancer types, including cancers of the bladder, kidney, breast, and prostate. The objective of our study is to decipher the complex genetic connections among these common disorders, emphasizing potential correlations and underlying biological processes. However, the genetic connections between these diseases remain largely unexplored. It fills a vacuum in the literature by using a new clustering approach based on Harris Hawks Optimization (HHO-C), which is a first for applying machine learning methods to this particular set of genetic data. To address this gap, we introduce HHO-C, a novel machine learning-based clustering approach, for the first time in this specific genetic dataset. The work accomplishes three noteworthy firsts: firstly, it is the first to apply machine learning to the study of the genetic interactions between OA and these cancers. Second, it creates a flexible genetic dataset that will be very helpful for further studies in this field. Finally, it presents the novel HHO-C approach, showcasing how well it manages intricate genetic data and providing fresh perspectives on genetic data analysis. It is anticipated that the results of this investigation will clarify the genetic relationships between OA and these malignancies, which could result in novel understandings of medical genetics and the creation of fresh approaches to diagnosis and treatment.

microRNA-875-5p-conjugated gold nanoparticles suppress breast cancer progression through the MTDH/PTEN/AKT signaling pathway

OBJECTIVE

A lack of effective delivery methods has hampered the study of therapeutics targeting miR-875-5p for breast cancer (BC).

METHODS

The miR-875-5p mimic was conjugated to AuNPs to produce AuNP-miR-875-5p. Then, the effect of AuNP-miR-875-5p on the proliferative, migratory, invasive activities, and apoptosis of BC cells was detected, as well as on tumor growth in animals. The involvement of the MTDH/PTEN/AKT pathway in miR-875-5p-mediated BC progression was identified.

RESULTS

AuNP-miR-875-5p was delivered to BC cells and hampered cell malignancy. MTDH was targeted by miR-875-5p. MTDH expression was negatively correlated with miR-875-5p expression in BC tissues. The anti-tumor effect of AuNP-miR-875-5p in BC cells was counteracted by MTDH overexpression. AuNP-miR-875-5p enhanced PTEN protein expression, thereby inhibiting AKT activation by targeting MTDH. AuNP-miR-875-5p blocked MCF-7 tumor growth in vivo.

CONCLUSION

AuNPs can deliver miR-875-5p to BC cells, and AuNP-miR-875-5p has clinical potential for treating unresectable BC.

Immunohistochemical distribution of Bcl-2 and p53 apoptotic markers in acetamiprid-induced nephrotoxicity

Pesticides, which adversely affect the critical metabolic processes of organisms, disrupt the physiological balance by specifically targeting enzymes and may lead to such consequences that may lead to death. It provides benefits in agricultural activities. The p53 protein antagonizes bcl-2, an anti-apoptotic protein character, and induces apoptosis by causing mitochondrial membrane permeability. This study aims to show the effect of acetamiprid, which is an insecticide from the neonicotinoid class, on bcl-2 and p53 immunoreactivity, which has an important place in the apoptotic mechanism in kidney tissue. A total of four groups including control and three experimental groups (the acetamiprid was administered 5, 10, and 15 mg kg-1) were formed in the study. After acetamiprid was administered via gavage for 14 days, the kidney tissues taken from the mice, which were sacrificed by cervical dislocation, were fixed in 10% formaldehyde solution for histological and immunohistochemical analyses, and as a result of routine tissue follow-up, the sections were blocked in paraffin and stained with haematoxylin-eosin and immunostaining. The histopathological examinations revealed that while the kidney tissue had a normal structure in the control group, degeneration in the distal and proximal tubules, glomerular degeneration, increase in the capsular area, glomerular atrophy, and haemorrhage were determined in the acetamiprid groups at increasing severity and frequency depending on the dose of the applied substance. In the kidney tissue, Bcl-2 and p53 immunoreactivity was observed in glomerular cells, sinusoidal epithelium, and proximal and distal tubule cells. The acetamiprid caused pathological changes in the kidneys in the dose range used. This effect also affects the expression of bcl-2 and p53 genes, which are biomarkers in the apoptotic mechanism. As acetamiprid accumulates in tissues, it increases the expression of p53 from cell death receptors, while suppressing the anti-apoptotic bcl-2 expression.

