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BackgroundChina has experienced one of the fastest increases in the incidence of acute lymphoid leukemia (ALL). The aim of this study was to assess the long-term trends of the incidence and mortality of ALL in mainland China between 1990 and 2019 and to project these trends through 2028.MethodsData on ALL were extracted from the Global Burden of Disease Study 2019; population data were extracted from World Population Prospects 2019. An age–period–cohort framework was used in the analysis.ResultsThe net drift for the incidence of ALL was 7.5% (95% confidence interval [CI]: 7.1%, 7.8%) per year in women and 7.1% (95% CI: 6.7%, 7.6%) in men, and local drift was found to be higher than 0 in every studied age group (p

BackgroundPostoperative adjuvant transarterial chemoembolization (PA-TACE) has been increasing widely used to improve the prognosis of hepatocellular carcinoma (HCC) patients. However, clinical outcomes vary from patient to patient, which calls for individualized prognostic prediction and early management.MethodsA total of 274 HCC patients who underwent PA-TACE were enrolled in this study. The prediction performance of five machine learning models was compared and the prognostic variables of postoperative outcomes were identified.ResultsCompared with other machine learning models, the risk prediction model based on ensemble learning strategies, including Boosting, Bagging, and Stacking algorithms, presented better prediction performance for overall mortality and HCC recurrence. Moreover, the results showed that the Stacking algorithm had relatively low time consumption, good discriminative ability, and the best prediction performance. In addition, according to time-dependent ROC analysis, the ensemble learning strategies were found to perform well in predicting both OS and RFS for the patients. Our study also found that BCLC Stage, hsCRP/ALB and frequency of PA-TACE were relatively important variables in both overall mortality and recurrence, while MVI contributed more to the recurrence of the patients.ConclusionAmong the five machine learning models, the ensemble learning strategies, especially the Stacking algorithm, could better predict the prognosis of HCC patients following PA-TACE. Machine learning models could also help clinicians identify the important prognostic factors that are clinically useful in individualized patient monitoring and management.

Liver hepatocellular carcinoma (LIHC) is one of the most common liver malignancies with high mortality and morbidity. Thus, it is crucial to identify potential biomarker that is capable of accurately predicting the prognosis and therapeutic response of LIHC. Kinesin family member 5A (KIF5A) is a microtubule-based motor protein involved in the transport of macromolecules such as organelle proteins in cells. Recent studies have illustrated that the high expression of KIF5A was related to poor prognosis of solid tumors, including bladder cancer, prostate cancer, and breast cancer. However, little is currently known concerning the clinical significance of KIF5A expression in LIHC. Herein, by adopting multi-omics bioinformatics analysis, we comprehensively uncovered the potential function and the predictive value of KIF5A in stratifying clinical features among patients with LIHC, for which a high KIF5A level predicted an unfavorable clinical outcome. Results from KIF5A-related network and enrichment analyses illustrated that KIF5A might involve in microtubule-based process, antigen processing and presentation of exogenous peptide antigen via MHC class II. Furthermore, immune infiltration and immune function analyses revealed upregulated KIF5A could predict a unique tumor microenvironment with more CD8+T cells and a higher level of anti-tumor immune response. Evidence provided by immunohistochemistry staining (IHC) further validated our findings at the protein level. Taken together, KIF5A might serve as a novel prognostic biomarker for predicting immunotherapy response and could be a potential target for anti-cancer strategies for LIHC.

