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Research into the characteristic molecules significantly affecting liver cancer immunotherapy.
BACKGROUND: The past decade has witnessed unprecedented scientific breakthroughs, including immunotherapy, which has great potential in clinical applications for liver cancer. METHODS: Public data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases and analyzed with R software. RESULTS: The LASSO and SVM-RFE machine learning algorithms identified 16 differentially expressed genes (DEGs) related to immunotherapy, namely, GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2. Moreover, a logistic model (CombinedScore) was established based on these DEGs, showing an excellent prediction performance for liver cancer immunotherapy. Patients with a low CombinedScore might respond better to immunotherapy. Gene Set Enrichment Analysis showed that many metabolism pathways were activated in patients with a high CombinedScore, including butanoate metabolism, bile acid metabolism, fatty acid metabolism, glycine serine and threonine metabolism, and propanoate metabolism. Our comprehensive analysis showed that the CombinedScore was negatively correlated with the levels of most tumor-infiltrating immune cells and the activities of key steps of cancer immunity cycles. Continually, the CombinedScore was negatively associated with the expression of most immune checkpoints and immunotherapy response-related pathways. Moreover, patients with a high and a low CombinedScore exhibited diverse genomic features. Furthermore, we found that CDCA7 was significantly correlated with patient survival. Further analysis showed that CDCA7 was positively associated with M0 macrophages and negatively associated with M2 macrophages, suggesting that CDCA7 could influence the progression of liver cancer cells by affecting macrophage polarization. Next, single-cell analysis showed that CDCA7 was mainly expressed in prolif T cells. Immunohistochemical results confirmed that the staining intensity of CDCA7 was prominently increased in the nucleus in primary liver cancer tissues compared to adjacent non-tumor tissues. CONCLUSIONS: Our results provide novel insights into the DEGs and factors affecting liver cancer immunotherapy. Meanwhile, CDCA7 was identified as a potential therapeutic target in this patient population.
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TNIK depletion induces inflammation and apoptosis in injured renal proximal tubule epithelial cells.
In the aftermath of acute kidney injury (AKI), surviving proximal tubule epithelia repopulate injured tubules to promote repair. However, a portion of cells fail to repair [termed failed-repair proximal tubule cells (FR-PTCs)] and exert ongoing proinflammatory and profibrotic effects. To better understand the molecular drivers of the FR-PTC state, we reanalyzed a mouse ischemia-reperfusion injury single-nucleus RNA-sequencing (snRNA-seq) atlas to identify Traf2 and Nck interacting kinase (Tnik) to be exclusively expressed in FR-PTCs but not in healthy or acutely injured proximal tubules after AKI (2 and 6 wk) in mice. We confirmed expression of Tnik protein in injured mouse and human tissues by immunofluorescence. Then, to determine the functional role of Tnik in FR-PTCs, we depleted TNIK with siRNA in two human renal proximal tubule epithelial cell lines (primary and immortalized hRPTECs) and analyzed each by bulk RNA-sequencing. Pathway analysis revealed significant upregulation of inflammatory signaling pathways, whereas pathways associated with differentiated proximal tubules such as organic acid transport were significantly downregulated. TNIK gene knockdown drove reduced cell viability and increased apoptosis, including differentially expressed poly(ADP-ribose) polymerase (PARP) family members, cleaved PARP-1 fragments, and increased annexin V binding to phosphatidylserine. Together, these results indicate that Tnik upregulation in FR-PTCs acts in a compensatory fashion to suppress inflammation and promote proximal tubule epithelial cell survival after injury. Modulating TNIK activity may represent a prorepair therapeutic strategy after AKI.NEW & NOTEWORTHY The molecular drivers of successful and failed repair in the proximal tubule after acute kidney injury (AKI) are incompletely understood. We identified Traf2 and Nck interacting kinase (Tnik) to be exclusively expressed in failed-repair proximal tubule cells after AKI. We tested the effect of siTNIK depletion in two proximal tubule cell lines followed by bulk RNA-sequencing analysis. Our results indicate that TNIK acts to suppress inflammatory signaling and apoptosis in injured renal proximal tubule epithelial cells to promote cell survival.
