Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma

A multi-parametric prognostic model based on clinicopathologic features: vessels encapsulating tumor clusters and hepatic plates predict overall survival in hepatocellular carcinoma patients

BACKGROUND

Vessels encapsulating tumor clusters (VETC) is a newly described vascular pattern that is distinct from microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Despite its importance, the current pathological diagnosis report does not include information on VETC and hepatic plates (HP). We aimed to evaluate the prognostic value of integrating VETC and HP (VETC-HP model) in the assessment of HCC.

METHODS

A total of 1255 HCC patients who underwent radical surgery were classified into training (879 patients) and validation (376 patients) cohorts. Additionally, 37 patients treated with lenvatinib were studied, included 31 patients in high-risk group and 6 patients in low-risk group. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish a prognostic model for the training set. Harrell's concordance index (C-index), time-dependent receiver operating characteristics curve (tdROC), and decision curve analysis were utilized to evaluate our model's performance by comparing it to traditional tumor node metastasis (TNM) staging for individualized prognosis.

RESULTS

A prognostic model, VETC-HP model, based on risk scores for overall survival (OS) was established. The VETC-HP model demonstrated robust performance, with area under the curve (AUC) values of 0.832 and 0.780 for predicting 3- and 5-year OS in the training cohort, and 0.805 and 0.750 in the validation cohort, respectively. The model showed superior prediction accuracy and discrimination power compared to TNM staging, with C-index values of 0.753 and 0.672 for OS and disease-free survival (DFS) in the training cohort, and 0.728 and 0.615 in the validation cohort, respectively, compared to 0.626 and 0.573 for TNM staging in the training cohort, and 0.629 and 0.511 in the validation cohort. Thus, VETC-HP model had higher C-index than TNM stage system(p < 0.01).Furthermore, in the high-risk group, lenvatinib alone appeared to offer less clinical benefit but better disease-free survival time.

CONCLUSIONS

The VETC-HP model enhances DFS and OS prediction in HCC compared to traditional TNM staging systems. This model enables personalized temporal survival estimation, potentially improving clinical decision-making in surveillance management and treatment strategies.

Active Components of Pueraria lobata through the MAPK/ERK Signaling Pathway Alleviate Iron Overload in Alcoholic Liver Disease

OBJECTIVE

To delve into the primary active ingredients and mechanism of Pueraria lobata for alleviating iron overload in alcoholic liver disease.

METHODS

Pueraria lobata's potential targets and signaling pathways in treating alcohol-induced iron overloads were predicted using network pharmacology analysis. Then, animal experiments were used to validate the predictions of network pharmacology. The impact of puerarin or genistein on alcohol-induced iron accumulation, liver injury, oxidative stress, and apoptosis was assessed using morphological examination, biochemical index test, and immunofluorescence. Key proteins implicated in linked pathways were identified using RT-qPCR, western blot analysis, and immunohistochemistry.

RESULTS

Network pharmacological predictions combined with animal experiments suggest that the model group compared to the control group, exhibited activation of the MAPK/ERK signaling pathway, suppression of hepcidin expression, and aggravated iron overload, liver damage, oxidative stress, and hepatocyte death. Puerarin and genistein, the active compounds in Pueraria lobata, effectively mitigated the aforementioned alcohol-induced effects. No statistically significant disparities were seen in the effects above between the two groups receiving drug therapy.

