What matters in aging is signaling for responsiveness.

Biological responsiveness refers to the capacity of living organisms to adapt to changes in both their internal and external environments through physiological and behavioral mechanisms. One of the prominent aspects of aging is the decline in this responsiveness, which can lead to a deterioration in the processes required for maintenance, survival, and growth. The vital link between physiological responsiveness and the essential life processes lies within the signaling systems. To devise effective strategies for controlling the aging process, a comprehensive reevaluation of this connecting loop is imperative. This review aims to explore the impact of aging on signaling systems responsible for responsiveness and introduce a novel perspective on intervening in the aging process by restoring the compromised responsiveness. These innovative mechanistic approaches for modulating altered responsiveness hold the potential to illuminate the development of action plans aimed at controlling the aging process and treating age-related disorders.

Effectiveness of pegfilgrastim prophylaxis in preventing febrile neutropenia during R-FC chemoimmunotherapy for chronic lymphocytic leukemia: A multicenter prospective phase II study.

Background: A chemotherapy of rituximab, fludarabine and cyclophosphamide (R-FC) has been accepted as a promising frontline chemotherapy in selected patients with chronic lymphocytic leukemia (CLL). Although R-FC regimen is a relatively dose-dense regimen and neutropenia incidence is more than 50%, primary prophylactic pegfilgrastim was not fully recommended in the clinical field. Therefore, the study evaluated the prophylactic effectiveness of pegfilgrastim to reduce the incidence of febrile neutropenia associated with R-FC of patients with CLL. Patients and methods: A single-arm, multicenter, prospective phase II study was designed to assess the efficacy of prophylactic pegfilgrastim. Thirty-four CLL patients were enrolled and analyzed for neutropenia and other related factors, and comparative analysis was performed with historical cohort. Results: Compared with our historical cohort, incidence of grade 3-4 neutropenia and febrile neutropenia was remarkably reduced during any cycle of chemotherapy (14.7% vs. 48.2% of study cohort vs. historical cohort during C1, 5.9% vs. 65.8% during C2, 12.9% vs. 80.6% during C3, 10% vs. 84.6% during C4, 3.4% vs. 83.6% during C5, and 10.7% vs. 85.7% during C6, p <0.001). Also, cumulative incidence of disrupted chemotherapy was noticeably reduced in study cohort on any cycles of R-FC regimen (8.8% vs. 22.2% of study cohort vs. historical cohort on C2, 9.7% vs. 25.2% on C3, 13.4% vs. 26.9% on C4, 13.8% vs. 45.2% on C5, 17.9% vs. 47.3% on C6, p=0.007). In addition, treatment-related mortality was 5.9%, which significantly reduced compared to 9.6% of our historical cohort (HR 0.64, 95% CI 0.42-0.79, P = 0.032). Conclusion: Primary prophylactic pegfilgrastim is effective in the prevention of neutropenia/febrile neutropenia, and infection-related mortality during R-FC regimen in patients with CLL.

Transcriptional landscape of oncogene-induced senescence: a machine learning-based meta-analytic approach.

Oncogene-induced senescence (OIS) is highly heterogeneous, varying by oncogenic signals and cellular context. While its dual role, in the initial inhibition potentially later leading to promotion of tumors through the senescence-associated secretory phenotype, is still a matter of debate, it is undeniable that OIS is critical to understanding tumorigenesis. A major obstacle to OIS research is the absence of a universally accepted marker. Here, we present a robust OIS-specific transcriptomic secretory phenotype, termed oncogene-induced senescence secretory phenotype (OIS-SP), which can identify OIS across multiple biological contexts from in vitro datasets to in vivo human samples. We apply a meta-analytic machine learning pipeline to harmonize a deliberately varied selection of Ras-Raf-MEK-induced senescence datasets of differing origins, oncogenic signals and cell types. Finally we make use of bypass data to identify key genes and eliminate genes associated with quiescence, so identifying 40 OIS-SP genes. Within this set, we determined a robust core of five OIS-SP genes (FBLN1, CXCL12, EREG, CST1 and MMP10). Importantly, these 5 OIS-SP genes showed clear, consistent regulation patterns across various human Ras-Raf-MEK-mutated tumor tissues, which suggests that OIS-SP may be a novel cancer driver phenotype with an unexpectedly critical role in tumorigenesis.