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Molecular clustering and prognostic features based on integrated databases predict survival and immune status in patients with gastric cancer.
BACKGROUND: Gastric cancer (GC) remains one of the most common malignancies worldwide with high mortality rates despite advances in treatment approaches. Patients frequently develop drug resistance to current therapies, highlighting the critical need for novel prognostic biomarkers that can enhance survival rates and guide immunotherapy decisions in patients with GC. METHODS: We conducted a comprehensive bioinformatics analysis using integrated clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. GC cases were categorized into two prognostic-related gene (PRG) clusters, and differentially expressed genes were identified. We established a prognostic model based on 11 key genes, stratified patients into high-risk and low-risk groups, and developed a nomogram model for survival prediction. Expression of selected genes was validated through quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry in clinical samples. RESULTS: The identified PRGs and gene clusters strongly associated with patient survival, immune system functions, and cancer-related pathways. Risk scores significantly correlated with immune cell abundance, checkpoint expression, and responses to immunotherapy and chemotherapy. For instance, the area under the curve (AUC) values of patients at 1-year, 3-year, and 5-year survival were all greater than 0.6 in the ROC curves (p < 0.05), which makes our prediction more accurate, and the line graphs predicted a 1-year survival rate exceeding 0.907, a 3-year survival rate exceeding 0.726, and a 5-year survival rate exceeding 0.633; the calibration curves are almost close to the predicted ones (p < 0.05). This implies that patients in the high-risk group demonstrated significantly poorer prognosis. Univariate Cox (UniCox) analysis and multivariate Cox (MultiCox) analysis indicate that CTHRC1 (Collagen Triple Helix Repeat Containing 1), CST6 (Cystatin E/M), and AKR1B1 (Aldo-Keto Reductase Family 1 Member B) are independent prognostic factors, and all are associated with poor survival prognosis (HR > 1, p < 0.05). Gene set enrichment analysis (GSEA) and single-cell analysis revealed significant enrichment of multiple biological pathways and variability in expression of these genes across different cell types within the tumor microenvironment. qRT-PCR and immunohistochemistry confirmed significant differences in mRNA and protein expression of CTHRC1, CST6, and AKR1B1 between normal and GC tissues (p < 0.05). CONCLUSION: Our research establishes a robust molecular signature for predicting survival of patients with GC and characterizing the tumor immune microenvironment. It aims not only to establish a prognostic model, but also to explore immunobiological functions. The identified prognostic features and key genes (CTHRC1, CST6, and AKR1B1) offer potential as biomarkers and therapeutic targets, potentially guiding more effective personalized treatment strategies for patients with GC.
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The conserved wobble uridine tRNA thiolase Ctu1 is required for angiogenesis and embryonic development.
Cytosolic thiouridylase is a conserved cytoplasmic tRNA thiolase composed of two different subunits, CTU1 and CTU2. CTU2 serves as a scaffold protein, while CTU1 catalyzes the 2-thiolation at the 34th wobble uridine of the anticodon loop. tRNAGlnUUG, tRNAGluUUC, and tRNALysUUU are the tRNA substrates that are modified with a thiol group at the C2 positions (s2) by CTU1, and also with a methoxycarbonylmethyl group at the C5 positions (mcm5) by Elongator and ALKBH8. mcm5s2U34 modification of the three tRNAs, and their modifying enzymes are involved in human disease and development. Elongator mutant animals exhibit severe phenotypes, while the biological function of Ctu1 in vertebrate animal models remains poorly characterized. Here, we applied antisense morpholino oligonucleotides targeting cytosolic thiouridylase subunit1 (ctu1) transcripts in a zebrafish model and small interfereing RNA against CTU1 transcript in human endothelial cells to define the phenotypes. We found that deficiency of ctu1 causes impaired angiogenesis and development in zebrafish embryos, and CTU1 is involved in proliferation, migration, and tube formation of human endothelial cells. We employed single-cell RNA sequencing to acquire the transcriptomic atlas from ctu1 and control morphant zebrafish. Comprehensive bioinformatics analysis, including pseudo-time, RNA velocity, cell-cell communication, and gene regulatory network inference revealed that ctu1 deficiency leads to the arrest of cell cycle, and the defects of nerve development and erythrocyte differentiation and the attenuation of several pro-angiogenic signaling pathways, e.g., angpt-tek and dll4-notch. Our findings show for the first time that CTU1 is essential for angiogenesis and embryonic development in vertebrates.
