Data pre-processing for analyzing microbiome data - A mini review.

The human microbiome is an emerging research frontier due to its profound impacts on health. High-throughput microbiome sequencing enables studying microbial communities but suffers from analytical challenges. In particular, the lack of dedicated preprocessing methods to improve data quality impedes effective minimization of biases prior to downstream analysis. This review aims to address this gap by providing a comprehensive overview of preprocessing techniques relevant to microbiome research. We outline a typical workflow for microbiome data analysis. Preprocessing methods discussed include quality filtering, batch effect correction, imputation of missing values, normalization, and data transformation. We highlight strengths and limitations of each technique to serve as a practical guide for researchers and identify areas needing further methodological development. Establishing robust, standardized preprocessing will be essential for drawing valid biological conclusions from microbiome studies.

Clinical atlas of rectal cancer highlights the barriers and insufficient interventions underlying the unfavorable outcomes in older patients.

Background: Aging confers an increased risk of developing cancer, and the global burden of cancer is cumulating as human longevity increases. Providing adequate care for old patients with rectal cancer is challenging and complex. Method: A total of 428 and 44,788 patients diagnosed with non-metastatic rectal cancer from a referral tertiary care center (SYSU cohort) and the Surveillance Epidemiology and End Results database (SEER cohort) were included. Patients were categorized into old (over 65 years) and young (aged 50-65 years) groups. An age-specific clinical atlas of rectal cancer was generated, including the demographic and clinicopathological features, molecular profiles, treatment strategies, and clinical outcomes. Results: Old and young patients were similar in clinicopathological risk factors and molecular features, including TNM stage, tumor location, tumor differentiation, tumor morphology, lymphovascular invasion, and perineural invasion. However, old patients had significantly worse nutritional status and more comorbidities than young patients. In addition, old age was independently associated with less systemic cancer treatment (adjusted odds ratio 0.294 [95% CI 0.184-0.463, P < 0.001]). We found that old patients had significantly worse overall survival (OS) outcomes in both SYSU (P < 0.001) and SEER (P < 0.001) cohorts. Moreover, the death and recurrence risk of old patients in the subgroup not receiving chemo/radiotherapy (P < 0.001 for OS, and P = 0.046 for time to recurrence [TTR]) reverted into no significant risk in the subgroup receiving chemo/radiotherapy. Conclusions: Although old patients had similar tumor features to young patients, they had unfavorable survival outcomes associated with insufficient cancer care from old age. Specific trials with comprehensive geriatric assessment for old patients are needed to identify the optimal treatment regimens and improve unmet cancer care. Study registration: The study was registered on the research registry with the identifier of researchregistry 7635.

Accurate tumor segmentation and treatment outcome prediction with DeepTOP.

BACKGROUND: Accurate outcome prediction prior to treatment can facilitate trial design and clinical decision making to achieve better treatment outcome. METHOD: We developed the DeepTOP tool with deep learning approach for region-of-interest segmentation and clinical outcome prediction using magnetic resonance imaging (MRI). DeepTOP was constructed with an automatic pipeline from tumor segmentation to outcome prediction. In DeepTOP, the segmentation model used U-Net with a codec structure, and the prediction model was built with a three-layer convolutional neural network. In addition, the weight distribution algorithm was developed and applied in the prediction model to optimize the performance of DeepTOP. RESULTS: A total of 1889 MRI slices from 99 patients in the phase III multicenter randomized clinical trial (NCT01211210) on neoadjuvant treatment for rectal cancer was used to train and validate DeepTOP. We systematically optimized and validated DeepTOP with multiple devised pipelines in the clinical trial, demonstrating a better performance than other competitive algorithms in accurate tumor segmentation (Dice coefficient: 0.79; IoU: 0.75; slice-specific sensitivity: 0.98) and predicting pathological complete response to chemo/radiotherapy (accuracy: 0.789; specificity: 0.725; and sensitivity: 0.812). DeepTOP is a deep learning tool that could avoid manual labeling and feature extraction and realize automatic tumor segmentation and treatment outcome prediction by using the original MRI images. CONCLUSION: DeepTOP is open to provide a tractable framework for the development of other segmentation and predicting tools in clinical settings. DeepTOP-based tumor assessment can provide a reference for clinical decision making and facilitate imaging marker-driven trial design.

