Imaging-based surrogate classification for risk stratification of hepatocellular carcinoma with microvascular invasion to adjuvant hepatic arterial infusion chemotherapy: a multicenter retrospective study.

BACKGROUND: Patients with microvascular invasion (MVI)-positive hepatocellular carcinoma (HCC) have shown promising results with adjuvant hepatic arterial infusion chemotherapy (HAIC) with FOLFOX after curative resection. We aim to develop an imaging-derived biomarker to depict MVI-positive HCC patients more precisely and promote individualized treatment strategies of adjuvant HAIC. MATERIALS AND METHODS: Patients with MVI-positive HCC were identified from five academic centers and utilized for model development (n=470). Validation cohorts were pooled from a previously reported prospective clinical study conducted (control cohort (n=145), adjuvant HAIC cohort (n=143)) (NCT03192618). The primary endpoint was recurrence-free survival (RFS). Imaging features were thoroughly reviewed, and multivariable logistic regression analysis was employed for model development. Transcriptomic sequencing was conducted to identify the associated biological processes. RESULTS: Arterial phase peritumoral enhancement, boundary of the tumor enhancement, tumor necrosis stratification, and boundary of the necrotic area were selected and incorporated into the nomogram for RFS. The imaging-based model successfully stratified patients into two distinct prognostic subgroups in both the training, control, and adjuvant HAIC cohorts (median RFS, 6.00 vs. 66.00 mo, 4.86 vs. 24.30 mo, 11.46 vs. 39.40 mo, all P<0.01). Furthermore, no significant statistical difference was observed between patients at high-risk of adjuvant HAIC and those in the control group (P=0.61). The area under the receiver operating characteristic curve at two years was found to be 0.83, 0.84, and 0.73 for the training, control, and adjuvant HAIC cohorts respectively. Transcriptomic sequencing analyses revealed associations between the radiological features and immune-regulating signal transduction pathways. CONCLUSION: The utilization of this imaging-based model could help to better characterize MVI-positive HCC patients and facilitate the precise subtyping of patients who genuinely benefit from adjuvant HAIC treatment.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Vessels encapsulating tumor clusters: a novel efficacy predictor of hepatic arterial infusion chemotherapy in unresectable hepatocellular carcinoma.

PURPOSE: Vessels encapsulating tumor clusters (VETC) is a novel vascular pattern structurally and functionally distinct from microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study aims to explore the prognostic value of VETC in patients receiving hepatic arterial infusion chemotherapy (HAIC) for unresectable HCC. METHODS: From January 2016 to December 2017, 145 patients receiving HAIC as the initial treatment for unresectable HCC were enrolled and stratified into two groups according to their VETC status. Overall survival (OS), progression-free survival (PFS), overall response rate (ORR), and disease control rate (DCR) were evaluated. RESULTS: The patients were divided into two groups: VETC+ (n = 31, 21.8%) and VETC- (n = 114, 78.2%). The patients in the VETC+ group had worse ORR and DCR than those in the VETC- group (RECIST: ORR: 25.8% vs. 47.4%, P = 0.031; DCR: 56.1% vs. 76.3%, P = 0.007; mRECIST: ORR: 41.0% vs. 52.6%, P = 0.008; DCR: 56.1% vs. 76.3%, P = 0.007). Patients with VETC+ had significantly shorter OS and PFS than those with VETC- (median OS: 10.2 vs. 21.6 months, P < 0.001; median PFS: 3.3 vs. 7.2 months, P < 0.001). Multivariate analysis revealed VETC status as an independent prognostic factor for OS (HR: 2.40; 95% CI: 1.46-3.94; P = 0.001) and PFS (HR: 1.97; 95% CI: 1.20-3.22; P = 0.007). CONCLUSION: VETC status correlates remarkably well with the tumor response and long-term survival in patients undergoing HAIC. It may be a promising efficacy predictor and help identify patients who will benefit from HAIC.

CCL2 is a key regulator and therapeutic target for periodontitis.