IntroductionUbiquitylation that mediated by ubiquitin ligases plays multiple roles not only in proteasome-mediated protein degradation but also in various cellular process including DNA repair, signal transduction and endocytosis. RING finger (RNF) proteins form the majority of these ubiquitin ligases. Recent studies have demonstrated the important roles of RNF finger proteins in tumorigenesis and tumor progression. Benzo[a]pyrene (BaP) is one of the most common environmental carcinogens causing lung cancer. The molecular mechanism of Bap carcinogenesis remains elusive. Considering the critical roles of RNF proteins in tumorigenesis and tumor progression, we speculate on whether Bap regulates RNF proteins resulting in carcinogenesis.MethodsWe used GEO analysis to identify the potential RING finger protein family member that contributes to Bap-induced NSCLC. We next used RT-qPCR, Western blot and ChIP assay to investigate the potential mechanism of Bap inhibits RNF182. BGS analyses were used to analyze the methylation level of RNF182.ResultsHere we reported that the carcinogen Bap suppresses the expression of ring finger protein 182 (RNF182) in non-small cell lung cancer (NSCLC) cells, which is mediated by abnormal hypermethylation in an AhR independent way and transcriptional regulation in an AhR dependent way. Furthermore, RNF182 exhibits low expression and hypermethylation in tumor tissues. RNF182 also significantly suppresses cell proliferation and induces cell cycle arrest in NSCLC cell lines.ConclusionThese results demonstrated that Bap inhibits RNF182 expression to promote lung cancer tumorigenesis through activating AhR and promoting abnormal methylation.

In most cancers, forkhead box N3 (FOXN3) acts as a transcriptional inhibitor to suppress tumor proliferation, but in pancreatic cancer, the opposite effect is observed. To confirm and investigate this phenomenon, FOXN3 expression in various carcinomas was determined using GEPIA2 and was found to be highly expressed in pancreatic cancer. Kaplan-Meier plotter was then used for survival analysis, revealing that high FOXN3 expression in pancreatic cancer might be associated with a poor prognosis. Similarly, clinical samples collected for immunohistochemical staining and survival analysis showed consistent results. The RNA-seq data of pancreatic cancer patients from the TCGA were then downloaded, and the differential expression gene set was obtained using R for gene set enrichment analysis (GSEA). The intersection of the above gene sets and FOXN3-related genes was defined as related differentially expressed gene sets (DEGs), and enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we analyzed the relationship between FOXN3 and immune infiltration in pancreatic cancer. Collectively, our findings reveal that FOXN3 is involved in the occurrence and progression of pancreatic cancer and may be useful as a prognostic tool in pancreatic cancer immunotherapy.

Colon cancer undergoes a traditional pathway from colon polyps to colon cancer. It is of great significance to investigate the key molecules involved in carcinogenesis from polyps to malignancies. Circular RNAs (circRNAs) are stably expressed in human body fluids such as plasma. Here, we demonstrated a differential expression pattern of plasma circRNAs in healthy individuals, colon polyp patients and colon cancer patients using circRNA Arraystar microarray. We explored that circRNA HADHA (circHADHA) was upregulated in plasma from polyp patients, whereas it was downregulated in plasma from colon cancer patients. Overexpression of circHADHA promoted autophagy in colon epithelial cells. Moreover, in colon cancer cells, overexpression of circHADHA promoted autophagy, whereas it inhibited cell proliferation and colony formation. CircHADHA increased the expression of ATG13 via miR-361 in both colon epithelial and cancer cells. ATG13 knockdown reduced autophagy even in the presence of circHADHA in colon cancer cells. Furthermore, the growth of circHADHA-overexpressing colon cancer cell-derived xenograft tumors was significantly decreased compared with control tumors in nude mice. In conclusion, circHADHA was differentially expressed in the plasma of healthy individuals, colon polyp patients and colon cancer patients. CircHADHA promoted autophagy by regulating ATG13 via miR-361 in both colon epithelial and cancer cells. CircHADHA suppressed tumor growth by inducing cell autophagy in colon cancer cells. CircHADHA potentially serves as a biomarker for screening of precursor colon cancer and a therapeutic target for colon cancer treatment.

