The dynamic role of nucleoprotein SHCBP1 in the cancer cell cycle and its potential as a synergistic target for DNA-damaging agents in cancer therapy.

BACKGROUND: Malignant tumours seriously threaten human life and health, and effective treatments for cancer are still being explored. The ability of SHC SH2 domain-binding protein 1 (SHCBP1) to induce cell cycle disturbance and inhibit tumour growth has been increasingly studied, but its dynamic role in the tumour cell cycle and corresponding effects leading to mitotic catastrophe and DNA damage have rarely been studied. RESULTS: In this paper, we found that the nucleoprotein SHCBP1 exhibits dynamic spatiotemporal expression during the tumour cell cycle, and SHCBP1 knockdown slowed cell cycle progression by inducing spindle disorder, as reflected by premature mitotic entry and multipolar spindle formation. This dysfunction was caused by G2/M checkpoint impairment mediated by downregulated WEE1 kinase and NEK7 (a member of the mammalian NIMA-related kinase family) expression and upregulated centromere/kinetochore protein Zeste White 10 (ZW10) expression. Moreover, both in vivo and in vitro experiments confirmed the significant inhibitory effects of SHCBP1 knockdown on tumour growth. Based on these findings, SHCBP1 knockdown in combination with low-dose DNA-damaging agents had synergistic tumouricidal effects on tumour cells. In response to this treatment, tumour cells were forced into the mitotic phase with considerable unrepaired DNA lesions, inducing mitotic catastrophe. These synergistic effects were attributed not only to the abrogation of the G2/M checkpoint and disrupted spindle function but also to the impairment of the DNA damage repair system, as demonstrated by mass spectrometry-based proteomic and western blotting analyses. Consistently, patients with low SHCBP1 expression in tumour tissue were more sensitive to radiotherapy. However, SHCBP1 knockdown combined with tubulin-toxic drugs weakened the killing effect of the drugs on tumour cells, which may guide the choice of chemotherapeutic agents in clinical practice. CONCLUSION: In summary, we elucidated the role of the nucleoprotein SHCBP1 in tumour cell cycle progression and described a novel mechanism by which SHCBP1 regulates tumour progression and through which targeting SHCBP1 increases sensitivity to DNA-damaging agent therapy, indicating its potential as a cancer treatment.

PRADclass: Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of Aggressive Prostate Adenocarcinoma.

BACKGROUND: Prostate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality. METHODS: Based on TCGA transcriptomic data pertaining to PRAD and the associated clinical metadata, we determined the sample Gleason grade, and used it to execute: (i) Gleason-grade wise linear modeling, followed by five contrasts against controls and ten contrasts between grades; and (ii) Gleason-grade wise network modeling via weighted gene correlation network analysis (WGCNA). Candidate biomarkers were obtained from the above analysis and the consensus found. The consensus biomarkers were used as the feature space to train ML models for classifying a sample as benign, indolent or aggressive. RESULTS: The statistical modeling yielded 77 Gleason grade-salient genes while the WGCNA algorithm yielded 1003 trait-specific key genes in grade-wise significant modules. Consensus analysis of the two approaches identified two genes in Grade-1 (SLC43A1 and PHGR1), 26 genes in Grade-4 (including LOC100128675, PPP1R3C, NECAB1, UBXN10, SERPINA5, CLU, RASL12, DGKG, FHL1, NCAM1, and CEND1), and seven genes in Grade-5 (CBX2, DPYS, FAM72B, SHCBP1, TMEM132A, TPX2, UBE2C). A RandomForest model trained and optimized on these 35 biomarkers for the ternary classification problem yielded a balanced accuracy ∼ 86% on external validation. CONCLUSIONS: The consensus of multiple parallel computational strategies has unmasked candidate Gleason grade-specific biomarkers. PRADclass, a validated AI model featurizing these biomarkers achieved good performance, and could be trialed to predict the differentiation of prostate cancers. PRADclass is available for academic use at: https://apalania.shinyapps.io/pradclass (online) and https://github.com/apalania/pradclass (command-line interface).

p66ShcA promotes malignant breast cancer phenotypes by alleviating energetic and oxidative stress.

