TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples.

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://github.com/hello-json/TMBstable for academic usage only.

PEcnv: accurate and efficient detection of copy number variations of various lengths.

Copy number variation (CNV) is a class of key biomarkers in many complex traits and diseases. Detecting CNV from sequencing data is a substantial bioinformatics problem and a standard requirement in clinical practice. Although many proposed CNV detection approaches exist, the core statistical model at their foundation is weakened by two critical computational issues: (i) identifying the optimal setting on the sliding window and (ii) correcting for bias and noise. We designed a statistical process model to overcome these limitations by calculating regional read depths via an exponentially weighted moving average strategy. A one-run detection of CNVs of various lengths is then achieved by a dynamic sliding window, whose size is self-adopted according to the weighted averages. We also designed a novel bias/noise reduction model, accompanied by the moving average, which can handle complicated patterns and extend training data. This model, called PEcnv, accurately detects CNVs ranging from kb-scale to chromosome-arm level. The model performance was validated with simulation samples and real samples. Comparative analysis showed that PEcnv outperforms current popular approaches. Notably, PEcnv provided considerable advantages in detecting small CNVs (1 kb-1 Mb) in panel sequencing data. Thus, PEcnv fills the gap left by existing methods focusing on large CNVs. PEcnv may have broad applications in clinical testing where panel sequencing is the dominant strategy. Availability and implementation: Source code is freely available at https://github.com/Sherwin-xjtu/PEcnv.

The study findings demonstrated a significant association between C-reactive protein levels and trabecular bone score : NHANES 2005-2008.

OBJECTIVES: The association between C-reactive protein and bone density has been primarily investigated in previous studies, with little to no research investigating its relationship with total bone trabecular score. METHODS: Data from the NHANES database (500 males and 633 females) were utilized in this study to perform a multiple weighted linear regression analysis to estimate this relationship of CRP and TBS. Subsequently, population characterization, univariate logistic regression analysis, subgroup and interaction analysis were in progress. RESULTS: Upon covariate adjustment, the analysis revealed a notable negative correlation between CRP and TBS(ß=-0.0081,95% CI (-0.0142, -0.0019), P = 0.009). Furthermore, no interactions were detected within any subgroups. CONCLUSION: This finding enhances our comprehension of the relationship in inflammation and bone health, offering the novel research outlook for the treatment and prevention of osteoporosis and osteoporotic fractures.

Selection of endovascular treatment strategies and analysis of the efficacy of different locations and types of splenic artery aneurysms.

PURPOSE: To analyze the selection of endovascular treatment strategies and the efficacy of various locations and types of splenic artery aneurysms (SAAs). METHODS: Sixty-three cases of patients diagnosed with SAA from January 2016 to October 2021 were collected, and their clinical data and follow-up results were analyzed. RESULTS: Among the 63 patients, 55 had true SAAs, and 8 had false SAAs. The average diameter of the true SAAs was 2.0 ± 0.8 cm. There were 10 cases of intra-aneurysm embolization, 24 cases of intra-aneurysm and aneurysm-bearing artery embolization, 10 cases of bare stent-assisted coil embolization, and 11 cases of stent grafts. The false SAAs had an average diameter of 2.3 ± 1.1 cm. Aneurysm-bearing artery embolization was applied in 5 cases, and stent grafts were applied in 3 cases. The incidence of complications after embolization of the aneurysm-bearing artery was higher (P < 0.01). Postembolization syndrome occurred in 10 patients; 7 patients developed splenic infarction to varying degrees, 1 patient had mildly elevated blood amylase, and 1 patient developed splenic necrosis with abscess formation, all of which improved after active treatment. The average length of hospital stay was 5.5 ± 3.2 days. The average follow-up time was 17.2 ± 16.1 months, and the aneurysm cavity of all patients was completely thrombotic. CONCLUSION: Endovascular treatments of SAAs are safe and effective. For various locations and types of SAAs, adequate selection of treatment is necessary. Stent grafts are recommended for their safety, economy, practicality, and preservation of the physiological functions of the human body.

Comparison of core needle biopsy and fine-needle aspiration methods in CT-guided percutaneous sampling of pancreatic tumors.

