Impact of Washing Processes on RNA Quantity and Quality in Patient-Derived Colorectal Cancer Tissues

Overexpression of TSPAN8 in consensus molecular subtype 3 colorectal cancer

BACKGROUND

Recently, consensus molecular subtypes (CMSs) have been proposed as a robust transcriptome-based classification system for colorectal cancer (CRC). Tetraspanins (TSPANs) are transmembrane proteins. They have been associated with the development of numerous malignancies, including CRC, through their role as "master organizers" for multi-molecular membrane complexes. No previous study has investigated the correlation between TSPANs and CMS classification. Herein, we investigated the expression of TSPANs in patient-derived primary CRC tissues and their CMS classifications.

METHODS

RNA samples were derived from primary CRC tissues (n = 100 patients diagnosed with colorectal adenocarcinoma) and subjected to RNA sequencing for transcriptome-based CMS classification and TSPAN-relevant analyses. Immunohistochemistry (IHC) and immunofluorescence (IF) stains were conducted to observe the protein expression level. To evaluate the relative biological pathways, gene-set enrichment analysis was performed.

RESULTS

Of the highly expressed TSPAN genes in CRC tissues (TSPAN8, TSPAN29, and TSPAN30), TSPAN8 was notably overexpressed in CMS3-classified primary tissues. The overexpression of TSPAN8 protein in CMS3 CRC was also observed by IHC and IF staining. As a result of gene-set enrichment analysis, TSPAN8 may potentially play a role in organizing signaling complexes for kinase-based metabolic deregulation in CMS3 CRC.

CONCLUSIONS

The present study reports the overexpression of TSPAN8 in CMS3 CRC. This study proposes TSPAN8 as a subtype-specific biomarker for CMS3 CRC. This finding provides a foundation for future CMS-based studies of CRC, a complex disease and the second leading cause of cancer mortality worldwide.

Deep volcanic residual U-Net for nodal metastasis (Nmet) identification from lung cancer

Lymph node metastasis detections are more clinically significant task associated with the presence and reappearance of lung cancer. The development of the computer-assisted diagnostic approach has greatly supported the diagnosis of human disorders in the field of medicine including lung cancer. Lung cancer treatment is possible if it is detected at the initial stage. Radiologists have great difficulty identifying and categorizing lung cancers in the initial phase. So, several methods were used to predict the lung cancer but does not provide accurate solutions with increased error rate. To overcome these issues, a Deep Volcanic Residual U-Net (DVR U-Net) for nodal metastasis is proposed in this manuscript which identifies the LC accurately in the early stage. Initially, the input images are taken from two datasets. After that, these input data are pre-processed using Anisotropic Diffusion Filter with a Fuzzy based Contrast-Limited Adaptive Histogram Equalization (ADFFCLAHE) method. Then the pre-processed images are given to the DVR U-Net to segment and extract the volume of interest for estimating the nodal stage of each volume of interest. Finally, DVR U-Net effectively detects and classifies the N + (nodal metastasis) or N- (non-nodal metastasis). The introduced method attains 99.9% higher accuracy as compared with the existing methods. Also, the statistical analysis of the Shapiro-Wilk test, Friedman test and Wilcoxon Signed-Rank test are executed to prove the statistical effectiveness of the implemented method.

Exploring extracellular matrix and prostaglandin pathway alterations across varying resection margin distances of right-sided colonic adenocarcinoma

BACKGROUND

Surgical resection followed by indicated adjuvant therapy offers potential curative treatment in colonic adenocarcinoma. Beyond the well-established seed and soil theory of colon cancer progression, the 'normal-appearing' tissues near the tumor are not genuinely normal and remain as remnants in patients following surgery. Our objective was to elucidate the alteration of gene expression and pathways across various distances of resection margins in right-sided colonic adenocarcinoma.

METHODS

Twenty-seven fresh samples of primary cancer and 56 matched non-tumor tissues adjacent to the tumor (NAT) were collected from patients with resectable right-sided colon cancer. NAT were systematically obtained at varying distances (1, 5, and 10 cm) on both proximal and distal sides. Comprehensive gene expression analysis was performed using 770-gene PanCancer Progression Panel, delineating distinctive pathways and functional predictions for each region.

RESULTS

Distinctive gene signatures and pathways exhibited by normal-appearing tissues were discovered at varying distances from cancer. Notably, SFRP2, PTGDS, COL1A1, IL1B, THBS2, PTGIS, COL1A2, NPR1, and BGN were upregulated, while ENPEP, MMP1, and NRCAM were downregulated significantly in 1-cm tissue compared to farther distances. Substantial alterations in the extracellular matrix (ECM) and prostaglandin/thromboxane synthesis were significantly evident at the 1-cm distance. Functional analysis indicated enhanced cell viability and survival, alongside reduced cellular death and apoptosis.

CONCLUSIONS

Different distances exerted a significant impact on gene alteration within the normal-looking mucosa surrounding primary cancer, influenced by various mechanisms. These findings may highlight potential therapeutic targets related to the ECM and prostaglandin/thromboxane pathways for treatment strategies.