Significant efforts have focused on identifying targetable genetic drivers that support the growth of solid tumors and/or increase metastatic ability. During tumor development and progression to metastatic disease, physiological and pharmacological selective pressures influence parallel adaptive strategies within cancer cell sub-populations. Such adaptations allow cancer cells to withstand these stressful microenvironments. This Darwinian model of stress adaptation often prevents durable clinical responses and influences the emergence of aggressive cancers with increased metastatic fitness. However, the mechanisms contributing to such adaptive stress responses are poorly understood. We now demonstrate that the p66ShcA redox protein, itself a ROS inducer, is essential for survival in response to physiological stressors, including anchorage independence and nutrient deprivation, in the context of poor outcome breast cancers. Mechanistically, we show that p66ShcA promotes both glucose and glutamine metabolic reprogramming in breast cancer cells, to increase their capacity to engage catabolic metabolism and support glutathione synthesis. In doing so, chronic p66ShcA exposure contributes to adaptive stress responses, providing breast cancer cells with sufficient ATP and redox balance needed to withstand such transient stressed states. Our studies demonstrate that p66ShcA functionally contributes to the maintenance of aggressive phenotypes and the emergence of metastatic disease by forcing breast tumors to adapt to chronic and moderately elevated levels of oxidative stress.

SHC4 orchestrates ß-catenin pathway-mediated metastasis in triple-negative breast cancer by promoting Src kinase autophosphorylation.

Triple-negative breast cancer (TNBC) is highly aggressive and metastatic, and has the poorest prognosis among all breast cancer subtypes. Activated ß-catenin is enriched in TNBC and involved in Wnt signaling-independent metastasis. However, the underlying mechanisms of ß-catenin activation in TNBC remain unknown. Here, we found that SHC4 was upregulated in TNBC and high SHC4 expression was significantly correlated with poor outcomes. Overexpression of SHC4 promoted TNBC aggressiveness in vitro and facilitated TNBC metastasis in vivo. Mechanistically, SHC4 interacted with Src and maintained its autophosphorylated activation, which activated ß-catenin independent of Wnt signaling, and finally upregulated the transcription and expression of its downstream genes CD44 and MMP7. Furthermore, we determined that the PxPPxPxxxPxxP sequence on CH2 domain of SHC4 was critical for SHC4-Src binding and Src kinase activation. Overall, our results revealed the mechanism of ß-catenin activation independent of Wnt signaling in TNBC, which was driven by SHC4-induced Src autophosphorylation, suggesting that SHC4 might be a potential prognostic marker and therapeutic target in TNBC.

Therapeutic potential of SHCBP1 inhibitor AZD5582 in pancreatic cancer treatment.

Pancreatic cancer (PC) is a highly aggressive and deadly malignancy with limited treatment options and poor prognosis. Identifying new therapeutic targets and developing effective strategies for PC treatment is of utmost importance. Here, we revealed that SHCBP1 is significantly overexpressed in PC and negatively correlated with patient prognosis. Knockout of SHCBP1 inhibits the proliferation and migration of PC cells in vitro, and suppresses the tumor growth in vivo. In addition, we identified AZD5582 as a novel inhibitor of SHCBP1, which efficiently restrains the growth of PC in cell lines, organoids, and patient-derived xenografts. Mechanistically, we found that AZD5582 induced the apoptosis of PC cells by inhibiting the activity of PI3K/AKT signaling and preventing the degradation of TP53. Collectively, our study highlights SHCBP1 as a potential therapeutic target and its inhibitor AZD5582 as a viable agent for PC treatment strategies.

[Study of senescence protein p66Shc on myocardial tissue repair in adult mice].