Aims: To compare the diagnostic efficacy and safety of CT-guided percutaneous core needle biopsy (CNB) and fine-needle aspiration (FNA) for pancreatic lesions. Methods and Material: A total of 176 patients with 176 pancreatic lesions who visited our hospital between January 2016 and March 2021 were retrospectively analyzed. They were divided into three groups: FNA group A (<1.5 cm between the lesion and great vessels necessitating FNA), FNA group B, and CNB (the latter two with ≥1.5 cm between the lesion and great vessels necessitating FNA). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and postoperative. The statistical analysis was done using Statistical Package for the Social Sciences version 17.0. Results: One hundred and seventy six patient's specimens all met the requirements. There were no statistically significant differences in sensitivity, specificity, positive predictive value, negative predictive value, and accuracy between the CNB group and FNA group B, (P > 0.05). Thirteen samples submitted for genetic testing (5 in CNB group, 4 in each of the FNA groups A and B) all met the standards of next-generation sequencing gene detection. The main complications of these groups included abdominal pain, fever, and hyperamylasemia. Conclusions: CT-guided percutaneous FNA and CNB have similar diagnostic efficacy for pancreatic biopsy. Furthermore, FNA has a wide range of puncture indications and is very safe. Like CNB, the obtained tissue through FNA can be genetically tested to guide clinical treatment.

A nomogram model based on the combination of the systemic immune-inflammation index, body mass index, and neutrophil/lymphocyte ratio to predict the risk of preoperative deep venous thrombosis in elderly patients with intertrochanteric femoral fracture: a retrospective cohort study.

OBJECTIVES: Deep vein thrombosis (DVT) has been considered as a frequent and serious consequence of intertrochanteric femoral fractures in the elderly. Several negative repercussions of DVT can be considerably mitigated by its timely recognition and treatment. The current work was aimed at exploring the factors independently predicting DVT among cases suffering from intertrochanteric femoral fractures and validate their predictive usefulness in diagnosing DVT. METHODS: Between April 2017 and July 2022, clinical information from 209 cases showing preoperative DVT for femoral intertrochanteric fractures were retrospectively evaluated. In patients with femoral intertrochanteric fractures, logistic regression analysis with a backward stepwise method was adopted for detecting independent predictors for the diagnosis of preoperative DVT. Using multivariate logistic regression, a nomogram prediction model was developed and verified with the testing group. RESULTS: According to multivariate logistic regression model, body mass index (BMI) (OR 0.79, 95% CI 0.63-0.99, P = 0.042), neutrophil/lymphocyte ratio (NLR) (OR 7.29, 95% CI 1.53, 34.64, P = 0.0012), and systemic immune-inflammation index (SII) (OR 6.61, 95% CI 2.35, 18.59, P = 0.001) were independent predictors for DVT before surgery among cases developing intertrochanteric femoral fracture. AUC values were 0.862 and 0.767 for training and testing groups, separately, while their mean errors in the calibration curve were 0.027 and 0.038 separately. Decision curve analysis (DCA) curve revealed a high value of clinical application for both groups. CONCLUSION: Upon admission, BMI, NLR, and SII are independent predictors of DVT before surgery among cases developing intertrochanteric femoral fractures. Additionally, the nomogram based on the BMI, NLR, and SII can assist clinicians in determining if preventive and symptomatic therapies are required to improve DVT prognosis and reduce its associated mortality.

What makes TMB an ambivalent biomarker for immunotherapy? A subtle mismatch between the sample-based design of variant callers and real clinical cohort.

Tumor mutation burden (TMB) is a widely recognized biomarker for predicting the efficacy of immunotherapy. However, its use still remains highly controversial. In this study, we examine the underlying causes of this controversy based on clinical needs. By tracing the source of the TMB errors and analyzing the design philosophy behind variant callers, we identify the conflict between the incompleteness of biostatistics rules and the variety of clinical samples as the critical issue that renders TMB an ambivalent biomarker. A series of experiments were conducted to illustrate the challenges of mutation detection in clinical practice. Additionally, we also discuss potential strategies for overcoming these conflict issues to enable the application of TMB in guiding decision-making in real clinical settings.

Cytokine concentration in peripheral blood of patients with colorectal cancer.