Toward drug-miRNA resistance association prediction by positional encoding graph neural network and multi-channel neural network

Drug discovery is a costly and time-consuming process, and most drugs exert therapeutic efficacy by targeting specific proteins. However, there are a large number of proteins that are not targeted by any drug. Recently, miRNA-based therapeutics are becoming increasingly important, since miRNA can regulate the expressions of specific genes and affect a variety of human diseases. Therefore, it is of great significance to study the associations between miRNAs and drugs to enable drug discovery and disease treatment. In this work, we propose a novel method named DMR-PEG, which facilitates drug-miRNA resistance association (DMRA) prediction by leveraging positional encoding graph neural network with layer attention (LAPEG) and multi-channel neural network (MNN). LAPEG considers both the potential information in the miRNA-drug resistance heterogeneous network and the specific characteristics of entities (i.e., drugs and miRNAs) to learn favorable representations of drugs and miRNAs. And MNN models various sophisticated relations and synthesizes the predictions from different perspectives effectively. In the comprehensive experiments, DMR-PEG achieves the area under the precision-recall curve (AUPR) score of 0.2793 and the area under the receiver-operating characteristic curve (AUC) score of 0.9475, which outperforms the most state-of-the-art methods. Further experimental results show that our proposed method has good robustness and stability. The ablation study demonstrates each component in DMR-PEG is essential for drug-miRNA drug resistance association prediction. And real-world case study presents that DMR-PEG is promising for DMRA inference.

MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network

MiRNAs can regulate genes encoding specific proteins which are related to the efficacy of drugs, and predicting miRNA-drug resistance associations is of great importance. In this work, we propose an attentive multimodal graph convolution network method (AMMGC) to predict miRNA-drug resistance associations. AMMGC learns the latent representations of drugs and miRNAs from four graph convolution sub-networks with distinctive combinations of features. Then, an attention neural network is employed to obtain attentive representations of drugs and miRNAs, and miRNA-drug resistance associations are predicted by the inner product of learned attentive representations. The computational experiments show that AMMGC outperforms other state-of-the-art methods and baseline methods, achieving the AUPR score of 0.2399 and the AUC score of 0.9467. The analysis demonstrates that leveraging multiple features of drugs and miRNAs can make a contribution to the miRNA-drug resistance association prediction. The usefulness of AMMGC is further validated by case studies.

Immune-related long non-coding RNAs can serve as prognostic biomarkers for clear cell renal cell carcinoma

Background

The immune microenvironment is a critical regulator of clear cell renal cell carcinoma (ccRCC) progression. However, the underlying mechanisms the regulatory role of immune-related long non-coding RNAs (irlncRNAs) in the ccRCC tumor microenvironment (TME) are still obscure. Herein, we investigated prognostics role of irlncRNAs for ccRCC.

Methods

The raw data of patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the ImmPort database. First, we investigated the correlation between the immune-related genes and irlncRNAs. Then, we identified the differentially expressed irlncRNA pairs (ILRPs) between normal and cancer tissue samples, and prognostic model was constructed with the differentially expressed ILRPs. We further explored whether the signature risk scores of ILRPs had a considerable impact on immune cell infiltration. Finally, we performed a drug sensitivity analysis based on risk score.

Results

There were 13 upregulated and 40 downregulated irlncRNAs between the ccRCC and normal tissue samples. We further selected the irlncRNAs that significantly affect the prognosis of patients with ccRCC via univariate Cox, lasso regression, and multivariate regression analyses. Twelve ILRPs were used to construct a prognostic signature. The model showed the ILRPs model could be used to assess the prognosis of ccRCC patients. Study of the influence of risk score and clinical characteristics on the prognosis of patients with ccRCC showed risk score to be an independent factor affecting the outcome of ccRCC. We further performed the difference analysis of immune cell abundance between ccRCC and normal tissue samples. The results showed that patients with higher abundance of M0 macrophages, plasma cells, follicular helper T cells, and regulatory T cells (Tregs) had a poor outcome. Finally, we performed a drug sensitivity analysis based on risk score. The results showed that high-risk score patients are sensitive to orafenib, sunitinib, temsirolimus, cisplatin, and gemcitabine.