Our previous study has shown that p66Shc plays an important role in the process of myocardial regeneration in newborn mice, and p66Shc deficiency leads to weakened myocardial regeneration in newborn mice. This study aims to explore the role of p66Shc protein in myocardial injury repair after myocardial infarction in adult mice, in order to provide a new target for the treatment of myocardial injury after myocardial infarction. Mouse myocardial infarction models of adult wild-type (WT) and p66Shc knockout (KO) were constructed by anterior descending branch ligation. The survival rate and heart-to-body weight ratio of two models were compared and analyzed. Masson's staining was used to identify scar area of injured myocardial tissue, and myocyte area was determined by wheat germ agglutinin (WGA) staining. TUNEL staining was used to detect the cardiomyocyte apoptosis. The protein expression of brain natriuretic peptide (BNP), a common marker of myocardial hypertrophy, was detected by Western blotting. The results showed that there was no significant difference in survival rate, myocardial scar area, myocyte apoptosis, and heart weight to body weight ratio between the WT and p66ShcKO mice after myocardial infarction surgery. Whereas the protein expression level of BNP in the p66ShcKO mice was significantly down-regulated compared with that in the WT mice. These results suggest that, unlike in neonatal mice, the deletion of p66Shc has no significant effect on myocardial injury repair after myocardial infarction in adult mice.

The function and immune role of cuproptosis associated hub gene in Barrett's esophagus and esophageal adenocarcinoma.

Barrett's esophagus (BE) is a precancerous lesion of esophageal adenocarcinoma (EAC), with approximately 3-5% of patients developing EAC. Cuproptosis is a kind of programmed cell death phenomenon discovered in recent years, which is related to the occurrence and development of many diseases. However, its role in BE and EAC is not fully understood. We used single sample Gene Set Enrichment Analysis (ssGSEA) for differential analysis of BE in the database, followed by enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) and GSEA, Protein-Protein Interaction (PPI), Weighted Gene Co-expression Network Analysis (WGCNA), Receiver Operating Characteristic Curve (ROC) and finally Quantitative Real Time Polymerase Chain Reaction (qRT-PCR) and immunohistochemistry (IHC) of clinical tissues. Two hub genes can be obtained by intersection of the results obtained from the cuproptosis signal analysis based on BE. The ROC curves of these two genes predicted EAC, and the Area Under the Curve (AUC) values could reach 0.950 and 0.946, respectively. The mRNA and protein levels of Centrosome associated protein E (CENPE) and Shc SH2 domain binding protein 1 (SHCBP1) were significantly increased in clinical EAC tissues. When they were grouped by protein expression levels, high expression of CENPE or SHCBP1 had a poor prognosis. The CENPE and SHCBP1 associated with cuproptosis may be a factor promoting the development of BE into EAC which associated with the regulation of NK cells and T cells.

A MACHINE LEARNING MODEL DERIVED FROM ANALYSIS OF TIME-COURSE GENE-EXPRESSION DATASETS REVEALS TEMPORALLY STABLE GENE MARKERS PREDICTIVE OF SEPSIS MORTALITY.

ABSTRACT: Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and the dynamic nature of the host immune response, identifying those at high risk of poor outcomes remains a critical challenge. Here, we performed secondary analysis of publicly available time-series gene-expression datasets from peripheral blood of patients admitted to the intensive care unit to elucidate temporally stable gene-expression markers between sepsis survivors and nonsurvivors. Using a limited set of genes that were determined to be temporally stable, we derived a dynamical model using a Support Vector Machine classifier to accurately predict the mortality of sepsis patients. Our model had robust performance in a test dataset, where patients' transcriptome was sampled at alternate time points, with an area under the curve of 0.89 (95% CI, 0.82-0.96) upon 5-fold cross-validation. We also identified 7 potential biomarkers of sepsis mortality (STAT5A, CX3CR1, LCP1, SNRPG, RPS27L, LSM5, SHCBP1) that require future validation. Pending prospective testing, our model may be used to identify sepsis patients with high risk of mortality accounting for the dynamic nature of the disease and with potential therapeutic implications.

Rucaparib inhibits lung adenocarcinoma cell proliferation and migration via the SHCBP1/CDK1 pathway.

