Purpose: Long noncoding RNAs (lncRNAs) have essential roles in diverse cellular processes, both in normal and diseased cell types, and thus have emerged as potential therapeutic targets. A specific member of this family, the SWI/SNF complex antagonist associated with prostate cancer 1 (SChLAP1), has been shown to promote aggressive prostate cancer growth by antagonizing the SWI/SNF complex and therefore serves as a biomarker for poor prognosis. Here, we investigated whether SChLAP1 plays a potential role in the development of human glioblastoma (GBM). Experimental Design: RNA-ISH and immunohistochemistry were performed on a tissue microarray to assess expression of SChLAP1 and associated proteins in human gliomas. Proteins complexed with SChLAP1 were identified using RNA pull-down and mass spectrometry. Lentiviral constructs were used for functional analysis in vitro and in vivo. Results: SChLAP1 was increased in primary GBM samples and cell lines, and knockdown of the lncRNA suppressed growth. SChLAP1 was found to bind heterogeneous nuclear ribonucleoprotein L (HNRNPL) which stabilized the lncRNA and led to an enhanced interaction with the protein actinin alpha 4 (ACTN4). ACTN4 was also highly expressed in primary GBM samples and was associated with poorer overall survival in glioma patients. The SChLAP1-HNRNPL complex led to stabilization of ACTN4 through suppression of proteasomal degradation, which resulted in increased nuclear localization of the p65 subunit of NF-κB and activation of NF-κB signaling, a pathway associated with cancer development. Conclusions: Our results implicated SChLAP1 as a driver of GBM growth as well as a potential therapeutic target in treatment of the disease.

Metabolic syndrome (MetS) is highly related to the excessive accumulation of visceral adipose tissue (VAT). Quantitative measurements of VAT are commonly applied in clinical practice for measurement of metabolic risks; however, it remains largely unknown whether the texture of VAT can evaluate visceral adiposity, stratify MetS and predict surgery-induced weight loss effects. 675 Chinese adult volunteers and 63 obese patients (with bariatric surgery) were enrolled. Texture features were extracted from VATs of the computed tomography (CT) scans and machine learning was applied to identify significant imaging biomarkers associated with metabolic-related traits. Combined with sex, ten VAT texture features achieved areas under the curve (AUCs) of 0.872, 0.888, 0.961, and 0.947 for predicting the prevalence of insulin resistance, MetS, central obesity, and visceral obesity, respectively. A novel imaging biomarker, RunEntropy, was identified to be significantly associated with major metabolic outcomes and a 3.5-year follow-up in 338 volunteers demonstrated its long-term effectiveness. More importantly, the preoperative imaging biomarkers yielded high AUCs and accuracies for estimation of surgery responses, including the percentage of excess weight loss (%EWL) (0.867 and 74.6%), postoperative BMI group (0.930 and 76.1%), postoperative insulin resistance (0.947 and 88.9%), and excess visceral fat loss (the proportion of visceral fat reduced over 50%; 0.928 and 84.1%). This study shows that the texture features of VAT have significant clinical implications in evaluating metabolic disorders and predicting surgery-induced weight loss effects. The complete list of funders can be found in the Acknowledgement section.

Long noncoding RNAs (lncRNAs) have emerged as potential prognostic markers in various human cancers as they participate in many malignant behaviors. However, the value of lncRNAs as prognostic markers among diverse human cancers is still under investigation, and a systematic signature based on these transcripts that related to pan-cancer prognosis has yet to be reported. In this study, we proposed a framework to incorporate statistical power, biological rationale and machine learning models for pan-cancer prognosis analysis. The framework identified a 5-lncRNA signature (ENSG00000206567, PCAT29, ENSG00000257989, LOC388282, and LINC00339) from TCGA training studies (n=1,878). The identified lncRNAs are significantly associated (all P1.48E-11) with overall survival (OS) of the TCGA cohort (n=4,231). The signature stratified the cohort into low- and high-risk groups with significantly distinct survival outcomes (median OS of 9.84 years versus 4.37 years, log-rank P=1.48E-38) and achieved a time-dependent ROC/AUC of 0.66 at 5 years. After routine clinical factors involved, the signature demonstrated a better performance for long-term prognostic estimation (AUC of 0.72). Moreover, the signature was further evaluated on two independent external cohorts (TARGET, n=1,122; CPTAC, n=391; National Cancer Institute) which yielded similar prognostic values (AUC of 0.60 and 0.75; log-rank P=8.6E-09 and P=2.7E-06). An indexing system was developed to map the 5-lncRNA signature to prognoses of pan-cancer patients. In silico functional analysis indicated that the lncRNAs are associated with common biological processes driving human cancers. The five lncRNAs, especially ENSG00000206567, ENSG00000257989 and LOC388282 that never reported before, may serve as viable molecular targets common among diverse cancers.

We have developed a platform, termed PathoFusion, which is an integrated system for marking, training, and recognition of pathological features in whole-slide tissue sections. The platform uses a bifocal convolutional neural network (BCNN) which is designed to simultaneously capture both index and contextual feature information from shorter and longer image tiles, respectively. This is analogous to how a microscopist in pathology works, identifying a cancerous morphological feature in the tissue context using first a narrow and then a wider focus, hence bifocal. Adjacent tissue sections obtained from glioblastoma cases were processed for hematoxylin and eosin (H&E) and immunohistochemical (CD276) staining. Image tiles cropped from the digitized images based on markings made by a consultant neuropathologist were used to train the BCNN. PathoFusion demonstrated its ability to recognize malignant neuropathological features autonomously and map immunohistochemical data simultaneously. Our experiments show that PathoFusion achieved areas under the curve (AUCs) of 0.985 ± 0.011 and 0.988 ± 0.001 in patch-level recognition of six typical pathomorphological features and detection of associated immunoreactivity, respectively. On this basis, the system further correlated CD276 immunoreactivity to abnormal tumor vasculature. Corresponding feature distributions and overlaps were visualized by heatmaps, permitting high-resolution qualitative as well as quantitative morphological analyses for entire histological slides. Recognition of more user-defined pathomorphological features can be added to the system and included in future tissue analyses. Integration of PathoFusion with the day-to-day service workflow of a (neuro)pathology department is a goal. The software code for PathoFusion is made publicly available.

Routine examination of entire histological slides at cellular resolution poses a significant if not insurmountable challenge to human observers. However, high-resolution data such as the cellular distribution of proteins in tissues, e.g., those obtained following immunochemical staining, are highly desirable. Our present study extends the applicability of the PathoFusion framework to the cellular level. We illustrate our approach using the detection of CD276 immunoreactive cells in glioblastoma as an example. Following automatic identification by means of PathoFusion’s bifocal convolutional neural network (BCNN) model, individual cells are automatically profiled and counted. Only discriminable cells selected through data filtering and thresholding were segmented for cell-level analysis. Subsequently, we converted the detection signals into the corresponding heatmaps visualizing the distribution of the detected cells in entire whole-slide images of adjacent H&E-stained sections using the Discrete Wavelet Transform (DWT). Our results demonstrate that PathoFusion is capable of autonomously detecting and counting individual immunochemically labelled cells with a high prediction performance of 0.992 AUC and 97.7% accuracy. The data can be used for whole-slide cross-modality analyses, e.g., relationships between immunochemical signals and anaplastic histological features. PathoFusion has the potential to be applied to additional problems that seek to correlate heterogeneous data streams and to serve as a clinically applicable, weakly supervised system for histological image analyses in (neuro)pathology.