CpG-biomarkers in tumor tissue and prediction models for the survival of colorectal cancer: A systematic review and external validation study.

The research aimed to identify previously published CpG-methylation-based prognostic biomarkers and prediction models for colorectal cancer (CRC) prognosis and validate them in a large external cohort. A systematic search was conducted, analyzing 298 unique CpGs and 12 CpG-based prognostic models from 28 studies. After adjustment for clinical variables, 48 CpGs and five prognostic models were confirmed to be associated with survival. However, the discrimination ability of the models was insufficient, with area under the receiver operating characteristic curves ranging from 0.53 to 0.62. Calibration accuracy was mostly poor, and no significant added prognostic value beyond traditional clinical variables was observed. All prognostic models were rated at high risk of bias. While a fraction of CpGs showed potential clinical utility and generalizability, the CpG-based prognostic models performed poorly and lacked clinical relevance.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies.

BACKGROUND: DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis. METHODS: We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively. RESULTS: Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques. CONCLUSIONS: There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

Identifying biomarkers associated with tumor growth rate: a longitudinal preoperative magnetic resonance imaging follow-up of low-grade gliomas.

Background: Although the influence of molecular biomarkers on the biological behavior of tumor cells has been investigated, their quantitative influence on the velocity of tumor growth remains unclear. This study aimed to identify the molecular biomarkers associated with tumor growth rates in World Health Organization (WHO) grade II gliomas, or low-grade gliomas (LGGs). Methods: Preoperative magnetic resonance imaging (MRI) data of patients with LGGs were retrospectively reviewed. Patients with at least 2 preoperative MRIs taken more than 90 days apart were enrolled. Patients with isocitrate dehydrogenase (IDH) wild-type tumors or with no recorded IDH status were excluded. A linear mixed-effects model was used to assess the velocity of tumor diameter expansion. The effect of biomarker expression on tumor growth rate was assessed using a multivariate linear mixed-effects regression model. Results: Data from 56 patients were used in our study. The overall velocity of diameter expansion (VDE) for LGGs was 2.1 mm/year. Higher expression level of mutant p53 were significantly associated with a higher tumor growth rate (+1.9 mm/year, P<0.01), while higher expression level of alpha-thalassemia/mental retardation syndrome X-linked protein (ATRX) were significantly associated with a lower tumor growth rate (-1.3 mm/year, P<0.01). Tumors with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation were found to grow significantly more slowly than those with no methylation (-3.1 mm/year, P<0.01). The telomerase reverse transcriptase (TERT) promoter type and expressions levels of Ki-67 and epidermal growth factor receptor (EGFR) showed no significant independent impact on tumor growth rates. Conclusions: The status of biomarkers is significantly associated with the tumor growth rate in LGGs.

Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning.

Purpose: The majority of solitary brain metastases appear similar to glioblastomas (GBMs) on magnetic resonance imaging (MRI). This study aimed to develop and validate an MRI-based model to differentiate intracranial metastases from GBMs using automated machine learning. Materials and Methods: Radiomics features from 354 patients with brain metastases and 354 with GBMs were used to build prediction algorithms based on T2-weighted images, contrast-enhanced (CE) T1-weighted images, or both. The data of these subjects were subjected to a nested 10-fold split in the training and testing groups to build the best algorithms using the tree-based pipeline optimization tool (TPOT). The algorithms were independently validated using data from 124 institutional patients with solitary brain metastases and 103 patients with GBMs from the cancer genome atlas. Results: Three groups of models were developed. The average areas under the receiver operating characteristic curve (AUCs) were 0.856 for CE T1-weighted images, 0.976 for T2-weighted images, and 0.988 for a combination in the testing groups, and the AUCs of the groups of models in the independent validation were 0.687, 0.831, and 0.867, respectively. A total of 149 radiomics features were considered as the most valuable features for the differential diagnosis of GBMs and metastases. Conclusion: The models established by TPOT can distinguish glioblastoma from solitary brain metastases well, and its non-invasiveness, convenience, and robustness make it potentially useful for clinical applications.

