FAM110B Inhibits Non-Small Cell Lung Cancer Cell Proliferation and Invasion Through Inactivating Wnt/β-Catenin Signaling

Pan-cancer analysis and single-cell analysis reveals FAM110B as a potential target for survival and immunotherapy

Background: FAM110B belongs to the family that has a 110 sequence similarity (FAM110) and is located in the centrosome and mitotic spindle. FAM110B has been linked to tumor cell growth in earlier research. Uncertainty exists regarding FAM110B's function within the tumor microenvironment is unclear as well as pan-cancer. Methods: In order to assess the variation in FAM110B expression within normal and pan-cancer tissues, we combined the TCGA and GTEx databases. The cBioPortal database and the GSCALite platform were used to examine the variation in genome and methylation alteration of FAM110B. Cox regression, Kaplan-Meier, and SangerBox were employed to examine the clinical features and prognosis of FAM110B and pan-cancer. The purpose of the correlational research was to investigate the associations within immunerelated genes, tumor mutation burden, microsatellite instability, immune-related genes, and immunological checkpoints and FAM110B expression. ESTIMATE, EPIC, QUANTISEQ, and MCPCOUNTER methods were used to calculate the interaction among FAM110B expression as well as the tumor immune microenvironment. The immunoinfiltration and function of FAM110B were analyzed by single-cell databases (TISCH and CancerSEA). Finally, we evaluated the sensitivity of FAM110B to small-molecule medications through GDSC and CTRP databases. Results: The transcription and protein expression of FAM110B varies significantly throughout cancer types, and this has predictive value for the prognosis of some tumors; including brain lower grade glioma (LGG), stomach adenocarcinoma (STAD), pancreatic adenocarcinoma (PAAD), etc. In the tumor microenvironment, the expression level of FAM110B was associated with immune cell infiltration, immune checkpoint immune regulatory genes, tumor mutational burden, and microsatellite fragility to a certain extent. Conclusion: This work investigates the possibility of utility of FAM110B as a marker to forecast pan-cancer immunotherapy response, providing a theoretical basis for cancer therapy.

Methylation of FAM110C is a synthetic lethal marker for ATR/CHK1 inhibitors in pancreatic cancer

Background and objectives

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies. An epigenetic-based synthetic lethal strategy provides a novel opportunity for PDAC treatment. Finding more DNA damage repair (DDR)-related or cell fate-related molecules with aberrant epigenetic changes is becoming very important. Family with sequence similarity 110C (FAM110C) is a cell fate-related gene and its function in cancer remains unclear.

Methods

Seven cell lines, 34 cases of intraductal papillary mucinous neoplasm (IPMN), 15 cases of mucinous cystic neoplasm (MCN) and 284 cases of PDAC samples were employed. Methylation-specific PCR, western blot, CRISPR knockout, immunoprecipitation and a xenograft mouse model were used in this study.

Results

FAM110C is methylated in 41.18% (14/34) of IPMN, 46.67% (7/15) of MCN and 72.89% (207/284) of PDAC, with a progression trend from IPMN/MCN to pancreatic cancer (P = 0.0001, P = 0.0389). FAM110C methylation is significantly associated with poor overall survival (OS) (P = 0.0065) and is an independent prognostic marker for poor OS (P = 0.0159). FAM110C inhibits PDAC cells growth both in vitro and in vivo, serving as a novel tumor suppressor. FAM110C activates ATM and NHEJ signaling pathways by interacting with HMGB1. Loss of FAM110C expression sensitizes PDAC cells to VE-822 (an ATR inhibitor) and MK-8776 (a CHK1 inhibitor).

Conclusion

FAM110C methylation is a potential diagnostic and prognostic marker in PDAC, and its epigenetic silencing sensitizes PDAC cells to ATR/CHK1 inhibitors.

Exploring the short linear motif-mediated protein-protein interactions of CrkL through ProP-PD

Adaptor proteins play a pivotal role in cellular signaling mediating a multitude of protein-protein interaction critical for cellular homeostasis. Dysregulation of these interactions has been linked to the onset of various cancer pathologies and exploited by viral pathogens during host cell takeover. CrkL is an adaptor protein composed of an N-terminal SH2 domain followed by two SH3 domains that mediate interactions with diverse partners through the recognition of specific binding motifs. In this study, we employed proteomic peptide-phage display (ProP-PD) to comprehensively explore the short linear motif (SLiM)-based interactions of CrkL. Furthermore, we scrutinized how the binding affinity for selected peptides was influenced in the context of the full-length CrkL versus the isolated N-SH3 domain. Importantly, our results provided insights into SLiM-binding sites within previously reported interactors, as well as revealing novel human and viral ligands, expanding our understanding of the interactions mediated by CrkL and highlighting the significance of SLiM-based interactions in mediating adaptor protein function, with implications for cancer and viral pathologies.