CONCLUSION

This study preliminarily demonstrated that puerarin and genistein in Pueraria lobata may increase hepcidin production to alleviate alcohol-induced iron overload by inhibiting the MAPK/ERK signaling pathway.

m6A RNA methylation regulators predict prognosis and indicate characteristics of tumour microenvironment infiltration in acute myeloid leukaemia

Patients with acute myeloid leukaemia (AML) have poor prognoses and low overall survival (OS) rates owing to its heterogeneity and the complexity of its tumour microenvironment (TME). N6-methyladenosine (m⁶A) modification plays a key role in the initiation and progression of haematopoietic malignancies. However, the underlying function of m⁶A regulators in AML remains elusive. This study thoroughly analysed the m⁶A modification features of 177 AML patients based on 22 m⁶A regulators. Utilizing unsupervised clustering, we determined three distinct m⁶A modification patterns related to different biological functions, TME cell-infiltrating characteristics and clinical outcomes. Additionally, a risk score was constructed based on six m⁶A regulators-associated prognostic signatures and was validated as an independent and valuable prognostic factor for AML. Patients with a low-risk score exhibited better survival than those with a high-risk score. Many m⁶A regulators were aberrantly expressed in AML, among which METTL14, YTHDC2, ZC3H13 and RBM15 were observed to be associated with the OS of AML. In addition, these four m⁶A regulators were found to be noticeably related to the immune checkpoint inhibitor (ICI) treatments. Finally, we verified the expression levels of these four m⁶A regulators in AML and healthy samples and three groups of AML patients with different risk categories. Collectively, our study indicates that the m⁶A modification pattern is involved in TME immune-infiltrating characteristics and prognosis in AML. A better understanding of the m⁶A modification pattern will help enhance our knowledge of the molecular mechanisms of AML and develop potential prognosis prediction indicators and more effective immunotherapeutic strategies.

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

Background and Aims

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action.

Methods

Gene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein-protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue.

Results

We identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest-scoring hub genes were screened to identify critical genes in the protein-protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para-cancer tissues.

Conclusion

Identifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms.

A novel pyroptosis-related signature predicts prognosis and indicates immunotherapy in oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) has been recognized as a frequently occurring oral malignant tumor. Pyroptosis plays an extremely important role in the occurrence and development of cancer, but the role of pyroptosis in OSCC remains unclear.

METHODS

OSCC-related data were obtained from the TCGA and GEO databases. A PSscore risk model was constructed through LASSO regression analysis. The GEO database was utilized as the validation set of the model. The "ESTIMATE" and "CIBERSORT" algorithms were utilized to additionally evaluate the relationship between the immune cell score and PSscore. TIDE and IPS algorithms were used to assess patient response to immunotherapy. In addition, Western blot analysis and MTT assay was used to further validate key genes.

RESULTS

Comprehensive bioinformatics analysis showed that a low-PSscore had a significant survival advantage, richer immune cell infiltration, more active immune-related pathways, higher TME scores, and lower tumor purity. The results of TIDE and IPS analysis indicated that the high-PSscore group had higher immune escape potential and was less sensitive to immunotherapy. In contrast, the low-PSscore group patients might be more sensitive to PD1 and CTLA4 + PD1 immunotherapy. Univariate and multivariate COX results indicated that PSscore was an independent prognostic factor in OSCC patients. Another important finding is that BAK1 is a potential target of OSCC and is related to the Nod-like receptor signaling pathway. Knockdown of BAK1 can significantly reduce the proliferation of OSCC cells.

CONCLUSION

The PSscore model could be utilized as a powerful prognostic indicator and can help in the development of new immunotherapies.

