Complement and coagulation cascades pathway correlates with chemosensitivity and overall survival in patients with soft tissue sarcoma

Pancancer Analysis of NSUN2 with a Focus on Prognostic and Immunological Roles in Endometrial Cancer

The significance of NSUN2 in carcinogenesis is gradually being recognized, yet a comprehensive analysis across pan-cancer remains a pivotal void in existing research. In our investigation, we capitalized on the UCSC Xena platform to evaluate NSUN2 expression levels and their prognostic implications across a range of cancer types. Furthermore, we employed the cBioPortal database to delve into the genomic variations of NSUN2 within human cancers. Our study encompassed the use of molecular docking, genomic tumor profiling, and an assessment of the gene's responsiveness to pharmacological treatments. Additionally, we utilized algorithmic techniques to measure the relationship between NSUN2 expression and key clinical biomarkers, such as microsatellite instability (MSI), tumor mutational burden (TMB), and immune cell infiltration. Our results have established a notable association between NSUN2 and endometrial cancer (UCEC), thereby confirming its clinical significance through an analysis of tumoral expression patterns, mutational spectra, methylation profiles, and drug sensitivity. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were crucial tools in elucidating the biological roles of NSUN2 in endometrial cancer. Consistently, elevated NSUN2 expression was associated with unfavorable clinical outcomes and was primarily observed in the context of genetic amplifications. Across 22 distinct tumor types, our analysis revealed a notable correlation between NSUN2 expression and various metrics related to immune cell infiltration, tumor stroma, and immune scores. Notably, higher levels of NSUN2 expression have been linked to a reduced response to certain chemotherapeutic agents, including PHA-793887. In UCEC, a positive correlation between NSUN2 methylation and gene expression hints at a potential epigenetic regulatory mechanism underlying cancer progression. Our study highlights the potential of NSUN2 as a key oncogene and its promising role as a therapeutic target as well as a prognostic biomarker for endometrial cancer. This underscores its potential importance in predicting responses to immunotherapy.

Genome-wide expression profiling reveals novel biomarkers in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the most aggressive and frequent malignancy detected among women worldwide. The pathophysiology of OC should, therefore be better understood to identify diagnostic, prognostic, and predictive novel biomarkers necessary for early detection, management, and prognostication. In this study, we aimed to investigate transcriptomic landscape and biomarker through RNA-seq data analysis. Further analysis by Protein Protein network identified top 10 Differentially Expressed Genes (DEGs). KEGG pathway enrichment analysis revealed the significant enrichment of DEGs in basal cell carcinoma, cell cycle and FoxO signalling pathway. The RNA-seq results of 10 DEGs were validated by QRT-PCR and TCGA database. Correlation studies were also performed between gene expression and clinical characteristics followed by survival analysis. Finally, 8 DEGs (CDKN1A, BCL6, CDC45, WNT2, TLR5, AQP5) including two novel DEGs (CSN1S1 and NKILA) were identified showing significant correlations with EOC characteristics. These may serve as interesting biomarkers and novel treatment targets and warrant further investigation into the functional outcome of EOC.

Identification of a coagulation-related signature correlated with immune infiltration and their prognostic implications in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a fatal form of lung cancer with a poor prognosis. Coagulation system had been confirmed closely related to tumor progression and the hypercoagulable state encouraged the immune infiltration and development of tumor cells, leading to a poor prognosis in cancer patients. However, the use of the coagulation-related genes (CRGs) for prognosis in LUAD has yet to be determined. In this study, we constructed an immune-related signature (CRRS) and identified a potential coagulation-related biomarker (P2RX1).

METHODS

We obtained a total of 209 CRGs based on two coagulation-related KEGG pathways, then developed the CRRS signature by using the TCGA-LUAD RNA-seq data via the procedure of LASSO-Cox regression, stepwise-Cox regression, univariate and multivariate Cox regression. Grouped by the CRRS, Kaplan-Meier survival curves and receiver operating characteristic curves were drawn for the training and validation sets, respectively. In addition, single-sample gene set enrichment analysis was exploited to explore immune infiltration level. Moreover, immunophenotypes and immunotherapy grouped by CRRS were further analyzed.

RESULTS

We developed an immune-related signature (CRRS) composed of COL1A2, F2, PLAUR, C4BPA, and P2RX1 in LUAD. CRRS was an independent risk factor for overall survival and displayed stable and powerful performance. Additionally, CRRS possessed distinctly superior accuracy than traditional clinical variables and molecular features. Functional analysis indicated that the differentially high expressed genes in the low-risk group significantly enriched in T cell and B cell receptor signaling pathways. The low-risk group was sensitive to anti-PD-1/PD-L1 immunotherapy and displayed abundant immune infiltration and immune checkpoint gene expression. Finally, we identified an independent prognostic gene P2RX1. Low expression of P2RX1 associated with poor overall survival and decreased immune infiltration.

CONCLUSIONS

Our study revealed a significant correlation between CRRS and immune infiltration. CRRS could serve as a promising tool to improve the clinical outcomes for individual LUAD patients.