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HK3 overexpression associated with epithelial-mesenchymal transition in colorectal cancer.
BACKGROUND: Colorectal cancer (CRC) is a common cancer worldwide. The main cause of death in CRC includes tumor progression and metastasis. At molecular level, these processes may be triggered by epithelial-mesenchymal transition (EMT) and necessitates specific alterations in cell metabolism. Although several EMT-related metabolic changes have been described in CRC, the mechanism is still poorly understood. RESULTS: Using CrossHub software, we analyzed RNA-Seq expression profile data of CRC derived from The Cancer Genome Atlas (TCGA) project. Correlation analysis between the change in the expression of genes involved in glycolysis and EMT was performed. We obtained the set of genes with significant correlation coefficients, which included 21 EMT-related genes and a single glycolytic gene, HK3. The mRNA level of these genes was measured in 78 paired colorectal cancer samples by quantitative polymerase chain reaction (qPCR). Upregulation of HK3 and deregulation of 11 genes (COL1A1, TWIST1, NFATC1, GLIPR2, SFPR1, FLNA, GREM1, SFRP2, ZEB2, SPP1, and RARRES1) involved in EMT were found. The results of correlation study showed that the expression of HK3 demonstrated a strong correlation with 7 of the 21 examined genes (ZEB2, GREM1, TGFB3, TGFB1, SNAI2, TWIST1, and COL1A1) in CRC. CONCLUSIONS: Upregulation of HK3 is associated with EMT in CRC and may be a crucial metabolic adaptation for rapid proliferation, survival, and metastases of CRC cells.
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The landscape of the mesenchymal signature in brain tumours.
The complexity of glioblastoma multiforme, the most common and lethal variant of gliomas, is reflected by cellular and molecular heterogeneity at both the inter- and intra-tumoural levels. Molecular subtyping has arisen in the past two decades as a promising strategy to give better predictions of glioblastoma multiforme evolution, common disease pathways, and rational treatment options. The Cancer Genome Atlas network initially identified four molecular subtypes of glioblastoma multiforme: proneural, neural, mesenchymal and classical. However, further studies, also investigated glioma stem cells, have only identified two to three subtypes: proneural, mesenchymal and classical. The proneural-mesenchymal transition upon tumour recurrence has been suggested as a mechanism of tumour resistance to radiation and chemotherapy treatment. Glioblastoma multiforme patients with the mesenchymal subtype tend to survive shorter than other subtypes when analysis is restricted to samples with low transcriptional heterogeneity. Although the mesenchymal signature in malignant glioma may seem at odds with the common idea of the ectodermal origin of neural-glial lineages, the presence of the mesenchymal signature in glioma is supported by several studies suggesting that it can result from: (i) intrinsic expression of tumour cells affected with accumulated genetic mutations and cell of origin; (ii) tumour micro-environments with recruited macrophages or microglia, mesenchymal stem cells or pericytes, and other progenitors; (iii) resistance to tumour treatment, including radiotherapy, antiangiogenic therapy and possibly chemotherapy. Genetic abnormalities, mainly NF1 mutations, together with NF-κB transcriptional programs, are the main driver of acquiring mesenchymal-signature. This signature is far from being simply tissue artefacts, as it has been identified in single cell glioma, circulating tumour cells, and glioma stem cells that are released from the tumour micro-environment. All these together suggest that the mesenchymal signature in glioblastoma multiforme is induced and sustained via cell intrinsic mechanisms and tumour micro-environment factors. Although patients with the mesenchymal subtype tend to have poorer prognosis, they may have favourable response to immunotherapy and intensive radio- and chemotherapy.
