A retrospective analysis of spinal teratomas and spinal lipomas: overlaps and differences in presentation, surgical treatments, and outcomes.

BACKGROUND: Spinal teratomas and lipomas, both adult and pediatric cases, are rare diseases with many similarities, but have yet to be systematically compared. PURPOSE: To systematically compare spinal teratomas and lipomas to optimize management. STUDY DESIGN: Retrospective. PATIENT SAMPLE: Symptomatic spinal teratoma and lipoma patients surgically treated at our center. OUTCOME MEASURES: Anatomical distribution, clinical manifestations, resection status, and outcomes. METHODS: Spinal teratoma and lipoma patients with complete data treated during 2008 to 2023 in our center were enrolled. Electrophysiological monitoring was routinely performed after 2012. Patient characteristics, anatomical distribution, clinical manifestations, surgical resection, and outcomes were analyzed. RESULTS: We enrolled 86 teratoma patients (71 adults) and 51 lipoma patients (39 adults). Most tumors were lumbosacral lesions; cervical/thoracic involvement was more common with lipomas. Pain, the most frequent manifestation, was more common in teratomas. Gross total resection (GTR) was achieved in 51.1% and 49% of teratomas and lipomas, respectively. Electrophysiological monitoring increased the GTR rate from 38.8% to 48.6%. Age independently predicted (OR: 1.040, 95% CI: 1.008-1.078) GTR/near-total resection (NTR). Symptom relief occurred in 81.4% teratoma patients and 64.7% lipoma patients. Recurrence/symptomatic progression occurred in 19 teratomas and 7 lipomas after a median of 95 and 115 months, respectively. Adult lipoma patients without spinal dysraphism had lower recurrence rates. GTR (HR: 0.172, 95% CI: 0.02557-0.7028) and lesion length (HR: 1.351, 95% CI: 1.138-1.607) independently predicted recurrence/progression. CONCLUSIONS: GTR should be pursued for adult/pediatric spinal teratomas and pediatric spinal lipomas. For adult spinal lipoma patients without dysraphism, conservative surgery could be considered.

BET degrader exhibits lower antiproliferative activity than its inhibitor via EGR1 recruiting septins to promote E2F1-3 transcription in triple-negative breast cancer.

The bromodomain and extraterminal domain (BET) family proteins serve as primary readers of acetylated lysine residues and play crucial roles in cell proliferation and differentiation. Dysregulation of BET proteins has been implicated in tumorigenesis, making them important therapeutic targets. BET-bromodomain (BD) inhibitors and BET-targeting degraders have been developed to inhibit BET proteins. In this study, we found that the BET inhibitor MS645 exhibited superior antiproliferative activity than BET degraders including ARV771, AT1, MZ1 and dBET1 in triple-negative breast cancer (TNBC) cells. Treatment with MS645 led to the dissociation of BETs, MED1 and RNA polymerase II from the E2F1-3 promoter, resulting in the suppression of E2F1-3 transcription and subsequent inhibition of cell growth in TNBC. In contrast, while ARV771 displaced BET proteins from chromatin, it did not significantly alter E2F1-3 expression. Mechanistically, ARV771 induced BRD4 depletion at protein level, which markedly increased EGR1 expression. This elevation of EGR1 subsequently recruited septin 2 and septin 9 to E2F1-3 promoters, enhancing E2F1-3 transcription and promoting cell proliferation rate in vitro and in vivo. Our findings provide valuable insights into differential mechanisms of BET inhibition and highlight potential of developing BET-targeting molecules as therapeutic strategies for TNBC.

Picea wilsonii NAC31 and DREB2A Cooperatively Activate ERD1 to Modulate Drought Resistance in Transgenic Arabidopsis.