Identification of a disulfidptosis-related genes signature for prognostic implication in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer. Additionally, disulfidptosis, a newly discovered type of cell death, has been found to be closely associated with the onset and progression of tumors.

METHODS

The study first identified genes related to disulfidptosis through correlation analysis. These genes were then screened using univariate cox regression and LASSO regression, and a prognostic model was constructed through multivariate cox regression. A nomogram was also created to predict the prognosis of LUAD. The model was validated in three independent data sets: GSE72094, GSE31210, and GSE37745. Next, patients were grouped based on their median risk score, and differentially expressed genes between the two groups were analyzed. Enrichment analysis, immune infiltration analysis, and drug sensitivity evaluation were also conducted.

RESULTS

In this study, we examined 21 genes related to disulfidptosis and developed a gene signature that was found to be associated with a poorer prognosis in LUAD. Our model was validated using three independent datasets and showed AUC values greater than 0.5 at 1, 3, and 5 years. Enrichment analysis revealed that the disulfidptosis-related genes signature had a multifaceted impact on LUAD, particularly in relation to tumor development, proliferation, and metastasis. Patients in the high-risk group exhibited higher tumor purity and lower stromal score, ESTIMATE score, and Immune score.

CONCLUSION

This study constructed a gene signature related to disulfidptosis in lung adenocarcinoma and analyzed its impact on the disease and its association with the tumor microenvironment. The findings of this research provide valuable insights into the understanding of lung adenocarcinoma and could potentially lead to the development of new treatment strategies.

Label-free quantitative proteomics reveals the potential mechanisms of insoluble dietary fiber from okara in improving hepatic lipid metabolism of high-fat diet-induced mice

The high purity insoluble dietary fiber (IDF) from okara is a natural component with a potentially positive effect on a high-fat diet (HFD)-induced hepatic metabolic disorders, although its regulatory mechanism remains unclear. This study aims to elucidate the potential pathways and key proteins of IDF for the amelioration of hepatic lipid metabolism in mice fed with HFD. Here, we used label-free quantitative proteomics technology to quantity and identify differentially expressed proteins in the liver that are associated with IDF treatment. The differentially expressed proteins were assessed by GO annotation and KEGG pathways. Western blot and qRT-PCR analyses were conducted to validate the potential targets regulated by IDF. In total, 73 differentially expressed proteins were identified, of which 27 were up-regulated (FC > 1.5) and 46 were down-regulated (FC < 0.667). GO analysis suggested that differentially expressed proteins were mainly located in the cell and organelles, regulated biological processes, and were associated with enzyme activity and molecular binding. The KEGG pathway enrichment analysis further demonstrated glycolysis/gluconeogenesis, pyruvate metabolism, TCA cycle, arginine biosynthesis, alanine, aspartate and glutamate metabolism, and retinol metabolism were affected. The combination of proteomics, Western blot, and qRT-PCR suggested that ACS, ACLY, GOT1, GLS2, NAGS, CYP4A10, CYP3A25, and CYP2A5 in these pathways might be key proteins for IDF intervention. Taken together, our findings elucidate new mechanisms involved in how IDF affects hepatic metabolism, provide important information for the functional food industries, and improve the added value of okara. SIGNIFICANCE: Okara is evidenced as a high-quality by-product with several nutritional components, especially dietary fiber (50-60%) labeled as "The Seventh Nutrient". Previous studies have shown that IDF has a positive potential effect on a high-fat diet (HFD)-induced hepatic metabolic disorders, but its molecular mechanism remains unclear. To elucidate the therapeutic mechanism of IDF at the protein level, a label-free quantitative proteomic analysis was used to identify the dynamic changes of the liver proteome between HIDF and HFD groups in this study. These results provide a new perspective for exploring the therapeutic mechanism of IDF at the protein level and enlightenment for promoting the comprehensive utilization of okara.

Perspectives of Cell Sensitivity/Resistance Assay in Soft Tissue Sarcomas Chemotherapy

Treatment of highly malignant soft tissue sarcomas (STSs) requires multicomponent therapy including surgery, radiotherapy, and chemotherapy. Despite the advancements in targeted cancer therapies, cytostatic drug combinations remain the gold standard for STS chemotherapy. The lack of algorithms for personalized selection of STS chemotherapy leads to unhelpful treatment of chemoresistant tumors, causing severe side effects in patients. The goal of our study is to assess the applicability of in vitro chemosensitivity/resistance assays (CSRAs) in predicting STS chemoresistance. Primary cell cultures were obtained from 148 surgery samples using enzymatic and mechanical disaggregation. CSRA was performed using resazurin-based metabolic activity measurement in cells cultured with doxorubicin, ifosfamide, their combination and docetaxel, gemcitabine, and also their combination for 7 days. Both the clinical data of patients and the CSRA results demonstrated a higher resistance of some cancer histotypes to specific drugs and their combinations. The correlation between the CSRA results for doxorubicin and ifosfamide and clinical responses to the combination chemotherapy with these drugs was demonstrated via Spearman rank order correlation. Statistically significant differences in recurrence-free survival were also shown for the groups of patients formed, according to the CSRA results. Thus, CSRAs may help both practicing physicians to avoid harmful and useless treatment, and researchers to study new resistance markers and to develop new STS drugs.