Construction of HBV gene-related prognostic and diagnostic models for hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a main cause of malignancy-related death all over the world with a poor prognosis. The current research is focused on developing novel prognostic and diagnostic models of Hepatocellular carcinoma from the perspective of hepatitis B virus (HBV)-related genes, and predicting its prognostic characteristics and potential reliable biomarkers for Hepatocellular carcinoma diagnosis. Methods: As per the information related to Hepatocellular carcinoma expression profile and the clinical data in multiple public databases, we utilized limma for assessing the differentially expressed genes (DEGs) in HBV vs non- hepatitis B virus groups, and the gene set was enriched, analyzed and annotated by WebGestaltR package. Then, STRING was employed to investigate the protein interactions. A risk model for evaluating Hepatocellular carcinoma prognosis was built with Lasso Cox regression analysis. The effect patients receiving immunotherapy was predicted using Tumor Immune Dysfunction and Exclusion (TIDE). Additionally, pRRophetic was used to investigate the drug sensitivity. Lastly, the Support Vector Machine (SVM) approach was utilized for building the diagnostic model. Results: The Hepatocellular Carcinoma Molecular Atlas 18 (HCCDB18) data set was utilized for the identification of 1344 HBV-related differentially expressed genes, mainly associated with cell division activities. Five functional modules were established and then we built a prognostic model in accordance with the protein-protein interaction (PPI) network. Five HBV-related genes affecting prognosis were identified for constructing a prognostic model. Then, the samples were assigned into RS-high and -low groups as per their relevant prognostic risk score (RS). High-risk group showed worse prognosis, higher mutation rate of TP53, lower sensitivity to immunotherapy but higher response to chemotherapeutic drugs than low-risk group. Finally, the hepatitis B virus diagnostic model of Hepatocellular carcinoma was established. Conclusion: In conclusion, the prognostic and diagnostic models of hepatitis B virus gene-related Hepatocellular carcinoma were constructed. ABCB6, IPO7, TIMM9, FZD7, and ACAT1, the five HBV-related genes that affect the prognosis, can work as reliable biomarkers for the diagnosis of Hepatocellular carcinoma, giving a new insight for improving the prognosis, diagnosis, and treatment outcomes of HBV-type Hepatocellular carcinoma.

Comprehensive analysis of cuproptosis-related genes and tumor microenvironment infiltration characterization in breast cancer

Background

Cuproptosis is a newly discovered programmed cell death dependent on overload copper-induced mitochondrial respiration dysregulation. The positive response to immunotherapy, one of the most important treatments for invasive breast cancer, depends on the dynamic balance between tumor cells and infiltrating lymphocytes in the tumor microenvironment (TME). However, cuproptosis-related genes (CRGs) in clinical prognosis, immune cell infiltration, and immunotherapy response remain unclear in breast cancer progression.

Methods

The expression and mutation patterns of 12 cuproptosis-related genes were systematically evaluated in the BRCA training group. Through unsupervised clustering analysis and developing a cuproptosis-related scoring system, we further explored the relationship between cuproptosis and breast cancer progression, prognosis, immune cell infiltration, and immunotherapy.

Results

We identified two distinct CuproptosisClusters, which were correlated with the different patterns between clinicopathological features, prognosis, and immune cell infiltration. Moreover, the differences of the three cuproptosis-related gene subtypes were evaluated based on the CuproptosisCluster-related DEGs. Then, a cuproptosis-related gene signature (PGK1, SLC52A2, SEC14L2, RAD23B, SLC16A6, CCL5, and MAL2) and the scoring system were constructed to quantify the cuproptosis pattern of BRCA patients in the training cohort, and the testing cohorts validated them. Specifically, patients from the low-CRG_score group were characterized by higher immune cell infiltration, immune checkpoint expression, immune checkpoint inhibitor (ICI) scores, and greater sensitivity to immunotherapy. Finally, we screened out RAD23B as a favorable target and indicated its expression was associated with breast cancer progression, drug resistance, and poor prognosis in BRCA patients by performing real-time RT-PCR, cell viability, and IC50 assay.

Conclusions

Our results confirmed the essential function of cuproptosis in regulating the progression, prognosis, immune cell infiltration, and response to breast cancer immunotherapy. Quantifying cuproptosis patterns and constructing a CRG_score could help explore the potential molecular mechanisms of cuproptosis regulating BRCA advancement and provide more effective immunotherapy and chemotherapy targets.

CDC20 May Serve as a Potential Biomarker-Based Risk Score System in Predicting the Prognosis of Patients with Hepatocellular Carcinoma

Background

The specificity and sensitivity of hepatocellular carcinoma (HCC) diagnostic markers are limited, hindering the early diagnosis and treatment of HCC patients. Therefore, improving prognostic biomarkers for patients with HCC is urgently needed.