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Identification of Ultrasound-Sensitive Prognostic Markers of LAML and Construction of Prognostic Risk Model Based on WGCNA.
BACKGROUND: Acute myeloid leukemia (LAML) is the most widely known acute leukemia in adults. Chemotherapy is the main treatment method, but eventually many individuals who have achieved remission relapse, the disease will ultimately transform into refractory leukemia. Therefore, for the improvement of the clinical outcome of patients, it is crucial to identify novel prognostic markers. METHODS: The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were utilized to retrieve RNA-Seq information and clinical follow-up details for patients with acute myeloid leukemia, respectively, whereas samples that received or did not receive ultrasound treatment were analyzed using differential expression analysis. For consistent clustering analysis, the ConsensusClusterPlus package was utilized, while by utilizing weighted correlation network analysis (WGCNA), important modules were found and the generation of the coexpression network of hub gene was generated using Cytoscape. CIBERSORT, ESTIMATE, and xCell algorithms of the "IOBR" R package were employed for the calculation of the relative quantity of immune infiltrating cells, whereas the mutation frequency of cells was estimated by means of the "maftools" R package. The pathway enrichment score was calculated using the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm of the "Gene Set Variation Analysis (GSVA)" R package. The IC50 value of the drug was predicted by utilizing the "pRRophetic." The indications linked with prognosis were selected by means of the least absolute shrinkage and selection operator (Lasso) Cox analysis. RESULTS: Two categories of samples were created as follows: Cluster 1 and Cluster 2 depending on the differential gene consistent clustering of ultrasound treatment. The prognosis of patients in Cluster 2 was better than that in Cluster 1, and a considerable variation was observed in the immune microenvironment of Cluster 1 and Cluster 2. Lasso analysis finally obtained an 8-gene risk model (GASK1A, LPO, LTK, PRRT4, UGT3A2, BLOCK1S1, G6PD, and UNC93B1). The model acted as an independent risk factor for the patients' prognosis, and it showed good robustness in different datasets. Considerable variations were observed in the abundance of immune cell infiltration, genome mutation, pathway enrichment score, and chemotherapeutic drug resistance between the low and high-risk groups in accordance with the risk score (RS). Additionally, model-based RSs in the immunotherapy cohort were significantly different between complete remission (CR) and other response groups. CONCLUSION: The prognosis of people with LAML can be predicted using the 8-gene signature.
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MAIP1-Related Tumor Immune Infiltration: As a Potential Prognostic Biomarker for Esophageal Cancer.
BACKGROUND: Esophageal cancer (EC), a common malignant tumor of digestive tract, is also one of the most deadly cancers. Accumulating studies have shown that the initiating and progressing multiple human diseases were closely related to the expression of MAIP. However, the specific roles and mechanisms of MAIP1 in EC remain incompletely defined. PURPOSE: This study aims to determine the clinical significance of MAIP1 in EC and explores its potential molecular mechanisms regulating tumor immune infiltration. METHODS: We obtained RNA-seq datasets and corresponding clinical data for EC patients from the Cancer Genome Atlas (TCGA) database via the UCSC Xena browser to extract MAIP1 expression and plot survival curves to determine their prognosis. Based on the differential expression of MAIP1, EC patients were divided into high and low group to investigate the mechanism of MAIP1 in EC. In addition, the single sample gene set enrichment analysis (ssGSEA) quantified the expression of various immune cell signature marker genes and assessed the degree of immune infiltration in EC. RESULTS: In the TCGA-EC cohort, the overexpression of MAIP1 was observed in tumor tissues compared to normal tissues (p = 0.0038). Overall survival analysis showed that EC patients with the overexpression of MAIP1 presented a lower overall survival and worse prognosis (p = 0.004). Enrichment analysis revealed that the differential genes (DEGs) between high and low group are involved in biological functions such as extracellular matrix and organization extracellular structure. The results of ssGSEA showed that DCs, iDCs, macrophages, mast cells, and NK cells were significantly different in MAIP1high and MAIP1low groups, and all showed high expression in the MAIP1low group. CONCLUSION: We proposed that MAIP1 overexpression was associated with poor prognosis and tumor immune infiltration in EC. At present, there are few MAIP1-related tumor immune infiltration studies in EC, and further investigation is needed.