The NAC family of transcription factors (TFs) regulate plant development and abiotic stress. However, the specific function and response mechanism of NAC TFs that increase drought resistance in Picea wilsonii remain largely unknown. In this study, we functionally characterized a member of the PwNAC family known as PwNAC31. PwNAC31 is a nuclear-localized protein with transcriptional activation activity and contains an NAC domain that shows extensive homology with ANAC072 in Arabidopsis. The expression level of PwNAC31 is significantly upregulated under drought and ABA treatments. The heterologous expression of PwNAC31 in atnac072 Arabidopsis mutants enhances the seed vigor and germination rates and restores the hypersensitive phenotype of atnac072 under drought stress, accompanied by the up-regulated expression of drought-responsive genes such as DREB2A (DEHYDRATION-RESPONSIVE ELEMENT BINDING PROTEIN 2A) and ERD1 (EARLY RESPONSIVE TO DEHYDRATION STRESS 1). Yeast two-hybrid and bimolecular fluorescence complementation assays confirmed that PwNAC31 interacts with DREB2A and ABF3 (ABSCISIC ACID-RESPONSIVE ELEMENT-BINDING FACTOR 3). Yeast one-hybrid and dual-luciferase assays showed that PwNAC31, together with its interaction protein DREB2A, directly regulated the expression of ERD1 by binding to the DRE element of the ERD1 promoter. Collectively, our study provides evidence that PwNAC31 activates ERD1 by interacting with DREB2A to enhance drought tolerance in transgenic Arabidopsis.

A deep learning method for multi-task intelligent detection of oral cancer based on optical fiber Raman spectroscopy.

In the fight against oral cancer, innovative methods like Raman spectroscopy and deep learning have become powerful tools, particularly in integral tasks encompassing tumor staging, lymph node staging, and histological grading. These aspects are essential for the development of effective treatment strategies and prognostic assessment. However, it is important to note that most research so far has focused on solutions to one of these problems and has not taken full advantage of the potential wealth of information in the data. To compensate for this shortfall, we conceived a method that combines Raman spectroscopy with deep learning for simultaneous processing of multiple classification tasks, including tumor staging, lymph node staging, and histological grading. To achieve this innovative approach, we collected 1750 Raman spectra from 70 tissue samples, including normal and cancerous tissue samples from 35 patients with oral cancer. In addition, we used a deep neural network architecture to design four distinct multi-task network (MTN) models for intelligent oral cancer diagnosis, named MTN-Alexnet, MTN-Googlenet, MTN-Resnet50, and MTN-Transformer. To determine their effectiveness, we compared these multitask models to each other and to single-task models and traditional machine learning methods. The preliminary experimental results show that our multi-task network model has good performance, among which MTN-Transformer performs best. Specifically, MTN-Transformer has an accuracy of 81.5%, a precision of 82.1%, a sensitivity of 80.2%, and an F1_score of 81.1% in terms of tumor staging. In the field of lymph node staging, the accuracy, precision, sensitivity, and F1_score of MTN-Transformer are 81.3%, 83.0%, 80.1%, and 81.5% respectively. Similarly, for the histological grading classification tasks, the accuracy was 83.0%, the precision 84.3%, the sensitivity 76.7%, and the F1_score 80.2%. This code is available at https://github.com/ISCLab-Bistu/MultiTask-OralRamanSystem.

Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms.

Introduction: Oral cancer, a predominant malignancy in developing nations, represents a global health challenge with a five-year survival rate below 50%. Nonetheless, substantial reductions in both its incidence and mortality rates can be achieved through early detection and appropriate treatment. Crucial to these treatment plans and prognosis predictions is the identification of the pathological type of oral cancer. Methods: Toward this end, fiber-optic Raman spectroscopy emerges as an effective tool. This study combines Raman spectroscopy technology with deep learning algorithms to develop a portable intelligent prototype for oral case analysis. We propose, for the first time, a multi-task network (MTN) Raman spectroscopy classification model that utilizes a shared backbone network to simultaneously achieve different clinical staging and histological grading diagnoses. Results: The developed model demonstrated accuracy rates of 94.88%, 94.57%, and 94.34% for tumor staging, lymph node staging, and histological grading, respectively. Its sensitivity, specificity, and accuracy compare closely with the gold standard: routine histopathological examination. Discussion: Thus, this prototype proposed in this study has great potential for rapid, non-invasive, and label-free pathological diagnosis of oral cancer.