Endothelial senescence in vascular diseases: current understanding and future opportunities in senotherapeutics.

Senescence compromises the essential role that the endothelium plays in maintaining vascular homeostasis, so promoting endothelial dysfunction and the development of age-related vascular diseases. Their biological and clinical significance calls for strategies for identifying and therapeutically targeting senescent endothelial cells. While senescence and endothelial dysfunction have been studied extensively, distinguishing what is distinctly endothelial senescence remains a barrier to overcome for an effective approach to addressing it. Here, we review the mechanisms underlying endothelial senescence and the evidence for its clinical importance. Furthermore, we discuss the current state and the limitations in the approaches for the detection and therapeutic intervention of target cells, suggesting potential directions for future research.

Nintedanib induces senolytic effect via STAT3 inhibition.

Selective removal of senescent cells, or senolytic therapy, has been proposed to be a potent strategy for overcoming age-related diseases and even for reversing aging. We found that nintedanib, a tyrosine kinase inhibitor, selectively induced the death of primary human dermal fibroblasts undergoing RS. Similar to ABT263, a well-known senolytic agent, nintedanib triggered intrinsic apoptosis in senescent cells. Additionally, at the concentration producing the senolytic effect, nintedanib arrested the cell cycle of nonsenescent cells in the G1 phase without inducing cytotoxicity. Interestingly, the mechanism by which nintedanib activated caspase-9 in the intrinsic apoptotic pathway differed from that of ABT263 apoptosis induction; specifically, nintedanib did not decrease the levels of Bcl-2 family proteins in senescent cells. Moreover, nintedanib suppressed the activation of the JAK2/STAT3 pathway, which caused the drug-induced death of senescent cells. STAT3 knockdown in senescent cells induced caspase activation. Moreover, nintedanib reduced the number of senescence-associated ß-galactosidase-positive senescent cells in parallel with a reduction in STAT3 phosphorylation and ameliorated collagen deposition in a mouse model of bleomycin-induced lung fibrosis. Consistently, nintedanib exhibited a senolytic effect through bleomycin-induced senescence of human pulmonary fibroblasts. Overall, we found that nintedanib can be used as a new senolytic agent and that inhibiting STAT3 may be an approach for inducing the selective death of senescent cells. Our findings pave the way for expanding the senolytic toolkit for use in various aging statuses and age-related diseases.

Co-inhibition of ATM and ROCK synergistically improves cell proliferation in replicative senescence by activating FOXM1 and E2F1.

The multifaceted nature of senescent cell cycle arrest necessitates the targeting of multiple factors arresting or promoting the cell cycle. We report that co-inhibition of ATM and ROCK by KU-60019 and Y-27632, respectively, synergistically increases the proliferation of human diploid fibroblasts undergoing replicative senescence through activation of the transcription factors E2F1 and FOXM1. Time-course transcriptome analysis identified FOXM1 and E2F1 as crucial factors promoting proliferation. Co-inhibition of the kinases ATM and ROCK first promotes the G2/M transition via FOXM1 activation, leading to accumulation of cells undergoing the G1/S transition via E2F1 activation. The combination of both inhibitors increased this effect more significantly than either inhibitor alone, suggesting synergism. Our results demonstrate a FOXM1- and E2F1-mediated molecular pathway enhancing cell cycle progression in cells with proliferative potential under replicative senescence conditions, and treatment with the inhibitors can be tested for senomorphic effect in vivo.

Endothelial cell senescence: A machine learning-based meta-analysis of transcriptomic studies.

