Finding the minimum number of retrieved lymph nodes in node-negative colorectal cancer using Real-world Data and the SEER database.

BACKGROUND: Current clinical guidelines recommend the removal of at least 12 lymph nodes (LNs) in resectable colorectal cancer (CRC). With advancements in lymphadenectomy technologies, the number of retrieved lymph nodes (rLNs) has markedly increased. This study aimed to investigate the lowest number of rLNs in node-negative patients. MATERIALS AND METHODS: A total of 1103 N0 and 208 N1a stage patients were enrolled in our cohort, while 8503 N0 and 1276 N1a patients from the Surveillance, Epidemiology, and End Results CRC database were included. Propensity score matching and multivariate Cox regression analyses were performed to mitigate the influence of selection bias and control for potential confounding variables. RESULTS: The median number of rLNs in N0 patients increased from 13.5 (interquartile range [IQR]: 9-18) in 2013 to 17 (IQR: 15-20) in 2019. The restrictive cubic spline illustrated a nonlinear relationship between rLNs and prognosis (nonlinearity, P =0.009), with a threshold ( N =16) influencing clinical outcomes. Patients at either N0 or N1a stage with sufficient rLNs (≥16) demonstrated superior prognoses to those with a limited rLNs (<16). After adjusting for clinical confounders, similar prognoses were observed in N0 limited and N1a adequate populations. Furthermore, Kaplan-Meier curves revealed that N0 limited patients who received chemotherapy exhibited better outcomes than those who did not. CONCLUSIONS: Among patients with node-negative CRC, it is crucial to remove 16 or more LNs effectively. Fewer than 16 rLNs should be regarded as an independent risk factor, implying the need for adjuvant chemotherapy.

Potential candidates for liver resection in liver-confined advanced HCC: a Chinese multicenter observational study.

Background: Advanced hepatocellular carcinoma (HCC) is characterized as symptomatic tumors [performance status (PS) score of 1-2], vascular invasion and extrahepatic spread, but patients with PS1 alone may be eliminated from this stage. Although liver resection is used for liver-confined HCC, its role in patients with PS1 alone remains controversial. Therefore, we aimed to explore its application in such patients and identify potential candidates. Methods: Eligible liver-confined HCC patients undergoing liver resection were retrospectively screened in 15 Chinese tertiary hospitals, with limited tumor burden, liver function and PS scores. Cox-regression survival analysis was used to investigate the prognostic factors and develop a risk-scoring system, according to which patients were substratified using fitting curves and the predictive values of PS were explored in each stratification. Results: From January 2010 to October 2021, 1535 consecutive patients were selected. In the whole cohort, PS, AFP, tumor size and albumin were correlated with survival (adjusted P<0.05), based on which risk scores of every patient were calculated and ranged from 0 to 18. Fitting curve analysis demonstrated that the prognostic abilities of PS varied with risk scores and that the patients should be divided into three risk stratifications. Importantly, in the low-risk stratification, PS lost its prognostic value, and patients with PS1 alone achieved a satisfactory 5-year survival rate of 78.0%, which was comparable with that PS0 patients (84.6%). Conclusion: Selected patients with PS1 alone and an ideal baseline condition may benefit from liver resection and may migrate forward to BCLC stage A.

Computational Characterizing Necroptosis Reveals Implications for Immune Infiltration and Immunotherapy of Hepatocellular Carcinoma.

Necroptosis is a programmed form of necrotic cell death in regulating cancer ontogenesis, progression, and tumor microenvironment (TME) and could drive tumor-infiltrating cells to release pro-inflammatory cytokines, incurring strong immune responses. Nowadays, there are few identified biomarkers applied in clinical immunotherapy, and it is increasingly recognized that high levels of tumor necroptosis could enhance the response to immunotherapy. However, comprehensive characterization of necroptosis associated with TME and immunotherapy in Hepatocellular carcinoma (HCC) remains unexplored. Here, we computationally characterized necroptosis landscape in HCC samples from TCGA and ICGA cohorts and stratified them into two necroptosis clusters (A or B) with significantly different characteristics in clinical prognosis, immune cell function, and TME-landscapes. Additionally, to further evaluate the necroptosis levels of each sample, we established a novel necroptosis-related gene score (NRGscore). We further investigated the TME, tumor mutational burden (TMB), clinical response to immunotherapy, and chemotherapeutic drug sensitivity of HCC subgroups stratified by the necroptosis landscapes. The NRGscore is robust and highly predictive of HCC clinical outcomes. Further analysis indicated that the high NRGscore group resembles the immune-inflamed phenotype while the low score group is analogous to the immune-exclusion or metabolism phenotype. Additionally, the high NRGscore group is more sensitive to immune checkpoint blockade-based immunotherapy, which was further validated using an external HCC cohort, metastatic melanoma cohort, and advanced urothelial cancer cohort. Besides, the NRGscore was demonstrated as a potential biomarker for chemotherapy, wherein the high NRGscore patients with more tumor stem cell composition could be more sensitive to Cisplatin, Doxorubicin, Paclitaxel-based chemotherapy, and Sorafenib therapy. Collectively, a comprehensive characterization of the necroptosis in HCC suggested its implications for predicting immune infiltration and response to immunotherapy of HCC, providing promising strategies for treatment.

