Secretome profiling of oral squamous cell carcinoma-associated fibroblasts reveals organization and disassembly of extracellular matrix and collagen metabolic process signatures

An important role has been attributed to cancer-associated fibroblasts (CAFs) in the tumorigenesis of oral squamous cell carcinoma (OSCC), the most common tumor of the oral cavity. Previous studies demonstrated that CAF-secreted molecules promote the proliferation and invasion of OSCC cells, inducing a more aggressive phenotype. In this study, we searched for differences in the secretome of CAFs and normal oral fibroblasts (NOF) using mass spectrometry-based proteomics and biological network analysis. Comparison of the secretome profiles revealed that upregulated proteins involved mainly in extracellular matrix organization and disassembly and collagen metabolism. Among the upregulated proteins were fibronectin type III domain-containing 1 (FNDC1), serpin peptidase inhibitor type 1 (SERPINE1), and stanniocalcin 2 (STC2), the upregulation of which was validated by quantitative PCR and ELISA in an independent set of CAF cell lines. The transition of transforming growth factor beta 1 (TGF-beta 1)-mediating NOFs into CAFs was accompanied by significant upregulation of FNDC1, SERPINE1, and STC2, confirming the participation of these proteins in the CAF-derived secretome. Type I collagen, the main constituent of the connective tissue, was also associated with several upregulated biological processes. The immunoexpression of type I collagen N-terminal propeptide (PINP) was significantly correlated in vivo with CAFs in the tumor front and was associated with significantly shortened survival of OSCC patients. Presence of CAFs in the tumor stroma was also an independent prognostic factor for OSCC disease-free survival. These results demonstrate the value of secretome profiling for evaluating the role of CAFs in the tumor microenvironment and identify potential novel therapeutic targets such as FNDC1, SERPINE1, and STC2. Furthermore, type I collagen expression by CAFs, represented by PINP levels, may be a prognostic marker of OSCC outcome.Peer reviewe

Tenascin-C and fibronectin expression divide early stage tongue cancer into low- and high-risk groups

Background:Oral tongue squamous cell carcinoma (OTSCC) metastasises early, especially to regional lymph nodes. There is an ongoing debate on which early stage (T1-T2N0) patients should be treated with elective neck dissection. We need prognosticators for early stage tongue cancer. Methods: Mice immunisation with human mesenchymal stromal cells resulted in production of antibodies against tenascin-C (TNC) and fibronectin (FN), which were used to stain 178 (98 early stage), oral tongue squamous cell carcinoma samples. TenascinC and FN expression in the stroma (negative, moderate or abundant) and tumour cells (negative or positive) were assessed. Similar staining was obtained using corresponding commercial antibodies. Results: Expression of TNC and FN in the stroma, but not in the tumour cells, proved to be excellent prognosticators both in all stages and in early stage cases. Among early stages, when stromal TNC was negative, the 5-year survival rate was 88%. Correspondingly, when FN was negative, no cancer deaths were observed. Five-year survival rates for abundant expression of TNC and FN were 43% and 25%, respectively. Conclusions: Stromal TNC and, especially, FN expressions differentiate patients into low-and high-risk groups. Surgery alone of early stage primary tumours might be adequate when stromal FN is negative. Aggressive treatments should be considered when both TNC and FN are abundant.Peer reviewe

Fascin promotes migration and invasion and is a prognostic marker for oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) prognosis is related to clinical stage and histological grade. However, this stratification needs to be refined. We conducted a comparative proteome study in microdissected samples from normal oral mucosa and OSCC to identify biomarkers for malignancy. Fascin and plectin were identified as differently expressed and both are implicated in several malignancies, but the clinical impacts of aberrant fascin and plectin expression in OSCCs remains largely unknown. Immunohistochemistry and real-time quantitative PCR were carried out in ex vivo OSCC samples and cell lines. A loss-of-function strategy using shRNA targeting fascin was employed to investigate in vitro and in vivo the fascin role on oral tumorigenesis. Transfections of microRNA mimics were performed to determine whether the fascin overexpression is regulated by miR-138 and miR-145. We found that fascin and plectin are frequently upregulated in OSCC samples and cell lines, but only fascin overexpression is an independent unfavorable prognostic indicator of disease-specific survival. In combination with advanced T stage, high fascin level is also an independent factor of disease-free survival. Knockdown of fascin in OSCC cells promoted cell adhesion and inhibited migration, invasion and EMT, and forced expression of miR-138 in OSCC cells significantly decreased the expression of fascin. In addition, fascin downregulation leads to reduced filopodia formation and decrease on paxillin expression. The subcutaneous xenograft model showed that tumors formed in the presence of low levels of fascin were significantly smaller compared to those formed with high fascin levels. Collectively, our findings suggest that fascin expression correlates with disease progression and may serve as a prognostic marker and therapeutic target for patients with OSCC.Peer reviewe