BackgroundInflammation, immunity, and nutrition status play important roles in tumorigenesis, progression, and metastasis. This study aimed to evaluate the prognostic value of Inflammation-Immunity-Nutrition Score (IINS) for overall survival (OS) and progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC) undergoing radical surgery.MethodsA total of 204 HCC patients who met the criteria were included in this retrospective study: 144 in the prediction model and 60 in the validation model. IINS was constructed based on the sum of classification scores of preoperative high-sensitivity C-reactive protein (hsCRP), lymphocyte (LYM), and albumin (ALB). The associations between the IINS group and the clinicopathologic characteristics were analyzed using Pearson’s χ2 test or Fisher’s exact test. Multivariate Cox regression analysis was used to evaluate variables significant on univariate analysis. Kaplan-Meier survival curves were conducted to investigate the prognostic values of IINS, Alpha-fetoprotein (AFP) and IINS-AFP classification. The prognostic performances of all the potential prognostic factors were further compared by receiver operating characteristic (ROC) curve, and time-dependent ROC curve. The internal validation and external validation were used to ensure the credibility of this prediction model.ResultsThe patients were divided into low and high IINS groups according to the median of IINS. According to multivariate Cox regression analyses, the Barcelona Clinic Liver Cancer (BCLC) Stage (P=0.003), AFP (P=0.013), and IINS (P=0.028) were independent prognostic factors for OS, and BCLC Stage (P=0.009), microvascular invasion (P=0.030), and IINS (P=0.031) were independent prognostic factors for PFS. High IINS group were associated with significantly worse OS and PFS compared with low IINS group (P

Human cancer statistics report that respiratory related cancers such as lung, laryngeal, oral and nasopharyngeal cancers account for a large proportion of tumors, and tumor metastasis remains the major reason for patient death. The metastasis of tumor cells requires actin cytoskeleton remodeling, in which fascin-1 plays an important role. Fascin-1 can cross-link F-actin microfilaments into bundles and form finger-like cell protrusions. Some studies have shown that fascin-1 is overexpressed in human tumors and is associated with tumor growth, migration and invasion. The role of fascin-1 in respiratory related cancers is not very clear. The main purpose of this study was to provide an updated literature review on the role of fascin-1 in the pathogenesis, diagnosis and management of respiratory related cancers. These studies suggested that fascin-1 can serve as an emerging biomarker and potential therapeutic target, and has attracted widespread attention.

AimsAnti-angiotherapy (Bevacizumab) is currently regarded as a promising option for glioma patients who are resistant to temozolomide (TMZ) treatment. But ongoing clinical research failed to meet therapeutic expectations. This study aimed to explore the pivotal genetic feature responsible for TMZ and Bevacizumab resistance in glioma patients.MethodsWe downloaded the transcriptomic and methylation data of glioma patients from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases and grouped these patients into resistant and non-resistant groups based on their clinical profiles. Differentially expressed genes and pathways were identified and exhibited with software in R platform. A TMZ-resistant cell line was constructed for validating the expression change of the candidate gene, ITGA5. An ITGA5-overexpressing cell line was also constructed to investigate its biological function using the CCK8 assay, Western blot, periodic acid–Schiff (PAS) staining, and transcriptional sequencing.ResultsChange of the cell morphology and polarity was closely associated with TMZ mono-resistance and TMZ/Bevacizumab dual resistance in glioma patients. The expression level of ITGA5 was effective in determining drug resistance and the outcome of glioma patients, which is regulated by methylation on two distinct sites. ITGA5 was augmented in TMZ-resistant glioma cells, while overexpressing ITGA5 altered the cell-promoted TMZ resistance through enhancing vascular mimicry (VM) formation correspondingly.ConclusionsBoth the epigenetic and transcriptional levels of ITGA5 are effective in predicting TMZ and Bevacizumab resistance, indicating that ITGA5 may serve as a predictor of the treatment outcomes of glioma patients.