Development and validation of a novel survival prediction model for newly diagnosed lower-grade gliomas.

OBJECTIVE: Diffuse gliomas are the most common primary gliomas with a poor prognosis. This study aimed to develop and validate prognostic models for predicting the survival probability in newly diagnosed lower-grade glioma (LGG) patients. METHODS: Detailed data were obtained for newly diagnosed LGG from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) cohorts. Survival was assessed using Cox proportional hazards regression with adjustment for known prognostic factors. The model was established using the TCGA cohort, and independently validated using the CGGA cohort, to predict the 3-, 5-, and 10-year survival probabilities of patients. RESULTS: Data from 293 patients with newly diagnosed LGG from the TCGA cohort were used to establish a prognostic model, and from 232 patients with primary LGG in the CGGA cohort to validate the model. Age, tumor grade, molecular subtype, tumor resection, and preoperative neurological deficits were included in the prediction model. The Cox regression model had a satisfactory corrected concordance index of 0.8508, 0.8510, and 0.8516 in the internal bootstrap validation at 3, 5, and 10 years, respectively. The calibration plots demonstrated high consistency of the predicted and observed outcomes. The CGGA cohort was used for external validation and showed satisfactory discrimination of 0.7776, 0.7682, and 0.7051 at 3, 5, and 10 years, respectively. The calibration plots demonstrated an acceptable calibration capability in the external validation. CONCLUSIONS: This study established and validated a prognostic model to predict the survival probability of patients with newly diagnosed LGG. The model performed well in discrimination and calibration with ease of use, speed, accessibility, interpretability, and generalizability. An easily used nomogram based on the Cox model was established for clinical application. Moreover, a free, easy-to-use software interface based on the nomogram is provided online.

Management of central nervous system Rosai-Dorfman disease: A single center treatment experience.

Rosai-Dorfman disease (RDD) is an idiopathic histiocytic proliferation disease with various clinical manifestations. A retrospective study of patients with pathological diagnosed RDD primarily involved in the central nervous system was conducted from January 2011 to December 2020 at a tertiary center. The clinical profile, imaging, and treatment data were collected. There were 16 male and 5 female patients with RDD-CNS. The patients were aged from 6 to 68 years with a median of 37 years. Of these 21 patients, 15 presented with intracranial RDD and 6 with spinal RDD. The main symptoms of RDD-CNS included headache, epilepsy, and neurological deficits. 76.19% (16/21) of the patients showed dura-based, homogeneous enhancement lesion on magnetic resonance imaging (MRI). Twenty patients received surgery as first treatment, and one patient received biopsy after steroid therapy. Total lesion resection was achieved in 42.9% (9/21) of the patients, subtotal resection in 47.6% (10/21), and biopsy in 0.9% (2/21). The symptoms were alleviated or stayed stable. Some RDDs (80%, 4/5) in the skull base had some complications. The patients were followed up for 11-108 months with a median duration of 47 months. Lesion progression or recurrence was found in two patients. The various clinical manifestations, as well as the dura-based and homogenous enhancement imaging profiles of RDD-CNS patients pose a great diagnostic challenge for clinicians. Surgery is effective for RDD-CNS requiring treatment. Medical therapy and radiotherapy would be feasible as noninvasive treatments, varying degrees of efficacy. The overall prognosis of RDD-CNS is acceptable. Periodic long-term follow-up is necessary.

The utility of intraoperative ECoG in tumor-related epilepsy: Systematic review.

OBJECT: Epilepsy is one of the most common clinical manifestations of primary brain tumors. Intraoperative electrocorticography (ECoG) has been widely used in tumor resection. We aim to describe the indication and utility of ECoG during brain tumor surgery. METHODS: We performed a systematic review of the literature on the prognosis of tumor-related epilepsy surgery guided by intraoperative ECoG. The published studies were searched in PubMed, Embase, and Web of Science using the keyword 'seizure' or 'epilepsy' and 'electrocorticography' or 'ECoG'. Two reviewer authors screened studies and extracted data independently. RESULTS: Thirteen studies included 569 patients were finally selected, of which eight investigated medically intractable epilepsy. Three publications described temporal tumor-related epilepsy. All included studies were retrospective, and the age of all patients ranged from 1 to 71 years. The duration of epilepsy ranged from 1 month to 30 years. Patients with tumor-related epilepsy underwent surgical treatment with Engel I outcomes ranging from 56.5%-100%. CONCLUSION: Intraoperative ECoG is generally considered a useful technique in delineating epileptogenic areas and improving the prognosis of surgical treatment of tumor-related epilepsy. However, large-scale randomized control trials are still needed to verify these findings and formulate appropriate surgical strategies.

Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas.

PURPOSE: The present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas. METHODS: This retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student's t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status. RESULTS: Six valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733-0.8755) and 0.758 (0.6879-0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201-0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group. CONCLUSION: Combined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.

Long non-coding RNA MT1DP interacts with miR-365 and induces apoptosis of nucleus pulposus cells by repressing NRF-2-induced anti-oxidation in lumbar disc herniation.

BACKGROUND: This study investigated the effects of the long non-coding (lnc) RNA MT1DP on the apoptosis of nucleus pulposus (NP) cells. The interactions between MT1DP and the microRNA miR-365, and its effects on the anti-oxidant activity of nuclear factor erythroid 2-related factor 2 (NRF-2) were investigated in lumbar disc herniation (LDH). METHODS: Human degenerative intervertebral disc NP tissues were obtained from 10 patients with LDH who underwent lumbar spine surgery. Normal intervertebral disc NP tissues were obtained from 10 patients with lumbar vertebrae fractures and used as negative controls (NCs). RESULTS: The gene expressions of MT1DP and miR-365 in human degenerative disc NP tissues and nucleus pulposus cells (NPCs) were significantly increased, while the level of NRF-2 was significantly decreased. Overexpression of MT1DP and miR-365 (MT1DP + miR-365) and inhibition of NRF-2 suppressed NP cell viability and induced apoptosis. MT1DP + miR-365 caused inflammation in NP cells by damaging the mitochondrial membrane. The combination of lnc-MT1DP and miR-365 reduced cell mitochondrial function and led to a decrease in the ability of cells to elimination reactive oxygen species (ROS). CONCLUSIONS: The combination of lnc-MT1DP and miR-365 damaged the cell mitochondrial membrane, reduced mitochondrial function and the ability to eliminate ROS, increased cell apoptosis, and caused LDH.

Ischemic Infarction of Pituitary Apoplexy: A Retrospective Study of 46 Cases From a Single Tertiary Center.

OBJECTIVE: Ischemic infarction of pituitary apoplexy (PA) is a rare type of pituitary apoplexy. This study aims to characterize ischemic PA via clinical presentations, imaging data, histopathological manifestations, and focus on the management and prognosis of the disease. METHODS: This study retrospectively identified 46 patients with ischemic PA confirmed using histopathology at a single institution from January 2013 to December 2020. The clinical presentations, imaging data, laboratory examination, management, and outcomes were collected. We then summarized the clinical presentations, imaging features, intraoperative findings, and histopathological manifestations, and compared the outcomes based on the timing of surgical intervention. RESULTS: Headache was the most common initial symptom (95.65%, 44/46), followed by visual disturbance (89.13%, 41/46), and nausea and vomiting (58.70%, 27/46). 91.3% of the patients had at least one pituitary dysfunction, with hypogonadism being the most common endocrine dysfunction (84.78%, 39/46). Cortisol dysfunction occurred in 24 (52.17%) patients and thyroid dysfunction occurred in 17 (36.96%). Typical rim enhancement and thickening of the sphenoid sinus on MRI were seen in 35 (85.37%) and 26 (56.52%) patients, respectively. Except for one patient with asymptomatic apoplexy, the remaining patients underwent early (≤ 1 week, 12 patients) and delayed (> 1 week, 33 patients) transsphenoidal surgery. Total tumor resection was achieved in 27 patients and subtotal tumor resection in 19 patients. At surgery, cottage cheese-like necrosis was observed in 50% (23/46) of the patients. At the last follow-up of 5.5 ± 2.7 years, 92.68% (38/41) of the patients had gained a significant improvement in visual disturbance regardless of surgical timing, and 65% of the patients were still receiving long-term hormone replacement therapy. CONCLUSION: Patients with ischemic PA can be accurately diagnosed by typical imaging characteristics preoperatively. The timing of surgical intervention does not significantly affect the resolution of neurological and endocrinological dysfunctions. Preoperative endocrine dysfunctions are common and usually appear to be poor after surgical intervention.