Methods

HCC-related datasets were downloaded from the public databases. Differentially expressed genes (DEGs) between HCC and adjacent nontumor liver tissues were then identified. Moreover, the intersection of DEGs in four datasets (GSE138178, GSE77509, GSE84006, and TCGA) was used in the functional enrichment, and module genes were obtained by a coexpression network. Cox and Kaplan-Meier analyses were used to identify overall survival- (OS-) related genes from module genes. Area under the curve (AUC) > 0.9 of OS-related genes was then carried out in order to perform the protein-protein interaction network. The feature genes were identified by least absolute shrinkage and selection operator (LASSO). Furthermore, the hub gene was identified through the univariate Cox model, after which the correlation analysis between the hub gene and pathways was explored. Finally, infiltration in immune cell types in HCC was analyzed.

Results

A total of 2,227 upregulated genes and 1,501 downregulated DEGs were obtained in all four datasets, which were mainly found to be involved in the cell cycle and retinol metabolism. Accordingly, 998 OS-related genes were screened to construct the LASSO model. Finally, 8 feature genes (BUB1, CCNB1, CCNB2, CCNA2, AURKB, CDC20, OIP5, and TTK) were obtained. CDC20 was shown to serve as a poor prognostic gene in HCC and was mainly involved in the cell cycle. Moreover, a positive correlation was noted between the high degree of infiltration with Th2 and CDC20.

Conclusion

High expression of CDC20 predicted poor survival, as potential target in the treatment for HCC.

Gene signatures, immune infiltration, and drug sensitivity based on a comprehensive analysis of m6a RNA methylation regulators in cervical cancer

BACKGROUND

Cervical cancer is the fourth most common cancer in women. N⁶-dimethyladenosine (m⁶A) mRNA methylation is closely associated with cervical cancer.

METHODS

Using TCGA database, we studied the expression and mutation of m⁶A-related genes in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and obtained genetic characteristics based on an m⁶A risk model and prognostic value of m⁶A. We studied the effects of the m⁶A risk score on immune features and genomic changes of patients with CESC, evaluated the sensitivity of patients with CESC to different small-molecule drugs based on the m⁶A risk score, and established a clinical prediction model.

RESULTS

Ten m⁶A-related genes were differentially expressed between CESC and normal tissues. High-risk patients had a low overall survival (OS) and significantly low immune scores but showed no significantly altered stromal scores. The tumor mutation burden (TMB) and tumor neoantigen levels significantly differed between the high- and low-risk groups. In the high-risk group, copy number variation (CNV) changes mainly led to gene amplification, while in the low-risk group, CNV changes primarily manifested as gene copy number deletions. ZC3H13 expression was low in CESC tissues. ZC3H13 knockdown promoted CESC cell proliferation, migration, and invasion, reducing the RNA methylation levels. Rapamycin suppressed the CESC cell proliferation, migration, and invasion abilities, increasing the m⁶A levels.

CONCLUSION

m⁶A mRNA methylation is closely related to the occurrence, development, immune invasion, drug sensitivity, and prognosis of cervical cancer. The prognostic m⁶A feature model of m⁶A signature genes can accurately predict the OS of patients with CESC. Drugs targeting factors regulating m⁶A mRNA methylation might offer a good prospect for treating cervical cancer.