Retinoid-related orphan receptor alpha (RORα) is involved in tumor development. However, the mechanisms underlying RORα inhibiting epithelial-to-mesenchymal transition (EMT) and invasion are poorly understood in gastric cancer (GC). This study revealed that the decreased expression of RORα is associated with GC development, progression, and prognosis. RORα suppressed cell proliferation, EMT, and invasion in GC cells through inhibition of the Wnt/β-catenin pathway. RORα overexpression resulted in the decreased Wnt1 expression and the increased RORα interaction with β-catenin, which could lead to the decreased intranuclear β-catenin and p-β-catenin levels, concomitant with downregulated T-cell factor-4 (TCF-4) expression and the promoter activity of c-Myc. The inhibition of Wnt/β-catenin pathway was coupled with the reduced expression of Axin, c-Myc, and c-Jun. RORα downregulated vimentin and Snail and upregulated E-cadherin protein levels in vitro and in vivo. Inversely, knockdown of RORα attenuated its inhibitory effects on Wnt/β-catenin pathway and its downstream gene expression, facilitating cell proliferation, EMT, migration, and invasion in GC cells. Therefore, RORα could play a crucial role in repressing GC cell proliferation, EMT, and invasion via downregulating Wnt/β-catenin pathway.

Background: Inflammation, immunity, and nutrition status play important roles in tumorigenesis, progression, and metastasis. This study aimed to evaluate the prognostic value of Inflammation-Immunity-Nutrition Score (IINS) for overall survival (OS) and progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC) undergoing radical surgery. Methods: A total of 204 HCC patients who met the criteria were included in this retrospective study: 144 in the prediction model and 60 in the validation model. IINS was constructed based on the sum of classification scores of preoperative high-sensitivity C-reactive protein (hsCRP), lymphocyte (LYM), and albumin (ALB). The associations between the IINS group and the clinicopathologic characteristics were analyzed using Pearson's c2 test or Fisher's exact test. Multivariate Cox regression analysis was used to evaluate variables significant on univariate analysis. Kaplan-Meier survival curves were conducted to investigate the prognostic values of IINS, Alpha-fetoprotein (AFP) and IINSAFP classification. The prognostic performances of all the potential prognostic factors were further compared by receiver operating characteristic (ROC) curve, and time-dependent ROC curve. The internal validation and external validation were used to ensure the credibility of this prediction model. Results: The patients were divided into low and high IINS groups according to the median of IINS. According to multivariate Cox regression analyses, the Barcelona Clinic Liver Cancer (BCLC) Stage (P=0.003), AFP (P=0.013), and IINS (P=0.028) were independent prognostic factors for OS, and BCLC Stage (P=0.009), microvascular invasion (P=0.030), and IINS (P=0.031) were independent prognostic factors for PFS. High IINS group were associated with significantly worse OS and PFS compared with low IINS group (P<0.001; P=0.004). In terms of clinical prognosis, IINS-AFP classification was good in group I, moderate in group II, and poor in group III. Group I had a longer OS (P<0.001) and PFS (P=0.008) compared with group II and III. ROC analysis revealed that IINS-AFP classification had a better prognostic performance for OS (AUC: 0.767) and PFS (AUC: 0.641) than other predictors, excluding its slightly lower predictive power for PFS than IINS. The time-dependent ROC curves also showed that both IINS (12-month AUC: 0.650; 24-month AUC: 0.670; 36-month AUC: 0.880) and IINS-AFP classification (12-month AUC: 0.720; 24-month AUC: 0.760; 36-month AUC: 0.970) performed well in predicting OS for HCC patients. Furthermore, the internal validation and external validation proved that IINS had good predictive performance, strong internal validity and external applicability, and could be used to establish the prediction model. Conclusion: Inflammation-immunity-nutrition score could be a powerful clinical prognostic indicator in HCC patients undergoing radical surgery. Furthermore, IINS-AFP classification presents better prognostic performance than IINS or AFP alone, and might serve as a practical guidance to help patients adjust treatment and follow-up strategies to improve future outcomes. [ABSTRACT FROM AUTHOR]