AIM: Our previous study revealed that the C-C motif chemokine receptor 2 (CCR2) is a promising target for periodontitis prevention and treatment. However, CCR2 is a receptor with multiple C-C motif chemokine ligands (CCLs), including CCL2, CCL7, CCL8, CCL13 and CCL16, and which of these ligands plays a key role in periodontitis remains unclear. The aim of the present study was to explore the key functional ligand of CCR2 in periodontitis and to evaluate the potential of the functional ligand as a therapeutic target for periodontitis. MATERIALS AND METHODS: The expression levels and clinical relevance of CCR2, CCL2, CCL7, CCL8, CCL13 and CCL16 were studied using human samples. The role of CCL2 in periodontitis was evaluated by using CCL2 knockout mice and overexpressing CCL2 in the periodontium. The effect of local administration of bindarit in periodontitis was evaluated by preventive and therapeutic medication in a mouse periodontitis model. Microcomputed tomography, haematoxylin and eosin staining, tartrate-resistant acid phosphatase staining, real-time quantitative polymerase chain reaction, enzyme-linked immunosorbent assay, bead-based immunoassays and flow cytometry were used for histomorphology, molecular biology and cytology analysis. RESULTS: Among different ligands of CCR2, only CCL2 was significantly up-regulated in periodontitis gingival tissues and was positively correlated with the severity of periodontitis. Mice lacking CCL2 showed milder inflammation and less bone resorption than wild-type mice, which was accompanied by a reduction in monocyte/macrophage recruitment. Adeno-associated virus-2 vectors overexpressing CCL2 in Ccl2-/- mice gingiva reversed the attenuation of periodontitis in a CCR2-dependent manner. In ligation-induced experimental periodontitis, preventive or therapeutic administration of bindarit, a CCL2 synthesis inhibitor, significantly inhibited the production of CCL2, decreased the osteoclast number and bone loss and reduced the expression levels of proinflammatory cytokines TNF-α, IL-6 and IL-1ß. CONCLUSIONS: CCL2 is a pivotal chemokine that binds to CCR2 during the progression of periodontitis, and targeting CCL2 may be a feasible option for controlling periodontitis.

BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data.

Advances in omics technology have enriched the understanding of the biological mechanisms of diseases, which has provided a new approach for cancer research. Multi-omics data contain different levels of cancer information, and comprehensive analysis of them has attracted wide attention. However, limited by the dimensionality of matrix models, traditional methods cannot fully use the key high-dimensional global structure of multi-omics data. Moreover, besides global information, local features within each omics are also critical. It is necessary to consider the potential local information together with the high-dimensional global information, ensuring that the shared and complementary features of the omics data are comprehensively observed. In view of the above, this article proposes a new tensor integrative framework called the strong complementarity tensor decomposition model (BioSTD) for cancer multi-omics data. It is used to identify cancer subtype specific genes and cluster subtype samples. Different from the matrix framework, BioSTD utilizes multi-view tensors to coordinate each omics to maximize high-dimensional spatial relationships, which jointly considers the different characteristics of different omics data. Meanwhile, we propose the concept of strong complementarity constraint applicable to omics data and introduce it into BioSTD. Strong complementarity is used to explore the potential local information, which can enhance the separability of different subtypes, allowing consistency and complementarity in the omics data to be fully represented. Experimental results on real cancer datasets show that our model outperforms other advanced models, which confirms its validity.

Multi-View Enhanced Tensor Nuclear Norm and Local Constraint Model for Cancer Clustering and Feature Gene Selection.