Procedure-specific prognostic impact of micropapillary subtype may guide resection strategy in small-sized lung adenocarcinomas: a multicenter study.

BACKGROUND: Limited resection has gradually become an acceptable treatment for lung adenocarcinomas (ADCs) presenting as ground-glass nodules (GGNs). However, its role in lung ADCs presenting as pure solid nodules (PSN) remains unclear. In this study, we aimed to identify potential candidates for limited resection in lung ADCs presenting as PSN. METHODS: We retrospectively reviewed 772 patients from seven hospitals with lung ADCs ⩽2 cm, presenting as PSN on computed tomography scans, who had undergone surgery between 2009 and 2013. Histological subtypes were listed in 5% increments. To investigate the value of histological subtypes in surgical decision making, five pathologists prospectively evaluated the feasibility of identifying histological subtypes using frozen section (FS) in two cohorts. RESULTS: The percentage of micropapillary (MIP) subtype had a striking impact on recurrence-free survival (RFS) and overall survival (OS) for lung ADCs ⩽2 cm presenting as PSNs. In multivariable Cox analysis, segmentectomy was significantly associated with worse RFS and OS in patients with MIP >5% than lobectomy, but not in those with MIP ⩽5%. With wedge resection, worse RFS and OS were observed in patients with MIP >5% and those with MIP ⩽5% than lobectomy. The sensitivity and specificity for detecting MIP by FS were 74.2% and 85.6%, respectively, with substantial inter-rater agreement. CONCLUSION: Segmentectomy and lobectomy had similar oncological outcomes in patients with lung ADCs ⩽2 cm presenting as PSN with MIP ⩽5%. Randomized trials are necessary to validate the feasibility of intraoperative FS to choose candidates for segmentectomy.

Association of tumor growth rates with molecular biomarker status: a longitudinal study of high-grade glioma.

To determine the association of molecular biomarkers with tumor growth in patients with high-grade gliomas (HGGs), the tumor growth rates and molecular biomarker status in 109 patients with HGGs were evaluated. Mean tumor diameter was assessed on at least two pre-surgical T2-weighted and contrast-enhancement T1-weighted magnetic resonance images (MRIs). Tumor growth rates were calculated based on tumor volume and diameter using various methods. The association of biomarkers with increased or decreased tumor growth was calculated using linear mixed-effects models. HGGs exhibited rapid growth rates, with an equivalent volume doubling time of 63.4 days and an equivalent velocity of diameter expansion of 51.6 mm/year. The WHO grade was an independent clinical factor of eVDEs. TERT promoter mutation C250T and MGMT promoter methylation was significantly associated with tumor growth in univariable analysis but not in multivariable analysis. Molecular groups of IDH1, TERT, and 1p/19q and IDH1 and MGMT were independently associated with tumor growth. In addition, tumor enhanced area had a faster growth rate than a tumor entity in incomplete enhanced HGGs (p = 0.006). Our findings provide crucial information for the prediction of preoperative tumor growth in HGGs, and aided in the decision making for aggressive resection and adjuvant treatment strategies.

Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach.