Conclusions

Our study has developed a novel and reasonable ILPRs model for prognostic prediction, which does not require transcriptional levels to be detected.

Circulating microRNA expression profiling in young obese Korean women

BACKGROUND/OBJECTIVES

This study investigates correlations between circulating microRNAs (miRNAs) and obesity-related parameters among young women (aged 20–30 years old) in Korea.

SUBJECTS/METHODS

We analyzed TaqMan low density arrays (TLDAs) of circulating miRNAs in 9 lean (body mass index [BMI] < 25 kg/m²) and 15 obese (BMI > 25 kg/m²) women. We also performed gene ontology (GO) analyses of the biological functions of predicted miRNA target genes, and clustered the results using the database for annotation, visualization and integrated discovery.

RESULTS

The TLDA cards contain 754 human miRNAs; of these, the levels of 8 circulating miRNAs significantly declined (> 2-fold) in obese subjects compared with those in lean subjects, including miR-1227, miR-144-5p, miR-192, miR-320, miR-320b, miR-484, miR-324-3p, and miR-378. Among them, miR-484 and miR-378 displayed the most significant inverse correlations with BMI (miR-484, r = −0.5484, P = 0.0056; miR-378, r = −0.5538, P = 0.0050) and visceral fat content (miR-484, r = −0.6141, P = 0.0014; miR-378, r = −0.6090, P = 0.0017). GO analysis indicated that genes targeted by miR-484 and miR-378 had major roles in carbohydrate and lipid metabolism.

CONCLUSION

Our result showed the differentially expressed circulating miRNAs in obese subjects compared to lean subjects. Although the mechanistic study to reveal the causal role of miRNAs remains, these miRNAs may be novel biomarkers for obesity.

Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword "bioinformatics"). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature.

Histone acetyltransferase 1 up regulates Bcl2L12 expression in nasopharyngeal cancer cells

The deregulation of Bcl2L12 expression in cancer has been recognized, but the causative factors are unknown. Histone acetyltransferases (HAT) play critical roles in the regulation gene transcription. This study tests a hypothesis that the aberrant activities of HAT induce deregulation of Bcl2L12 in nasopharyngeal cancer (NPC). In this study, human NPC tissues were collected from the clinic. The expression of Bcl2L12 and HATs in NPC cells was analyzed by real time RT-PCR and Western blotting. NPC cell apoptosis was analyzed by flow cytometry. The results showed that by screening the subtypes of HAT, the levels of HAT1 were uniquely higher in NPC as compared with non-cancer nasopharyngeal tissue. The levels of Bcl2L12 in NPC cells were positively correlated with HAT1. HAT1 involved in the STAT5 binding to the Bcl2L12 promoter. HAT1 increased the expression of Bcl2L12. Bcl2L12 mediated the effects of HAT1 on suppressing NPC cell apoptosis. Absorption of the HAT1 shRNA plasmid-carrying liposomes induced NPC cell apoptosis. In conclusion, inhibition of HAT1 can induce NPC cell apoptosis via increasing Bcl2L12 expression, which can be a potential therapy for NPC treatment.

MicroRNA‑30a‑5p suppresses tumor cell proliferation of human renal cancer via the MTDH/PTEN/AKT pathway

The present study aimed to explore the effects of microRNA (miRNA)‑30a‑5p on tumor proliferation and to seek a potential therapeutic target for the treatment of human renal cancer. The results demonstrated that the expression levels of miRNA‑30a‑5p were reduced in tumor samples from patients with renal cancer compared with in normal tissue samples. Overall survival and disease‑free survival were increased in patients with renal cancer and high miRNA‑30a‑5p expression compared with in those with low miRNA‑30a‑5p. Furthermore, overexpression of miRNA‑30a‑5p suppressed cell proliferation, induced apoptosis, and promoted caspase‑3/9 activities and B‑cell lymphoma 2‑associated X protein (Bax) protein expression in Caki‑2 cells. In addition, the results confirmed that overexpression of miRNA‑30a‑5p inhibited metadherin (MTDH), upregulated phosphatase and tensin homolog (PTEN) and suppressed phosphorylated (p)‑protein kinase B (AKT) protein expression levels in Caki‑2 cells. Furthermore, transfection with small interfering RNA‑MTDH, increased the effects of miRNA‑30a‑5p on the inhibition of cell proliferation, and promotion of apoptosis, caspase‑3/9 activities and Bax protein expression levels in Caki‑2 cells. Knockdown of MTDH expression also upregulated PTEN and suppressed p‑AKT protein expression in Caki‑2 cells. In conclusion, the present study is the first, to the best of our knowledge, to provide evidence suggesting that miRNA‑30a‑5p suppresses tumor human renal cancer cell proliferation via the MTDH/PTEN/AKT pathway.