The analysis of cancer data from multi-omics can effectively promote cancer research. The main focus of this article is to cluster cancer samples and identify feature genes to reveal the correlation between cancers and genes, with the primary approach being the analysis of multi-view cancer omics data. Our proposed solution, the Multi-View Enhanced Tensor Nuclear Norm and Local Constraint (MVET-LC) model, aims to utilize the consistency and complementarity of omics data to support biological research. The model is designed to maximize the utilization of multi-view data and incorporates a nuclear norm and local constraint to achieve this goal. The first step involves introducing the concept of enhanced partial sum of tensor nuclear norm, which significantly enhances the flexibility of the tensor nuclear norm. After that, we incorporate total variation regularization into the MVET-LC model to further augment its performance. It enables MVET-LC to make use of the relationship between tensor data structures and sparse data while paying attention to the feature details of the tensor data. To tackle the iterative optimization problem of MVET-LC, the alternating direction method of multipliers is utilized. Through experimental validation, it is demonstrated that our proposed model outperforms other comparison models.

Precise microdissection of gastric mixed adeno-neuroendocrine carcinoma dissects its genomic landscape and evolutionary clonal origins.

Gastric mixed adenoneuroendocrine carcinoma (MANEC) is a clinically aggressive and heterogeneous tumor composed of adenocarcinoma (ACA) and neuroendocrine carcinoma (NEC). The genomic properties and evolutionary clonal origins of MANEC remain unclear. We conduct whole-exome and multiregional sequencing on 101 samples from 33 patients to elucidate their evolutionary paths. We identify four significantly mutated genes, TP53, RB1, APC, and CTNNB1. MANEC resembles chromosomal instability stomach adenocarcinoma in that whole-genome doubling in MANEC is predominant and occurs earlier than most copy-number losses. All tumors are of monoclonal origin, and NEC components show more aggressive genomic properties than their ACA counterparts. The phylogenetic trees show two tumor divergence patterns, including sequential and parallel divergence. Furthermore, ACA-to-NEC rather than NEC-to-ACA transition is confirmed by immunohistochemistry on 6 biomarkers in ACA- and NEC-dominant regions. These results provide insights into the clonal origin and tumor differentiation of MANEC.

Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model.

Esophageal cancer (EC) is a malignant tumor with high mortality. We aimed to find the optimal examined lymph node (ELN) count threshold and develop a model to predict survival of patients after radical esophagectomy. Two cohorts were analyzed: the training cohort which included 734 EC patients from the Chinese registry and the external testing cohort which included 3208 EC patients from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox proportional hazards regression analysis was used to determine the prognostic value of ELNs. The cut-off point of the ELNs count was determined using R-statistical software. The prediction model was developed using random survival forest (RSF) algorithm. Higher ELNs count was significantly associated with better survival in both cohorts (training cohort: HR = 0.98, CI = 0.97-0.99, P < 0.01; testing cohort: HR = 0.98, CI = 0.98-0.99, P < 0.01) and the cut-off point was 18 (training cohort: P < 0.01; testing cohort: P < 0.01). We developed the RSF model with high prediction accuracy (AUC: training cohort: 87.5; testing cohort: 79.3) and low Brier Score (training cohort: 0.122; testing cohort: 0.152). The ELNs count beyond 18 is associated with better overall survival. The RSF model has preferable clinical capability in terms of individual prognosis assessment in patients after radical esophagectomy.

Dynamic peripheral blood immune cell markers for predicting the response of patients with metastatic cancer to immune checkpoint inhibitors.

PURPOSE: Immune checkpoint inhibitors (ICIs) have shown durable responses in various malignancies. However, the response to ICI therapy is unpredictable, and investigation of predictive biomarkers needs to be improved. EXPERIMENTAL DESIGN: In total, 120 patients receiving ICI therapy and 40 patients receiving non-ICI therapy were enrolled. Peripheral blood immune cell markers (PBIMs), as liquid biopsy biomarkers, were analyzed by flow cytometry before ICI therapy, and before the first evaluation. In the ICI cohort, patients were randomly divided into training (n = 91) and validation (n = 29) cohorts. Machine learning algorithms were applied to construct the prognostic and predictive immune-related models. RESULTS: Using the training cohort, a peripheral blood immune cell-based signature (BICS) based on four hub PBIMs was developed. In both the training and the validation cohorts, and the whole cohort, the BICS achieved a high accuracy for predicting overall survival (OS) benefit. The high-BICS group had significantly shorter progression-free survival and OS than the low-BICS group. The BICS demonstrated the predictive ability of patients to achieve durable clinical outcomes. By integrating these PBIMs, we further constructed and validated the support vector machine-recursive and feature elimination classifier model, which robustly predicts patients who will achieve optimal clinical benefit. CONCLUSIONS: Dynamic PBIM-based monitoring as a noninvasive, cost-effective, highly specific and sensitive biomarker has broad potential for prognostic and predictive utility in patients receiving ICI therapy.