C1QC is a prognostic biomarker with immune-related value in kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is a representative histologic subtype of renal cell carcinoma (RCC). RCC exhibits a strong immunogenicity with a prominent dysfunctional immune infiltration. Complement C1q C chain (C1QC) is a polypeptide in serum complement system and is involved in tumorigenesis and the modulation of tumor microenvironment (TME). However, researches have not explored the effect of C1QC expression on prognosis and tumor immunity of KIRC.

Methods: The difference in a wide variety of tumor tissues and normal tissues in terms of the C1QC expression was detected using TIMER and TCGA portal databases, and further validation of protein expression of C1QC was conducted via Human Protein Atlas. Then, the associations of C1QC expression with clinicopathological data and other genes were studied with the use of UALCAN database. Subsequently, the association of C1QC expression with prognosis was predicted by searching the Kaplan-Meier plotter database. A protein-protein interaction (PPI) network with the Metascape database was built using STRING software, such that the mechanism underlying the C1QC function can be studied in depth. The TISCH database assisted in the evaluation of C1QC expression in different cell types in KIRC at the single-cell level. Moreover, the association of C1QC and the infiltration level of tumor immune cell was assessed using TIMER platform. The TISIDB website was selected to deeply investigate the Spearman correlation between C1QC and immune-modulator expression. Lastly, how C1QC affected the cell proliferation, migration, and invasion in vitro was assessed using knockdown strategies.

Results: KIRC tissues had notably upregulated C1QC level in comparison with adjacent normal tissues, with showed a positive relevance to clinicopathological features including tumor stage, grade, and nodal metastasis, and a negative relevance to clinical prognosis in KIRC. C1QC knockdown inhibited KIRC cell proliferation, migration, and invasion, as indicated by the results of the in vitro experiment. Furthermore, functional and pathway enrichment analysis demonstrated that C1QC was involved in immune system-related biological processes. According to single-cell RNA analysis, C1QC exhibited a specific upregulation in macrophages cluster. Additionally, there was an obvious association of C1QC and a wide variety of tumor-infiltrating immune cells in KIRC. Also, high C1QC expression presented inconsistent prognosis in different enriched immune cells subgroups in KIRC. Immune factors might contribute to C1QC function in KIRC.

Conclusion: C1QC is qualified to predict KIRC prognosis and immune infiltration biologically. Targeting C1QC may bring new hope for the treatment of KIRC.

A pathway-based mutation signature to predict the clinical outcomes and response to CTLA-4 inhibitors in melanoma

Immune checkpoint inhibitor (ICI) therapy has become a powerful clinical strategy for treating melanoma. The relationship between somatic mutations and the clinical benefits of immunotherapy has been widely recognized. However, the gene-based predictive biomarkers are less stable due to the heterogeneity of cancer at the individual gene level. Recent studies have suggested that the accumulation of gene mutations in biological pathways may activate antitumor immune responses. Herein, a novel pathway mutation signature (PMS) was constructed to predict the survival and efficacy of ICI therapy. In a dataset of melanoma patients treated with anti-CTLA-4, we mapped the mutated genes into the pathways and then identified seven significant mutation pathways associated with survival and immunotherapy response, which were used to construct the PMS model. According to the PMS model, the patients in the PMS-high group showed better overall survival (hazard ratio (HR) = 0.37; log-rank test, p < 0.0001) and progression-free survival (HR = 0.52; log-rank test, p = 0.014) than those in the PMS-low group. The PMS-high patients also showed a significantly higher objective response rate to anti-CTLA-4 therapy than the PMS-low patients (Fisher's exact test, p = 0.0055), and the predictive power of the PMS model was superior to that of TMB. Finally, the prognostic and predictive value of the PMS model was validated in two independent validation sets. Our study demonstrated that the PMS model can be considered a potential biomarker to predict the clinical outcomes and response to anti-CTLA-4 therapy in melanoma patients.

Development and validation of focal adhesion-related genes signature in gastric cancer

Background: This study aims to build a focal adhesion-related genes-based prognostic signature (FAS) to accurately predict gastric cancer (GC) prognosis and identify key prognostic genes related to gastric cancer. Results: Gene expression and clinical data of gastric cancer patients were sourced from Gene Expression Omnibus and The Cancer Genome Atlas. Subsequently, the GEO dataset was randomly distributed into training and test cohorts. The TCGA dataset was used to validate the external cohort. Lasso Cox regression was used to detect OS-related genes in the GEO cohort. A risk score model was established according to the screened genes. A nomogram, based on the clinical characteristics and risk score, was generated to predict the prognosis of gastric cancer patients. Using time-dependent receiver operating characteristic (ROC) and calibration performances, we evaluated the models' validity. The patients were grouped into a high- or low-risk group depending on the risk score. Low-risk patients exhibited higher OS than high-risk patients (entire cohort: p < 0.001; training cohort: p < 0.001, test cohort: p < 0.001). Furthermore, we found a correlation between high-risk gastric cancer and extracellular matrix (ECM) receptor interaction, high infiltration of macrophages, CD44, and HLA-DOA. Conclusion: The generated model based on the genetic characteristics of the focal adhesion prognostic gene can aid in the prognosis of gastric cancer patients in the future.