A Nomogram-Based Risk Classification System Predicting the Overall Survival of Childhood with Clear Cell Sarcoma of the Kidney Based on the SEER Database

Objective. Clear cell sarcoma of the kidney (CCSK) is a lethal pediatric renal malignancy with poor prognosis. A prognostic nomogram needs to be established for overall survival (OS) prediction of patients with CCSK. Methods. Eligible 2588 CCSK patients (age 0–19) diagnosed between 2000 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomized into training and validation cohorts (7 : 3). Independent prognostic factors were identified by univariate and multifactorial Cox regression analyses and used to construct a nomogram. Receiver operating characteristics (ROC) analysis, calibration curves, and decision curve analysis (DCA) were used to validate the nomogram. Moreover, a risk classification system was established based on the risk scores of the nomogram. Results. Cox analyses revealed that age, combined stage, and origin were most significant prognostic factors. Based on these prognostic factors, a nomogram was established for predicting 3- and 5-year OS of patients with CCSK. The area under the ROC curve (AUC) of 3- and 5-year OS was 0.733 and 0.728 in the training cohort, corresponding to 0.69 and 0.674 in the validation cohort. The C-index of calibration curves in the training and validation cohorts was 0.724 and 0.686. DCAs indicated the clinical utility of this nomogram. A risk classification system stratified CCSK patients into three different risk cohorts. The OS time of low-, intermediate-, and high-risk patients was 76, 68, and 65 months in the training cohort, corresponding to 69.5, 66, and 72 months in the validation cohort. Conclusion. A nomogram-based risk classification system has high accuracy for the prognostic prediction of CCSK.

mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning

Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA stemness index-related metabolic risk score model, which was examined in The Cancer Genome Atlas and Gene Expression Omnibus databases (1323 patients) and validated using the Zhongshan Hospital cohort (200 patients). The high-risk group showed more immune infiltrations; higher levels of immunosuppressive checkpoints, such as CD274, tumor mutation burden, and resistance to chemotherapeutics; potentially better response to immune therapy; worse prognosis; and advanced stage of tumor node metastasis than the low-risk group. The combination of risk score and clinical characteristics was effective in predicting overall survival. Zhongshan cohort validated that high-risk score group correlated with malignant progression, worse prognosis, inferior adjuvant chemotherapy responsiveness of CRC, and shaped an immunoevasive contexture. This tool may provide a more accurate risk stratification in CRC and screening of patients with CRC responsive to immunotherapy.

Fuzheng Xiaozheng prescription exerts anti-hepatocellular carcinoma effects by improving lipid and glucose metabolisms via regulating circRNA-miRNA-mRNA networks

BACKGROUND

Hepatocellular carcinoma (HCC) is a major threat to human health due to its high lethality. Our previous studies suggested that Fuzheng Xiaozheng prescription (FZXZP), an effective Chinese medicine, demonstrated significant suppressive effects on HCC. However, its underlying mechanism remains largely unclear.

PURPOSE

This study aimed to investigate the anti-HCC mechanisms of FZXZP from transcriptomic sequencing based on a holistic perspective.

METHODS

Rat HCC model was induced by diethylnitrosamine, and then the model was administered with two doses of FZXZP, high and low. Sodium demethylcantharidate was used as a positive control. Subsequently, microarrays of circRNA, miRNA and mRNA were performed on the blank, model, high and low dose groups, respectively, and the competitive binding mechanisms among them were further analyzed by bioinformatics. Then, the circRNA-miRNA-mRNA networks were constructed to mine the targeted-RNAs of FZXZP in HCC, as well as to explore their potential regulatory mechanisms. Finally, functions and pathways of the FZXZP targeted genes in rat HCC were annotated with GO and KEGG, and qRT-PCR was performed to validate the accuracy of the above analyses in this study.

RESULTS

The results showed that FZXZP significantly inhibited the development and progression of HCC in rats, improved the pathological conditions and suppressed the proliferation of HCC cells. Subsequently, after a series of screening, the competing endogenous RNA networks (circRNA-miRNA-mRNA), consisting of 2 circRNAs, 7 miRNAs and 104 mRNAs, were finally established. KEGG and GO analyses of the networks revealed that lipid metabolism related pathways, such as fatty acid metabolism, bile secretion and PPAR pathway, were significantly enriched. In the further hubgene network analysis, in addition to lipid metabolism, aberrant glucose metabolism was found to be ameliorated by G6pc and Pklr in hubgenes. Finally, the qRT-PCR analyses confirmed that the expression tendencies of the above targeted genes were correct and believable in transcriptomic sequencings, and qRT-PCR results of the genes closely related to proliferation, invasion and apoptosis of HCC also indicated the inhibitory effects of FZXZP on HCC obviously.