Immune Subtype Profiling and Establishment of Prognostic Immune-Related lncRNA Pairs in Human Ovarian Cancer.

This study collected immune-related genes (IRGs) and used gene expression data from TCGA database to construct a molecular subtype of ovarian cancer (OV) based on immune-related lncRNA gene pairs (IRLnc_GPs). The relationships between molecular subtypes and prognosis and clinical characteristics were further explored. IRGs were acquired from the ImmPort database, and round-robin pairing of immune-related lncRNAs was performed. The NMF algorithm was used to identify molecular subtypes, and the immune score of a single sample was calculated through ESTIMATE, TIMER, ssGSEA, MCPcounter, and CIBERSORT. The relationship between molecular subtypes and immune microenvironments was identified. A hypergeometric test was used to test the lncRNA pairs among the OV molecular subtypes (C1 and C2 subtypes). The BH method was used to screen the different lncRNA pairs, and a predictive risk model was constructed and verified. Finally, correlation analysis between the risk model, immune checkpoint genes, and chemotherapy drugs was carried out. Based on IRLnc_GP to classify 373 OV samples of TCGA, the samples were divided into two subtypes, and the prognosis between the subtypes showed significant differences. The C1 subtype with a poor prognosis was more related to the pathways of tumor occurrence and development. We identified 180 differential lncRNA pairs between subtypes and constructed a prognostic risk model based on 8 IRLnc_GPs. In the independent dataset, the distribution of subtypes in functional modules was different and highly repeatable. There were significant differences in the molecular and clinical characteristics of the subtypes and the drug sensitivity of immunotherapy/chemotherapy. In conclusion, the risk model established based on IRLnc_GP can better evaluate the prognosis of OV samples and can also assess the effects of different drug treatments in the high- and low-risk groups, providing new insights and ideas for the treatment of OV.

Identification and Validation of Invasion-Related Molecular Subtypes and Prognostic Features for Cervical Cancer.

Background: As one of the main causes leading to female cancer deaths, cervical cancer shows malignant features of local infiltration and invasion into adjacent organs and tissues. This study was designed to categorize novel molecular subtypes according to cervical cancer invasion and screen reliable prognostic markers. Methods: Invasion-related gene sets and expression profiles of invasion-related genes were collected from the CancerSEA database and The Cancer Genome Atlas (TCGA), respectively. Samples were clustered by nonnegative matrix factorization (NMF) to obtain different molecular subgroups, immune microenvironment characteristics of which were further systematically compared. Limma was employed to screen differentially expressed gene sets in different subtypes, followed by Lasso analysis for dimension reduction. Multivariate and univariate Cox regression analysis was performed to determine prognostic characteristics. The Kaplan-Meier test showed the prognostic differences of patients with different risks. Additionally, receiver operating characteristic (ROC) curves were applied to validate the prognostic model performance. A nomogram model was developed using clinical and prognostic characteristics of cervical cancer, and its prediction accuracy was reflected by calibration curve. Results: This study filtered 19 invasion-related genes with prognosis significance in cervical cancer and 2 molecular subtypes (C1, C2). Specifically, the C1 subtype had an unfavorable prognosis, which was associated with the activation of the TGF-beta signaling pathway, focal adhesion, and PI3K-Akt signaling pathway. 875 differentially expressed genes were screened, and 8 key genes were finally retained by the dimension reduction analysis. An 8-gene signature was established as an independent factor predictive of the prognosis of cervical cancer. The signature performance was even stronger when combined with N stage. Conclusion: Based on invasion-related genes, the present study categorized two cervical cancer subtypes with distinct TME characteristics and established an 8-gene marker that can accurately and independently predict the prognosis of cervical cancer.

Efficacy evaluation of vaginal-assisted laparoscopic radical hysterectomy and laparoscopic radical hysterectomy for cervical cancer: a single-center retrospective case series study.

