Construction of a co-expression network and prediction of metastasis markers in colorectal cancer patients with liver metastasis

Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis

The task of predicting liver tumors is critical as part of medical image analysis and genomics area since diagnosis and prognosis are important in making correct medical decisions. Silent characteristics of liver tumors and interactions between genomic and imaging features are also the main sources of challenges toward reliable predictions. To overcome these hurdles, this study presents two integrated approaches namely, - Attention-Guided Convolutional Neural Networks (AG-CNNs), and Genomic Feature Analysis Module (GFAM). Spatial and channel attention mechanisms in AG-CNN enable accurate tumor segmentation from CT images while providing detailed morphological profiling. Evaluation with three control databases TCIA, LiTS, and CRLM shows that our model produces more accurate output than relevant literature with an accuracy of 94.5%, a Dice Similarity Coefficient of 91.9%, and an F1-Score of 96.2% for the Dataset 3. More considerably, the proposed methods outperform all the other methods in different datasets in terms of recall, precision, and Specificity by up to 10 percent than all other methods including CELM, CAGS, DM-ML, and so on.•Utilization of Attention-Guided Convolutional Neural Networks (AG-CNN) enhances tumor region focus and segmentation accuracy.•Integration of Genomic Feature Analysis (GFAM) identifies molecular markers for subtype-specific tumor classification.

Single-cell RNA sequencing and spatial transcriptome reveal potential molecular mechanisms of lung cancer brain metastasis

BACKGROUND

Lung cancer is a highly aggressive and prevalent disease worldwide. By the time it is first diagnosed, distant metastases have usually already occurred. Among them, the prognosis of patients with brain metastasis from lung cancer is very poor. Therefore, it is particularly important to identify the evolutionary status of tumor cells during lung cancer brain metastases and discover the underlying mechanisms of lung cancer brain metastases.

METHODS

In this study, we analysed three types of data: single-cell RNA sequencing, bulk RNA sequencing, and spatial transcriptome. Firstly, we identified early metastatic epithelial cell clusters (EMEC) using CNV and trajectory analysis in scRNA-seq data. Secondly, we integrated scRNA-seq and spatial transcriptome data with the help of MIA (Multimodal intersection analysis) to explore the biological characteristics of EMEC. Finally, we used bulk RNA-seq data to validate the molecular characteristics of EMEC.

RESULT

A total of 55,763 single cells were obtained and divided into 9 cell types. In brain metastasis, we found a significantly higher proportion of epithelial cells. In addition, we identified a specific subpopulation of epithelial cells, which was named as "early metastatic epithelial cell clusters (EMEC)". It is enriched in oxidative phosphorylation, coagulation, complement. Moreover, we also found that EMEC underwent cellular communication with other immune cells through ligand-receptor pairs such as MIF-(CD74 + CXCR4) and MIF-(CD74 + CD44). Next, we validated that EMEC were associated with poor clinical prognosis using three independent external datasets. Finally, spatial transcriptome analysis revealed specificity in the spatial distribution of EMEC, which shifted from the peripheral regions to the central regions of the tumour as the depth of tumor invasion progressed.

CONCLUSION

This study reveals the potential molecular mechanisms of lung cancer brain metastasis from both single-cell and spatial transcriptomic perspectives, providing biological insights and clinical reference value for detecting patients suffering from lung cancer brain metastasis.

Prediction and verification of targets for α-hederin/oxaliplatin dual-loaded rHDL modified liposomes: Reversing effector T-cells dysfunction and improving anti-COAD efficiency in vitro and in vivo