Role of Human Mesangial-Tubular Crosstalk in Secretory IgA-Induced IgA Nephropathy.

BACKGROUND: IgA nephropathy (IgAN) is characterized by the mesangial deposition of pathogenic IgA. We previously detected the deposition of pathogenic secretory IgA (SIgA) in the mesangium of about one-third of IgAN patients. Tubulointerstitial injury has an important role in the development of IgAN. However, the relationship between SIgA and tubulointerstitial damage is currently unclear. In this work, the role of the mesangial-tubular crosstalk was explored in the tubulointerstitial damage in SIgA-induced IgAN. METHODS: SIgA deposition in renal tissues of IgAN patients was detected by immunofluorescence. Flow cytometry was used to assess the binding of SIgA to human renal mesangial cells (HRMC) and human proximal tubule epithelial (HK-2) cells. HK-2 was co-cultured with HRMC added with SIgA isolated from patients or normal volunteers. Protein synthesis and gene expressions of TNF-α, TGF-ß1, and MCP-1 were determined by ELISA and PCR, respectively. The expressions of the above cytokines in renal tissues of patients and normal controls were detected by immunohistochemistry. RESULTS: Twenty-nine of 96 patients had SIgA deposition in the mesangium, but SIgA was rarely detected in the tubulointerstitium. The binding rate of SIgA to HK-2 (2.79%) was significantly lower than that of HRMC (81.6%) (p < 0.001). The expressions of TNF-α, TGF-ß1, and MCP-1 in HRMC were significantly higher than in SIgA-stimulated HK-2 (p < 0.05), and their expressions were significantly higher in the SIgA-stimulated co-culture group compared with SIgA-stimulated HRMC (p < 0.05). The expressions of the above cytokines were mainly detected in tubulointerstitium of IgAN patients with positive and negative SIgA deposition, without significant difference between the 2 groups, but to a significantly higher level than that in normal controls, and their expressions positively correlated with tubulointerstitial injury. CONCLUSION: Inflammatory factors released from the mesangium after SIgA deposition might mediate tubulointerstitial damage via mesangial-tubular crosstalk in IgAN.

The TLR4-MyD88-NF-κB pathway is involved in sIgA-mediated IgA nephropathy.

Previous studies have shown that secretory IgA (sIgA) was critically involved in IgA nephropathy (IgAN) immune responses. Toll-like receptors (TLRs), especially TLR4 which participates in mucosal immunity, may be involved in the pathogenesis of IgAN. The purpose of this study was to investigate whether sIgA and TLR4 interact to mediate kidney damage in IgAN patients. IgAN patients with positive sIgA deposition in renal tissues were screened by immunofluorescence assay. Patient salivary sIgA (P-sIgA) was collected and purified by jacalin affinity chromatography. Salivary sIgA from healthy volunteers was used as a control (N-sIgA). Expression of TLR4, MyD88, NF-κB, TNF-α, IL-6, and MCP-1 were detected in the mesangial area of IgAN patients by immunohistochemistry, the expression levels in patients with positive sIgA deposition were higher than that with negative sIgA deposition. Human renal mesangial cells (HRMCs) were cultured in vitro, flow cytometry showed that P-sIgA bound HRMCs significantly better than N-sIgA. HRMCs were cultured in the presence of sIgA (400 µg/mL) for 24 h, compared with cells cultured with N-sIgA, HRMCs cultured in vitro with P-sIgA showed enhanced expression of TLR4, increased secretion of TNF-α, IL-6, and MCP-1, and increased expression of MyD88/NF-κB. TLR4 shRNA silencing and NF-κB inhibition both reduced the ability of HRMCs to synthesize TNF-α, IL-6, and MCP-1. Our results indicate that sIgA may induce high expression of TLR4 in HRMCs and further activate downstream signalling pathways, prompting HRMCs to secrete multiple cytokines and thereby mediating kidney damage in IgAN patients.