Background: To investigate the survival outcomes of abdominal radical hysterectomy (ARH), laparoscopic radical hysterectomy (LRH), and vaginal-assisted laparoscopic radical hysterectomy (VALRH) in the treatment of cervical cancer patients. Methods: This was a retrospective study. We collected the clinical data of 654 patients with cervical cancer (406 ARH, 172 LRH, and 76 VALRH), then compared the effects of different surgical methods on recurrence and survival. Results: Total overall survival (OS) were no significant differences in three groups (P>0.05). Total disease-free survival (DFS) was significantly higher in ARH group than in LRH group [hazard ratio (HR) =2.8, 95% confidence interval (CI): 1.199-3.607, P=0.004]; however, there were no significant differences between the VALRH (94.7%) and ARH (93.3%) groups. Subgroup stratification analysis showed that the overall recurrence rate in LRH group was significantly higher than that of the ARH groups for patients with a tumor size from ≥2 to <4 cm, negative postoperative lymph nodes, and no postoperative adjuvant therapy (all P<0.05). However, in the subgroup with tumor sizes of ≥2, <4, and ≥4 cm, no matter whether the lymph nodes were positive or not, and those with no postoperative supplementary adjuvant therapy, LRH was associated with a significantly higher local pelvic recurrence rate than ARH (all P<0.05). No significant differences between VALRH and ARH in any of the subgroup analyses (all P>0.05). A Cox analysis indicated that LRH increased the risk of overall and local pelvic recurrence after surgery compared with ARH (HR =2.338, 95% CI: 1.186-4.661, P=0.014; HR =10.313, 95% CI: 2.839-37.460, P<0.001); however, no significant difference between VALRH and ARH (all P>0.05). Sensitivity analysis of surgeons did not change the conclusions. Conclusions: Our analyses showed that the local pelvic recurrence rates and overall recurrence rates of LRH were significantly higher than ARH. VALRH could avoid tumor intraperitoneal exposure and achieve the same tumor prognosis as open surgery. By improving the standardization of minimally invasive surgery for early cervical cancer and paying close attention to the tumor-free concept, minimally invasive radical hysterectomy may achieve the same tumor outcome as open surgery.

Diverse spectral band-based deep residual network for tongue squamous cell carcinoma classification using fiber optic Raman spectroscopy.

The research is to propose a new classification framework, called diverse spectral band-based deep residual network (DSB-ResNet), which can distinguish tongue squamous cell carcinoma (TSCC) from non-cancerous tissue. A fiber optic Raman spectroscopy system is used to collect Raman spectral data of TSCC and normal tissues. DSB-ResNet takes advantage of diverse spectral band-based spectra without processing to derive spectral representations from different spectral bands of Raman spectra, which improves the ability to identify TSCC. To show the superiority of the proposed method, the existing methods are used as the competitive methods to compare with the DSB-RestNet, the results demonstrate our method has the highest performance with 97.38 %, 98.75 %, and 98.25 % for sensitivity, specificity, and accuracy, respectively. The experimental results show that the DSB-ResNet is able to distinguish TSCC from non-cancerous tissue successfully. The proposed method is expected to provide a theoretical and methodological base for accurate detection of TSCC.

LncRNA miR503HG Regulates the Drug Resistance of Recurrent Cervical Squamous Cell Carcinoma Cells by Regulating miR-155/Caspase-3.

INTRODUCTION: The purpose of this study is to investigate the role of long non-coding RNA (lncRNA) miR503 Host Gene (miR503HG) in cervical squamous cell carcinoma (CSCC). METHODS: Analysis of TCGA dataset revealed that expression levels of miR503HG in CSCC tissues were over 12 times lower than those in non-tumor tissues, indicating its involvement in CSCC. RESULTS: In this study, we observed that levels of miR503HG in plasma were significantly lower in CSCC patients than in healthy participants. The cisplatin-based treatment further downregulated miR503HG in both patients and CSCC cells. MiR503HG overexpression in CSCC cells led to the suppression of miR-155 and elevation of Caspase-3, acting as the downstream target of miR-155. Cell apoptosis analysis showed that miR503HG and Caspase-3 overexpression led to an increased cell apoptosis rate under Cisplatin treatment. MiR-155 played the opposite role and attenuated the functions of Caspase-3 and miR503HG overexpression. CONCLUSION: Therefore, miR503HG may regulate the drug resistance of CSCC cells by regulating mir-155/Caspase-3.