Numerous systemic vascular dysfunction that leads to age-related diseases is highly associated with endothelial cell (EC) senescence; thus, identifying consensus features of EC senescence is crucial in understanding the mechanisms and identifying potential therapeutic targets. Here, by utilizing a total of 8 screened studies from different origins of ECs, we have successfully obtained common features in both gene and pathway level via sophisticated machine learning algorithms. A total of 400 differentially expressed genes (DEGs) were newly discovered with meta-analysis when compared to the usage of individual studies. The generated parsimonious model established 36 genes and 57 pathways features with non-zero coefficient, suggesting remarkable association of phosphoglycerate dehydrogenase and serine biosynthesis pathway with endothelial cellular senescence. For the cross-validation process to measure model performance of 36 deduced features, leave-one-study-out cross-validation (LOSOCV) was employed, resulting in an overall area under the receiver operating characteristic (AUROC) of 0.983 (95 % CI, 0.952, 1.000) showing excellent discriminative performance. Moreover, pathway-level analysis was performed by Pathifier algorithm, obtaining a total of 698 pathway deregulation scores from the 10,416 merged genes. In this process, high dimensional data was eventually narrowed down to 57 core pathways with AUROC value of 0.982 (95 % CI, 0.945, 1.000). The robust model with high performance underscores the merit of utilizing sophisticated meta-analysis in finding consensus features of endothelial cell senescence, which may lead to the development of therapeutic targets and advanced understanding of vascular dysfunction pathogenesis with further elucidation.

Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning.

Thyroid nodules are neoplasms commonly found among adults, with papillary thyroid carcinoma (PTC) being the most prevalent malignancy. However, current diagnostic methods often subject patients to unnecessary surgical burden. In this study, we developed and validated an automated, highly accurate multi-study-derived diagnostic model for PTCs using personalized biological pathways coupled with a sophisticated machine learning algorithm. Surprisingly, the algorithm achieved near-perfect performance in discriminating PTCs from non-tumoral thyroid samples with an overall cross-study-validated area under the receiver operating characteristic curve (AUROC) of 0.999 (95% confidence interval [CI]: 0.995-1) and a Brier score of 0.013 on three independent development cohorts. In addition, the algorithm showed excellent generalizability and transferability on two large-scale external blind PTC cohorts consisting of The Cancer Genome Atlas (TCGA), which is the largest genomic PTC cohort studied to date, and the post-Chernobyl cohort, which includes PTCs reported after exposure to radiation from the Chernobyl accident. When applied to the TCGA cohort, the model yielded an AUROC of 0.969 (95% CI: 0.950-0.987) and a Brier score of 0.109. On the post-Chernobyl cohort, it yielded an AUROC of 0.962 (95% CI: 0.918-1) and a Brier score of 0.073. This algorithm also is robust against other various types of clinical scenarios, discriminating malignant from benign lesions as well as clinically aggressive thyroid cancer with poor prognosis from indolent ones. Furthermore, we discovered novel pathway alterations and prognostic signatures for PTC, which can provide directions for follow-up studies.

Global transcriptional downregulation of TREX and nuclear trafficking machinery as pan-senescence phenomena: evidence from human cells and tissues.

Nucleocytoplasmic trafficking (NCT) of macromolecules is a fundamental process in eukaryotes that requires tight controls to maintain proper cell functions. Downregulation of the classical NCT pathway in senescent cells has been reported. However, whether this is a hallmark that exists across all types of cellular senescence remains unknown, and whether the mRNA export machinery is altered during senescence has not been demonstrated. Here, we show that the global transcriptomic downregulation of both the TREX (transcription-export) machinery and classical NLS-dependent protein transport machinery is a hallmark of varying types of senescence. A gene set-based approach using 25 different studies showed that the TREX-NCT gene set displays distinct common downregulated patterns in senescent cells versus its expression in their nonsenescent counterparts regardless of the senescence type, such as replicative senescence (RS), tumor cell senescence (TCS), oncogene-induced senescence (OIS), stem cell senescence (SCS), progeria and endothelial cell senescence (ECS). Similar patterns of TREX-NCT gene downregulation were also shown in two large human tissue genomic databases, the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. We also found that early-stage cancer tissues show consistent age-related patterns of TREX-NCT enrichment, suggesting the potential significance of TREX-NCT genes in determining cell fate in the early stage of tumorigenesis. Moreover, human cancer tissues exhibit an opposite TREX-NCT enrichment pattern with aging, indicating that deviation from age-related changes in TREX-NCT genes may provide a novel but critical clue for the age-dependent pathogenesis of cancer and increase in cancer incidence with aging.