Cisapride induced hypoglycemia via the KCNH6 potassium channel.

Mutations in KCNH6 has been proved to cause hypoinsulinemia and diabetes in human and mice. Cisapride is a stomach-intestinal motility drug used to treat gastrointestinal dysfunction. Cisapride has been reported to be a potential inhibitor of the KCNH family, but it remained unclear whether cisapride inhibited KCNH6. Here, we discovered the role of cisapride on glucose metabolism, focusing on the KCNH6 potassium channel protein. Cisapride reduced blood glucose level and increased serum insulin secretion in wild-type (WT) mice fed standard normal chow/a high-fat diet or in db/db mice, especially when combined with tolbutamide. This effect was much stronger after 4 weeks of intraperitoneal injection. Whole-cell patch-clamp showed that cisapride inhibited KCNH6 currents in transfected HEK293 cells in a concentration-dependent manner. Cisapride induced an increased insulin secretion through the disruption of intracellular calcium homeostasis in a rat pancreatic ß-cell line, INS-1E. Further experiments revealed that cisapride did not decrease blood glucose or increase serum insulin in KCNH6 ß-cell knockout (Kcnh6-ß-KO) mice when compared with WT mice. Cisapride also ameliorated glucose-stimulated insulin secretion (GSIS) in response to high glucose in WT but not Kcnh6-ß-KO mice. Thus, our data reveal a novel way for the effect of KCNH6 in cisapride-induced hypoglycemia.

Value of shear wave elasticity in predicting the efficacy of neoadjuvant chemotherapy in different molecular types.

PURPOSE: The purpose of this study was to evaluate the response to neoadjuvant chemotherapy (NAC) of different molecular subtypes of breast cancer using shear wave ultrasound elastography (SWE). METHODS: Ninety-eight patients with final diagnoses of breast cancer prior to NAC were examined with SWE and B-mode ultrasound. These parameters were compared with the response evaluation criteria in solid tumors (RECIST) index and pathological diagnoses. Then, we recorded the area under the receiver operating characteristic (ROC) curve. Immunohistochemical markers, including estrogen receptor (ER), progesterone receptor (PR), Ki67 index, and human epidermal growth factor receptor 2 (HER2) score, were examined before neoadjuvant treatment. Then, the diagnostic efficacy of SWE in different molecular subtypes was evaluated. RESULTS: One-way analysis of variance revealed that age, tumor margin, the change in tumor size after chemotherapy, and the average (Emean), minimum (Emin), and maximum (Emax) values from shear wave elastography ultrasound before chemotherapy were related to the RECIST index. Multivariate regression revealed that age, tumor margin, the change in tumor size after chemotherapy, and Emax were independently correlated with the RECIST index. According to the ROC curve, the area under the curve (AUC) of Emax was 0.773. The AUC of Emean, Emin, and the ratio between tumor tissue and normal tissue (EI) were 0.630, 0.617, and 0.510, respectively. The Emax, Emin, Emean, and EI before and after NAC were significantly different (p ≤ 0.05). There was statistical significance in Luminal B type and HER2 enriched type in correlation results of the Emax values and RECIST indexes of different molecular subtypes. However, there was no significant difference in the Luminal A type and Triple negative types. There was no significant difference in the Emax values before NAC between the pathologic complete response (pCR) group and the non-pCR group (p > 0.05). CONCLUSION: Emax can be used to predict the response to NAC in women with invasive breast cancer, especially those with the Luminal B type and HER2 enriched type, but it cannot be used to predict pCR to NAC.