A comprehensive characterization of alternative splicing events related to prognosis and the tumor microenvironment in lung adenocarcinoma.

Background: Alternative splicing (AS) is a critical mechanism of post-transcriptional regulation and has been widely reported to be associated with the tumor progression and tumor microenvironment (TME) formation. However, the role of AS in lung adenocarcinoma (LUAD) has not been clearly elucidated. This study presents a comprehensive analysis exploring the impact of AS on prognosis and TME in LUAD. Methods: The gene expression transcriptome profiles and survival data were obtained from The Cancer Genome Atlas (TCGA) database, and the splicing profiles were obtained from the TCGA SpliceSeq database. Base on prognostic AS events, a prognostic signature was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression followed by multivariate Cox regression analysis. Survival outcomes was analyzed using the Kaplan-Meier method and the predictive performance of the signature was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the landscape of the TME was assessed by ESTIMATE, Microenvironment Cell Population (MCP)-counter, and single-sample Gene-Set Enrichment Analysis (ssGSEA) algorithms. Results: A total of 127 prognostic AS events with P value <0.001 from 89 genes in LUAD were confirmed. A prognostic signature was constructed based on 20 prognostic AS events. Kaplan-Meier survival analysis demonstrated that higher risk scores were associated with poorer overall survival (OS). The area under the ROC curve of risk scores predicting the 1-, 3-, and 5-year survival probability were 0.791, 0.847, and 0.832, respectively. Furthermore, significant relationship was observed between the prognostic signature and the landscape of the TME. High-risk patients had lower stromal/immune scores, higher tumor purity, and significantly decreased abundance of majority immune cells, and immune-related signatures (P<0.05). Finally, a potential regulatory mechanism of the AS events is displayed in a regulatory network. Conclusions: This research highlights the prognostic value of AS events for patients with LUAD and provide new insight into the regulation of the TME by AS. Notably, AS may affect the patient's prognosis by altering the TME. Our findings provide important guidance for the development of novel biomarkers and therapeutic targets in patients with LUAD.

Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer.

BACKGROUND: Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. METHODS: We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. RESULTS: A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). CONCLUSION: We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

Identify optimal HAP series scores for unresectable HCC patients undergoing TACE plus sorafenib: A Chinese multicenter observational study.

Background: Hepatoma arterial-embolization prognostic (HAP) series scores have been proposed for prognostic prediction in patients with unresectable hepatocellular carcinoma (uHCC) undergoing transarterial chemoembolization (TACE). However, their prognostic value in TACE plus sorafenib (TACE-S) remains unknown. Here, we aim to evaluate their prognostic performance in such conditions and identify the best model for this combination therapy. Methods: Between January 2012 and December 2018, consecutive patients with uHCC receiving TACE-S were recruited from 15 tertiary hospitals in China. Cox regression analyses were used to investigate the prognostic values of baseline factors and every scoring system. Their prognostic performance and discriminatory performance were evaluated and confirmed in subgroup analyses. Results: A total of 404 patients were enrolled. In the whole cohort, the median follow-up period was 44.2 (interquartile range (IQR), 33.2-60.7) months, the median overall survival (OS) time was 13.2 months, and 336 (83.2%) patients died at the end of the follow-up period. According to multivariate analyses, HAP series scores were independent prognostic indicators of OS. In addition, the C-index, Akaike information criterion (AIC) values, and time-dependent area under the receiver operating characteristic (ROC) curve (AUC) indicated that modified HAP (mHAP)-III had the best predictive performance. Furthermore, the results remained consistent in most subsets of patients. Conclusion: HAP series scores exhibited good predictive ability in uHCC patients accepting TACE-S, and the mHAP-III score was found to be superior to the other HAP series scores in predicting OS. Future prospective high-quality studies should be conducted to confirm our results and help with treatment decision-making.

Analysis and Construction of a Molecular Diagnosis Model of Drug-Resistant Epilepsy Based on Bioinformatics.