Introduction: The role of tumour secretory cytokines and peripheral circulatory cytokines in tumour progression has received increasing attention; however, the role of tumour-related inflammatory cytokines in colorectal cancer (CRC) remains unclear. In this study, the concentrations of various cytokines in the peripheral blood of healthy controls and patients with CRC at different stages were compared. Methods: Peripheral blood samples from 4 healthy participants and 22 colorectal cancer patients were examined. Luminex beads were used to evaluate concentration levels of 40 inflammatory cytokines in peripheral blood samples. Results: In peripheral blood, compared with healthy controls and early stage (I + II) CRC patients, advanced CRC (III + IV) patients had increased concentrations of mononuclear/macrophage chemotactic-related proteins (CCL7, CCL8, CCL15, CCL2, and MIF), M2 polarization-related factors (IL-1ß, IL-4), neutrophil chemotactic and N2 polarization-related cytokines (CXCL2, CXCL5, CXCL6, IL-8), dendritic cells (DCs) chemotactic-related proteins (CCL19, CCL20, and CCL21), Natural killer (NK) cell related cytokines (CXCL9, CXCL10), Th2 cell-related cytokines (CCL1, CCL11, CCL26), CXCL12, IL-2, CCL25, and CCL27, and decreased IFN-γ and CX3CL1 concentrations. The differential upregulation of cytokines in peripheral blood was mainly concentrated in CRC patients with distant metastasis and was related to the size of the primary tumour; however, there was no significant correlation between cytokine levels in peripheral blood and the propensity and mechanism of lymph node metastasis. Discussion: Different types of immune cells may share the same chemokine receptors and can co-localise in response to the same chemokines and exert synergistic pro-tumour or anti-tumour functions in the tumour microenvironment. Chemokines and cytokines affect tumour metastasis and prognosis and may be potential targets for treatment.

DelInsCaller: An Efficient Algorithm for Identifying Delins and Estimating Haplotypes from Long Reads with High Level of Sequencing Errors.

Delins, as known as complex indel, is a combined genomic structural variation formed by deleting and inserting DNA fragments at a common genomic location. Recent studies emphasized the importance of delins in cancer diagnosis and treatment. Although the long reads from PacBio CLR sequencing significantly facilitate delins calling, the existing approaches still encounter computational challenges from the high level of sequencing errors, and often introduce errors in genotyping and phasing delins. In this paper, we propose an efficient algorithmic pipeline, named delInsCaller, to identify delins on haplotype resolution from the PacBio CLR sequencing data. delInsCaller design a fault-tolerant method by calculating a variation density score, which helps to locate the candidate mutational regions under a high-level of sequencing errors. It adopts a base association-based contig splicing method, which facilitates contig splicing in the presence of false-positive interference. We conducted a series of experiments on simulated datasets, and the results showed that delInsCaller outperformed several state-of-the-art approaches, e.g., SVseq3, across a wide range of parameter settings, such as read depth, sequencing error rates, etc. delInsCaller often obtained higher f-measures than other approaches; specifically, it was able to maintain advantages at ~15% sequencing errors. delInsCaller was able to significantly improve the N50 values with almost no loss of haplotype accuracy compared with the existing approach as well.

HRD-MILN: Accurately estimate tumor homologous recombination deficiency status from targeted panel sequencing data.

Homologous recombination deficiency (HRD) is a critical feature guiding drug and treatment selection, mainly for ovarian and breast cancers. As it cannot be directly observed, HRD status is estimated on a small set of genomic instability features from sequencing data. The existing methods often perform poorly when handling targeted panel sequencing data; however, the targeted panel is the most popular sequencing strategy in clinical practices. Thus, we proposed HRD-MILN to overcome the computational challenges from targeted panel sequencing. HRD-MILN incorporated a multi-instance learning framework to discover as many loss of heterozygosity (LOH) associated with HRD status to cluster as possible. Then the HRD score is obtained based on the association between the LOHs and the cluster in the sample to be estimated, and finally, the HRD status is estimated based on the score. In comparison experiments on targeted panel sequencing data, the Precision of HRD-MILN could achieve 87%, significantly improved from 63% reported by the existing methods, where the highest margin of improvement reached 14%. It also presented advantages on whole exome sequencing data. Based on our best knowledge, HRD-MILN is the first practical tool for estimating HRD status from targeted panel sequencing data and could benefit clinical applications.

Is an SV caller compatible with sequencing data? An online recommendation tool to automatically recommend the optimal caller based on data features.

