Down-Regulation of an Autophagy-Related Gene SERPINA1 as a Superior Prognosis Biomarker Associates with Relapse and Distant Metastasis in Colon Adenocarcinoma

An integrated framework for prognosis prediction and drug response modeling in colorectal liver metastasis drug discovery

BACKGROUND

Colorectal cancer (CRC) is the third most prevalent cancer globally, and liver metastasis (CRLM) is the primary cause of death. Hence, it is essential to discover novel prognostic biomarkers and therapeutic drugs for CRLM.

METHODS

This study developed two liver metastasis-associated prognostic signatures based on differentially expressed genes (DEGs) in CRLM. Additionally, we employed an interpretable deep learning model utilizing drug sensitivity databases to identify potential therapeutic drugs for high-risk CRLM patients. Subsequently, in vitro and in vivo experiments were performed to verify the efficacy of these compounds.

RESULTS

These two prognostic models exhibited superior performance compared to previously reported ones. Obatoclax, a BCL-2 inhibitor, showed significant differential responses between high and low risk groups classified by prognostic models, and demonstrated remarkable effectiveness in both Transwell assay and CT26 colorectal liver metastasis mouse model.

CONCLUSIONS

This study highlights the significance of developing specialized prognostication approaches and investigating effective therapeutic drugs for patients with CRLM. The application of a deep learning drug response model provides a new drug discovery strategy for translational medicine in precision oncology.

Role of protein degradation systems in colorectal cancer

Protein degradation is essential for maintaining protein homeostasis. The ubiquitin‒proteasome system (UPS) and autophagy-lysosome system are the two primary pathways responsible for protein degradation and directly related to cell survival. In malignant tumors, the UPS plays a critical role in managing the excessive protein load caused by cancer cells hyperproliferation. In this review, we provide a comprehensive overview of the dual roles played by the UPS and autolysosome system in colorectal cancer (CRC), elucidating their impact on the initiation and progression of this disease while also highlighting their compensatory relationship. Simultaneously targeting both protein degradation pathways offers new promise for enhancing treatment efficacy against CRC. Additionally, apoptosis is closely linked to ubiquitination and autophagy, and caspases degrade proteins. A thorough comprehension of the interplay between various protein degradation pathways is highly important for clarifying the mechanism underlying the onset and progression of CRC.

Identification of AP002498.1 and LINC01871 as prognostic biomarkers and therapeutic targets for distant metastasis of colorectal adenocarcinoma

BACKGROUND

Increasing evidence suggests that lncRNA (Long non-coding RNA, lncRNA)-mediated ceRNA (competing endogenous RNA, ceRNA) networks are involved in the occurrence and progression of colorectal cancer (CRC). However, the roles of the lncRNA-miRNA-mRNA ceRNA network in distant metastasis of CRC are still unclear.

METHODS

In this study, we constructed a specific ceRNA network to identify potential biomarkers and therapeutic targets for distant metastasis of CRC. Specifically, RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to screen for differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) related to metastasis. After validation and selection by qRT-PCR and univariate and multivariate analysis of the metastasis- and prognosis-related lncRNAs, the regulated microRNAs (miRNAs) and coexpressed mRNAs were used to construct a ceRNA network for distant metastasis of CRC.

RESULTS

Two key distant metastasis-related DElncRNAs, AP002498.1 and LINC01871, were identified by univariate and multivariate analysis in combination with analyses of clinical data and expression levels. Furthermore, lncRNA-associated ceRNA subnetworks were constructed from the predicted miRNAs and 13 coexpressed DEmRNAs (SERPINA1, ITLN1, REG4, L1TD1, IGFALS, MUC5B, CIITA, CXCL9, CXCL10, GBP4, GNLY, IDO1, and NOS2). The AP002498.1- and LINC01871-associated ceRNA subnetworks regulated the expression of the target genes SERPINA1 and MUC5B and GNLY, respectively, through the associated miRNAs.

CONCLUSION

The DElncRNA AP002498.1 and the LINC01871/miR-4644 and miR-185-5p/GNLY axes were identified as being closely associated with distant metastasis and could represent independent prognostic biomarkers or therapeutic targets in colorectal adenocarcinoma.