Src homolog and collagen homolog binding protein 1 (SHCBP1) binds to the SH2 domain of SHC-transforming protein 1 (SHC1) and is involved in midbody organization and cytokinesis completion. SHCBP1 has been reported to be a cancer driver gene, promoting cancer progression. However, the functional role and underlying mechanism of SHCBP1 in regulating lung adenocarcinoma (LUAD) cell proliferation and migration are incompletely understood. Here, we discovered that SHCBP1 is overexpressed in LUAD tissues and is associated with a poor prognosis. SHCBP1 knockdown inhibited LUAD cell proliferation and migration by arresting the cell cycle and preventing epithelial-mesenchymal transition (EMT) via decreasing cyclin-dependent kinase 1 (CDK1) expression. Mechanistically, CDK1 overexpression reversed SHCBP1 knockdown-induced inhibition of proliferation and migration, confirming CDK1 as a key downstream target of SHCBP1. In addition, we proposed that rucaparib may be a small-molecule inhibitor of SHCBP1 and validated both in vitro and in vivo that rucaparib inhibits cell proliferation and migration via suppression of the SHCBP1/CDK1 pathway in LUAD. Our study elucidates a newly identified role of SHCBP1 in promoting cell proliferation and migration in LUAD, and suggests rucaparib as a potential inhibitor for LUAD treatment.

Prognostic significance of SH2D5 expression in lung adenocarcinoma and its relation to immune cell infiltration.

Objective: Through analyzing the SH2D5 expression profiles, clinical features, and immune infiltration in lung adenocarcinoma (LUAD), the study was intended to discuss the correlations of SH2D5 with prognosis and immune infiltration in LUAD. Methods: We downloaded transcriptome and clinical data of LUAD patients from TCGA, GEO, and CCLE databases. Sangerbox, R language, GEPIA, UALCAN, and Kaplan-Meier Plotter were adopted to analyze the SH2D5 expression patterns, prognosis, and clinical features. Spearman correlation analysis was performed to determine the association between SH2D5 expression and immune cell infiltration and immune checkpoint genes. The miRNA-SH2D5 relations were predicted by miRDB and starbase. Lastly, quantitative PCR, IHC and Western blot were implemented for validation. Results: A prominent up-regulation of SH2D5 was noted in the LUAD group relative to the normal group, which was validated by quantitative PCR, IHC and Western blot. SH2D5 expression was inversely related to overall survival (OS) of LUAD patients as well as B cell immune infiltration. Additionally, SH2D5 expression was negatively correlated with dendritic cells resting (p < 0.001), plasma cells (p < 0.001), mast cells resting (p = 0.031) and T cells CD4 memory resting (p = 0.036) in LUAD patients with abundant SH2D5 expression correlated with poor prognosis. Furthermore, enrichment analysis suggested that SH2D5 was associated with lung cancer and immunity. Lastly, we investigated the relationship between the expression of SH2D5 and the use of antitumor drugs. Conclusion: High SH2D5 expression shares an association with unfavorable prognosis in LUAD, and SH2D5 may also provide new ideas for immunotherapy as a potential therapeutic target.

Characterization of SHCBP1 to prognosis and immunological landscape in pan-cancer: novel insights to biomarker and therapeutic targets.

BACKGROUND: Previous studies have revealed the significant roles of SHC SH2 domain-binding protein 1 (SHCBP1) in occurrence and progression of cancers, but there is no pan-cancer analysis of SHCBP1. METHODS: In this study, we explored the potential carcinogenic role of SHCBP1 across 33 tumors from the TCGA and GTEx databases. We investigated SHCBP1 expression, prognosis, genetic alterations, tumor mutational burden (TMB) score, microsatellite instability (MSI) and tumor microenvironment from TIMER2, GEPIA2, UALCAN and cBioPortal databases. Moreover, the cellular functions and potential mechanisms were evaluated by GO and KEGG analysis. Besides, the mRNA expression of SHCBP1 was examined using qRT-PCR assay in gastrointestinal cancers. RESULTS: SHCBP1 was significantly upregulated in various cancers, and apparent relationship existed between SHCBP1 and survival prognosis in patients. The TMB, MSI, and tumor microenvironment analysis indicated that SHCBP1 was closely related to immune checkpoints, immune targets, as well as CD4+ naive T cell, CD8+ T cell, and neutrophil. Moreover, the cellular functions of SHCBP1 were mainly in regulating cell cycle motor protein activity. In addition, we validated that SHCBP1 mRNA expression was over-expressed in gastrointestinal cancers. CONCLUSIONS: This study was the first to systematically determine the prognostic value of SHCBP1, providing a forward-looking perspective on immunotherapy and cellular processes in pan-cancer.

