Lowered expression level of INSC predicts poor prognosis in patients with colon cancer

The aim of this study was to investigate the prognostic value of inscuteable spindle orientation adaptor protein (INSC) in colon cancer (CC). Firstly, transcriptional change of INSC was analysed using the data from public databases. Next, INSC protein expression was assessed by immunohistochemistry. Its correlation with clinicopathological features and the prognostic values of patients were also investigated. Then, an INSC-based nomogram was built to predict CC prognosis. Compared to normal tissues, INSC was significantly downregulated at the transcriptional level in CC tissues. A low INSC mRNA level not only positively correlated with TNM stage (tumour-nodus-metastases), advanced T stage, and N stage, but also with the shorter 5- and 8-year overall survival (OS) and disease-specific survival. Concerning protein level, INSC downregulation was confirmed in CC samples. In terms of the correlation with N stage and 5- and 8-year OS, it was also consistent with mRNA levels. Cox regression analysis indicated that INSC protein expression was an independent prognostic factor for OS. The nomogram showed better prognostic accuracy and clinical net benefit for 5-year OS than TNM staging. Altogether, downregulation of INSC is related to inferior clinicopathological features and patient outcomes, and it may be a novel independent prognostic biomarker in CC

Aging characteristics of colorectal cancer based on gut microbiota

Abstract Background Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). Methods A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age‐differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age‐differentiated bacteria. Results The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age‐related differential bacteria and model of CRC patients based on the 58 age‐related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease‐differentiated bacteria and the age‐differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85). Conclusion Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC

The anatomical areas that showed significantly reduced FAvalues in aMCI compared to normal controls (TFCE- corrected, p<0.0001).

<p>The anatomical areas that showed significantly reduced FAvalues in aMCI compared to normal controls (TFCE- corrected, p<0.0001).</p

Demographic Characteristics of both groups.

<p>Demographic Characteristics of both groups.</p

The regions having significantly reduced FA values in the aMCI group compared to the control group (TFCE corrected, p<0.0001).

<p>The regions having significantly reduced FA values in the aMCI group compared to the control group (TFCE corrected, p<0.0001).</p

The regions having significantly increased MD values in the aMCI group compared to the control group (TFCE corrected, p<0.05).

<p>The regions having significantly increased MD values in the aMCI group compared to the control group (TFCE corrected, p<0.05).</p

The anatomical regions that showed significantly increased MD values in aMCI compared to normal controls (TCFE-corrected, p<0.0001).

<p>The anatomical regions that showed significantly increased MD values in aMCI compared to normal controls (TCFE-corrected, p<0.0001).</p

The regions having significantly increased MD values in the aMCI group compared to the control group (TFCE corrected, p<0.0001).

<p>The regions having significantly increased MD values in the aMCI group compared to the control group (TFCE corrected, p<0.0001).</p