Liver-specific lncRNA FAM99A may be a tumor suppressor and promising prognostic biomarker in hepatocellular carcinoma

Background

Increasing evidence shows that liver-specific long non-coding RNAs (lncRNAs) play important roles in the development of hepatocellular carcinoma (HCC). We identified a novel liver-specific lncRNA, FAM99A, and examined its clinical significance and biological functions in HCC.

Methods

The expression level and clinical value of FAM99A in HCC were examined using The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases, and were further verified using quantitative real-time polymerase chain reaction (qRT–PCR) in our HCC cohort. Univariate and multivariate Cox proportional hazards regression models were also applied to identify independent prognostic indicators for HCC patients. Cell counting kit-8, colony formation, and Transwell assays were performed to evaluate the effects of FAM99A on the proliferation, migration, and invasion abilities of HCC cells in vitro. A subcutaneous xenograft tumor model was implemented to determine the effect of FAM99A on the tumor growth of HCC cells in vivo. RNA pull-down and mass spectrometry assays were performed to reveal the potential molecular mechanisms of FAM99A in HCC.

Results

The three public online databases and qRT–PCR data showed that FAM99A was frequently downregulated in HCC tissues and inversely correlated with microvascular invasion and advanced histological grade of HCC patients. Kaplan–Meier survival analysis indicated that decreased FAM99A was significantly associated with poor overall survival of HCC patients based on TCGA database (P = 0.040), ICGC data portal (P < 0.001), and our HCC cohort (P = 0.010). A multivariate Cox proportional hazards regression model based on our HCC cohort suggested that FAM99A was an independent prognostic factor of overall survival for HCC patients (hazard ratio: 0.425, P = 0.039). Upregulation of FAM99A suppressed the proliferation, colony formation, migration, and invasion capacities of HCC cells in vitro, and knockdown of FAM99A had the opposite effects. A subcutaneous xenograft tumor model demonstrated that overexpression of FAM99A significantly inhibited the tumor growth of HCC cells in vivo. Seven tumor-related proteins (PCBP1, SRSF5, SRSF6, YBX1, IGF2BP2, HNRNPK, and HNRNPL) were recognized as possible FAM99A-binding proteins by the RNA pull-down and mass spectrometry assays.

Conclusion

Our results suggest that FAM99A exerts cancer-inhibiting effects on HCC progression, and it may be a promising prognostic indicator for HCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10186-2.

Construction and validation of a novel coagulation-related 7-gene prognostic signature for gastric cancer

Background: Gastric cancer (GC) is the most common malignant tumor. Due to the lack of practical molecular markers, the prognosis of patients with advanced gastric cancer is still poor. A number of studies have confirmed that the coagulation system is closely related to tumor progression. Therefore, the purpose of this study was to construct a coagulation-related gene signature and prognostic model for GC by bioinformatics methods.

Methods: We downloaded the gene expression and clinical data of GC patients from the TCGA and GEO databases. In total, 216 coagulation-related genes (CRGs) were obtained from AmiGO 2. Weighted gene co-expression network analysis (WGCNA) was used to identify coagulation-related genes associated with the clinical features of GC. Last absolute shrinkage and selection operator (LASSO) Cox regression was utilized to shrink the relevant predictors of the coagulation system, and a Coag-Score prognostic model was constructed based on the coefficients. According to this risk model, GC patients were divided into high-risk and low-risk groups, and overall survival (OS) curves and receiver operating characteristic (ROC) curves were drawn in the training and validation sets, respectively. We also constructed nomograms for predicting 1-, 2-, and 3-year survival in GC patients. Single-sample gene set enrichment analysis (ssGSEA) was exploited to explore immune cells’ underlying mechanisms and correlations. The expression levels of coagulation-related genes were verified by real-time quantitative polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).

Results: We identified seven CRGs employed to construct a Coag-Score risk model using WGCNA combined with LASSO regression. In both training and validation sets, GC patients in the high-risk group had worse OS than those in the low-risk group, and Coag-Score was identified as an independent predictor of OS, and the nomogram provided a quantitative method to predict the 1-, 2-, and 3-year survival rates of GC patients. Functional analysis showed that Coag-Score was mainly related to the MAPK signaling pathway, complement and coagulation cascades, angiogenesis, epithelial–mesenchymal transition (EMT), and KRAS signaling pathway. In addition, the high-risk group had a significantly higher infiltration enrichment score and was positively associated with immune checkpoint gene expression. Conclusion: Coagulation-related gene models provide new insights and targets for the diagnosis, prognosis prediction, and treatment management of GC patients.