The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its performance with regard to the prediction of mutations in pTERT in patients with World Health Organization (WHO) grade II gliomas. In total, 164 patients with WHO grade II gliomas were enrolled in this retrospective study. We extracted a total of 1,293 radiomics features from multi-parametric magnetic resonance imaging scans. Elastic net (used for feature selection) and support vector machine with linear kernel were applied in nested 10-fold cross-validation loops. The predictive model was evaluated by receiver operating characteristic and precision-recall analyses. We performed an unpaired t-test to compare the posterior predictive probabilities among patients with differing pTERT statuses. We selected 12 valuable radiomics features using nested 10-fold cross-validation loops. The area under the curve (AUC) was 0.8446 (95% confidence interval [CI], 0.7735-0.9065) with an optimal summed value of sensitivity of 0.9355 (95% CI, 0.8802-0.9788) and specificity of 0.6197 (95% CI, 0.5071-0.7371). The overall accuracy was 0.7988 (95% CI, 0.7378-0.8598). The F1-score was 0.8406 (95% CI, 0.7684-0.902) with an optimal precision of 0.7632 (95% CI, 0.6818-0.8364) and recall of 0.9355 (95% CI, 0.8802-0.9788). Posterior probabilities of pTERT mutations were significantly different between patients with wild-type and mutant TERT promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting pTERT status in patients with WHO grade II glioma and may aid in glioma management.

A Texture Analysis-Based Prediction Model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma.

BACKGROUND: Some clinical N0 lung adenocarcinomas have been pathologically diagnosed as N1 or N2. To improve the preoperative diagnostic accuracy of lymph node disease, we developed a prediction model for lymph node metastasis in cT1 N0 M0 lung adenocarcinoma based on computed tomography texture analysis and clinical characteristics to estimate the probability of lymph node metastasis. METHODS: The records of 501 consecutive patients with cT1 N0 M0 lung adenocarcinoma who underwent computed tomography scan and pulmonary resection with systematic lymph nodes dissection or lymph nodes sampling were reviewed. Each nodule was manually segmented, and its computerized texture features were extracted. Multivariate logistic regression with fivefold validation was used to estimate independent predictors and build the prediction model. The prediction model was then externally validated. A nomogram was developed based on logistic regression results. RESULTS: Among 501 patients, 41 were diagnosed with positive lymph nodes (8.18%). Four independent predictors were identified: the skewness and 90th percentile of computed tomography number, nodule compactness, and carcinoembryonic antigen level. This model showed good calibration (Hosmer-Lemeshow test, p = 0.337), with an area under the curve of 0.883 (95% confidence interval, 0.842 to 0.924; p < 0.001). The area under the curve was 0.808 (95% confidence interval, 0.735 to 0.880) when validated with independent data. CONCLUSIONS: A model based on computerized textures and carcinoembryonic antigen level can assess the lymph node status of patients with cT1 N0 M0 lung adenocarcinoma preoperatively, which could assist surgeons in making subsequent clinical decisions.

Molecular profiles for insular low-grade gliomas with putamen involvement.

BACKGROUND: The newly proposed putamen classification system shows good prognostic value in patients with insular LGGs, yet no study towards the molecular profiles of putamen involved LGGs has been proposed. METHODS: Clinical information and imaging data of patients diagnosed with insular low-grade gliomas were collected retrospectively. Genetic information of the 34 tumors was assessed using RNA-sequencing. Gene set enrichment analysis was further performed to identify the genes showing differential expression between putamen-involved tumors and putamen non-involved tumors. The level of Ki-67 expression was also evaluated. RESULTS: There were 843 genes identified to be differentially expressed between putamen-involved and non-involved gliomas. Specifically, Gene set enrichment analysis discovered 13 Kyoto Encyclopedia of Genes and Genomes pathways and 37 Gene Ontology Biological Process term were upregulated in putamen-involved low-grade glioma cells. The enriched GO sets with the highest gene counts included cell cycle (42 genes), mitotic cell cycle (24 genes), and cell division (19 genes). Furthermore, high expression of Ki-67 was associated with putamen involvement in insular gliomas. CONCLUSIONS: There is clear genetic variation between putamen-involved and non-involved insular low-grade gliomas. The differential expression of genes related to the processes of cell proliferation, cell migration, or DNA repair may lead to putamen involvement. The findings suggest that among the two subtypes, putamen-involved insular low-grade gliomas have higher malignancy, and the clinical treatment towards the putamen-involved insular low-grade gliomas should be more active.

Three-dimensional printing of navigational template in localization of pulmonary nodule: A pilot study.