Effectiveness of Quantitative Shear Wave Elastography for the Prediction of Axillary Lymph Node Metastasis.

Objective: Invasive breast cancer can be metastasized through axillary lymph nodes (LNs). This study was to evaluate the effectiveness of quantitative shear wave elastography (SWE) to predict axillary LN metastasis, which also provides prognostic implication of SWE as a histopathologic element of invasive breast cancer. Methods: 72 prospectively enrolled patients received B-mode ultrasound (BUS) and SWE, and the elasticity index (EI) of SWE at the stiffest part of lymph nodes (LNs) was measured. EI of SWE was closely associated with pathologic results and the histopathologic elements. The receiver operating characteristics (ROC) curve was drawn to evaluate the optimal cut-off value for the assessment of disease severity. Results: A significantly longer short-axis diameter and a larger maximal cortex were observed in malignant LNs than that in healthy LNs. The absence of the hilum was associated with metastatic LNs. The EI of SWE varied markedly between the benign and malignant LNs. The combination of E max and BUS showed higher area under the curve (AUC) than BUS alone to predict metastatic LNs (0.7762 vs. 0.7230). EI of SWE in malignant lymph nodes those with extranodal extension are higher than those without extranodal extension. Conclusions: Quantitative SWE provides a viable alternative for the assessment of axillary LN and shows great potential to predict pathological prognostic elements of metastatic axillary LNs in invasive breast cancer. Joint use of SWE and BUS allows examination of the predictive outcome of BUS for axillary lymph node metastasis in invasive breast cancer.

Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data.

BACKGROUND: The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of bioinformatics. The purpose of cancer clustering is to find expression patterns of different cancer types, so that the samples with similar expression patterns can be gathered into the same type. In order to improve the accuracy and reliability of cancer clustering, many clustering methods begin to focus on the integration analysis of cancer multi-omics data. Obviously, the methods based on multi-omics data have more advantages than those using single omics data. However, the high heterogeneity and noise of cancer multi-omics data pose a great challenge to the multi-omics analysis method. RESULTS: In this study, in order to extract more complementary information from cancer multi-omics data for cancer clustering, we propose a low-rank subspace clustering method called multi-view manifold regularized compact low-rank representation (MmCLRR). In MmCLRR, each omics data are regarded as a view, and it learns a consistent subspace representation by imposing a consistence constraint on the low-rank affinity matrix of each view to balance the agreement between different views. Moreover, the manifold regularization and concept factorization are introduced into our method. Relying on the concept factorization, the dictionary can be updated in the learning, which greatly improves the subspace learning ability of low-rank representation. We adopt linearized alternating direction method with adaptive penalty to solve the optimization problem of MmCLRR method. CONCLUSIONS: Finally, we apply MmCLRR into the clustering of cancer samples based on multi-omics data, and the clustering results show that our method outperforms the existing multi-view methods.

The clinicopathological significance and predictive value for immunotherapy of programmed death ligand-1 expression in Epstein-Barr virus-associated gastric cancer.

The effect of anti-programmed cell death 1 (PD-1) antibody in Epstein-Barr virus-associated gastric cancer (EBVaGC) was debatable, and no predictive biomarkers for efficacy have been reported. Public reports on anti-PD-1 antibody monotherapy-treated EBVaGC with available programmed death ligand-1 (PD-L1) expression status were summarized and analyzed. Relevance with clinicopathologic characteristics of PD-L1 expression by immunohistochemistry was analyzed in 159 patients diagnosed with EBVaGC. Relevance with genomic transcriptome and mutation profile of PD-L1 status in EBVaGC was assessed with three datasets, the cancer genome atlas (TCGA), Gene Expression Omnibus (GEO) GSE51575, and GSE62254. Based on the data from 8 reports, patients with positive PD-L1 expression (n = 30) had significantly superior objective response rate (ORR) than patients with negative PD-L1 expression (n = 9) (63.3% vs. 0%, P = .001) in EBVaGC receiving anti-PD-1 antibody monotherapy. PD-L1 positivity was associated with less aggressive clinicopathological characteristics and was an independent predictor for a longer disease-free survival (hazard ratio [HR] and 95% CI: 0.45 [0.22-0.92], P = .03) and overall survival (HR and 95% CI: 0.17 [0.06-0.43], P < .001). Analysis of public EBVaGC transcriptome and mutation datasets revealed enhanced immune-related signal pathways in PD-L1high EBVaGC and distinct mutation patterns in PD-L1low EBVaGC. PD-L1 positivity indicates a subtype of EBVaGC with 'hot' immune microenvironment, lower aggressiveness, better prognosis, and higher sensitivity to anti-PD-1 immunotherapy.