A lot of bioinformatics tools were released to detect structural variants from the sequencing data during the past decade. For a data analyst, a natural question is about the selection of a tool fits for the data. Thus, this study presents an automatic tool recommendation method to facilitate data analysis. The optimal variant calling tool was recommended from a set of state-of-the-art bioinformatics tools by given a sequencing data. This recommendation method was implemented under a meta-learning framework, identifying the relationships between data features and the performance of tools. First, the meta-features were extracted to characterize the sequencing data and meta-targets were identified to pinpoint the optimal caller for the sequencing data. Second, a meta-model was constructed to bridge the meta-features and meta-targets. Finally, the recommendation was made according to the evaluation from the meta-model. A series of experiments were conducted to validate this recommendation method on both the simulated and real sequencing data. The results revealed that different SV callers often fit different sequencing data. The recommendation accuracy averaged more than 80% across all experimental configurations, outperforming the random- and fixed-pick strategy. To further facilitate the research community, we incorporated the recommendation method into an online cloud services for genomic data analysis, which is available at https://c.solargenomics.com/ via a simple registration. In addition, the source code and a pre-trained model is available at https://github.com/hello-json/CallerRecommendation for academic usages only.

Percutaneous Ablation of Hepatic Tumors at the Hepatocaval Confluence Using Irreversible Electroporation: A Preliminary Study.

BACKGROUND: Tumors at the hepatocaval confluence are difficult to treat, either surgically or ablatively. METHODS: A retrospective longitudinal study on patients ineligible for thermal ablation who underwent computed tomography-guided IRE for hepatic tumors at the hepatocaval confluence was conducted. Factors analyzed included patient and tumor characteristics, IRE procedure details, treatment-related complications, and prognosis. RESULTS: Between 2017 and 2021, 21 patients at our institute received percutaneous IRE. Of the 38 lesions, 21 were at the hepatocaval confluence. Complete ablation was achieved in all cases. Local and distant recurrence was observed in 4.8% (1/21) and 42.6% (9/21) of the ablated tumors, respectively. All postcava remained perfused at follow-up, except for 1 (4.8%) hepatic vein near the lesion found to be temporarily occluded and restored within 1 month. The ratio of the maximum diameter of ablation area at 1, 3, and 6 months post procedure compared to that immediately after IRE was 0.68 (0.50-0.84), 0.49 (0.27-0.61), and 0.38 (0.25-0.59), respectively. Progression-free survival of the patients with recurrence was 121 (range, 25-566) days. Four (19.0%) patients died at the end of follow-up with median overall survival of 451.5 (range, 25-716) days. CONCLUSIONS: IRE could be a safe and effective treatment for hepatic tumors at the hepatocaval confluence. This article provides valuable prognostic data; further clinical research is needed for better prognosis.

Overexpression of CXCR2 predicts poor prognosis in patients with colorectal cancer.

Colorectal cancer is a heterogeneous disease. Although many risk factors are used to predict colorectal cancer patients' prognosis after surgical resection, new prognostic factors are still needed to be defined to promote predictive efficacy of prognosis and further guide therapies. Herein, we identified the prognostic significance of CXCR2 in colorectal cancer patients. We retrospectively analysed 134 patients with colorectal cancer who underwent minimally invasive surgery between 2010 and 2011. The overall cohort was divided into a training set (n = 78) and a validation set (n = 56). We detected CXCR2 expression using immunohistochemical staining and defined the cut-off value using X-tile program. Next, we analysed the association between CXCR2 expression and clinicopathologic features in training and validation sets. High expression of CXCR2 was associated with Dukes stage (P = 0.018), tumor invasion (P = 0.018) and liver metastasis (P = 0.047). Multivariate COX regression analyses confirmed that high CXCR2 level was an independent prognostic risk factor for both overall survival and disease free survival. Kaplan-Meier survival analysis demonstrated that patients with high expression of CXCR2 had a poor overall survival and disease free survival even in low-risk group (I + II). This indicated that CXCR2 can help to refine individual risk stratification. In addition, we established Nomograms of all significant factors to predict 3- or 5-years overall survival and disease free survival. Moreover, we found the combination of CXCR2 and its ligand CXCL5 had more significant value in predicting the prognosis than single CXCR2 factor.