BACKGROUND: Small pulmonary nodules are a common problem, especially with the wide implementation of lung cancer-screening program. This poses a great challenge to thoracic surgeons because of the difficulty of nodule localization. We recently built an efficient, customized navigational template using 3-dimensional (3D) printing technology to facilitate the procedure of lung nodule localization. This study aims to investigate its feasibility in clinical application. METHODS: Patients with peripheral lung nodules (<2 cm) were enrolled. Preadmission computed tomography images were downloaded and reconstructed into a 3D model. A digital model of the navigational template was designed via computer-aided design software and then exported into 3D printer to produce physical template. The precision of the template-guided nodule localization and associated complications were evaluated. RESULTS: A total of 16 patients were enrolled, and 18 nodules were localized through template-guided localization. The success rate of lung nodule localization was 100%, and the median time of localization was 13 minutes (range 10-16 minutes). In our series, no significant complication occurred, except for 2 asymptomatic pneumothoraxes. The median deviation between the localizer and the center of the nodule was 10.0 mm, ranging from 5 to 20 mm. CONCLUSIONS: This novel navigational template created by 3D printing technology is feasible, and it has acceptable accuracy for the application in lung nodule localization. The use of this navigational template could facilitate the procedure of lung nodule localization and may potentially break the dependence of percutaneous localization on computed tomography scanning.

Relationship of Lymph Node Micrometastasis and Micropapillary Component and Their Joint Influence on Prognosis of Patients With Stage I Lung Adenocarcinoma.

This study aimed to investigate the relationship between lymph node micrometastasis and histologic patterns of adenocarcinoma, with a particular focus on their joint effect on prognosis. We retrospectively reviewed 235 patients with stage I adenocarcinoma from January 2009 to December 2009. Lymph node micrometastasis was evaluated by immunohistochemical staining for cytokeratin (AE1/AE3) and thyroid transcription factor-1. A logistic regression model was applied to confirm the predictive factors of micrometastasis. Survival analysis was performed to evaluate the effect of micrometastasis on prognosis. Lymph node micrometastasis was observed in 35 patients (15%). Patients with micrometastasis had significantly worse recurrence-free survival (P<0.001) and overall survival (P<0.001) compared with those without micrometastasis. Micropapillary component was confirmed as an independent predictor of increased frequency of micrometastasis (P<0.001). Among 62 patients with adenocarcinoma with a micropapillary component, 23 (37%) had lymph node micrometastasis. Micropapillary-positive/micrometastasis-positive patients had significantly worse survival compared with micropapillary-positive/micrometastasis-negative patients (RFS, P=0.039; OS, P=0.002) and micropapillary-negative patients (recurrence-free survival, P<0.001; overall survival, P<0.001). Moreover, the presence of micrometastasis correlated with a higher risk of locoregional recurrence (P=0.031) rather than distant recurrence (P=0.456) in micropapillary-positive patients. In summary, lymph node micrometastasis was more frequently observed in adenocarcinoma with a micropapillary component. Moreover, lymph node micrometastasis could provide helpful prognostic information in patients with resected stage I lung adenocarcinoma with a micropapillary component; thus, immunohistochemical detection of micrometastatic tumor cells in lymph nodes should be recommended.

Tumor Spread through Air Spaces Affects the Recurrence and Overall Survival in Patients with Lung Adenocarcinoma >2 to 3 cm.

