Innate tumor killers in colorectal cancer

Colorectal cancer (CRC) ranks as the third most aggressive tumor globally, and it can be categorized into two forms: colitis-mediated CRC and sporadic CRC. The therapeutic approaches for CRC encompass surgical intervention, chemotherapy, and radiotherapy. However, even with the implementation of these techniques, the 5-year survival rate for metastatic CRC remains at a mere 12-14%. In the realm of CRC treatment, gene therapy has emerged as a novel therapeutic approach. Among the crucial molecular pathways that govern tumorigenesis, STAT3 plays a significant role. This pathway is subject to regulation by cytokines and growth factors. Once translocated into the nucleus, STAT3 influences the expression levels of factors associated with cell proliferation and metastasis. Literature suggests that the upregulation of STAT3 expression is observed as CRC cells progress towards metastatic stages. Consequently, elevated STAT3 levels serve as a significant determinant of poor prognosis and can be utilized as a diagnostic factor for cancer patients. The biological and malignant characteristics of CRC cells contribute to low survival rates in patients, as the upregulation of STAT3 prevents apoptosis and promotes pro-survival autophagy, thereby accelerating tumorigenesis. Furthermore, STAT3 plays a role in facilitating the proliferation of CRC cells through the stimulation of glycolysis and promoting metastasis via the induction of epithelial-mesenchymal transition (EMT). Notably, an intriguing observation is that the upregulation of STAT3 can mediate resistance to 5-fluorouracil, oxaliplatin, and other anti-cancer drugs. Moreover, the radio-sensitivity of CRC diminishes with increased STAT3 expression. Compounds such as curcumin, epigallocatechin gallate, and other anti-tumor agents exhibit the ability to suppress STAT3 and its associated pathways, thereby impeding tumorigenesis in CRC. Furthermore, it is worth noting that nanostructures have demonstrated anti-proliferative and anti-metastatic properties in CRC. 

STAT3 as a newly emerging target in colorectal cancer therapy: Tumorigenesis, therapy response, and pharmacological/nanoplatform strategies.

The RNA cap methyltransferase CMTR1 methylates the first transcribed nucleotide of RNA polymerase II transcripts, impacting gene expression mechanisms, including during innate immune responses. Using mass spectrometry, we identify a multiply phosphorylated region of CMTR1 (phospho-patch [P-Patch]), which is a substrate for the kinase CK2 (casein kinase II). CMTR1 phosphorylation alters intramolecular interactions, increases recruitment to RNA polymerase II, and promotes RNA cap methylation. P-Patch phosphorylation occurs during the G1 phase of the cell cycle, recruiting CMTR1 to RNA polymerase II during a period of rapid transcription and RNA cap formation. CMTR1 phosphorylation is required for the expression of specific RNAs, including ribosomal protein gene transcripts, and promotes cell proliferation. CMTR1 phosphorylation is also required for interferon-stimulated gene expression. The cap-snatching virus, influenza A, utilizes host CMTR1 phosphorylation to produce the caps required for virus production and infection. We present an RNA cap methylation control mechanism whereby CK2 controls CMTR1, enhancing co-transcriptional capping. 

CK2 phosphorylation of CMTR1 promotes RNA cap formation and influenza virus infection

Immunotherapies such as immune checkpoint blockade have achieved remarkable success in treating cancer. Unfortunately, response rates have been limited in multiple cancers including hepatocellular carcinoma (HCC). The critical function of epigenetics in tumor immune evasion and anti-tumor immunity supports harnessing epigenetic regulators as a potential strategy to enhance the efficacy of immunotherapy. Here, we discovered a tumor-promoting function of FTSJ3, an RNA 2'-O-methyltransferase, in HCC by suppressing anti-tumor immune responses. FTSJ3 was upregulated in hepatocellular carcinoma, and high FTSJ3 expression correlated with reduced patient survival. Deletion of FTSJ3 blocked HCC growth and induced robust anti-tumor immune responses. Mechanistically, FTSJ3 suppressed double-stranded RNA (dsRNA)-induced IFNβ signaling in a 2'-O-methyltransferase manner. Deletion of RNA sensors in HCC cells or systemic knockout of type I IFN receptor IFNAR in mice rescued the in vivo tumor growth defect caused by FTSJ3 deficiency, indicating that FTSJ3 deletion suppresses tumor growth by activating the RNA sensor-mediated type I IFN pathway. Furthermore, FTSJ3 deletion significantly enhanced the efficacy of PD-1 immune checkpoint blockade. The combination of FTSJ3 deficiency and anti-PD-1 antibody treatment effectively eradicated tumors and increased the survival time. In conclusion, this study reveals an epigenetic mechanism of tumor immune evasion and, importantly, suggests FTSJ3-targeting therapies as potential approach to overcome immunotherapy resistance in HCC patients.