Overlooked Ecological Roles of Influent Wastewater Microflora in Improving Biological Phosphorus Removal in an Anoxic/Aerobic MBR Process.

The ecological roles of influent microflora in activated sludge communities have not been well investigated. Herein, parallel lab-scale anoxic/aerobic (A/O) membrane bioreactors (MBRs), which were fed with raw (MBR-C) and sterilized (MBR-T) municipal wastewater, were operated. The MBRs showed comparable nitrogen removal but superior phosphorus removal in MBR-C than MBR-T over the long-term operation. The MBR-C sludge community had higher diversity and deterministic assembly than the MBR-T sludge community as revealed by 16S rRNA gene sequencing and null model analysis. Moreover, the MBR-C sludge community had higher abundance of polyphosphate accumulating organisms (PAOs) and hydrolytic/fermentative bacteria (HFB) but lower abundance of glycogen-accumulating organisms (GAOs), in comparison with MBR-T sludge. Intriguingly, the results of both the net growth rate and Sloan's neutral model demonstrated that HFB in the sludge community were generally slow-growing or nongrowing and their consistent presence in activated sludge was primarily attributed to the HFB immigration from influent microflora. Positive correlations between PAOs and HFB and potential competitions between HFB and GAOs were observed, as revealed by the putative species-species associations in the ecological networks. Taken together, this work deciphers the positive ecological roles of influent microflora, particularly HFB, in system functioning and highlights the necessity of incorporating influent microbiota for the design and modeling of A/O MBR plants.

Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery.

The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 < p < 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.

IDSSIM: an lncRNA functional similarity calculation model based on an improved disease semantic similarity method.

BACKGROUND: It has been widely accepted that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human diseases. Many association prediction models have been proposed for predicting lncRNA functions and identifying potential lncRNA-disease associations. Nevertheless, among them, little effort has been attempted to measure lncRNA functional similarity, which is an essential part of association prediction models. RESULTS: In this study, we presented an lncRNA functional similarity calculation model, IDSSIM for short, based on an improved disease semantic similarity method, highlight of which is the introduction of information content contribution factor into the semantic value calculation to take into account both the hierarchical structures of disease directed acyclic graphs and the disease specificities. IDSSIM and three state-of-the-art models, i.e., LNCSIM1, LNCSIM2, and ILNCSIM, were evaluated by applying their disease semantic similarity matrices and the lncRNA functional similarity matrices, as well as corresponding matrices of human lncRNA-disease associations coming from either lncRNADisease database or MNDR database, into an association prediction method WKNKN for lncRNA-disease association prediction. In addition, case studies of breast cancer and adenocarcinoma were also performed to validate the effectiveness of IDSSIM. CONCLUSIONS: Results demonstrated that in terms of ROC curves and AUC values, IDSSIM is superior to compared models, and can improve accuracy of disease semantic similarity effectively, leading to increase the association prediction ability of the IDSSIM-WKNKN model; in terms of case studies, most of potential disease-associated lncRNAs predicted by IDSSIM can be confirmed by databases and literatures, implying that IDSSIM can serve as a promising tool for predicting lncRNA functions, identifying potential lncRNA-disease associations, and pre-screening candidate lncRNAs to perform biological experiments. The IDSSIM code, all experimental data and prediction results are available online at https://github.com/CDMB-lab/IDSSIM .