Biological functions and therapeutic potential of SHCBP1 in human cancer.

The incidence of cancer is increasing globally, and it is the most common cause of death. The identification of novel cancer diagnostic and prognostic biomarkers is important for developing cancer treatment strategies and reducing mortality. SHCSH2 domain-binding protein 1 (SHCBP1) is a protein that specifically binds to the SH2 domain of Src homology-collagen. It participates in the regulation of a variety of signal transduction pathways and can activate a variety of signaling molecules to perform a series of physiological functions. SHCBP1 is expressed in a variety of human tissues, but its abnormal expression in various systems is associated with cancer. SHCBP1 is abnormally expressed in a variety of tumors, and plays roles in almost all aspects of cancer biology (such as cell proliferation, apoptosis prevention, invasion, and metastasis) through various possible mechanisms. Its expression level is related to the clinicopathological characteristics of patients. In addition, the SHCBP1 expression pattern is closely related to cancer type, stage, and other clinical variables. Therefore, SHCBP1 is a promising tumor biomarker for cancer diagnosis and prognosis and a potential therapeutic target. This article reviews the expression, biological functions, mechanisms, and potential clinical significance of SHCBP1 in various human tumors to provide a new theoretical basis for clinical molecular diagnosis, molecular targeted therapy, and scientific research on cancer.

ROS1 as a possible prognostic biomarker of cervical adenocarcinoma: An exploratory analysis with next-generation sequencing.

OBJECTIVES: Given the differences in clinical and biological characteristics between cervical adenocarcinoma and squamous cell carcinoma, this study aimed to conduct an exploratory analysis to examine the molecular characteristics of cervical adenocarcinoma in a Japanese population. METHODS: This study explored the simultaneous testing of multiple mutations targeting cervical adenocarcinoma using next-generation sequencing (NGS). The following genes were analyzed: BCAR4, CD274, PDCD1LG2, KRAS, ARID1A, PTEN, ALK, EGFR, ROS1, BRAF, PIK3CA, EP300, EBXW7, SHCBP1, TGFBR2, SMAD4, ERBB2, ERBB3, and KLF5. Tumor tissue and blood samples were obtained at the time of primary treatment. The NGS-based molecular profiles obtained from Tokai University (49 specimens) were compared with the registered data in The Cancer Genome Atlas (TCGA) database (133 specimens). RESULTS: The study cohort had higher rates of adenocarcinoma than the TCGA cohort (44.9% vs. 18.0%; P = 0.001). The adenocarcinomas in the study cohort had more alterations in ROS1, EGFR, EP300, SHCBP1, ALK, and PIK3CA than those in the TCGA cohort. Among them, ROS1 had the highest number of gene alterations (median, 7.00 ± 2.63). In the study cohort, patients with a high number of ROS1 alterations had a significantly higher recurrence rate (5-year recurrence rate, 48.8% vs. 14.6%; hazard ratio [HR], 4.32; 95% confidence interval [CI], 1.20-15.50; P = 0.014) and lower overall survival than those with low alterations (5-year survival rate, 70.7% vs. 93.1%; HR, 7.15; 95% CI, 1.08-58.22; P = 0.032). CONCLUSION: The current exploratory analysis suggests that ROS1 gene alteration may be a prognostic biomarker in cervical adenocarcinoma in Japanese patients.

Construction of a lncRNA-mediated ceRNA network and a genomic-clinicopathologic nomogram to predict survival for breast cancer patients.