RNA methyltransferase FTSJ3 regulates the type I interferon pathway to promote hepatocellular carcinoma immune evasion.

The Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), also known as CD66a, is a member of the immunoglobulin superfamily. CEACAM1 was shown to be a prognostic marker in patients suffering from cancer. In this review, we summarize pre-clinical and clinical evidence linking CEACAM1 to tumorigenicity and cancer progression. Furthermore, we discuss potential CEACAM1-based mechanisms that may affect cancer biology.

The role of carcinoembryonic antigen-related cell adhesion molecule 1 in cancer

CMTR1, also called IFN-stimulated gene 95 kDa protein (ISG95), is elevated by viral infection in a variety of cells. However, the functions of CMTR1 in colorectal cancer (CRC), especially its roles in tumorigenesis and immune regulation, remain unclear. Here, we first identified CMTR1 as a novel oncogene in colorectal cancer. Based on The Cancer Genome Atlas (TCGA) database exploration and human tissue microarray (TMA) analysis, we found that CMTR1 expression was markedly higher in CRC tissues than in adjacent normal tissues. High CMTR1 expression was correlated with poor prognosis in CRC patients. Knockdown (KD) of CMTR1 significantly suppressed cell proliferation and tumorigenicity both in vitro and in vivo, whereas overexpression of CMTR1 resulted in the opposite effects. KEGG pathway analysis revealed differential enrichment in the JAK/STAT signaling pathway in colorectal cancer cells with CMTR1 KD. Mechanistically, suppression of CMTR1 expression inhibited RNAPII recruitment to the transcription start site (TSS) of STAT3 and suppressed STAT3 expression and activation. Furthermore, the efficacy of PD1 blockade immunotherapy was prominently enhanced in the presence of CMTR1 KD via increased infiltration of CD8 + T cells into the tumor microenvironment. Overall, it appears that CMTR1 plays a key role in regulating tumor cell proliferation and antitumor immunity. 

/pdf/cmtr1-promotes-colorectal-cancer-cell-growth-and-immune-3emjqvnc.pdf

CMTR1 promotes colorectal cancer cell growth and immune evasion by transcriptionally regulating STAT3

Background Chemotherapy-induced amenorrhea (CIA) is one of universal phenomena in breast cancer (BC) patients, and it causes difficulties in evaluating the actual menopausal status which is important for the oncologists to choose appropriate treatment. Currently, serum estradiol (E2) and follicle-stimulating hormone (FSH) levels are the most commonly used clinical parameters for the assessment of menopausal status in BC patients. However, the optimal cut-off points of serum E2 and FSH have little been explored in southern Chinese population. Objective This study is aimed to determine the optimal cut-off values of the serum E2 and FSH levels for evaluating the menopausal status of BC patients in a southern Chinese population. Methods A retrospective analysis was done among a total of 206 patients with BC from a southern Chinese area. The data of serum E2, FSH, and luteinizing hormone (LH) levels were collected and analyzed for the comparison purpose. The receiver-operating curve (ROC) was generated to assess the specificity and sensitivity of the three biomarkers in discriminating the menopausal status of BC patients. The optimal cut-off values were determined according to the Youden index and then compared with the recommended reference values by the Chinese Anti-cancer Association (CACA) and those recommended by the manufacturers. Results The areas under the ROC curves (AUCs) of E2, FSH, and LH were 0.846 (95% CI: 0.790-0.903), 0.781 (95% CI: 0.714-0.847) and 0.608 (95% CI: 0.526-0.690), respectively. The optimal cut-off values were 130.0 pg/mL for E2, 23.325 IU/L for FSH, and 11.625 IU/L for LH with a maximum of the Youden index. When E2, FSH, and LH were used in combination for ROC analysis, the AUC increased to 0.847 (95% CI: 0.790-0.904), which was higher than that of any other biomarker alone. In this study, the sensitivity and specificity of E2 and FSH were 91.6% and 73.70% and 94.4% and 58.6%, respectively, in comparison with 85.0% and 75.80% and 76.6% and 65.7% according to the CACA-recommended cut-off points, or 92.5% and 68.7% and 96.3% and 53.5% according to the manufacturer recommended cut-off points. Conclusion Considering the sensitivity and specificity of serum E2 and FSH for assessing the menopausal status, the optimal cut-off values determined in the present study were similar to the manufacturer's recommendations, but obviously superior to the cut-off points suggested by CACA. These cut-off points calculated in this study seem to be valuable in southern Chinese population and might be used by clinicians to make a correct medical decision for BC patients who would benefit from endocrine therapy of aromatase inhibitor (AI).