Atonal bHLH transcription factor 1 is an important factor for maintaining the balance of cell proliferation and differentiation in tumorigenesis.

Establishing the link between cellular processes and oncogenesis may aid the elucidation of targeted and effective therapies against tumor cell proliferation and metastasis. Previous studies have investigated the mechanisms involved in maintaining the balance between cell proliferation, differentiation and migration. There is increased interest in determining the conditions that allow cancer stem cells to differentiate as well as the identification of molecules that may serve as novel drug targets. Furthermore, the study of various genes, including transcription factors, which serve a crucial role in cellular processes, may present a promising direction for future therapy. The present review described the role of the transcription factor atonal bHLH transcription factor 1 (ATOH1) in signaling pathways in tumorigenesis, particularly in cerebellar tumor medulloblastoma and colorectal cancer, where ATOH1 serves as an oncogene or tumor suppressor, respectively. Additionally, the present review summarized the associated therapeutic interventions for these two types of tumors and discussed novel clinical targets and approaches.

Quantitative shear wave elastography in primary invasive breast cancers, based on collagen-S100A4 pathology, indicates axillary lymph node metastasis.

BACKGROUND: The purpose of this study was to evaluate the value of quantitative shear wave elastography (SWE) in indicating the axillary lymph node metastasis (LNM) of invasive breast cancers (IBCs) and to investigate if S100A4 plays a key role in promoting metastasis and increasing stiffness in IBC. METHODS: The differences in SWE of 223 IBC patients were compared between the LNM+ and LNM- groups and the optimal cutoff values of SWE for diagnosing LNM were calculated. We searched the gene expression omnibus (GEO) to determine whether S100A4 was more highly expressed in IBCs that were LNM+ than in those that were LNM-. Sirius red and immunohistochemical staining were used to examine the collagen deposition and S100A4 expression of included tissue samples, and correlations of SWE and S100A4 expression with collagen deposition were analyzed. RESULTS: The optimal cutoff values for Emax (the maximum stiff value), Emean (the mean stiff value), and EmeanR (the ratio of Emean between mass and parenchyma) for diagnosing axillary LNM were 111.05 kPa, 79.80 kPa, and 6.89, respectively. GSE9893 exhibited more increased S100A4 expression in IBCs that were LNM+ than in those that were LNM-. Collagen volume fraction (CVF) and the average optical density of S100A4 (AODS100A4) in the LNM+ group were significantly higher than those in the LNM- group. Emax, Emean, EmeanR, and AODS100A4 were all positively correlated with CVF. CONCLUSIONS: SWE in primary IBC could be useful for indicating axillary LNM. S100A4 may be a factor that regulates cancer-associated collagen deposition and metastasis; however, prospective molecular biological studies are needed.

LncRNA-Disease Associations Prediction Using Bipartite Local Model With Nearest Profile-Based Association Inferring.

There is much evidence that long non-coding RNA (lncRNA) is associated with many diseases. However, it is time-consuming and expensive to identify meaningful lncRNA-disease associations (LDAs) through medical or biological experiments. Therefore, investigating how to identify more meaningful LDAs is necessary, and at the same time it is conducive to the prevention, diagnosis and treatment of complex diseases. Considering the limitations of some current prediction models, a novel model based on bipartite local model with nearest profile-based association inferring, BLM-NPAI, is developed for predicting LDAs. This model predicts novel LDAs from the lncRNA side and the disease side, respectively. More importantly, for some lncRNAs and diseases without any association, the model can also be predicted by their nearest neighbors. Leave-one-out cross validation (LOOCV) and 5-fold cross validation are implemented for BLM-NPAI to evaluate the performance of this model. Our model is superior to current advanced methods in most cases. In addition, to verify the validity and reliability of BLM-NPAI, three disease cases and three lncRNA cases are analyzed to further evaluate BLM-NPAI. Finally, these predicted novel LDAs are confirmed by using the LncRNA-disease database.