Identification of MMP1 as a Potential Prognostic Biomarker and Correlating with Immune Infiltrates in Cervical Squamous Cell Carcinoma.

Cervical squamous cell carcinoma (CESC) is a human papillomavirus-driven tumor that the underlying molecular mechanisms are unclear. This study aimed to elucidate the key candidate genes and potential mechanism in CESC by bioinformatics analysis. A total of 132 common differentially expressed genes (DEGs) were identified based on three expression profile data sets. A multivariate Cox proportional regression model was used to develop a four-gene prognostic signature. Mechanistically, the correlationship between MMP1 and tumor-infiltrating lymphocytes was further analyzed. Furthermore, annotations were investigated by Gene Ontology (GO) and The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Finally, potential drugs for CESC treatment were predicted by Connectivity Map. We profiled four genes (EFNA1, ANLN, MMP1, and ZWINT) with significant prognostic values for CESC. Multiple public available data sets were used for mRNA expression and prognostic characterization. Subsequently, GO and KEGG pathway analyses showed DEGs were mainly enriched in cell cycle, immunity, and metabolic-related pathway. We then conducted an integrated analysis of MMP1, and the expression of MMP1 showed significantly inverse association with the amount of CD8+ T cells, CD4+ T cells, and macrophages infiltration. Our findings suggest the four-gene signature may be associated with prognosis. We further revealed that MMP1 may be a novel biomarker for immunotherapy, and prognostic judgment of patients with cervical cancer.

lncRNA MEG3 modified epithelial-mesenchymal transition of ovarian cancer cells by sponging miR-219a-5p and regulating EGFR.

This study was aimed to verify whether there existed any associations between long noncoding RNA MEG3/miR-219a-5p/EGFR axis and the development of ovarian cancer (OC). As a whole, we gathered 317 pairs of OC tissues and surgical marginal normal tissues and simultaneously acquired four OC cell lines (ie, A2780, Caov-3, OVCAR-3, and SKOV-3) and human normal ovarian surface epithelial cell line. Moreover, pcDNA3.1-MEG3, si-MEG3, miR-219a-5p mimic, miR-219a-5p inhibitor, pcDNA3.1-EGFR, and si-EGFR were, respectively, transfected into the OC cells, and their impacts on viability, proliferation, apoptosis, invasion, and migration of OC cells were assessed via conduction of MTT assay, colony formation assay, flow cytometry assay, transwell assay, and scratch assay. Ultimately, dual-luciferase reporter gene assay was performed to testify the targeted relationships among maternally expressed gene 3 (MEG3), miR-219a-5p, and estimated glomerular filtration rate (EGFR). It was indicated that underexpressed MEG3 and miR-219a-5p were significantly associated with unfavorable prognosis of patients with OC when compared with overexpressed MEG3 and miR-219a-5p (P < .05). In addition, the OC cells transfected with si-MEG3 or miR-219a-5p inhibitor exhibited stronger viability, proliferation, invasion, and migration than untreated cells (P < .05). Correspondingly, the apoptotic percentage of OC cells was reduced observably under treatments of si-MEG3 and miR-219a-5p inhibitor (P < .05). Moreover, MEG3 exerted modulatory effects on the expression of miR-219a-5p (P < .05), and there was a sponging relationship between them (P < .05). Finally, EGFR expression was modified by both MEG3 and miR-219a-5p significantly (P < .05), and raising EGFR expression could changeover the impacts of MEG3 and miR-219a-5p on the above-mentioned activity of OC cells (P < .05). Conclusively, MEG3 could serve as a promising biomarker for diagnosis and treatment of OC, considering its involvement with OC etiology via regulation of miR-219a-5p/EGFR axis.

Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy.