Network Pharmacology, Integrated Bioinformatics, and Molecular Docking Reveals the Anti-Ovarian Cancer Molecular Mechanisms of Cinnamon (Cinnamomum cassia (L.) J. Presl)

Cinnamon ( Cinnamomum cassia (L.) J. Presl) is a popular natural spice with various pharmacological properties. This study was based on network pharmacology integrating bioinformatics and molecular docking to explore the potential molecular mechanisms of cinnamon in the treatment of ovarian cancer (OC). The chemical composition of cinnamon was collected from the TCMSP database to predict its targets and construct a “cinnamon active component target” network. OC-related genes were retrieved from Genecards and DisGeNET databases. The “disease-target” network was established, and the drug targets were mapped to the disease targets, and the key targets obtained from the mapping were subjected to DAVID analysis to construct a “component-target-pathway” network diagram. The active ingredients of cinnamon were molecularly docked to the core targets to predict the molecular mechanism of cinnamon in the treatment of ovarian cancer. From cinnamon, 105 chemical components were screened and de-duplicated to obtain 15 active components and 74 drug target proteins, and 26 common targets were obtained after mapping drug targets to disease targets. 368 entries were identified by GO enrichment analysis, mainly including biological progresses such as regulation of smooth muscle contraction and regulation of tube diameter, and molecular functions such as antioxidant activity, and peroxidase activity. The KEGG pathway enrichment analysis identified 4 signaling pathways, neuroactive ligand-receptor interaction, HIF-1 signaling pathway, regulation of lipolysis in adipocytes, and complement and coagulation cascades. Molecular docking analysis showed good affinity of these key targets with representative components of OC. There was a stable interaction between DIBP and ADRB2 and NR3C1. There is a stable interaction between oleic acid and C2K, EDN1, ERBB2, PLAU, PLG, PRSS3, PTGS1, PTGS2, SERPINE1 and SLC2A1. Cinnamon exerted its therapeutic effects on OC through multiple pathways and targets.

Novel Plasma Proteomic Biomarkers for Early Identification of Induction Chemotherapy Beneficiaries in Locoregionally Advanced Nasopharyngeal Carcinoma

Background

Induction chemotherapy (IC) can alleviate locoregionally advanced nasopharyngeal carcinoma (LA-NPC), but effectiveness differs between patients, toxicity is problematic, and effective blood-based IC efficacy predictors are lacking. Here, we aimed to identify biomarkers for early identification of IC beneficiaries.

Methods

Sixty-four pairs of matched plasma samples collected before and after IC from LA-NPC patients including 34 responders and 30 non-responders, as well as 50 plasma samples of healthy individuals, were tested using data-independent acquisition mass spectrometry. The proteins associated with clinical traits or IC benefits were investigated by weighted gene co-expression network analysis (WGCNA) and soft cluster analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional annotations were performed to determine the potential function of the identified proteins. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of candidate biomarkers in predicting IC beneficiaries.

Results

Compared with healthy individuals, 1027 differentially expressed proteins (DEPs) were found in the plasma of LA-NPC patients. Based on feedback from IC outcomes, 463 DEPs were identified in the pre-IC plasma between responders and non-responders. A total of 1212 DEPs represented the proteomic changes before and after IC in responders, while 276 DEPs were identified in post-IC plasma between responders and non-responders. WGCNA identified nine protein co-expression modules correlated with clinical traits. Soft cluster analysis identified four IC benefits-related protein clusters. Functional enrichment analysis showed that these proteins may play a role in IC via immunity, complement, coagulation, glycosaminoglycan and serine. Four proteins differentially expressed in all group comparisons, paraoxonase/arylesterase 1 (PON1), insulin-like growth factor-binding protein 3 (IGFBP-3), rheumatoid factor D5 light chain (v-kappa-3) and RNA helicase (DDX55), were associated with clinical traits or IC benefits. A four-protein model accurately identified potential IC beneficiaries (AUC=0.95) while diagnosing LA-NPC (AUC=0.92), and the prediction performance was verified using the models to confirm the effective IC (AUC=0.97) and evaluate IC outcome (AUC=0.94).

Conclusion

The plasma protein profiles among IC responders and non-responders were different. PON1, IGFBP3, v-kappa-3 and DDX55 could serve as potential biomarkers for early identification of IC beneficiaries for individualised treatment of LA-NPC.