Heterogeneity in liver histopathology is associated with GSK-3ß activity and mitochondrial dysfunction in end-stage diabetic rats on differential diets.

While liver histopathology is heterogeneous in diabetes, the underlying mechanisms remain unclear. We investigated whether glycemic variation resulting from differential diets can induce heterogeneity in diabetic liver and the underlying molecular mechanisms. We generated end-stage non-obese diabetic model rats by subtotal-pancreatectomy in male Sprague- Dawley rats and ad libitum diet for 7 weeks (n = 33). The rats were then divided into three groups, and fed a standard- or a low-protein diet (18 or 6 kcal%, respectively), for another 7 weeks: to maintain hyperglycemia, 11 rats were fed ad libitum (18AL group); to achieve euglycemia, 11 were calorierestricted (18R group), and 11 were both calorie- and proteinrestricted with the low-protein diet (6R group). Overnightfasted liver samples were collected after the differential diets together with sham-control (18S group), and histology and molecular changes were compared. Hyperglycemic-18AL showed glycogenic hepatopathy (GH) without steatosis, with the highest GSK-3ß inactivation because of Akt activation during hyperglycemia; mitochondrial function was not impaired, compared to the 18S group. Euglycemic-18R showed neither GH nor steatosis, with intermediate GSK-3ß activation and mitochondrial dysfunction. However, euglycemic-6R showed both GH and steatosis despite the highest GSK-3ß activity and no molecular evidence of increased lipogenesis or decreased ApoB expression, where mitochondrial dysfunction was highest among the groups. In conclusion, heterogeneous liver histopathology developed in end-stage non-obese diabetic rats as the glycemic levels varied with differential diets, in which protein content in the diets as well as glycemic levels differentially influenced GSK-3ß activity and mitochondrial function in insulin-deficient state. [BMB Reports 2020; 53(2): 100-105].

A meta-analysis to identify novel diagnostic and therapeutic targets for Dupuytren's disease.

The aim of this study was to determine novel candidate genes for Dupuytren's disease by performing a meta-analysis. We identified 261 genes (111 up-regulated and 150 down-regulated) that were consistently expressed differentially in Dupuytren's disease across the studies. We performed functional enrichment on total sets of the identified 261 genes and confirmed that most of the genes were closely related to common processes of diseases in general. From the integrated studies of the gene-correlation network and the protein-protein interaction network, we identified three functional modules in the gene co-expression network and four hub gene clusters in the protein-protein interaction network that shared the same genes and represented similar biological functions, implying that the seven groups identified in the systematic analysis of these two networks might be involved in the pathogenesis of Dupuytren's disease. This work demonstrates potential in developing experimental and clinical strategies for understanding and treating Dupuytren's disease.

Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach.

Epidermal growth factor receptor (EGFR) inhibitors have benefitted cancer patients worldwide, but resistance inevitably develops over time, resulting in treatment failures. An accurate prediction model for acquired resistance (AR) to EGFR inhibitors is critical for early diagnosis and according intervention, but is not yet available due to personal variations and the complex mechanisms of AR. Here, we have developed a novel pipeline to build a meta-analysis-based, multivariate model for personalized pathways in AR to EGFR inhibitors, using sophisticated machine learning algorithms. Surprisingly, the model achieved excellent predictive performance, with a cross-study validation area under curve (AUC) of over 0.9, and generalization performance on independent cohorts of samples, with a perfect AUC score of 1. Furthermore, the model showed excellent transferability across different cancer cell lines and EGFR inhibitors, including gefitinib, erlotinib, afatinib, and cetuximab. In conclusion, our model achieved high predictive accuracy through robust cross study validation, and enabled individualized prediction on newly introduced data. We also discovered common pathway alteration signatures for AR to EGFR inhibitors, which can provide directions for other follow-up studies.