With deep convolutional neural networks and fiber optic Raman spectroscopy, this study presents a novel classification method that discriminates tongue squamous cell carcinoma (TSCC) from non-tumorous tissue. To achieve this purpose, 24 tissues spectral data were first collected from 12 patients who had undergone a surgical resection due to the tongue squamous cell carcinomas. Then 6 blocks with each block including 1 convolutional layer and 1 max-pooling layer are used to extract the nonlinear feature representations from Raman spectra. The derived features form a representative vector, which is fed into a fully-connected network for performing classification task. Experimental results demonstrated that the proposed method achieved high sensitivity (99.31%) and specificity (94.44%). To show the superiority for the ConvNets classifier, comparison results with the state-of-the-art methods show it had a competitive classification accuracy. Moreover, these promising results may pave the way to apply the deep ConvNets model in the fiber optic Raman instrument for intra-operative evaluation of TSCC resection margins and improve patient survival.

Mechanism of Low Expression of miR-30a-5p on Epithelial-Mesenchymal Transition and Metastasis in Ovarian Cancer.

Metastasis of ovarian cancer is regulated by microRNAs. This study focused on the effects of miR-30a-5p on ovarian cancer migration and invasion. Our results showed that the miR-30a-5p and mucin type O-glycan biosynthesis are closely related to ovarian cancer, and that miR-30a-5p was downregulated in ovarian cancer cells. miR-30a-5p overexpression reduced cell viability and inhibited migration and invasion in HO-8910 and HO-8910PM cells. S phase kinase-associated protein 2 (SKP2), B cell lymphoma 9 (BCL9), and NOTHC1 are direct target genes of miR-30a-5p. MTDH, SKP2, BCL9, and NOTCH1 genes were overexpressed in ovarian cancer cells, and they are direct target genes of miR-30a-5p. miR-30a-5p overexpression inhibited epithelial-mesenchymal transition (EMT) process, while upregulation of SKP2, BCL9, and NOTCH1 gene expression levels reduced the inhibition of EMT process by miR-30a-5p. miR-30a-5p was lowly expressed in ovarian cancer, and such a phenomenon is related to ovarian cancer metastasis. miR-30a-5p might inhibit the migration and invasion of ovarian cancer cells by downregulating the expression of SKP2, BCL9, and NOTCH1 genes.

Prognostic value of transcranial facial nerve motor-evoked potentials in predicting facial nerve function following cerebellopontine angle tumorectomy.

Facial nerve paralysis is a common complication following cerebellopontine angle (CPA) surgery. This study investigated the prognostic value of facial nerve motor-evoked potentials (FNMEPs) elicited by transcranial electrical stimulation for facial nerve outcome after CPA tumorectomy.A total of 95 patients were enrolled in this study between January 2014 and January 2016. All these patients underwent CPA tumorectomy (unilateral, n = 95; bilateral, n = 1). Intraoperative FNMEP elicited by transcranial electrical stimulation was recorded. The short- and long-term postoperative facial nerve functions were evaluated according to the House-Brackmann (HB) scale. The correlation between perioperative changes in the FNMEP stimulus threshold (delta FNMEP = postoperative stimulus threshold level-preoperative stimulus threshold level) and postoperative facial nerve functions were analyzed.On the first day postoperatively, the facial nerve function was HB grade I in 67, grade II in 17, grade III in 7, and grade IV in 5 facial nerves. One year postoperatively, the facial nerve function was grade I in 80, grade II in 11, grade III in 3, and grade IV in 2 facial nerves. The delta FNMEP was significantly correlated with the short- and long-term facial nerve function; receiver operating characteristic (ROC) curves yielded a cut-off delta FNMEP value of 30 V (sensitivity, 91.3%; specificity, 98.6%) and 75 V (sensitivity, 100%; specificity, 98.8%) for predicting short- and long-term facial nerve function damage, respectively.FNMEP elicited by transcranial electrical stimulation is an effective and safe approach for predicting facial nerve function in CPA tumorectomy. A high delta FNMEP is a potential indicator for the prediction of postoperative facial nerve damage.