Breast cancer (BC) is the most common cancer among women and a leading cause of cancer-related deaths worldwide. The diagnosis of early patients and the prognosis of advanced patients have not improved over the past several decades. The purpose of the present study was to identify the lncRNA-related genes based on ceRNA network and construct a credible model for prognosis in BC. Based on The Cancer Genome Atlas (TCGA) database, prognosis-related differently expressed genes (DEGs) and a lncRNA-associated ceRNA regulatory network were obtained in BC. The patients were randomly divided into a training group and a testing group. A ceRNA-related prognostic model as well as a nomogram was constructed for further study. A total of 844 DElncRNAs, 206 DEmiRNAs and 3295 DEmRNAs were extracted in BC, and 12 RNAs (HOTAIR, AC055854.1, ST8SIA6-AS1, AC105999.2, hsa-miR-1258, hsa-miR-7705, hsa-miR-3662, hsa-miR-4501, CCNB1, UHRF1, SPC24 and SHCBP1) among them were recognized for the construction of a prognostic risk model. Patients were then assigned to high-risk and low-risk groups according to the risk score. The Kaplan-Meier (K-M) analysis demonstrated that the high-risk group was closely associated with poor prognosis. The predictive nomogram combined with clinical features showed performance in clinical practice. In a nutshell, our ceRNA-related gene model and the nomogram graph are accurate and reliable tools for predicting prognostic outcomes of BC patients, and may make great contributions to modern precise medicine.

Identification and characterization of sex-dependent gene expression profile in glioblastoma.

Glioblastoma (GBM) is the most lethal primary tumor in the human brain and lacks favorable treatment options. Sex differences in the outcome of GBM are broadly acknowledged, but the underlying molecular mechanisms remain largely unknown. To identify the sex-dependent critical genes in the progression of GBM, raw data from several microarray datasets with the same array platform were downloaded from the Gene Expression Omnibus (GEO) database. These datasets included tumorous and normal tissue from patients with GBM and crucial sex features. Then, the differentially expressed genes (DEGs) in female and male tumors were identified via bioinformatics analysis, respectively. Functional signatures of the identified DEGs were further annotated by Gene Ontology (GO) and pathway enrichment analyses. Venn diagram and functional protein-protein interaction (PPI) network analyses were performed to screen out the sex-specific DEGs. Survival analysis of patients with differences in the expression level of selected genes was then carried out using the data from The Cancer Genome Atlas (TCGA). Here, we showed that ECT2, AURKA, TYMS, CDK1, NCAPH, CENPU, OIP5, KIF14, ASPM, FBXO5, SGOL2, CASC5, SHCBP1, FN1, LOX, IGFBP3, CSPG4, and CD44 were enriched in female tumor samples, whereas TNFSF13B, CXCL10, CXCL8, CXCR4, TLR2, CCL2, and FCGR2A were enriched in male tumor samples. Among these key genes, interestingly, ECT2 was associated with increased an survival rate for female patients, whileTNFSF13B could be regarded as a potential marker of poor prognosis in male patients. These results suggested that sex differences in patients may be attributed to the heterogeneous gene activity, which might influence the oncogenesis and the outcomes of GBM.

Identification of Immune Infiltration and Prognostic Biomarkers in Small Cell Lung Cancer Based on Bioinformatic Methods from 3 Studies.

AIMS: This study aimed to investigate the correlation between gene expression and immune cell infiltration and the overall survival rate in tumor tissues, which may contribute to the therapy and prognosis of small cell lung cancer (SCLC) patients. BACKGROUND: SCLC is the most aggressive type of lung neoplasm. There is no proper marker for the treatment and prediction of prognosis in SCLC. OBJECTIVES: Three gene expression profiles of SCLC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified between normal lung samples and SCLC lung samples. METHODS: Functional enrichment analysis of all DEGs was performed to explore the linkage among DEGs, the tumor immune microenvironment, and SCLC tumorigenesis. The common genes among the 3 groups in the Venn diagram and hub genes in protein-protein interaction (PPI) networks were considered potential key genes in SCLC patients. The TIMER (tumor immune estimation resource) database calculation and Kaplan-Meier survival curves were used to investigate the association between potential key genes and immune infiltrate prognosis of SCLC patients. RESULTS: A total of 750 (top 250 from each study) differentially expressed genes (DEGs) were identified. CLDN18 and BRIP1 were significantly related to immune infiltration in the tumor microenvironment. SHCBP1 and KIF23 were related mostly to prognosis in SCLC patients. CONCLUSION: The present study may provide some potential biomarkers for the therapy and prognosis of SCLC.