N7-Methylguanosine-Related lncRNAs: Integrated Analysis Associated With Prognosis and Progression in Clear Cell Renal Cell Carcinoma

N7-Methylguanosine (m7G) and long non-coding RNAs (lncRNAs) have been widely reported to play an important role in cancer. However, there is little known about the relationship between m7G-related lncRNAs and clear cell renal cell carcinoma (ccRCC). To find new potential biomarkers and construct an m7G-related lncRNA prognostic signature for ccRCC, we retrieved transcriptome data and clinical data from The Cancer Genome Atlas (TCGA), and divided the entire set into train set and test set with the ratio of 1:1 randomly. The m7G-related lncRNAs were identified by Pearson correlation analysis (|coefficients| &gt; 0.4, and p &lt; 0.001). Then we performed the univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a 12 m7G-related lncRNA prognostic signature. Next, principal component analysis (PCA), the Kaplan–Meier method, time-dependent receiver operating characteristics (ROC) were made to verify and evaluate the risk signature. A nomogram based on the risk signature and clinical parameters was developed and showed high accuracy and reliability for predicting the overall survival (OS). Functional enrichment analysis (GO, KEGG and GSEA) was used to investigate the potential biological pathways. We also performed the analysis of tumor mutation burden (TMB), immunological analysis including immune scores, immune cell infiltration (ICI), immune function, tumor immune escape (TIE) and immunotherapeutic drug in our study. In conclusion, using the 12 m7G-related lncRNA risk signature as a prognostic indicator may offer us insight into the oncogenesis and treatment response prediction of ccRCC.

Identification of Prognosis-Associated Biomarkers in Thyroid Carcinoma by a Bioinformatics Analysis

Background

This study aimed to identify the key genes associated with prognosis in thyroid cancer (TC), and to explore potential pathways.

Methods

GSE66783, GSE58545, and GSE129562 datasets were used to identify differentially expressed genes (DEGs) between tumor and normal tissues, followed by KEGG analyses on DEGs. The protein-protein interaction (PPI) network of DEGs was subsequently constructed to find the top 10 hub genes and seed genes in the whole network. Furthermore, the mRNA expressions of hub genes and prognostic values were explored. Regarding the seed gene, pathway activity score and GSEA analyses were conducted as well.

Results

1) A total of 183 DEGs were consistently expressed in three datasets comprising 76 up-regulated and 107 down-regulated genes. DEGs were mainly enriched in the p53 signaling pathway, complement and coagulation cascades, and hedgehog signaling pathway. 2) The top 10 hub genes, including CCND1, TIMP1, ICAM1, MET, PLAU, LDLR, PLAUR, ITGA2, ITGA3, and LGALS3, were identified. All hub genes were highly expressed in TC compared with normal samples. 3) High expression of CCND1, TIMP1, MET, and LGALS3 statistically correlated with a favorable prognosis of patients. Poor survival was observed in patients with ITGA2 and ITGA3 high expression. There was no association between ICAM1, PLAU, and PLAUR expression and survival of patients. LGALS3 and TIMP1 were further identified as independent prognostic factors in TC. 4) Among 10 hub genes, TIMP1 was determined as the seed gene, indicating its significance in the whole network. We further found that in most of the famous cancer-related pathways, TIMP1 higher expression caused a lower pathway activity, suggesting its inhibitory effect to these pathways in TC. In addition, TIMP1 positively correlated with the p53 signaling pathway, complement, and coagulation cascades involved in TC.

Conclusion

The present study provided seven prognosis-associated genes in TC and revealed several significant pathways, which contributed to elucidate the pathogenesis of TC.

Quantitative proteomic analysis of the effects of dietary deprivation of methionine and cystine on A549 xenograft and A549 xenograft-bearing mouse

Methionine (Met) and cystine (CySS) are key sulfur donors in cell metabolism and are important nutrients for sustaining tumor growth; however, the molecular effects associated with their deprivation remain to be characterized. Here, we applied a xenograft mouse model to assess the impact of their deprivation on A549 xenografts and the xenograft-bearing animal. Results show that Met and CySS deprivation inhibits A549 growth in vitro, not in vivo. Deprivation was detrimental to the xenograft-bearing mouse, as demonstrated by weight loss and renal dysfunction. Differentially expressed proteins in A549 xenograft and mouse kidneys were characterized using quantitative proteomics. Functional annotation and protein-protein interaction network analysis revealed the enriched signaling pathways, including focal adhesion (Fn1) in the A549 xenograft, and xenobiotic metabolism (Cyp2e1) and glutathione metabolism (Ggt1) in the mouse kidney. Met and CySS deprivation inhibits the migratory and invasive properties of cancer cells, as evidenced by reduced expression of the epithelial to mesenchymal transition marker N-cadherin in A549 cells in vitro. Moreover, IGFBP1 protein expression was inhibited in both A549 xenograft and mouse kidneys. This study provides the first insights into changes within the proteome profile and biological processes upon Met and CySS deprivation in a A549 xenograft mouse model.