This study tried to develop the α-Hederin/Oxaliplatin (OXA) dual-loaded rHDL (α-Hederin-OXA-rHDL) modified liposomes to improve the therapeutic index on colon adenocarcinoma (COAD). The α-Hederin-OXA-rHDL were prepared and evaluated for characterizations, accumulate to tumor tissues, and antitumor activity. A thorough investigation into oxaliplatin resistant and KRAS-mutant related hub keg genes were identified and performed to assess the prognosis role of the genetic signature in COAD. The potential immune signatures and molecular docking for verifing the predicted targets of α-Hederin-OXA-rHDL in tumor-bearing mice. Results suggested that α-Hederin-OXA-rHDL could enhance the sensitivity of oxaliplatin in HCT116/L-OHP cells via the regulation of KEAP1/NRF2 -mediated signaling and HO1 or GPX4 proteins. Furthermore, α-Hederin-OXA-rHDL regulated the predicted targets of PRDM1 interaction with miR-140-5p, efficient activing CD8 T cell to improve therapeutic response in vivo. Collectively, this work provides drug delivery with rHDL dual-loaded α-Hederin and oxaliplatin synergistically targets cancer cells and effectory T cells combating COAD.

Comprehensive Proteogenomic Profiling Reveals the Molecular Characteristics of Colorectal Cancer at Distinct Stages of Progression

Colorectal cancer is the second most common malignant tumor worldwide. Analysis of the changes that occur during colorectal cancer progression could provide insights into the molecular mechanisms driving colorectal cancer development and identify improved treatment strategies. In this study, we performed an integrated multiomic analysis of 435 trace tumor samples from 148 patients with colorectal cancer, covering nontumor, intraepithelial neoplasia (IEN), infiltration, and advanced stage colorectal cancer phases. Proteogenomic analyses demonstrated that KRAS and BRAF mutations were mutually exclusive and elevated oxidative phosphorylation in the IEN phase. Chr17q loss and chr20q gain were also mutually exclusive, which occurred predominantly in the IEN and infiltration phases, respectively, and impacted the cell cycle. Mutations in TP53 were frequent in the advanced stage colorectal cancer phase and associated with the tumor microenvironment, including increased extracellular matrix rigidity and stromal infiltration. Analysis of the profiles of colorectal cancer based on consensus molecular subtype and colorectal cancer intrinsic subtype classifications revealed the progression paths of each subtype and indicated that microsatellite instability was associated with specific subtype classifications. Additional comparison of molecular characteristics of colorectal cancer based on location showed that ANKRD22 amplification by chr10q23.31 gain enhanced glycolysis in the right-sided colorectal cancer. The AOM/DSS-induced colorectal cancer carcinogenesis mouse model indicated that DDX5 deletion due to chr17q loss promoted colorectal cancer development, consistent with the findings from the patient samples. Collectively, this study provides an informative resource for understanding the driving events of different stages of colorectal cancer and identifying the potential therapeutic targets. Significance: Characterization of the proteogenomic landscape of colorectal cancer during progression provides a multiomic map detailing the alterations in each stage of carcinogenesis and suggesting potential diagnostic and therapeutic approaches for patients.

An unusual presentation of solitary ovarian metastasis from colorectal cancer in an elderly woman: a case report

Background

There have been cases of colorectal cancer (CRC) metastasizing into the ovary. This study reports a case involving solitary ovarian metastasis (OM) from CRC, which is very rare in the absence of other pelvic and peritoneal metastases. This atypical clinical presentation added to the complexity of the diagnosis.

Case Description

We report a case of solitary OM-CRC in a 48-year-old woman. The patient underwent CRC surgery and refused follow-up after three rounds of chemotherapy. Approximately 14 months later, the patient presented with vaginal bleeding for 2 months. The magnetic resonance imaging (MRI) showed a huge solid cystic mass in the right adnexa. Intraoperatively, the right ovary was found to be enlarged and smooth without adhesions. By careful examination of the abdominal cavity, no metastatic lesions were found in the left ovary and uterus, and no seedings were found in the rest of the pelvis and abdomen. After removal of the uterus and bilateral adnexa, the histologic examination revealed metastatic adenocarcinoma of the right ovary with a considered rectal carcinoma of origin. Positive staining for multiple tumor-associated markers, which further established the primary nature of CRC. These findings support a possible diagnosis of primary CRC and ovarian metastases. The patient recovered well after the operation and no recurrence or metastasis was seen 18 months after the operation.