Objective: To investigate the efficiency of pre-therapy magnetic resonance imaging (MRI) features in predicting the prognosis of desmoid-type fibromatosis patients treated with imatinib. Materials and Methods: A total of 38 desmoid-type fibromatosis patients treated with imatinib were collected in this retrospective study. The high signal intensity on pre-therapy MRI was evaluated on axial T2 and T1 contrast-enhanced sequences with fat suppression. Cox regression and Kaplan–Meier analyses explored the correlation between clinical or radiographic characteristics and progression-free survival (PFS). Results: Hyperintense T1 contrast enhancement (CE) proportion (≥ 75%) was identified as an independent predictor for PFS. Patients with hyperintense T1 CE proportion <75% demonstrated no progression, while patients with hyperintense T1 CE proportion ≥75% demonstrated a progression rate of 78.4%. Conclusion: Hyperintense T1 CE proportion in the tumor is a potential predictor of disease progression in patients with desmoid-type fibromatosis treated with imatinib. Hyperintense T1 CE proportion <75% indicates progression-free during treatment. [ABSTRACT FROM AUTHOR]

Gliomas are the most aggressive and common type of malignant brain tumor, with limited treatment options and a dismal prognosis. Angiogenesis, a hallmarks of cancer, is one of two critical events in the progression of gliomas. Accumulating evidence has demonstrated that in glioma dysregulated molecules like long noncoding RNAs (lncRNAs), are closely linked to tumorigenesis and prognosis. However, the effects of and mechanisms of action of lncRNAs during tumor angiogenesis are poorly understood. The effect of lncRNA RP11-732M18.3 on angiogenesis was elucidated through an intracranial orthotopic glioma model, immunohistochemistry, and an in vitro angiogenesis assay. Co-culture experiments and cell migration assays were performed to investigate the function of lncRNA RP11-732M18.3 in vitro. lncRNA RP11-732M18.3 increased CD31+ microvessel density, and overexpression of lncRNA RP11-732M18.3 resulted in poor mouse survival. lncRNA RP11-732M18.3 promoted endothelial cell migration and tube formation. Nomogram and Kaplan-Meier survival analyses indicated that higher VEGFA is correlated with a poor prognosis. Mechanistically, lncRNA RP11-732M18.3 promotes angiogenesis by increasing the nuclear level of EP300 and facilitating the transcription and secretion of VEGFA. Our study contributes to the latest understanding of glioma angiogenesis and prognosis. lncRNA RP11-732M18.3 may be a potential treatment target in glioma.

ObjectiveTo investigate the efficiency of pre-therapy magnetic resonance imaging (MRI) features in predicting the prognosis of desmoid-type fibromatosis patients treated with imatinib.Materials and MethodsA total of 38 desmoid-type fibromatosis patients treated with imatinib were collected in this retrospective study. The high signal intensity on pre-therapy MRI was evaluated on axial T2 and T1 contrast-enhanced sequences with fat suppression. Cox regression and Kaplan–Meier analyses explored the correlation between clinical or radiographic characteristics and progression-free survival (PFS).ResultsHyperintense T1 contrast enhancement (CE) proportion (≥ 75%) was identified as an independent predictor for PFS. Patients with hyperintense T1 CE proportion

Background and PurposePretreatment prediction of the response to neoadjuvant chemoradiotherapy (NCRT) helps to determine the subsequent plans for the patients with locally advanced rectal cancer (LARC). If the good responders (GR) and non-good responders (non-GR) can be accurately predicted, they can choose to intensify the neoadjuvant chemoradiotherapy to decrease the risk of tumor progression during NCRT and increase the chance of organ preservation. Compared with radiomics methods, deep learning (DL) may adaptively extract features from the images without the need of feature definition. However, DL suffers from limited training samples and signal discrepancy among different scanners. This study aims to construct a DL model to predict GRs by training apparent diffusion coefficient (ADC) images from different scanners.MethodsThe study retrospectively recruited 700 participants, chronologically divided into a training group (n = 500) and a test group (n = 200). Deep convolutional neural networks were constructed to classify GRs and non-GRs. The networks were designed with a max-pooling layer parallelized by a center-cropping layer to extract features from both the macro and micro scale. ADC images and T2-weighted images were collected at 1.5 Tesla and 3.0 Tesla. The networks were trained by the image patches delineated by radiologists in ADC images and T2-weighted images, respectively. Pathological results were used as the ground truth. The deep learning models were evaluated on the test group and compared with the prediction by mean ADC value.ResultsArea under curve (AUC) of receiver operating characteristic (ROC) is 0.851 (95% CI: 0.789–0.914) for DL model with ADC images (DL_ADC), significantly larger (P = 0.018, Z = 2.367) than that of mean ADC with AUC = 0.723 (95% CI: 0.637–0.809). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of DL_ADC model are 94.3%, 68.3%, 87.4% and 83.7%, respectively. The DL model with T2-weighted images (DL_T2) produces an AUC of 0.721 (95% CI: 0.640–0.802), significantly (P = 0.000, Z = 3.554) lower than that of DL_ADC model.ConclusionDeep learning model reveals the potential of pretreatment apparent diffusion coefficient images for the prediction of good responders to neoadjuvant chemoradiotherapy.