Integrative analysis reveals the prognostic value and functions of splicing factors implicated in hepatocellular carcinoma

Splicing factors (SFs) play critical roles in the pathogenesis of various cancers through regulating tumor-associated alternative splicing (AS) events. However, the clinical value and biological functions of SFs in hepatocellular carcinoma (HCC) remain obscure. In this study, we identified 40 dysregulated SFs in HCC and established a prognostic model composed of four SFs (DNAJC6, ZC3H13, IGF2BP3, DDX19B). The predictive efficiency and independence of the prognostic model were confirmed to be satisfactory. Gene Set Enrichment Analysis (GSEA) illustrated the risk score calculated by our prognostic model was significantly associated with multiple cancer-related pathways and metabolic processes. Furthermore, we constructed the SFs-AS events regulatory network and extracted 108 protein-coding genes from the network for following functional explorations. Protein–protein interaction (PPI) network delineated the potential interactions among these 108 protein-coding genes. GO and KEGG pathway analyses investigated ontology gene sets and canonical pathways enriched by these 108 protein-coding genes. Overlapping the results of GSEA and KEGG, seven pathways were identified to be potential pathways regulated by our prognostic model through triggering aberrant AS events in HCC. In conclusion, the present study established an effective prognostic model based on SFs for HCC patients. Functional explorations of SFs and SFs-associated AS events provided directions to explore biological functions and mechanisms of SFs in HCC tumorigenesis.

Serum proteomic profiling in patients with advanced Schistosoma japonicum-induced hepatic fibrosis

Background

Schistosoma japonicum is a parasitic flatworm that is the aetiological agent of human schistosomiasis, an important cause of hepatic fibrosis. Schistosomiasis-induced hepatic fibrosis is a consequence of the highly fibrogenic nature of egg-induced granulomatous lesions, which are the main pathogenic features of schistosomiasis. Although global awareness of the association between schistosomiasis-induced hepatic fibrosis and S. japonicum infection is increasing, little is known about the molecular differences associated with rapid progression to schistosomiasis in cirrhotic patients.

Methods

We systematically used data-independent acquisition (DIA)-based liquid chromatography-mass spectrometry to identify differentially expressed proteins in serum samples from patients with advanced S. japonicum-induced hepatic fibrosis.

Results

Our analysis identified 1144 proteins, among which 66 were differentially expressed between the healthy control group and the group of patients with advanced S. japonicum-induced hepatic fibrosis stage F2 (SHF-F2) and 214 were differentially expressed between the SHF-F2 and SHF-F4 groups (up- or downregulation of at least 1.5-fold in serum samples). The results also indicated that two selected proteins (C1QA and CFD) are potential biomarkers for distinguishing between patients with SHF-F2 and those with SHF-F4 due to S. japonicum infection.

Conclusions

We provide here the first global proteomic profile of serum samples from patients with advanced S. japonicum-induced hepatic fibrosis. The proteins C1QA and CFD are potential diagnostic markers for patients with SHF-F2 and SHF-F4 due to S. japonicum infection, although further large-scale studies are needed. Our DIA-based quantitative proteomic analysis revealed molecular differences among individuals at different stages of advanced S. japonicum-induced hepatic fibrosis and may provide fundamental information for further detailed investigations.

Graphic Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04734-1.

Complement in Tumourigenesis and the Response to Cancer Therapy

In recent years, our knowledge of the complement system beyond innate immunity has progressed significantly. A modern understanding is that the complement system has a multifaceted role in malignancy, impacting carcinogenesis, the acquisition of a metastatic phenotype and response to therapies. The ability of local immune cells to produce and respond to complement components has provided valuable insights into their regulation, and the subsequent remodeling of the tumour microenvironment. These novel discoveries have advanced our understanding of the immunosuppressive mechanisms supporting tumour growth and uncovered potential therapeutic targets. This review discusses the current understanding of complement in cancer, outlining both direct and immune cell-mediated roles. The role of complement in response to therapies such as chemotherapy, radiation and immunotherapy is also presented. While complement activities are largely context and cancer type-dependent, it is evident that promising therapeutic avenues have been identified, in particular in combination therapies.

C3AR1 mRNA as a Potential Therapeutic Target Associates With Clinical Outcomes and Tumor Microenvironment in Osteosarcoma

Objective: Targeting cancer-specific messenger RNAs (mRNAs) may offer novel insights into therapeutic strategies in osteosarcoma. This study aimed to discover possible osteosarcoma-specific mRNA and probe its biological functions.

Methods: Based on mRNA-seq data from the TARGET database, stromal and immune scores were estimated for each osteosarcoma sample via the ESTIMATE algorithm. Stromal and immune mRNAs were obtained via integration of differentially expressed mRNAs between high and low stromal / immune score groups. Among hub and prognostic mRNAs, C3AR1 mRNA was focused and its prognostic value was assessed. The associations between C3AR1 mRNA and immune cells were analyzed via the CIBERSORT algorithm. Its expression was verified in osteosarcoma tissues and cells by RT-qPCR and western blot. The functions of C3AR1 were investigated by a series of experiments.

Results: Low stromal and immune scores were both indicative of unfavorable outcomes for osteosarcoma patients. Eighty-eight up-regulated and seven down-regulated stromal and immune mRNAs were identified. Among 30 hub mRNAs, low expression of C3AR1 mRNA indicated worse outcomes than its high expression. There was a lower mRNA expression of C3AR1 in metastatic than non-metastatic osteosarcoma. C3AR1 mRNA was closely correlated to various immune cells such as macrophages. C3AR1 was verified to be down-regulated in osteosarcoma tissues and cells. Its overexpression suppressed proliferation, migration and invasion and induced apoptosis in osteosarcoma cells.