Conclusions

Solitary ovarian metastases from CRC can be better managed and treated by increasing clinicians' vigilance for this rare condition. This helps to improve the patient's prognosis and quality of life.

Identification of ROS and KEAP1-related genes and verified targets of α-hederin induce cell death for CRC

In this study, we analyzed and verified differentially expressed genes (DEGs) in ROS and KEAP1 crosstalk in oncogenic signatures using GEO data sets (GSE4107 and GSE41328). Multiple pathway enrichment analyses were finished based on DEGs. The genetic signature for colorectal adenocarcinoma (COAD) was identified by using the Cox regression analysis. Kaplan-Meier survival and receiver operating characteristic curve analysis were used to explore the prognosis value of specific genes in COAD. The potential immune signatures and drug sensitivity prediction were also analyzed. Promising small-molecule agents were identified and predicted targets of α-hederin in SuperPred were validated by molecular docking. Also, expression levels of genes and Western blot analysis were conducted. In total, 48 genes were identified as DEGs, and the hub genes such as COL1A1, CXCL12, COL1A2, FN1, CAV1, TIMP3, and IGFBP7 were identified. The ROS and KEAP1-associated gene signatures comprised of hub key genes were developed for predicting the prognosis and evaluating the immune cell responses and immune infiltration in COAD. α-hederin, a potential anti-colorectal cancer (CRC) agent, was found to enhance the sensitivity of HCT116 cells, regulate CAV1 and COL1A1, and decrease KEAP1, Nrf2, and HO-1 expression significantly. KEAP1-related genes could be an essential mediator of ROS in CRC, and KEAP1-associated genes were effective in predicting prognosis and evaluating individualized CRC treatment. Therefore, α-hederin may be an effective chemosensitizer for CRC treatments in clinical settings.

New treatment alternatives for primary and metastatic colorectal cancer by an integrated transcriptome and network analyses

Metastatic colorectal cancer (CRC) is still in need of effective treatments. This study applies a holistic approach to propose new targets for treatment of primary and liver metastatic CRC and investigates their therapeutic potential in-vitro. An integrative analysis of primary and metastatic CRC samples was implemented for alternative target and treatment proposals. Integrated microarray samples were grouped based on a co-expression network analysis. Significant gene modules correlated with primary CRC and metastatic phenotypes were identified. Network clustering and pathway enrichments were applied to gene modules to prioritize potential targets, which were shortlisted by independent validation. Finally, drug-target interaction search led to three agents for primary and liver metastatic CRC phenotypes. Hesperadin and BAY-1217389 suppress colony formation over a 14-day period, with Hesperadin showing additional efficacy in reducing cell viability within 48 h. As both candidates target the G2/M phase proteins NEK2 or TTK, we confirmed their anti-proliferative properties by Ki-67 staining. Hesperadinin particular arrested the cell cycle at the G2/M phase. IL-29A treatment reduced migration and invasion capacities of TGF-β induced metastatic cell lines. In addition, this anti-metastatic treatment attenuated TGF-β dependent mesenchymal transition. Network analysis suggests IL-29A induces the JAK/STAT pathway in a preventive manner.

ANGPTL2+cancer-associated fibroblasts and SPP1+macrophages are metastasis accelerators of colorectal cancer

Background

Liver metastasis (LM) is a leading cause of cancer-related deaths in CRC patients, whereas the associated mechanisms have not yet been fully elucidated. Therefore, it is urgently needed to deeply explore novel metastasis accelerators and therapeutic targets of LM-CRC.