Gene Prioritization and Network Topology Analysis of Targeted Genes for Acquired Taxane Resistance by Meta-Analysis.

Network topology-based approaches prove to be highly efficient in addressing multifactorial phenomena such as acquired drug resistance in cancer. The aim of this study was to identify differentially expressed genes across multiple microarray datasets (meta-DEGs), to prioritize meta-DEGs to find the most promising genes linked to acquired taxane resistance (ATR), and to analyze the relevant biological networks using topology analysis. A total of 771 meta-DEGs were identified by performing a cross-platform meta-analysis of ATR-related microarray datasets. A gene prioritization method was used to simultaneously identify activated or deactivated genes on a co-expression map and protein-protein interaction (PPI) network. The top 10 prioritized genes in the gene co-expression and the top 1% highly ranked genes in the PPI network were identified. The selected meta-DEGs were used to construct biological networks, and topological analysis was performed using network centrality measures. Using integrative analyses, we identified ATR candidate genes, including several previously unidentified genes that were found to be associated with ATR. From the gene co-expression network, PRSS23 was the highest-ranking gene at local average connectivity measure and ADAM9 was ranked highest in other centralities. In protein interaction network, HSPA1A, ANXA1, and PA2G4 showed highest ranks in network centrality analyses. This study provides a comprehensive overview of the gene expression patterns associated with ATR. Furthermore, it presents a new approach to identification of unveiled candidate genes to ATR, using a gene prioritization method and network analysis.

Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method.

PURPOSE: This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR). MATERIALS AND METHODS: Penalized regression, combinedwith an individualized pathway score algorithm,was applied to construct a predictive model using publically available genomic cohorts of ATR and intrinsic taxane resistance (ITR). To develop a model with enhanced generalizability, we merged multiple ATR studies then updated the learning parameter via robust cross-study validation. RESULTS: For internal cross-study validation, the ATR model produced a perfect performance with an overall area under the receiver operating curve (AUROC) of 1.000 with an area under the precision-recall curve (AUPRC) of 1.000, a Brier score of 0.007, a sensitivity and a specificity of 100%. The model showed an excellent performance on two independent blind ATR cohorts (overall AUROC of 0.940, AUPRC of 0.940, a Brier score of 0.127). When we applied our algorithm to two large-scale pharmacogenomic resources for ITR, the Cancer Genome Project (CGP) and the Cancer Cell Line Encyclopedia (CCLE), an overall ITR cross-study AUROC was 0.70, which is a far better accuracy than an almost random level reported by previous studies. Furthermore, this model had a high transferability on blind ATR cohorts with an AUROC of 0.69, suggesting that general predictive features may be at work across both ITR and ATR. CONCLUSION: We successfully constructed a multi-study-derived personalized prediction model for ATR with excellent accuracy, generalizability, and transferability.

Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor.

PURPOSE: Acquired resistance (AR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) is a major issue worldwide, for both patients and healthcare providers. However, precise prediction is currently infeasible due to the lack of an appropriate model. This study was conducted to develop and validate an individualized prediction model for automated detection of acquired EGFR-TKI resistance. METHODS: Penalized regression was applied to construct a predictive model using publically available genomic cohorts of acquired EGFR-TKI resistance. To develop a model with enhanced generalizability, we merged multiple cohorts then updated the learning parameter via robust cross-study validation. Model performance was evaluated mainly using the area under the receiver operating characteristic curve. RESULTS: Using a multi-study-derived machine learning method, we developed an extremely parsimonious model with generalized predictors (DDK3, CPS1, MOB3B, KRT6A), which has excellent prediction performance on blind cohorts for AR to EGFR-TKIs (gefitinib, erlotinib and afatinib) and monoclonal antibody against EGFR (cetuximab). In addition, our model also showed high performance for predicting intrinsic resistance (IR) to EGFR-TKIs from two large-scale pharmacogenomic resources, the Cancer Genome Project and the Cancer Cell Line Encyclopedia, suggesting that these general predictive features may work across AR and IR. CONCLUSIONS: We successfully constructed a multi-study-derived prediction model for acquired EGFR-TKI resistance with excellent accuracy, generalizability and transferability.

Development of second primary cancer in Korean breast cancer survivors.

PURPOSE: Breast cancer survivors have slightly increased the risk of second primary cancers. Breast, colon, uterine, and ovarian cancers are common secondary cancers in breast cancer survivors. In this study, we assessed the development of second primary cancers of breast cancer survivors in Korea. METHODS: Medical records of patients with breast cancer in 3 tertiary medical institutions were reviewed retrospectively. We evaluated secondary malignancy diagnosed at least 2 months after the breast cancer diagnosis. Based on the International Classification of Disease-9 codes of malignancies, secondary primary breast cancer records were evaluated with person-year adjustment. The standardized incidence ratio (SIR) was assessed using national cancer incidence. RESULTS: A total of 3,444 treatment records were included from 3 medical centers. The cumulative incidence of overall second primary cancers was 2.8% (n = 93). The SIR was significantly higher in all sites (1.56; 95% confidence interval [CI], 1.26-1.91), endometrial cancer (5.65; 95% CI, 2.06-12.31), biliary tract cancer (3.96; 95% CI, 1.19-8.60), and thyroid cancer (2.29; 95% CI, 1.67-3.08). CONCLUSION: The incidence of cancer was higher in breast cancer survivors compared to general population. Surveillance of secondary cancer in this group should be recommended individually considering the benefit related to the prognosis of primary breast cancer.

Establishment of Patient-Derived Keloid Xenograft Model.

Recent advances on preclinical model based on patient-derived tumor xenografts have new insight into many clinical fields. According to our literature review, many authors believe that immunodeficient animals such as athymic rats and mice should be used to prevent tissue loss caused by acute rejection to establish patient-derived tumor xenografts models.However, recent advances showed that the microenvironment has gained attention as an important factor responsible for disease progression. Additionally, researchers attempt to come up with novel findings in chemotherapy drugs and immune modulator to control development of keloid. For these reasons, establishment of reliable animal model of keloids is very important.In this new model using an immunocompetent animal as a humanized-xenografts model, human keloid scar has been maintained for as long as 4 months. Results of migration assay have demonstrated that typical morphology of keloid fibroblast was preserved based on multiple time point observations despite its aging change. Quantitative real time polymerase chain reaction findings suggested that after implantation, there has been significant increase of vascular endothelial growth factor, CD34, and transforming growth factor beta 1 expression despite insignificant changes of hypoxia inducible factor 1 an matrix metallopeptidase 1, and matrix metallopeptidase 9 gene expression. These findings suggested that implantation of keloids within the immunocompetent animals yields is very useful experimental model in terms of fibrosis.In summary, the authors have successfully established and propagated patient-derived keloid model using the immunocompetent animals. This model could be used to test novel materials as well as combination therapies and is superior to the conventional cell line experiment models. In addition, the biology of the keloids can easily be assessed to identify predictive markers for responses to treatment regimens that are currently actively under research in various centers.

Presurgical Botulinum Toxin A Treatment Increases Angiogenesis by Hypoxia-Inducible Factor-1α/Vascular Endothelial Growth Factor and Subsequent Superiorly Based Transverse Rectus Abdominis Myocutaneous Flap Survival in a Rat Model.