Background: Epilepsy is a complex chronic disease of the nervous system which influences the health of approximately 70 million patients worldwide. In the past few decades, despite the development of novel antiepileptic drugs, around one-third of patients with epilepsy have developed drug-resistant epilepsy. We performed a bioinformatic analysis to explore the underlying diagnostic markers and mechanisms of drug-resistant epilepsy. Methods: Weighted correlation network analysis (WGCNA) was applied to genes in epilepsy samples downloaded from the Gene Expression Omnibus database to determine key modules. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen the genes resistant to carbamazepine, phenytoin, and valproate, and sensitivity of the three-class classification SVM model was verified through the receiver operator characteristic (ROC) curve. A protein-protein interaction (PPI) network was utilized to analyze the protein interaction relationship. Finally, ingenuity pathway analysis (IPA) was adopted to conduct disease and function pathway and network analysis. Results: Through WGCNA, 72 genes stood out from the key modules related to drug resistance and were identified as candidate resistance genes. Intersection analysis of the results of the LASSO and SVM-RFE algorithms selected 11, 4, and 5 drug-resistant genes for carbamazepine, phenytoin, and valproate, respectively. Subsequent union analysis obtained 17 hub resistance genes to construct a three-class classification SVM model. ROC showed that the model could accurately predict patient resistance. Expression of 17 hub resistance genes in healthy subjects and patients was significantly different. The PPI showed that there are six resistance genes (CD247, CTSW, IL2RB, MATK, NKG7, and PRF1) that may play a central role in the resistance of epilepsy patients. Finally, IPA revealed that resistance genes (PRKCH and S1PR5) were involved in "CREB signaling in Neurons." Conclusion: We obtained a three-class SVM model that can accurately predict the drug resistance of patients with epilepsy, which provides a new theoretical basis for research and treatment in the field of drug-resistant epilepsy. Moreover, resistance genes PRKCH and S1PR5 may cooperate with other resistance genes to exhibit resistance effects by regulation of the cAMP-response element-binding protein (CREB) signaling pathway.

Identification of a robust signature for clinical outcomes and immunotherapy response in gastric cancer: based on N6-methyladenosine related long noncoding RNAs.

BACKGROUND: Gastric cancer (GC) is a globally prevalent cancer, ranking fifth for incidence and fourth for mortality worldwide. The N6-methyladenosine (m6A) related long noncoding RNAs (lncRNAs) were widely investigated in recent studies. Nevertheless, the underlying prognostic implication and tumor immune mechanism of m6A-related lncRNA in GC remain unknown. METHODS: We systematically assessed the m6A modification expression of 407 GC clinical samples based on 23 m6A regulators and comprehensively associated these genes with lncRNAs. Then, we constructed a m6A-related lncRNA prognostic signature (m6A-LPS) to evaluate both status and prognosis of the disease. Immune-related mechanisms were explored via dissecting tumor-infiltrating cells as well as applying tumor immune dysfunction and the exclusion algorithm. Furthermore, we validated the latent regulative mechanism of m6A-related lncRNA in GC cell lines. RESULTS: The m6A-LPS containing nine hub lncRNAs was built, which possessed a superior capability to predict the outcomes of GC patients. Meanwhile, we found an intimate correlation between the m6A-LPS and tumor infiltrating cells, and that the low-risk group had a higher expression of immune checkpoints and responsed more to immunotherapy than the high-risk group. Clinically, these crucial lncRNAs expression levels were verified in ten pairs of GC samples. In in vitro experiments, the abilities of migration and proliferation were significantly enhanced via downregulating the lncRNA AC026691.1. Both migrative and proliferative capabilities of tumor cells were significantly enhanced via downregulating the lncRNA AC026691.1. in vitro. CONCLUSIONS: Collectively, the m6A-LPS could provide a novel prediction insight into the prognosis of GC patients and serve as an independent clinical factor for GC. These m6A-related lncRNAs might remodel the tumor microenvironment and affect the anti-cancer ability of immune checkpoint blockers. Importantly, lncRNA AC026691.1 could inhibit both migration and proliferation of GC by means of FTO regulation.

The prognosis of epilepsy patients with CACNA1H missense variants: A longitudinal cohort study.