Low miR-143/miR-145 Cluster Levels Induce Activin A Overexpression in Oral Squamous Cell Carcinomas, Which Contributes to Poor Prognosis

Deregulated expression of activin A is reported in several tumors, but its biological functions in oral squamous cell carcinoma (OSCC) are unknown. Here, we investigate whether activin A can play a causal role in OSCCs. Activin A expression was assessed by qPCR and immunohistochemistry in OSCC tissues. Low activin A-expressing cells were treated with recombinant activin A and assessed for apoptosis, proliferation, adhesion, migration, invasion and epithelial-mesenchymal transition (EMT). Those phenotypes were also evaluated in high activin A-expressing cells treated with follistatin (an activin A antagonist) or stably expressing shRNA targeting activin A. Transfections of microRNA mimics were performed to determine whether the overexpression of activin A is regulated by miR-143/miR-145 cluster. Activin A was overexpressed in OSCCs in comparison with normal oral mucosa, and high activin A levels were significantly associated with lymph node metastasis, tumor differentiation and poor survival. High activin A levels promoted multiple properties associated with malignant transformation, including decreased apoptosis and increased proliferation, migration, invasion and EMT. Both miR-143 and miR-145 were markedly downregulated in OSCC cell lines and in clinical specimens, and inversely correlated to activin A levels. Forced expression of miR-143 and miR-145 in OSCC cells significantly decreased the expression of activin A. Overexpression of activin A in OSCCs, which is controlled by downregulation of miR-143/miR-145 cluster, regulates apoptosis, proliferation and invasiveness, and it is clinically correlated with lymph node metastasis and poor survival.Peer reviewe

Estimulação cerebral profunda na Doença de Parkinson: evidências de estudos de longa duração

A Doença de Parkinson (DP) é uma condição neurodegenerativa crônica que afeta principalmente idosos, mas pode ocorrer em adultos jovens. É a segunda doença neurodegenerativa mais comum, após o Alzheimer. A DP afeta 1% dos indivíduos acima de 60 anos em países industrializados. Sua causa envolve fatores genéticos e ambientais, como exposição a pesticidas e envelhecimento. A Estimulação Cerebral Profunda (DBS) é um tratamento que simula lesões cerebrais, melhorando sintomas motores e não motores. O presente estudo tem como objetivo analisar evidências de estudos sobre a eficácia da DBS no tratamento da DP. Trata-se de uma revisão sistemática de estudos quantitativos que utiliza as bases de dados PubMed (Medline), Cochrane Library e Scientific Electronic Library Online (SciELO) para selecionar artigos científicos. Os estudos incluídos abrangem o período de 2013 a 2023 e estão em inglês, abordando a DBS no tratamento da DP. A DBS melhora diversos sintomas motores e não motores, resultando em uma melhor qualidade de vida para os pacientes. Tais benefícios são sustentados mesmo em estágios avançados da Doença de Parkinson, a qual consiste em fornecer pulsos de corrente elétrica a áreas cerebrais profundas através de eletrodos implantados cirurgicamente, geralmente quando a terapia medicamentosa já não é eficaz. Em um estudo com 82 pacientes, a terapia com DBS resultou em uma redução de ± 52% nos sintomas motores do UPDRS sob medicação antes da cirurgia. A melhora nos sintomas motores com a estimulação, em comparação com a ausência de estimulação e medicação, foi de ± 61% no primeiro ano e ± 39% de 8 a 15 anos após a cirurgia (antes da reprogramação). A medicação foi reduzida em ± 55% após 1 ano e ± 44% após 8 a 15 anos, com a maioria dos pacientes mostrando melhorias após a reprogramação. De acordo com as literaturas analisadas, a DBS é uma terapia eficaz para a DP. Enfatiza-se a importância da inovação contínua e dos novos estudos para explorar as facetas não investigadas desse campo. Com a abordagem dos aspectos clínicos, cirúrgicos, tecnológicos e científicos, destacam-se os benefícios, limitações e desafios a serem superados. Ademais, inovações tecnológicas na DBS, como a estimulação direcional, adaptativa e a telemedicina estão sendo exploradas. Em suma, este artigo fornece evidências sobre os benefícios da DBS na DP, ressaltando a necessidade de pesquisas adicionais para otimizar tal intervenção terapêutica e melhorar a qualidade de vida dos pacientes

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost