NUCB2/Nesfatin-1 drives breast cancer metastasis through the up-regulation of cholesterol synthesis via the mTORC1 pathway.

BACKGROUND: Reprogramming lipid metabolism for tumor metastasis is essential in breast cancer, and NUCB2/Nesfatin-1 plays a crucial role in regulating energy metabolism. Its high expression is associated with poor prognosis in breast cancer. Here, we studied whether NUCB2/Nesfatin-1 promotes breast cancer metastasis through reprogramming cholesterol metabolism. METHODS: ELISA was employed to measure the concentration of Nesfatin-1 in the serum of breast cancer patients and the control group. Database analysis suggested that NUCB2/Nesfatin-1 might be acetylated in breast cancer, which was confirmed by treating the breast cancer cells with acetyltransferase inhibitors. Transwell migration and Matrigel invasion assays were conducted, and nude mouse lung metastasis models were established to examine the effect of NUCB2/Nesfatin-1 on breast cancer metastasis in vitro and in vivo. The Affymetrix gene expression chip results were analyzed using IPA software to identify the critical pathway induced by NUCB2/Nesfatin-1. We evaluated the effect of NUCB2/Nesfatin-1 on cholesterol biosynthesis through the mTORC1-SREBP2-HMGCR axis by utilizing mTORC1 inhibitor and rescue experiments. RESULTS: NUCB2/Nesfatin-1 was found to be overexpressed in the breast cancer patients, and its overexpression was positively correlated with poor prognosis. NUCB2 was potentially acetylated, leading to high expression in breast cancer. NUCB2/Nesfatin-1 promoted metastasis in vitro and in vivo, while Nesfatin-1 rescued impaired cell metastasis induced by NUCB2 depletion. Mechanistically, NUCB2/Nesfatin-1 upregulated cholesterol synthesis via the mTORC1 signal pathway, contributing to breast cancer migration and metastasis. CONCLUSIONS: Our findings demonstrate that the NUCB2/Nesfatin-1/mTORC1/SREBP2 signal pathway is critical in regulating cholesterol synthesis, essential for breast cancer metastasis. Thus, NUCB2/Nesfatin-1 might be utilized as a diagnostic tool and also used in cancer therapy for breast cancer in the future.

Abdominal perivascular epithelioid cell tumor (PEComa) without visible fat: a clinicopathologic and radiological analysis of 16 cases.

OBJECTIVE: To retrospectively review the clinicopathological features and computed tomography (CT) and magnetic resonance imaging (MRI) findings of abdominal perivascular epithelioid cell tumor without visible fat (PEComawvf). MATERIALS AND METHODS: Sixteen patients with surgically and pathologically confirmed perivascular epithelioid cell tumor without visible fat were enrolled. Their clinicopathological data and imaging findings were retrospectively reviewed. The CT and MRI features, including location, size, shape, margin, density, calcification, cystic necrosis and enhancement pattern, were analyzed. RESULTS: There were 4 males and 12 females (median age, 46 years; range, 21-65 years) in this study. All 16 patients were diagnostic asymptomatic unenhanced CT or MRI and revealed a well-defined (n = 13), oval (n = 10), mass with heterogeneous (n = 6) or homogeneous density/signal intensity (n = 7), calcification and hemorrhage was no found in any cases. On enhanced CT/MRI, markedly enhancement patterns (n = 14) were observed. The "peripheral enhancement" sign was observed in 13 cases. One in 16 cases recurrence was discovered during the follow-up period. CONCLUSIONS: Dynamic CT, MRI and pathology of PEComawvf had some characteristics of non-aggressive pattern of performance, and MRI would provide beneficial detection of microscopic fat. Enhanced imaging showed PEComawvf is characterized by a "peripheral enhancement" with a marked enhancement pattern. Knowing these characteristics could contribute to improving the understanding abdominal PEComawvf and related palliative care.

Role of SSH1 in colorectal cancer prognosis and tumor progression.

BACKGROUND AND AIM: Slingshot 1 protein (SSH1) plays a critical role in cytoskeleton dynamic regulation. Increasing evidence suggest that SSH1 expression is upregulated in several cancers and relates to tumor progression and drug resistance. Here, we evaluated the role of SSH1 in colorectal cancer (CRC) development and its prognostic value in patients with CRC. METHODS: SSH1 expression was examined by quantitative real-time polymerase chain reaction, western blot analysis, or immunohistochemistry. The association between SSH1 expression and clinical characteristics and prognosis was evaluated. Stable SSH1 knockdown cells were used for in vitro assays and xenograft models. Correlation between SSH1 expression and epithelial-mesenchymal transition (EMT) was analyzed by western blot and online data analysis. RESULTS: SSH1 expression was upregulated in cancer tissue compared with paired non-cancerous tissue in patients with CRC. SSH1 expression level in CRC tissue was associated with tumor stage, lymph node metastasis, and correlated with poor prognosis as indicated by univariate and multivariate analyses. In vitro, loss of SSH1 impaired colony formation, migration, and invasion of CRC cells. In vivo data suggest that SSH1 could promote the progression and metastasis of CRC. Interestingly, E-cadherin, ZEB1, and Snail, which are markers of EMT, had a significant expression correlation with SSH1. CONCLUSIONS: SSH1 expression is associated with CRC progression and predicts poor prognosis. SSH1 may promote CRC tumor progression by regulating EMT.

Epigenetic silencing of Rab39a promotes epithelial to mesenchymal transition of cervical cancer through AKT signaling.

The objective of this study was to investigate the functional role of Rab39a in human cervical cancer (CC) and the underlying molecular mechanisms. We first measured Rab39a mRNA expression in CC tissues and paired non-tumor tissues by quantitative real-time PCR (QRT-PCR). Overall survival of CC patients with different mRNA levels of Rab39a in The Cancer Genome Atlas (TCGA) database was assessed by Kaplan-Meier survival curves analysis. Next methylation-specific PCR (MSP) was performed to determine the expression mechanism of Rab39a. Then cell proliferation, migration and invasion of Rab39a-transfected or mock-transfected cervical cancer cells were determined by CCK-8, flow cytometry, wound healing, transwell migration and invasion assays, respectively. Finally, the molecular mechanism by which Rab39a modulated CC cell epithelial-mesenchymal transition (EMT) was explored. It was found that Rab39a mRNA was significantly down-regulated in the high-risk patients compared to the low-risk patients (p = 0.0054). Six of seven cancer tissues with lymph node metastasis express low Rab39a mRNA compared to the surrounding non-tumor tissues. Cervical cancer patients with low level of Rab39a were showed a poorly clinical outcome (p = 0.004). Loss of Rab39a expression in cervical cancer tissues was associated with the aberrant DNA methylation in the promoter of Rab39a gene. Disrupted Rab39a expression in cervical cancer cells could be restored after treatment with the demethylated agent 5-Aza-2'-deoxycytidine. Furthermore, it was found that Rab39a hardly influenced cell growth but significantly suppressed cell migration, invasion and EMT process. Rab39a exerted its potential suppressor functions through inhibiting AKT phosphorylation. The inhibition effects of Rab39a could be blocked by AKT pathway inhibitor. Collectively, our data shows that Rab39a is a potential epigenetic silenced tumor suppressor inhibiting cancer invasion and migration through modulating the AKT signaling.

[Association of NLRC3 with prognosis and tumor immunity in patients with stage III colorectal cancer].

OBJECTIVE: To explore the association of nucleotide binding oligomerization domain-like receptor family caspase recruitment domain containing 3 (NLRC3) with prognosis and tumor immunity in patients with stage III colorectal cancer.
 Methods: Data of 122 patients with stage III colorectal cancer, who underwent radical resection from 2012 to 2013 in Xiangya Hospital of Central South University, were retrospectively collected. The expressions of NLRC3 and CD8+ were examined by immumohistochemical (IHC) staining. The preoperative clinical data were used to obtain neutrophil to lymphocyte ratio (NLR), and the stability of microsatellite was determined. The relationship between NLRC3 and clinicopathological factors was analyzed by χ2 test, and the independent prognostic factors for patients with stage III colorectal cancer were determined by COX regression model.
 Results: The expression of NLRC3 was significantly associated with CD8+ T cells infiltration (χ2=27.79, P<0.01), NLR (χ2=6.35, P<0.05), lymph node metastasis (LN) (χ2=10.12, P<0.01) and microsatellite stability (χ2=6.05, P<0.05). NLRC3 (OR=0.066, 95% CI 0.020 to 0.218), vascular emboli (OR=3.119, 95% CI 1.547 to 6.286) and NLR (OR=5.103, 95% CI 2.465 to 10.563) had an effect on overall survival (OS) for patients with stage III colorectal cancer (all P<0.05). In addition, NLRC3 (OR=0.144, 95% CI 0.055 to 0.377), vascular emboli (OR=3.589, 95% CI 1.859 to 6.932) and NLR (OR=2.939, 95% CI 1.509 to 5.723) also had an effect on disease-free survival (DFS) for patients with stage III colorectal cancer (all P<0.05).
 Conclusion: NLRC3, intravascular emboli and NLR are independent prognostic factors for patients with stage III colorectal cancer. NLRC3 might be a good prognostic factor for patients with stage III colorectal cancer due to its capacity of inhibiting systemic inflammation and promoting local anti-tumor immunity.

Multi-Algorithm Analysis Reveals Pyroptosis-Linked Genes as Pancreatic Cancer Biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at late stages, limiting treatment options and survival rates. Pyroptosis-related gene signatures hold promise as PDAC prognostic markers, but limited gene pools and small sample sizes hinder their utility. We aimed to enhance PDAC prognosis with a comprehensive multi-algorithm analysis. Using R, we employed natural language processing and latent Dirichlet allocation on PubMed publications to identify pyroptosis-related genes. We collected PDAC transcriptome data (n = 1273) from various databases, conducted a meta-analysis, and performed differential gene expression analysis on tumour and non-cancerous tissues. Cox and LASSO algorithms were used for survival modelling, resulting in a pyroptosis-related gene expression-based prognostic index. Laboratory and external validations were conducted. Bibliometric analysis revealed that pyroptosis publications focus on signalling pathways, disease correlation, and prognosis. We identified 357 pyroptosis-related genes, validating the significance of BHLHE40, IL18, BIRC3, and APOL1. Elevated expression of these genes strongly correlated with poor PDAC prognosis and guided treatment strategies. Our accessible nomogram model aids in PDAC prognosis and treatment decisions. We established an improved gene signature for pyroptosis-related genes, offering a novel model and nomogram for enhanced PDAC prognosis.

A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years.

Background: Triple-negative breast cancer (TNBC) is proposed at the beginning of this century, which is still the most challenging breast cancer subtype due to its aggressive behavior, including early relapse, metastatic spread, and poor survival. This study uses machine learning methods to explore the current research status and deficiencies from a macro perspective on TNBC publications. Methods: PubMed publications under "triple-negative breast cancer" were searched and downloaded between January 2005 and 2022. R and Python extracted MeSH terms, geographic information, and other abstracts from metadata. The Latent Dirichlet Allocation (LDA) algorithm was applied to identify specific research topics. The Louvain algorithm established a topic network, identifying the topic's relationship. Results: A total of 16,826 publications were identified, with an average annual growth rate of 74.7%. Ninety-eight countries and regions in the world participated in TNBC research. Molecular pathogenesis and medication are most studied in TNBC research. The publications mainly focused on three aspects: Therapeutic target research, Prognostic research, and Mechanism research. The algorithm and citation suggested that TNBC research is based on technology that advances TNBC subtyping, new drug development, and clinical trials. Conclusion: This study quantitatively analyzes the current status of TNBC research from a macro perspective and will aid in redirecting basic and clinical research toward a better outcome for TNBC. Therapeutic target research and Nanoparticle research are the present research focus. There may be a lack of research on TNBC from a patient perspective, health economics, and end-of-life care perspectives. The research direction of TNBC may require the intervention of new technologies.

Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years.

Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) term 'Pancreatic Neoplasms' from January 1996 to October 2021 were downloaded from PubMed. Using the statistical computing language R and the interpreted, high-level, general-purpose programming language Python, we extracted publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analyses. The generative statistical algorithm "latent Dirichlet allocation" (LDA) was applied to identify specific research topics and trends. The unsupervised "Louvain algorithm" was used to establish a network to identify relationships between single topics. A total of 60,296 publications were identified and analyzed. The publications were derived from 133 countries, mostly from the Northern Hemisphere. For the term "pancreatic cancer research", 12,058 MeSH terms appeared 1,395,060 times. Among them, we identified the four main topics "Clinical Manifestation and Diagnosis", "Review and Management", "Treatment Studies", and "Basic Research". The number of publications has increased rapidly during the past 25 years. Based on the number of publications, the algorithm predicted that "Immunotherapy", Prognostic research", "Protein expression", "Case reports", "Gemcitabine and mechanism", "Clinical study of gemcitabine", "Operation and postoperation", "Chemotherapy and resection", and "Review and management" as current research topics. To our knowledge, this is the first study on this subject of pancreatic cancer research, which has become possible due to the improvement of algorithms and hardware.

Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing.

Objective: This study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies' publications in the PubMed database to understand the historical growth, current state, and potential expansion trend. Methods: All MDD radiology studies publications from January 2002 to January 2022 were downloaded from PubMed using R, a statistical computing language. R and the interpretive general-purpose programming language Python were used to extract publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analysis. The generative statistical algorithm "Latent Dirichlet allocation" (LDA) was applied to identify specific research focus and trends. The unsupervised Leuven algorithm was used to build a network to identify relationships between research focus. Results: A total of 5,566 publications on MDD and radiology research were identified, and there is a rapid upward trend. The top-cited publications were 11,042, and the highly-cited publications focused on improving diagnostic performance and establishing imaging standards. Publications came from 76 countries, with the most from research institutions in the United States and China. Hospitals and radiology departments take the lead in research and have an advantage. The extensive field of study contains 12,058 Medical Subject Heading (MeSH) terms. Based on the LDA algorithm, three areas were identified that have become the focus of research in recent years, "Symptoms and treatment," "Brain structure and imaging," and "Comorbidities research." Conclusion: Latent Dirichlet allocation analysis methods can be well used to analyze many texts and discover recent research trends and focus. In the past 20 years, the research on MDD and radiology has focused on exploring MDD mechanisms, establishing standards, and constructing imaging methods. Recent research focuses are "Symptoms and sleep," "Brain structure study," and "functional connectivity." New progress may be made in studies on MDD complications and the combination of brain structure and metabolism.

Web-Based Dynamic Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Breast Cancer Patients with Lung Metastases.

BACKGROUND: 60-70% of patients who die from breast cancer have lung metastases. However, there is a lack of readily available tools for accurate risk stratification in patients with breast cancer lung metastases (BCLM). Therefore, a web-based dynamic nomogram was developed for BCLM to quickly, accurately, and intuitively assess overall and cancer-specific survival rates. METHODS: Patients diagnosed with BCLM between 2004 and 2016 were extracted from the Surveillance, Epidemiology, and Final Results (SEER) database. After excluding incomplete data, all patients were randomly assigned to training and validation cohorts (2:1). Patients' basic clinical information, detailed pathological staging and treatment information, and sociological information were included in further analysis. Nomograms were constructed following the evaluations of the Cox regression model and verified using the concordance index (C-index), calibration curves, time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Web-based dynamic nomograms were published online. RESULTS: 3916 breast cancer patients with lung metastases were identified from the SEER database. Based on multivariate Cox regression analysis, overall survival (OS) and cancer-specific survival (CSS) are significantly correlated with 13 variables: age, marital status, race, grade, T stage, surgery, chemotherapy, bone metastatic, brain metastatic, liver metastatic, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). These are included in the construction of the nomogram of OS and CSS. The time-dependent receiver operating characteristic curve, decision curve analysis, consistency index, and calibration curve prove the distinct advantages of the nomogram. CONCLUSIONS: Our web-based dynamic nomogram effectively integrates patient molecular subtype and sociodemographic characteristics with clinical characteristics and guidance and can be easily used. ER-Negative should receive attention in diagnosing and treating BCLM.

Alpha-Lipoic Acid Prevents Side Effects of Therapeutic Nanosilver without Compromising Cytotoxicity in Experimental Pancreatic Cancer.

Silver nanoparticles (AgNPs) have attracted attention in cancer therapy and might support the treatment of pancreatic ductal adenocarcinoma (PDAC). Silver is in clinical use in wound dressings, catheters, stents and implants. However, the side effects of systemic AgNP treatment due to silver accumulation limit its therapeutic application. We evaluated whether the antioxidant and natural agent α-lipoic acid might prevent these side effects. We synthesized AgNPs using an Ionic-Pulser® Pro silver generator and determined the concentration by inductively coupled plasma-optical emission spectrometry. The effect of α-lipoic acid was examined in four PDAC and two nonmalignant cell lines by MTT, FACS analysis, TEM, xenotransplantation and immunohistochemistry. The viability of PDAC cells was nearly totally abolished by AgNP treatment, whereas nonmalignant cells largely resisted. α-Lipoic acid prevented AgNP-induced cytotoxicity in nonmalignant cells but not in PDAC cells, which might be due to the higher sensitivity of malignant cells to silver-induced cytotoxicity. α-Lipoic acid protected mitochondria from AgNP-induced damage and led to precipitation of AgNPs. AgNPs reduced the growth of tumor xenografts, and cotreatment with α-lipoic acid protected chick embryos from AgNP-induced liver damage. Together, α-lipoic acid strongly reduced AgNP-induced side effects without weakening the therapeutic efficacy.

A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed.

BACKGROUND AND AIMS: The aim of this study was to analyse the landscape of publications on rectal cancer (RC) over the past 25 years by machine learning and semantic analysis. METHODS: Publications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Rectal Neoplasms' from 1994 to 2018 were downloaded in September 2019. R and Python were used to extract publication date, MeSH terms and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation was applied to analyse the text from the articles' abstracts to identify more specific research topics. Louvain algorithm was used to establish a topic network resulting in identifying the relationship between the topics. RESULTS: A total of 23,492 papers published were identified and analysed in this study. The changes of research focus were analysed by the changing of MeSH terms. Studied contents extracted from the publications were divided into five areas, including surgical intervention, radiotherapy and chemotherapy intervention, clinical case management, epidemiology and cancer risk as well as prognosis studies. CONCLUSIONS: The number of publications indexed on RC has expanded rapidly over the past 25 years. Studies on RC have mainly focused on five areas. However, studies on basic research, postoperative quality of life and cost-effective research were relatively lacking. It is predicted that basic research, inflammation and some other research fields might become the potential hotspots in the future.

Functional polymorphism of PIN1 rs2233679 is associated with the progression of CIN to early cervical cancer in Hunan Chinese.

OBJECTIVE: Peptidyl-prolyl cis/trans isomerase NIMA-interacting 1 (PIN1) involves alteration of the structure, function, intracellular localization and/or stability of the phosphorylated protein on serine or threonine residues which relates to inflammation and tumorigenesis. Association between PIN1 promoter polymorphisms and cancer risk were reported in several cancers. We intend to study the relationship between the polymorphism of PIN1 promoter and cervical cancer initiation and development. MATERIALS AND METHODS: We genotyped two common single nucleotide polymorphisms (SNPs) (rs2233678 and rs2233679) in the promoter of the PIN1 gene in healthy controls, patients with CIN or cervical cancer. We used polymerase chain reaction and DNA sequencing methods to analyze these two SNPs in 179 patients and 223 healthy controls. Luciferase activity assay was used to detect PIN1 expression driven by the rs2233679. RESULTS: The results revealed that the carriers of rs2233679 genotypes CT/TT had a significantly increased risk of cervical cancer in patients with CIN compared with genotype CC (odds ration [OR] = 2.924, 95% confidence interval [CI] = 1.093-7.819, P = 0.033). Luciferase activity assay results revealed that PIN1 expression driven by the rs2233679 genotype TT was higher than the genotype CC (P < 0.05). On the other hand, no significant correlation between the healthy controls and patients was found for PIN1 rs2233678 which showed that rs2233678 genotypes CG/GG is 95% in healthy controls and 100% in patients. CONCLUSION: PIN1 rs2233679 genotype CT/TT may be a risk factor of early cervical cancer compared with genotype CC in Hunan populations. Our findings suggest that PIN1 rs2233679 genotype CT/TT might involve in the progression of the precancerous stage developing to early cancer by enhancing PIN1 expression.

Association Study of Single-Nucleotide Polymorphisms of STAT2/STAT3/IFN-γ Genes in Cervical Cancer in Southern Chinese Han Women.

OBJECTIVE: Interferon-γ (IFN-γ) and signal transducers and activators of transcription (STATs) each play an important role in carcinogenesis associated with viral infection. Cervical cancer is almost invariably associated with infection by human papillomavirus (HPV), and previous studies suggested that dysregulation of the signal pathway involved in IFN-γ and STATs is associated. Our objective was to evaluate the association of SNPs in STAT2, STAT3, and IFN-γ with cervical cancer susceptibility in Chinese Han women in Hunan province. MATERIALS AND METHODS: Genomic DNA was extracted from peripheral blood samples of 234 cervical cancer patients and 216 healthy female controls. STAT2 and STAT3 genotyping was performed using polymerase chain reaction-restriction enzyme (PCR-RE) analysis. IFN-γ genotyping was detected by PCR-amplification of specific allele (PASA). RESULTS: For STAT2 rs2066807 polymorphisms, there was no significant difference of genotype distribution (P=0.827) and allele frequencies (P=0.830, OR=1.09, 95% CI: 0.51-2.31) between cases and controls. For STAT3 rs957970 polymorphisms, there was no significant difference of genotype distribution (P=0.455) and allele frequencies (P=0.560, OR=0.92, 95% CI: 0.71-1.20) between cases and controls. For IFN-γ +874A/T polymorphisms, there was no significant difference of genotype distribution (P=0.652) and allele frequencies (P=0.527, OR=1.12, 95% CI: 0.79-1.59) between cases and controls. CONCLUSION: These results suggest that polymorphisms in STAT2, STAT3 and IFN-γ genes are not likely to be strong predictors of cervical cancer in Han women in southern China.