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Comprehensive analysis pinpoints CCNA2 as a prognostic and immunological biomarker in non-small cell lung cancer.
BACKGROUND: Lung cancer is a leading cause of morbidity and mortality globally. Despite advances in targeted and immunotherapies, overall survival (OS) rates remain suboptimal. Cyclin-A2 (CCNA2), known for its upregulation in various tumors and role in tumorigenesis, has an undefined function in non-small cell lung cancer (NSCLC). METHODS: We analyzed three microarray datasets from the Gene Expression Omnibus (GEO) repository to identify differentially expressed genes. Using STRING, we constructed a protein-protein interaction (PPI) network to pinpoint hub genes. The expression and prognostic relevance of CCNA2 were validated using GEPIA and the Kaplan-Meier plotter. Clinicopathological correlations were assessed via the Human Protein Atlas (HPA) and UALCAN databases. qRT-PCR and immunohistochemistry (IHC) were performed to validate CCNA2 mRNA and protein levels. Loss-of-function assays in lung cancer cell lines evaluated the biological role of CCNA2. Immune infiltration and single-cell sequencing were also explored. RESULTS: Analysis of GSE18842, GSE101929, and GSE116959 datasets identified 321 upregulated and 623 downregulated genes in NSCLC. CCNA2 was confirmed to be highly expressed in NSCLC through qRT-PCR and IHC, with overexpression correlating with advanced pathological stages and lymph node metastasis. The area under the curve (AUC) of CCNA2 indicating high diagnostic accuracy. Immune infiltration and single-cell sequencing revealed that CCNA2 expression was significantly associated with immune cell infiltration, particularly in Tprolif cells. CONCLUSION: CCNA2 is upregulated in NSCLC and shows significant correlation with clinicopathological characteristics. Our findings suggest that CCNA2 may serve as a promising biomarker for both the prognosis and diagnosis of NSCLC.
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Identification of Prognostic Biomarkers of Cutaneous Melanoma Based on Analysis of Tumor Mutation Burden.
BACKGROUND: Immunotherapy offers a novel approach for the treatment of cutaneous melanoma, but the clinical efficiency varies for individual patients. In consideration of the high cost and adverse effects of immunotherapy, it is essential to explore the predictive biomarkers of outcomes. Recently, the tumor mutation burden (TMB) has been proposed as a predictive prognosticator of the immune response. METHOD: RNA-seq and somatic mutation datasets of 472 cutaneous melanoma patients were downloaded from The Cancer Genome Atlas (TCGA) database to analyze mutation type and TMB. Differently expressed genes (DEGs) were identified for functional analysis. TMB-related signatures were identified via LASSO and multivariate Cox regression analysis. The association between mutants of signatures and immune cells was evaluated from the TIMER database. Furthermore, the Wilcox test was applied to assess the difference in immune infiltration calculated by the CIBERSORT algorithm in risk groupings. RESULTS: C>T substitutions and TTN were the most common SNV and mutated gene, respectively. Patients with low TMB presented poor prognosis. DEGs were mainly implicated in skin development, cell cycle, DNA replication, and immune-associated crosstalk pathways. Furthermore, a prognostic model consisting of eight TMB-related genes was developed, which was found to be an independent risk factor for treatment outcome. The mutational status of eight TMB-related genes was associated with a low level of immune infiltration. In addition, the immune infiltrates of CD4+ and CD8+ T cells, NK cells, and M1 macrophages were higher in the low-risk group, while those of M0 and M2 macrophages were higher in the high-risk group. CONCLUSION: Our study demonstrated that a higher TMB was associated with favorable survival outcome in cutaneous melanoma. Moreover, a close association between prognostic model and immune infiltration was identified, providing a new potential target for immunotherapy.