Methods

The bulk RNA sequencing data and clinicopathological information of CRC patients were enrolled from the TCGA and GEO databases. The single-cell RNA sequencing (scRNA-seq) datasets of CRC were collected from and analyzed in the Tumor Immune Single-cell Hub (TISCH) database. The infiltration levels of cancer-associated fibroblasts (CAFs) and macrophages in CRC tissues were estimated by multiple immune deconvolution algorithms. The prognostic values of genes were identified by the Kaplan-Meier curve with a log-rank test. GSEA analysis was carried out to annotate the significantly enriched gene sets. The biological functions of cells were experimentally verified.

Results

In the present study, hundreds of differentially expressed genes (DEGs) were selected in LM-CRC compared to primary CRC, and these DEGs were significantly associated with the regulation of endopeptidase activity, blood coagulation, and metabolic processes. Then, SPP1, CAV1, ANGPTL2, and COLEC11 were identified as the characteristic DEGs of LM-CRC, and higher expression levels of SPP1 and ANGPTL2 were significantly associated with worse clinical outcomes of CRC patients. In addition, ANGPTL2 and SPP1 mainly distributed in the tumor microenvironment (TME) of CRC tissues. Subsequent scRNA-seq analysis demonstrated that ANGPTL2 and SPP1 were markedly enriched in the CAFs and macrophages of CRC tissues, respectively. Moreover, we identified the significantly enriched gene sets in LM-CRC, especially those in the SPP1+macrophages and ANGPTL2+CAFs, such as the HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION and the HALLMARK_COMPLEMENT. Finally, our in vitro experiments proved that ANGPTL2+CAFs and SPP1+macrophages promote the metastasis of CRC cells.

Conclusion

Our study selected four characteristic genes of LM-CRC and identified ANGPTL2+CAFs and SPP1+macrophages subtypes as metastasis accelerators of CRC which provided a potential therapeutic target for LM-CRC.

Identification of diagnostic biomarkers via weighted correlation network analysis in colorectal cancer using a system biology approach

Colorectal cancer (CRC) is the third most frequent cancer to be diagnosed in both females and males necessitating identification of effective biomarkers. An in-silico system biology approach called weighted gene co-expression network analysis (WGCNA) can be used to examine gene expression in a complicated network of regulatory genes. In the current study, the co-expression network of DEGs connected to CRC and their target genes was built using the WGCNA algorithm. GO and KEGG pathway analysis were carried out to learn more about the biological role of the DEmRNAs. These findings revealed that the genes were mostly enriched in the biological processes that were involved in the regulation of hormone levels, extracellular matrix organization, and extracellular structure organization. The intersection of genes between hub genes and DEmRNAs showed that DKC1, PA2G4, LYAR and NOLC1 were the clinically final hub genes of CRC.

Identification of key modules and micro RNAs associated with colorectal cancer via a weighted gene co-expression network analysis and competing endogenous RNA network analysis

Background

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide, and the incidence of CRC has increased rapidly in recent years. Due to the high invasiveness of colonoscopy and the low accuracy of alternative diagnostic methods, the diagnosis of CRC remains a serious problem. Thus, molecular biomarkers for CRC need to be identified.

Methods

In this study, RNA-sequencing data from The Cancer Genome Atlas (TCGA) database were used to identify the long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), and micro RNAs (miRNAs) that were differentially expressed between the CRC and normal tissues. Based on the gene expression and clinical features, the results of the weighted gene co-expression network analysis (WGCNA) and the binding relationships between miRNAs and lncRNAs and mRNAs were used to establish a CRC-related competing endogenous RNA (ceRNA) network.

Results

The core miRNAs (i.e., mir-874, mir-92a-1, and mir-940) in the network were identified. Among them, mir-874 was negatively correlated with the overall survival (OS) of patients. The protein-coding genes in the ceRNA network included IZUMO4, WT1, NPEPL1, TEX22, PPFIA4, and SFXN3, and the lncRNAs were LINC00858 and PRR7-AS1. These genes were significantly highly expressed in CRC according to validations in other independent data sets.

Conclusions

In conclusion, this study established a network of the co-expressed ceRNAs associated with CRC and identified the genes and miRNAs related to the prognosis of CRC patients.