Objective: Determine the performance of a computed tomography (CT) -based radiomics model in predicting early response to immunotherapy in patients with metastatic melanoma.Methods: This retrospective study examined 50 patients with metastatic melanoma who received immunotherapy treatment in our hospital with an anti-programmed cell death-1 (PD-1) agent or an inhibitor of cytotoxic T lymphocyte antigen-4 (CTLA-4). Thirty-four patients who received an anti-PD-1 agent were in the training sample and 16 patients who received a CTLA-4 inhibitor were in the validation sample. Patients with true progressive disease (PD) were in the poor response group, and those with pseudoprogression, complete response (CR), partial response (PR), or stable disease (SD) were in the good response group. CT images were examined at baseline and after the first and second cycles of treatment, and the imaging data were extracted for radiomics modeling.Results: The radiomics model based on pre-treatment, post-treatment, and delta features provided the best results for predicting response to immunotherapy. Receiver operating characteristic (ROC) analysis for good response indicated an area under the curve (AUC) of 0.882 for the training group and an AUC of 0.857 for the validation group. The sensitivity, specificity, and accuracy of model were 85.70% (6/7), 66.70% (6/9), and 75% (12/16) for predicting a good response.Conclusion: A CT-based radiomics model for metastatic melanoma has the potential to predict early response to immunotherapy and to identify pseudoprogression.

ObjectiveTo develop and validate a radiomics model of diffusion kurtosis imaging (DKI) and T2 weighted imaging for discriminating pancreatic neuroendocrine tumors (PNETs) from solid pseudopapillary tumors (SPTs).Materials and MethodsSixty-six patients with histopathological confirmed PNETs (n = 31) and SPTs (n = 35) were enrolled in this study. ROIs of tumors were manually drawn on each slice at T2WI and DWI (b = 1,500 s/mm2) from 3T MRI. Intraclass correlation coefficients were used to evaluate the interobserver agreement. Mean diffusivity (MD) and mean kurtosis (MK) were derived from DKI. The least absolute shrinkage and selection operator regression were used for feature selection.ResultsMD and MK had a moderate diagnostic performancewith the area under curve (AUC) of 0.71 and 0.65, respectively. A radiomics model, which incorporated sex and age of patients and radiomics signature of the tumor, showed excellent discrimination performance with AUC of 0.97 and 0.86 in the primary and validation cohort. Moreover, the new model had better diagnostic performance than that of MD (P = 0.023) and MK (P = 0.004), and showed excellent differentiation with a sensitivity of 95.00% and specificity of 91.67% in primary cohort, and the sensitivity of 90.91% and specificity of 81.82% in the validation cohort. The accuracy of radiomics analysis, radiologist 1, and radiologist 2 for diagnosing SPTs and PNETs were 92.42, 77.27, and 78.79%, respectively. The accuracy of radiomics analysis was significantly higher than that of subjective diagnosis (P < 0.05).ConclusionsRadiomics model could improve the diagnostic accuracy of SPTs and PNETs and contribute to determining an appropriate treatment strategy for pancreatic tumors.