Gel-Based Proteomic Identification of Suprabasin as a Potential New Candidate Biomarker in Endometrial Cancer

Endometrial cancer (EC) is the most frequent gynaecologic cancer in postmenopausal women. We used 2D-DIGE and mass spectrometry to identify candidate biomarkers in endometrial cancer, analysing the serum protein contents of 10 patients versus 10 control subjects. Using gel-based proteomics, we identified 24 candidate biomarkers, considering only spots with a fold change in volume percentage ≥ 1.5 or intensity change ≤ 0.6, which were significantly different between cases and controls (p < 0.05). We used Western blotting analysis both in the serum and tissue of 43 patients for data validation. Among the identified proteins, we selected Suprabasin (SBSN), an oncogene previously associated with poor prognosis in different cancers. SBSN principal isoforms were subjected to Western blotting analysis in serum and surgery-excised tissue: both isoforms were downregulated in the tissue. However, in serum, isoform 1 was upregulated, while isoform 2 was downregulated. Data-mining on the TCGA and GTEx projects, using the GEPIA2.0 interface, indicated a diminished SBSN expression in the Uterine Corpus Endometrial Cancer (UCEC) database compared to normal tissue, confirming proteomic results. These results suggest that SBSN, specifically isoform 2, in tissue or serum, could be a potential novel biomarker in endometrial cancer.

Seven Genes Associated With Lymphatic Metastasis in Thyroid Cancer That Is Linked to Tumor Immune Cell Infiltration

Objective

Since there are few studies exploring genes associated with lymphatic metastasis of thyroid carcinoma (THCA), this study was conducted to explore genes associated with lymphatic metastasis of THCA and to investigate the relationship with immune infiltration.

Methods

Differentially expressed genes associated with THCA lymphatic metastasis were analyzed based on The Cancer Genome Atlas Program (TCGA) database; a protein-protein interaction(PPI)network was constructed to screen for pivotal genes. Based on the identified hub genes, their expression in THCA with and without lymphatic metastasis were determined. Functional enrichment analysis was performed. The correlation between the identified genes and immune cell infiltration was explored. LASSO logistic regression analysis was performed to determine the risk score of the most relevant gene constructs and multifactor COX regression analysis based on genes in the risk score formula.

Results

A total of 115 genes were differentially expressed in THCA with and without lymphatic metastasis, including 28 upregulated genes and 87 downregulated genes. The PPI network identified seven hub genes (EVA1A, TIMP1, SERPINA1, FAM20A, FN1, TNC, MXRA8); the expression of all seven genes was upregulated in the group with lymphatic metastasis; Immuno-infiltration analysis showed that all seven genes were significantly positively correlated with macrophage M1 and NK cells and negatively correlated with T-cell CD4+ and myeloid dendritic cells. LASSO logistic regression analysis identified the five most relevant genes (EVA1A, SERPINA1, FN1, TNC, MXRA8), and multi-factor COX regression analysis showed EVA1A, SERPINA1 and FN1 as independent prognostic factors.

Conclusion

Seven genes were associated with lymphatic metastasis of THCA and with tumor immune cell infiltration.

Integrated weighted gene co-expression network analysis reveals biomarkers associated with prognosis of high-grade serous ovarian cancer

Background

Ovarian cancer is the gynecologic tumor with the highest fatality rate, and high‐grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer. One important reason for the poor prognosis of HGSOC is the lack of effective diagnostic and prognostic biomarkers. New biomarkers are necessary for the improvement of treatment strategies and to ensure appropriate healthcare decisions.

Methods

To construct the co‐expression network of HGSOC samples, we applied weighted gene co‐expression network analysis (WGCNA) to assess the proteomic data obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and module‐trait relationship was then analyzed and plotted in a heatmap to choose key module associated with HGSOC. Subsequently, hub genes with high connectivity in key module were identified by Cytoscape software. Furthermore, the biomarkers were selected through survival analysis, followed by evaluation using the relative operating characteristic (ROC) analysis.

Results

A total of 9 modules were identified by WGCNA, and module‐trait analysis revealed that the brown module was significantly associated with HGSOC (cor = 0.7). Ten hub genes with the highest connectivity were selected by protein‐protein interaction analysis. After survival and ROC analysis, ALB, APOB and SERPINA1 were suggested to be the biomarkers, and their protein levels were positively correlated with HGSOC prognosis.

Conclusion

We conducted the first gene co‐expression analysis using proteomic data from HGSOC samples, and found that ALB, APOB and SERPINA1 had prognostic value, which might be applied for the treatment of HGSOC in the future.