Establishment and verification of a prognostic model based on coagulation and fibrinolysis-related genes in hepatocellular carcinoma

BACKGROUND

Studies have shown that coagulation and fibrinolysis (CFR) are correlated with Hepatocellular carcinoma (HCC) progression and prognosis. We aim to build a model based on CFR-correlated genes for risk assessment and prediction of HCC patient.

METHODS

HCC samples were selected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases respectively. The Molecular Signatures Database (MSigDB) was used to select the CFR genes. RiskScore model were established by single sample gene set enrichment analysis (ssGSEA), weighted correlation network analysis (WGCNA), multivariate Cox regression analysis, LASSO regression analysis.

RESULTS

PCDH17, PGF, PDE2A, FAM110D, FSCN1, FBLN5 were selected as the key genes and designed a RiskScore model. Those key genes were Differential expressions in HCC cell and patients. Overexpression PDE2A inhibited HCC cell migration and invasion. The higher the RiskScore, the lower the probability of survival. The model has high AUC values in the first, third and fifth year prediction curves, indicating that the model has strong prediction performance. The difference analysis of clinicopathological features found that a great proportion of high clinicopathological grade samples showed higher RiskScore. RiskScore were positively correlated with immune scores and TIDE scores. High levels of immune checkpoints and immunomodulators were observed in high RiskScore group. High RiskScore groups may benefit greatly from taking traditional chemotherapy drugs.

CONCLUSIONS

We screened CFR related genes to design a RiskScore model, which could accurately evaluate the prognosis and survival status of HCC patients, providing certain value for optimizing the clinical treatment of cancer in the future.

Role of Sox9 in BPD and its effects on the Wnt/β-catenin pathway and AEC-II differentiation

The excessive activation of the Wnt/β-catenin signaling pathway is an important regulatory mechanism that underlies the excessive proliferation and impaired differentiation of type 2 alveolar epithelial cells (AEC-II) in bronchopulmonary dysplasia (BPD). Sox9 has been shown to be an important repressor of the Wnt/β-catenin signaling pathway and plays an important regulatory role in various pathophysiological processes. We found that the increased expression of Sox9 in the early stages of BPD could downregulate the expression of β-catenin and promote the differentiation of AEC-II cells into AEC-I, thereby alleviating the pathological changes in BPD. The expression of Sox9 in BPD is regulated by long noncoding RNA growth arrest-specific 5. These findings may provide new targets for the early intervention of BPD.

Exploration and Validation of Pancreatic Cancer Hub Genes Based on Weighted Gene Co-Expression Network Analysis and Immune Infiltration Score Analysis

Objective

To find pancreatic cancer (PC)-related hub genes based on weighted gene co-expression network analysis (WGCNA) construction and immune infiltration score analysis and validate them immunohistochemically by clinical cases, to generate new concepts or therapeutic targets for the early diagnosis and treatment of PC.

Material and Methods

In this study, WGCNA and immune infiltration score were utilized to identify the relevant core modules of PC and the hub genes within these core modules.

Results

Using WGCNA analysis, data from PC and normal pancreas integrated with TCGA and GTEX were analyzed and brown modules were chosen from the six modules. Five hub genes, including DPYD, FXYD6, MAP6, FAM110B, and ANK2, were discovered to have differential survival significance via validation tests utilizing survival analysis curves and the GEPIA database. The DPYD gene was the only gene associated with PC survival side effects. Validation of the Human Protein Atlas (HPA) database and immunohistochemical testing of clinical samples showed positive results for DPYD expression in PC.

Conclusion

In this study, we identified DPYD, FXYD6, MAP6, FAM110B, and ANK2, as immune-related candidate markers for PC. Only the DPYD gene had a negative impact on the survival of PC patients. Through validation of the HPA database and immunohistochemical testing of clinical cases, we believe that the DPYD gene brings novel ideas and therapeutic targets in the diagnosis and treatment of PC.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Pan-cancer analysis reveals potential of FAM110A as a prognostic and immunological biomarker in human cancer

Background

Despite great success, immunotherapy still faces many challenges in practical applications. It was previously found that family with sequence similarity 110 member A (FAM110A) participate in the regulation of the cell cycle and plays an oncogenic role in pancreatic cancer. However, the prognostic value of FAM110A in pan-cancer and its involvement in immune response remain unclear.