To date, there have been several experimental studies to assess tissue viability of transverse rectus abdominis myocutaneous (TRAM) flaps. Botulinum toxin A (BoTA) has gained popularity in many clinical fields, for a variety of therapeutic and aesthetic purposes. In addition, there have been reports regarding the positive effect of BoTA on flap survival by various mechanisms. In this study, we hypothesized that pretreatment with BoTA could augment the survival of TRAM flaps via increased hypoxia-inducible factor (HIF)1α/vascular endothelial growth factor (VEGF)-dependent angiogenesis.Twenty-four Sprague-Dawley rats were randomly divided into 2 groups: a control group and a BoTA group. Five days before superiorly based TRAM flap elevation, the BoTA group was pretreated with BoTA, whereas the control group was pretreated with normal saline. Gross flap survival rates were assessed, and quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) and Western blotting were performed for the evaluation of angiogenesis-related factors (CD34, HIF-1α, and VEGF).In the BoTA group, the gross flap survival rate was significantly higher than that in the control group on both ipsilateral (92.78.3 ± 5.05% vs 86.8 ± 3.88%, P = 0.009) and contralateral (91.57 ± 5.79% vs 74.28 ± 11.83%, P < 0.001) sides.The relative mRNA expression of CD34 and VEGF was significantly higher in the BoTA group than that in the control group in every zone, whereas the relative mRNA expression of HIF-1α was significantly higher in the BoTA group than that in the control group on contralateral sides. The relative protein expression of CD34, VEGF, and HIF-1α was significantly higher in the BoTA group than that in the control group in every zone.In conclusion, we demonstrate that presurgical BoTA treatment might increase angiogenesis by HIF-1α/VEGF, subsequently increase superiorly based TRAM flap survival in a rat model.

Bilirubin provides perforator flap protection from ischaemia-reperfusion injury in a rat model: a preliminary result.

The use of bilirubin, a well-known and powerful antioxidant, has gained popularity in recent years because of its role in the prevention of ischaemic heart disease in patients with Gilbert's syndrome. We investigate the effects of bilirubin on ischaemia-reperfusion (I/R) injury using a rat perforator flap model. Forty-eight rats were randomly divided into two groups: experimental (bilirubin) group (n = 24) and control group (n = 24). In each group, elevated bilateral deep inferior epigastric perforator (DIEP) flaps were created. The right (no ischaemia side) and left (ischaemia side) DIEP flaps were separated according to the presence of ischaemia induction. Ischaemia was induced in anaesthetised rats by perforator clamping for 15 or 30 minutes. After surgery, the flap survival was assessed daily on postoperative days 0 to 5, and overall histological changes of DIEP flaps above the perforator were analysed at postoperative day 5. The flap survival rate in the bilirubin group was significantly higher than that in the control group at the ischaemia side following perforator clamping for 15 or 30 minutes (93·42 ± 4·48% versus 89·63 ± 3·98%, P = 0·002; and 83·96 ± 4·23% versus 36·46 ± 6·38%, P < 0·001, respectively). The difference in flap survival between the two groups was the most prominent on the ischaemic side following 30 minutes of perforator clamping. From a morphologic perspective, pre-treatment with bilirubin was found to alleviate perforator flap necrosis caused by I/R injury in this experimental rat model.

Identification of novel therapeutic target genes in acquired lapatinib-resistant breast cancer by integrative meta-analysis.

Acquired resistance to lapatinib is a highly problematic clinical barrier that has to be overcome for a successful cancer treatment. Despite efforts to determine the mechanisms underlying acquired lapatinib resistance (ALR), no definitive genetic factors have been reported to be solely responsible for the acquired resistance in breast cancer. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets related to breast cancer with ALR, using the R-based RankProd package. From the meta-analysis, we were able to identify a total of 990 differentially expressed genes (DEGs, 406 upregulated, 584 downregulated) that are potentially associated with ALR. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs showed that "response to organic substance" and "p53 signaling pathway" may be largely involved in ALR process. Of these, many of the top 50 upregulated and downregulated DEGs were found in oncogenesis of various tumors and cancers. For the top 50 DEGs, we constructed the gene coexpression and protein-protein interaction networks from a huge database of well-known molecular interactions. By integrative analysis of two systemic networks, we condensed the total number of DEGs to six common genes (LGALS1, PRSS23, PTRF, FHL2, TOB1, and SOCS2). Furthermore, these genes were confirmed in functional module eigens obtained from the weighted gene correlation network analysis of total DEGs in the microarray datasets ("GSE16179" and "GSE52707"). Our integrative meta-analysis could provide a comprehensive perspective into complex mechanisms underlying ALR in breast cancer and a theoretical support for further chemotherapeutic studies.