In Vitro and Ex Vivo Permeability Studies of Paclitaxel and Doxorubicin From Drug-Eluting Biodegradable Ureteral Stents

A drug-eluting biodegradable ureteral stent (BUS) has been developed as a new approach for the treatment of urothelial tumors of upper urinary tract cancer. In a previous work, this system has proven to be a good carrier for anticancer drugs as a potential effective and sustainable intravesical drug delivery system. BUS has revealed to reduce in 75% the viability of human urothelial cancer cells (T24) after 72 h of contact and demonstrated minimal cytotoxic effect on human umbilical vein endothelial cells (HUVECs) which were used as a control. In this work, we studied the permeability of the anticancer drugs, such as paclitaxel and doxorubicin, alone or released from the BUS developed. We used 3 different membranes to study the permeability: polyethersulfone (PES) membrane, HUVECs cell monolayer, and an ex vivo porcine ureter. The ureter thickness was measured (864.51 μm) and histological analysis was performed to confirm the integrity of urothelium. Permeability profiles were measured during 8 h for paclitaxel and doxorubicin. The drugs per se have shown to have a different profile and as expected, increasing the complexity of the membrane to be permeated, the permeability decreased, with the PES being more permeable and the ex vivo ureter tissue being less permeable. The molecular weight has also shown to influence the permeability of each drug and a higher percentage for doxorubicin (26%) and lower for paclitaxel (18%) was observed across the ex vivo ureter. The permeability (P), diffusion (D), and partition (K_d) coefficients of paclitaxel and doxorubicin through the permeable membranes were calculated. Finally, we showed that paclitaxel and doxorubicin drugs released from the BUS were able to remain in the ex vivo ureter and only a small amount of the drugs can across the different permeable membranes with a permeability of 3% for paclitaxel and 11% for doxorubicin. The estimated amount of paclitaxel that remains in the ex vivo ureter tissue is shown to be effective to affect the cancer cell and not affect the noncancer cells.

Drug-eluting biodegradable ureteral stent: New approach for urothelial tumors of upper urinary tract cancer

Upper urinary tract urothelial carcinoma (UTUC) accounts for 5-10% of urothelial carcinomas and is a disease that has not been widely studied as carcinoma of the bladder. To avoid the problems of conventional therapies, such as the need for frequent drug instillation due to poor drug retention, we developed a biodegradable ureteral stent (BUS) impregnated by supercritical fluid CO₂ (scCO₂) with the most commonly used anti-cancer drugs, namely paclitaxel, epirubicin, doxorubicin, and gemcitabine. The release kinetics of anti-cancer therapeutics from drug-eluting stents was measured in artificial urine solution (AUS). The in vitro release showed a faster release in the first 72h for the four anti-cancer drugs, after this time a plateau was achieved and finally the stent degraded after 9days. Regarding the amount of impregnated drugs by scCO₂, gemcitabine showed the highest amount of loading (19.57μg _drug/mg _polymer: 2% loaded), while the lowest amount was obtained for paclitaxel (0.067μg _drug/mg _polymer: 0.01% loaded). A cancer cell line (T24) was exposed to graded concentrations (0.01-2000ng/ml) of each drugs for 4 and 72h to determine the sensitivities of the cells to each drug (IC₅₀). The direct and indirect contact study of the anti-cancer biodegradable ureteral stents with the T24 and HUVEC cell lines confirmed the anti-tumoral effect of the BUS impregnated with the four anti-cancer drugs tested, reducing around 75% of the viability of the T24 cell line after 72h and demonstrating minimal cytotoxic effect on HUVECs.