OBJECTIVE: CACNA1H is regarded as a gene conferring susceptibility to generalised epilepsy. However, the prognosis of epilepsy patients carrying the CACNA1H missense variants of uncertain significance (VUS) is unknown. A prospective cohort was generated to determine the deleterious effects of these variants and to check whether the presence of these variants affects the prognosis of epilepsy patients. METHOD: This study was conducted at Xijing Hospital in Xian, China. All patients were followed up for at least 1 year. Previous reports were searched for previously reported variants. Ensembl database was searched for variants in the general population. Combined Annotation Dependent Depletion (CADD) was used to evaluate the deleterious effect of variants. Logistic regression and Cox regression were used for data analysis. RESULTS: The study included 176 epilepsy patients with or without CACNA1H variants. In epilepsy patients with missense variants, we found 35 different variants, including 33 variants with uncertain significance and 2 likely benign variants. No significant difference was observed between the distribution of CADD scores of the variants from this cohort, of the general population, and of those found in previous reports. Among epilepsy patients with missense variants, the number of antiepileptic drugs (AEDs) administered to the patients, a first-degree family history of epilepsy, and possibly the presence of abnormalities in brain radiology findings were correlated with the poorer prognosis. Among the entire cohort, the type of epilepsy, number of AEDs administered, and presence of abnormalities in brain radiology findings were associated with the prognosis of these patients. The deleterious effect of CACNA1H missense variants or their presence was not related to the prognosis of epilepsy patients. CONCLUSION: The results of our study suggest that CACNA1H variants are related to multiple epilepsy syndromes. However, there is no strong evidence of the correlation between CACNA1H missense variants and a certain type of epilepsy. In our study cohort, both the deleterious effects and the presence of CACNA1H variants were found to be unrelated to the prognosis of patients with epilepsy. These findings suggest that CACNA1H missense variants that are classified as VUS might not influence the outcome of epilepsy.

Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best?

PURPOSE: This study aims to incorporate informative histogram indicator analyses and advanced multimodal MRI parameters to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to explore the features associated with patients' survival. PATIENTS AND METHODS: A total of 120 patients with pathologically confirmed LGGs or HGGs receiving conventional and advanced MRI such as three-dimensional arterial spin labeling (3D-ASL), intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) were included. The mean and histogram indicators from advanced MRI were calculated from the entire tumor. The efficacies of a single indicator or multiple parameters were tested in distinguishing HGGs from LGGs and predicting patients' survival. Receiver operating characteristic (ROC) curve and multivariable stepwise logistic regression were used to evaluate the diagnostic efficacies. Leave-one-out cross-validation was further used to validate the accuracy of the parameter sets in glioma grading. Log-rank test using the Kaplan-Meier curve was utilized to predict patients' survival. RESULTS: Overall, parameters from DCE-MRI performed better than those from 3D-ASL or IVIM-DWI in both glioma grading and survival prediction. The histogram metrics of Ve were demonstrated to have higher accuracies (the accuracies for Extended Tofts_Ve mean and Extended Tofts_Ve median were 68.33% and 71.67%, respectively, while those for the Incremental_Ve mean and Incremental_Ve 75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_Ve histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_Ktrans 95th (AUC = 0.9265) and Extended Tofts_Ve 95th (AUC = 0.9154) performed better than their corresponding means (Patlak_Ktrans mean: AUC = 0.9118 and Extended Tofts_Ve mean: AUC = 0.9044) in predicting patients' overall survival (OS) at 18-month follow-up. CONCLUSION: DCE-MRI-derived histogram features from the entire tumor were promising metrics for glioma grading and OS prediction. Combining single modal histogram features improved glioma grading. TRIAL REGISTRATION: NCT02622620.

Neurovascular decoupling in type 2 diabetes mellitus without mild cognitive impairment: Potential biomarker for early cognitive impairment.

Type 2 diabetes mellitus (T2DM) is a significant risk factor for mild cognitive impairment (MCI) and the acceleration of MCI to dementia. The high glucose level induce disturbance of neurovascular (NV) coupling is suggested to be one potential mechanism, however, the neuroimaging evidence is still lacking. To assess the NV decoupling pattern in early diabetic status, 33 T2DM without MCI patients and 33 healthy control subjects were prospectively enrolled. Then, they underwent resting state functional MRI and arterial spin labeling imaging to explore the hub-based networks and to estimate the coupling of voxel-wise cerebral blood flow (CBF)-degree centrality (DC), CBF-mean amplitude of low-frequency fluctuation (mALFF) and CBF- mean regional homogeneity (mReHo). We further evaluated the relationship between NV coupling pattern and cognitive performance (false discovery rate corrected). T2DM without MCI patients displayed significant decrease in the absolute CBF-mALFF, CBF-mReHo coupling of CBFnetwork and in the CBF-DC coupling of DCnetwork. Besides, networks which involved CBF and DC hubs mainly located in the default mode network (DMN). Furthermore, less severe disease and better cognitive performance in T2DM patients were significantly correlated with higher coupling of CBF-DC, CBF-mALFF or CBF-mReHo, especially for the cognitive dimensions of general function and executive function. Thus, coupling of CBF-DC, CBF-mALFF and CBF-mReHo may serve as promising indicators to reflect NV coupling state and to explain the T2DM related early cognitive impairment.