Prognosis and immunotherapy significances of a cancer-associated fibroblasts-related gene signature in lung adenocarcinoma.

Cells associated with cancer (CAFs) contribute significantly to the stroma of a tumor microenvironment (TME), which is related to the occurrence, treatment, and prognosis of lung adenocarcinoma (LUAD). Therefore, this study investigated the function of CAF-associated genes in the microenvironment of LUAD. The Cancer Genome Atlas (TCGA) database was used to download RNA-seq data from the TCGA Lung Adenocarcinoma cohort (TCGA-LUAD). The GSE68465 dataset, as the external validation set, was from the Gene Expression Omnibus (GEO) database. Besides, CAF-associated genes were sourced from the GeneCards and Molecular Signatures Database (MsigDB). For LUAD, differentially expressed CAF-related genes were selected from overlapping CAF and LUAD patient and control samples. Next, LASSO and Univariate Cox analyses were used to construct the risk model. Additionally, an analysis of Cox regression was used to construct a nomogram. Next, the immune infiltration in malignant tumour tissues was compared between high- and low-risk groups using Estimation of STromal and Immune cells in MAlignant Tumours (ESTIMATE) tissues and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT). The sensitivity differences of immunotherapy between the two risk groups were estimated by Tumor Immune Dysfunction and Exclusion (TIDE), and compared by rank-sum test. Finally, the model genes were detected by fluorescent real-time quantitative polymerase chain reaction (qRT-PCR). A total of 57 DE-CAFGs were acquired, and 9 of them (SHCBP1, CCNA2, AKAP12, CCNB1, GALNT3, SCGB1A1, CPS1, CDC6, and CXCL13) were selected as prognostic biomarkers. The Cox independent prognosis revealed the RiskScore and Stage were the two LUAD independent prognosis factors Moreover, 11 types of immune cells (memory B cells, resting natural killer cells (NK cells), Eosinophils, Macrophages M0, CD4 memory resting T cells, CD4 memory activated T cells, resting Mast cells, naive B cells, T cells regulatory (Tregs), neutrophils, and plasma cell), and 18 human leukocyte antigen (HLA) genes were different with the two risk groups. Lastly, the TIDE analysis showed differences between the two risk groups for TIDE, T cell dysfunction, and T cell exclusion, PD-L1 treatment scores. Lastly, Both LUAD and normal samples expressed the 9 model genes differently. A CAF-related prognostic model was constructed, which may have potential immunotherapy guiding significance for LUAD patients.

SHCBP1 Is a Prognostic Biomarker Related to the Tumour Immune Microenvironment in Pan-Cancer.

OBJECTIVE: Shc SH2 domain-binding protein (SHCBP1) is involved in regulating tumour progression in a variety of tumour types. The aim of this study was to analyze the prognostic landscape of SHCBP1 in pan-cancer and investigate the relationship between SHCBP1 expression and tumour immune microenvironment. METHODS: The Cancer Genome Atlas (TCGA) and genotype tissue expression (GTEx) database were used for obtaining the required data for the evaluation of SHCBP1 expression. The clinical characteristics and prognostic role of SHCBP1 were analyzed by using TCGA cohort. This was followed by gene set enrichment analysis by R software. Based on TCGA pan-cancer data, the correlation between SHCBP1 expression and immune infiltration, immune-related genes, microsatellite instability (MSI), and tumour mutational burden (TMB) was discussed. Finally, the half-maximum inhibitory concentration (IC50) values of 192 anti-cancer drugs were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) database and their correlation with SHCBP1 was analyzed. RESULTS: SHCBP1 was upregulated in most tumours showing a significant association with poor prognosis. Gene set enrichment analysis revealed SHCBP1 relation to the immunity, cell cycle, and cancer pathway in various types of tumour. SHCBP1 expression also showed a positive correlation with tumour-associated macrophages (TAMs) and immunosuppressive genes including TGFBR1, PD-L1 and TGFB1 and so on. In addition, high expression of SHCBP1 in patients was associated with resistance to a variety of anti-tumour drugs. CONCLUSION: The present study confirms SHCBP1 as a poor prognostic marker of cancers. Its high expression is suggested to be involved in immunological resistance to a variety of anti-tumour drugs.