/pdf/determining-the-optimal-cut-off-values-of-serum-e2-and-fsh-2xcx4yzq.pdf

Determining the Optimal Cut-Off Values of Serum E2 and FSH for Evaluating the Menopausal Status of Breast Cancer Patients in a Southern Chinese Population

Immune checkpoint blockade (ICB) therapy is a promising treatment for hepatocellular carcinoma (HCC). However, it is limited by low response rates. Tumour‐infiltrating immune cells (TIICs), specifically CD8+ T cells, in the tumour microenvironment (TME) are related to tumour immune responses and are potential predictors for immunotherapeutic and survival outcomes. Therefore, we established a new CD8+ T cell‐related genes prognostic index (CD8T‐RGPI) for evaluating immune responses and prognostic outcomes for HCC. Using the TCGA databases, we evaluated the RNA‐seq data of CD8+ T cell‐related genes with gene co‐expression network. Then, the CD8T‐RGPI model was built by combining univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The nomogram exhibited a good prediction ability for HCC patients' prognosis in either TCGA or ICGC databases. The CD8T‐RGPI was elevated in patients with high T, M and TNM stages. Functional enrichment analyses revealed that in CD8T‐RGPI high‐risk group, tumour‐related and immunosuppressive pathways, such as focal adhesion, aerobic glycolysis, HIF1 transcription factor pathway, IL‐4 and IL‐13 signalling were significantly enriched. Moreover, for CD8T‐RGPI high‐risk HCC patients, immune tolerance and immunosurveillance escape was associated with poor therapeutic outcomes for ICB therapy. The CD8T‐RGPI low‐risk HCC patients with elevated immune infiltration levels were associated with good immunotherapeutic responses to ICB. Our findings provide prospective prognostic biomarkers and elucidate on HCC immunotherapy.

A CD8+ T cell‐related genes prognostic model for hepatocellular carcinoma patients

Epithelial-mesenchymal transition (EMT) contributes to high tumor heterogeneity and the immunosuppressive environment of the HCC tumor microenvironment (TME). Here, we developed EMT-related genes phenotyping clusters and systematically evaluated their impact on HCC prognosis, the TME, and drug efficacy prediction. We identified HCC specific EMT-related genes using weighted gene co-expression network analysis (WGCNA). An EMT-related genes prognostic index (EMT-RGPI) capable of effectively predicting HCC prognosis was then constructed. Consensus clustering of 12 HCC specific EMT-related hub genes uncovered two molecular clusters C1 and C2. Cluster C2 preferentially associated with unfavorable prognosis, higher stemness index (mRNAsi) value, elevated immune checkpoint expression, and immune cell infiltration. The TGF-β signaling, EMT, glycolysis, Wnt β-catenin signaling, and angiogenesis were markedly enriched in cluster C2. Moreover, cluster C2 exhibited higher TP53 and RB1 mutation rates. The TME subtypes and tumor immune dysfunction and exclusion (TIDE) score showed that cluster C1 patients responded well to immune checkpoint inhibitors (ICIs). Half-maximal inhibitory concentration (IC50) revealed that cluster C2 patients were more sensitive to chemotherapeutic and antiangiogenic agents. These findings may guide risk stratification and precision therapy for HCC patients. 

Epithelial-mesenchymal transition-related gene prognostic index and phenotyping clusters for hepatocellular carcinoma patients.

Abstract The aim of this study is to explore the prognostic value of vascular invasion (VI) in hepatocellular carcinoma (HCC) by searching for competing endogenous RNAs (ceRNA) network and constructing a new prognostic model for HCC. The differentially expressed genes (DEGs) between HCC and normal tissues were identified from GEO and TCGA. StarBase and miRanda prediction tools were applied to construct a circRNA-miRNA-mRNA network. The DEGs between HCC with and without VI were also identified. Then, the hub genes were screened to build a prognostic risk score model through the method of least absolute shrinkage and selection operator. The prognostic ability of the model was assessed using the Kaplan−Meier method and Cox regression analysis. In result, there were 221 up-regulated and 47 down-regulated differentially expressed circRNAs (DEcircRNAs) in HCC compared with normal tissue. A circRNA-related ceRNA network was established, containing 11 DEcircRNAs, 12 DEmiRNAs, and 161 DEmRNAs. Meanwhile, another DEG analysis revealed 625 up-regulated and 123 down-regulated DEGs between HCC with and without VI, and then a protein–protein interaction (PPI) network was built based on 122 VI-related DEGs. From the intersection of DEGs within the PPI and ceRNA networks, we obtained seven hub genes to build a novel prognostic risk score model. HCC patients with high-risk scores had shorter survival time and presented more advanced T/N/M stages as well as VI occurrence. In conclusion a novel prognostic model based on seven VI-associated DEGs within a circRNA-related ceRNA network was constructed in this study, with great ability to predict the outcome of HCC patients.

Construction of a ceRNA network to reveal a vascular invasion associated prognostic model in hepatocellular carcinoma

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Lu Yang

Author Tools

Chat about Author