Conclusion: C3AR1 mRNA could be a promising therapeutic target for osteosarcoma, linked with prognosis and tumor microenvironment.

Identification of a Prognostic Signature Model with Tumor Microenvironment for predicting Disease-free Survival after Radical Prostatectomy

Background: The tumor microenvironment (TME) and immune checkpoint inhibitors have been shown to promote active immune responses through different mechanisms. We attempted to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).

Methods: The gene transcriptome profiles and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA) database, and the immune and stromal scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of the risk score (RS) model based on univariate Cox analysis and least absolute shrinkage and selection operation (LASSO) Cox regression analysis and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 dataset was utilized for external validation. Twenty-two subsets of tumor-infiltrating immune cells were analyzed using the CIBERSORT algorithm.

Results: In this study, the patients with higher immune/stromal scores were associated with a worse DFS, higher Gleason score, and higher pathological T stage. Based on the immune and stromal scores, 515 differentially expressed genes (DEGs) were identified. The univariate Cox and LASSO Cox regression models were employed to select 18 DEGs from 515 DEGs and construct an RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUCs for the 1-year, 3-year and 5-year DFS rates in the RS model were 0.890, 0.877 and 0.841, respectively. A nomogram of DFS was established based on the RS and Gleason score, and the AUCs for the 1-year, 3-year and 5-year DFS rates in the nomogram were 0.907, 0.893, and 0.872, respectively. These results were further validated in the GSE70768 dataset. In addition, the proportion of Tregs was determined to be higher in high-RS patients (P<0.05), and the expression levels of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) were observed to be higher in high-RS patients (P<0.05).

Conclusions: Our study established and validated an 18-gene prognostic signature model associated with TME, which might serve as a prognosis stratification tool to predict DFS in PCa patients after radical prostatectomy.

C8B in Complement and Coagulation Cascades Signaling Pathway is a predictor for Survival in HBV-Related Hepatocellular Carcinoma Patients

OBJECTIVE

The role of the complement and coagulation cascades signaling pathway in the pathogenesis of cancers remains uncertain. This study aimed to investigate the associations between enriched differentially expressed genes (DEGs) in this pathway and hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients.

MATERIALS AND METHODS

Clinical and gene expression data of the Gene Expression Omnibus (GEO) series profile GSE14520 were downloaded. The "Limma" package was used to screen the DEGs and the "clusterProfiler" package was used to identify the complement and coagulation cascades pathway and enriched significant genes. Cox regression analysis, the Kaplan-Meier method, and the nomogram model were used to address the correlations between significantly enriched DEGs in the complement and coagulation cascades pathway and HCC survival.

RESULTS

A total of 220 HBV-related HCC patients were enrolled in this study. The complement and coagulation cascades pathway was significantly enriched by 37 DEGs (p-value < 0.05 and adjusted p-value < 0.05). Complement 8 beta chain (C8B) expression levels had protective effects on overall survival (OS) and recurrence-free survival (RFS) in HBV-related HCC patients. High levels of C8B contributed to favorable OS and RFS in this population (both p < 0.01), even after adjustment of clinicopathological characteristics including tumor node metastasis (TNM) staging, Barcelona Clinic liver cancer (BCLC) staging, gender, and fibrinogen beta chain (FGB) expression (all p < 0.05).

CONCLUSION

C8B in the complement and coagulation cascades signaling pathway serves as a predictive candidate for survival in HBV-related HCC patients.

Upregulation of ATIC in multiple myeloma tissues based on tissue microarray and gene microarrays

PURPOSE

Multiple myeloma (MM) is characterized by the malignant proliferation of plasma cells, which produce a monoclonal immunoglobulin protein. The role of 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase (ATIC) has not yet been well studied in the area of MM. Thus, in the current study, we sought to examine the expression levels, including mRNA and protein levels of ATIC in MM.

METHODS

Multiple myeloma microarray and RNA-seq data were screened from the SRA, GEO, ArrayExpress, and Oncomine databases. The mRNA level of ATIC was extracted from the high throughput data, and the prognostic value was studied. The protein level of ATIC was also detected by in-house immunohistochemistry on a tissue microarray. Potential signaling pathways were enriched with ATIC-related genes in MM.

RESULTS

Both the mRNA and protein levels of ATIC were significantly upregulated in MM samples as compared to normal samples. Furthermore, the summarized Standardized Mean Difference was 1.66 with 674 cases of MM based on 10 independent studies including the in-house tissue microarray. The overall hazard ratio of ATIC in MM was 1.7 with 1631 cases of MM based on five microarrays. In the KEGG pathway analysis, the ATIC-related genes were mainly enriched in the pathway of complement and coagulation cascades.

CONCLUSION

We provided the first evidence supporting the upregulation of ATIC may play an essential part in the tumorigenesis and development of MM. The promoting cancer capacity may be related to the pathway of complement and coagulation cascades.