Comprehensive analysis of a TPX2-related TRHDE-AS1/PKIA ceRNA network involving prognostic signatures in Hepatitis B virus-infected hepatocellular carcinoma.

Hepatitis B virus (HBV) infection is a main carcinogenic factor of hepatocellular carcinoma (HCC). TPX2 microtubule nucleation factor is recently recommended as a novel prognostic biomarker in HBV-infected HCC tissues. This study aimed to explore a TPX2-related ceRNA regulatory network in HBV-infected HCC and the potential impact on HCC prognosis. We comprehensively identified 541 differential expressed lncRNAs (DElncRNAs), 37 DEmiRNAs and 439 DEmRNAs from HBV-related TCGA-HCC cohorts in TPX2low and TPX2high groups. Based on their RNA-RNA interaction and expression analysis, four DElncRNAs (TRHDE-AS1, DLX6-AS1, SNHG14, HOXA11-AS), four DEmiRNAs (miR-23b, miR-320a, miR-589, miR-126) and five DEmRNAs (PKIA, PCDHA2, SHCBP1, PRSS16, KIF18A) in HCC tumor vs normal groups were subjected to the hub regulatory networks analysis and further prognostic value analysis. Importantly, the TRHDE-AS1/miR-23b/PKIA ceRNA network was associated with HCC prognosis. Furthermore, cellular location analysis and base-base interaction analysis indicated that the cytoplasmic lncRNA TRHDE-AS1 was regarded as a ceRNA to sponging miR-23b and then regulating PKIA. Interestingly, correlation analysis suggested the expression correlation between TRHDE-AS1 and PKIA in HCC. Finally, we further performed the methylation and immune infiltration analysis to explore the functional process of PKIA in HCC. We proposed a ceRNA regulatory network may help elucidate the mechanism by which TPX2 contributes to the prognosis of HBV-related HCC.

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer.

Background: Breast cancer is a big threat to the women across the world with substantial morbidity and mortality. The pressing matter of our study is to establish a prognostic gene model for breast cancer based on mRNAsi for predicting patient's prognostic survival. Methods: From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded the expression profiles of genes in breast cancer. On the basis of one-class logistic regression (OCLR) machine learning algorithm, mRNAsi of samples was calculated. Kaplan-Meier (K-M) and Kruskal-Wallis (K-W) tests were utilized for the assessment of the connection between mRNAsi and clinicopathological variables of the samples. As for the analysis on the correlation between mRNAsi and immune infiltration, ESTIMATE combined with Spearman test was employed. The weighted gene coexpression network analysis (WGCNA) network was established by utilizing the differentially expressed genes in breast cancer, and the target module with the most significant correlation with mRNAsi was screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to figure out the biological functions of the target module. As for the construction of the prognostic model, univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on genes in the module. The single sample gene set enrichment analysis (ssGSEA) and tumor mutational burden were employed for the analysis on immune infiltration and gene mutations in the high- and low-risk groups. As for the analysis on whether this model had the prognostic value, the nomogram and calibration curves of risk scores and clinical characteristics were drawn. Results: Nine mRNAsi-related genes (CFB, MAL2, PSME2, MRPL13, HMGB3, DCTPP1, SHCBP1, SLC35A2, and EVA1B) comprised the prognostic model. According to the results of ssGSEA and gene mutation analysis, differences were shown in immune cell infiltration and gene mutation frequency between the high- and low-risk groups. Conclusion: Nine mRNAsi-related genes screened in our research can be considered as the biomarkers to predict breast cancer patients' prognoses, and this model has a potential relationship with individual somatic gene mutations and immune regulation. This study can offer new insight into the development of diagnostic and clinical treatment strategies for breast cancer.