OBJECTIVES: Tumor spread through air spaces (STAS) is a novel invasive pattern in lung adenocarcinoma (ADC). The effects of the combination of STAS and tumor size on survival have not been well studied. METHODS: A total of 383 patients with ADC 3 cm or smaller (stage IA) and 161 patients with stage IB ADC were identified from 2009 to 2010. Recurrence-free survival (RFS) and overall survival (OS) were compared between patients as stratified by STAS and tumor size. A validation cohort was included in this study. RESULTS: STAS was observed in 116 ADCs 3 cm or smaller (30.3%). In cases involving ADCs 3 cm or smaller, patients with STAS had worse RFS (p = 0.006) and OS rates (p < 0.001) than those without STAS. Furthermore, comparable RFS (p = 0.091) and OS (p = 0.443) rates were observed in patients with ADCs 3 cm or smaller with STAS present and those with stage IB ADC. Multivariate analysis revealed STAS to be an independent prognostic factor in ADCs 3 cm or smaller (RFS, p = 0.043; OS, p = 0.009). Among patients with ADCs larger than 2 to 3 cm, STAS still stratified the prognosis. Moreover, the unfavorable prognosis of patients with ADCs larger than 2 to 3 cm with STAS present was similar to that of patients with stage IB ADC. Among patients with ADCs 2 cm or smaller, STAS failed to stratify the prognosis significantly. Similar results were obtained in the validation cohort. CONCLUSIONS: These results provide preliminary evidence that STAS could be considered as a factor in a staging system to predict prognosis more precisely, especially in ADCs larger than 2 to 3 cm.

Long non-coding RNA TUG1 contributes to tumorigenesis of human osteosarcoma by sponging miR-9-5p and regulating POU2F1 expression.

Recent studies have shown that long non-coding RNAs (lncRNAs) have critical roles in tumorigenesis, including osteosarcoma. The lncRNA taurine-upregulated gene 1 (TUG1) was reported to be involved in the progression of osteosarcoma. Here, we investigated the role of TUG1 in osteosarcoma cells and the underlying mechanism. TUG1 expression was measured in osteosarcoma cell lines and human normal osteoblast cells by quantitative real-time PCR (qRT-PCR). The effects of TUG1 on osteosarcoma cells were studied by RNA interference in vitro and in vivo. The mechanism of competing endogenous RNA (ceRNA) was determined using bioinformatic analysis and luciferase assays. Our data showed that TUG1 knockdown inhibited cell proliferation and colony formation, and induced G0/G1 cell cycle arrest and apoptosis in vitro, and suppressed tumor growth in vivo. Besides, we found that TUG1 acted as an endogenous sponge to directly bind to miR-9-5p and downregulated miR-9-5p expression. Moreover, TUG1 overturned the effect of miR-9-5p on the proliferation, colony formation, cell cycle arrest, and apoptosis in osteosarcoma cells, which involved the derepression of POU class 2 homeobox 1 (POU2F1) expression. In conclusion, our study elucidated a novel TUG1/miR-9-5p/POU2F1 pathway, in which TUG1 acted as a ceRNA by sponging miR-9-5p, leading to downregulation of POU2F1 and facilitating the tumorigenesis of osteosarcoma. These findings may contribute to the lncRNA-targeted therapy for human osteosarcoma.

Histone deacetylase inhibitor sodium butyrate suppresses proliferation and promotes apoptosis in osteosarcoma cells by regulation of the MDM2-p53 signaling.

Histone deacetylase inhibitors have been reported to induce tumor cell growth arrest, differentiation, and apoptosis. This study aimed to investigate the effects of one histone deacetylase inhibitor - sodium butyrate (SB) - on osteosarcoma (OS) cell proliferation and apoptosis and also the molecular mechanisms by which SB exerts regulatory effects on OS cells. U2OS and MG63 cells were treated with SB at various concentrations. Then, cell proliferation and apoptosis were determined by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide and flow cytometry assays, respectively; the expression of Ki67, Bax, Bcl-2, MDM2, and p53 proteins was determined by using Western blot assay. The results showed that SB suppressed proliferation in a concentration-dependent manner and promoted apoptosis of OS cells. In addition, SB enhanced p53 expression and decreased MDM2 expression, indicating that SB can regulate MDM2-p53 feedback loop. p53 inhibited proliferation and promoted apoptosis, whereas MDM2 promoted proliferation and suppressed apoptosis, which indicated that functional effect of SB on OS cell lines at least in part depended on the MDM2-p53 signaling. We also explored the effect of SB on OS cells in vivo and found that SB suppressed the growth of OS cells with no noticeable effect on activity and body weight of mice in vivo. These findings will offer new clues for OS development and progression and offer SB as a potent targeted agent for OS treatment.