Methods

The Human Protein Atlas (HPA) database was used to detect the expression of FAM110A in human normal tissues, the Tumor Immune Estimation Resource (TIMER) and TIMER 2.0 databases were used to explore the association of FAM110A expression with immune checkpoint genes and immune infiltration, and the Gene Set Cancer Analysis (GSCA) database was used to explore the correlation between FAM110A expression and copy number variations (CNV) and methylation. The LinkedOmics database was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Statistical analysis and visualization of data from the The Cancer Genome Atlas (TCGA) or the Genotype-Tissue Expression (GTEx) databases were performed using the R software (version 3.6.3). Clinical samples were validated using immunohistochemistry.

Results

FAM110A expression was elevated in most tumor tissues compared with that in normal tissues. CNV and methylation were associated with abnormal FAM110A mRNA expression in tumor tissues. FAM110A affected prognosis and was associated with the expression of multiple immune checkpoint genes and abundance of tumor-infiltrating immune cells across multiple types of cancer, especially in liver hepatocellular carcinoma (LIHC). FAM110A-related genes were involved in multiple immune-related processes in LIHC.

Conclusion

FAM110A participates in regulating the immune infiltration and affecting the prognosis of patients in multiple cancers, especially in LIHC. FAM110A may serve as a prognostic and immunological biomarker for human cancer.

Characterization of aging cancer-associated fibroblasts draws implications in prognosis and immunotherapy response in low-grade gliomas

Background: Due to the highly variable prognosis of low-grade gliomas (LGGs), it is important to find robust biomarkers for predicting clinical outcomes. Aging cancer-associated fibroblasts (CAFs) within the senescent stroma of a tumor microenvironment (TME) have been recently reported to play a key role in tumor development. However, there are few studies focusing on this topic in gliomas.

Methods and Results: Based on the transcriptome data from TCGA and CGGA databases, we identified aging CAF-related genes (ACAFRGs) in LGGs by the weighted gene co-expression network analysis (WGCNA) method, followed by which LGG samples were classified into two aging CAF-related gene clusters with distinct prognosis and characteristics of the TME. Machine learning algorithms were used to screen out eight featured ACAFRGs to characterize two aging CAF-related gene clusters, and a nomogram model was constructed to predict the probability of gene cluster A for each LGG sample. Then, a powerful aging CAF scoring system was developed to predict the prognosis and response to immune checkpoint blockage therapy. Finally, the ACAFRGs were verified in two glioma-related external datasets. The performance of the aging CAF score in predicting the immunotherapy response was further validated in two independent cohorts. We also confirmed the expression of ACAFRGs at the protein level in glioma tissues through the Human Protein Atlas website and Western blotting analysis.

Conclusion: We developed a robust aging CAF scoring system to predict the prognosis and immunotherapy response in LGGs. Our findings may provide new targets for therapeutics and contribute to the exploration focusing on aging CAFs.

Effect and Mechanism of Transthyretin over-Expression on Proliferation and Cell Cycle of Lung Cancer A549 Cells

Background

The effects of transthyretin (TTR) over-expression on the proliferation and cell cycle of non-small cell lung cancer (NSCLC) A549 cells and its possible mechanism were verified.

Methods

A total of 196 LC patients and 20 healthy controls were enrolled at Tianjin Hospital, Tianjin, China between Apr 2017 and Oct 2017. The serum TTR content was detected by ELISA. Through lentiviral transfection method, NSCLC cells were divided into non-transfected group (group A), negative control group (group B) transfected with empty vector and experimental group (group C) transfected with TTR over-expression. Cell proliferation was detected by CCK-8 method, TTR mRNA expression was detected by real-time quantitative polymerase chain reaction (RT-qPCR), and TTR protein expression was tested by Western blot (WB). Cell cycle was detected by flow cytometry, Wnt3a/β-catenin protein expression was detected by WB, and mRNA expression was detected by RT-qPCR.

Results

The serum TTR content in early, middle and late LC group was remarkably lower than that in healthy group (P<0.05). Compared with late stage, TTR content in early and middle stages of LC group was higher, and the difference was statistically marked (P < 0.05). The absorbance value of group C was lower than that of groups A and B, indicating that the cell proliferation activity dramatically decreased, with statistically marked difference (P<0.05). LC A549 cells in group C were obviously blocked in G2M, with statistical significance (P<0.05).

Conclusion

TTR over-expression can inhibit the proliferation of NSCLC A549 cells, and the expression is related to Wnt3a/β-catenin pathway. TTR in serum of patients was helpful for diagnosing LC and has certain clinical value.