CONCLUSION

FZXZP demonstrated significant anti-HCC effects through improving lipid and glucose metabolism, restoring the metabolic homeostasis of the liver via circRNA-miRNA-mRNA networks.

Use of Single Cell Transcriptomic Techniques to Study the Role of High-Risk Human Papillomavirus Infection in Cervical Cancer

High-risk human papillomavirus (hrHPV) infection has been associated with a higher probability of progression to cervical cancer. However, several extensive studies have reported that the presence of hrHPV can lead to a better prognosis, but the mechanism of how this occurs is unclear. In this study, microbiological analysis was used to identify HPV infection as a factor for the prognosis of patients with cervical squamous cell carcinoma (CSCC). Comparing the interactions of HPV⁺ and HPV^- malignant cells with immune cells as well as the trajectory of malignant cells either with or without HPV, we found that most of the HPV⁺ cells are well differentiated while HPV^- cells appear to be hypo-fractionated. Using transcriptomic and immunostaining data, we validated a set of unfavourable molecules in the HPV^- CSCC cells, including KRT16, ITGB1, CXCR1, VEGFA, CRCT1 and TNFRSF10B/DR5. This study provides a basis for the development of a rational post-operative follow-up programme and the development of an appropriate treatment plan for patients with cervical cancer.

A Distinct Glucose Metabolism Signature of Lung Adenocarcinoma With Prognostic Value

Background: Lung adenocarcinoma (LUAD) remains the most common type of lung cancer and is the main cause of cancer-related death worldwide. Reprogramming of glucose metabolism plays a crucial role in tumorigenesis and progression. However, the regulation of glucose metabolism is still being explored in LUAD. Determining the underlying clinical value of glucose metabolism will contribute in increasing clinical interventions. Our study aimed to conduct a comprehensive analysis of the landscape of glucose metabolism-related genes in LUAD and develop a prognostic risk signature.

Methods: We extracted the RNA-seq data and relevant clinical variants from The Cancer Genome Atlas (TCGA) database and identified glucose metabolism-related genes associated with the outcome by correlation analysis. To generate a prognostic signature, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed.

Results: Finally, ten genes with expression status were identified to generate the risk signature, including FBP2, ADH6, DHDH, PRKCB, INPP5J, ABAT, HK2, GNPNAT1, PLCB3, and ACAT2. Survival analysis indicated that the patients in the high-risk group had a worse survival than those in the low-risk group, which is consistent with the results in validated cohorts. And receiver operating characteristic (ROC) curve analysis further validated the prognostic value and predictive performance of the signature. In addition, the two risk groups had significantly different clinicopathological characteristics and immune cell infiltration status. Notably, the low-risk group is more likely to respond to immunotherapy.

Conclusion: Overall, this study systematically explored the prognostic value of glucose metabolism and generated a prognostic risk signature with favorable efficacy and accuracy, which help select candidate patients and explore potential therapeutic approaches targeting the reprogrammed glucose metabolism in LUAD.

A Risk Model Developed Based on Necroptosis Predicts Overall Survival for Hepatocellular Carcinoma and Identification of Possible Therapeutic Drugs

Background

Necroptosis is a form of regulatory cell death (RCD) that attracts and activates immune cells, resulting in pro-tumor or anti-tumor effects. The purpose of this study was to investigate genes associated with necroptosis, to construct a risk score for predicting overall survival in patients with hepatocellular carcinoma, and to find potentially effective drugs.

Methods

The three algorithms ssGSEA, EPIC, and ESTIMATE were used to quantify the immune cell infiltration of the samples, differentially expressed genes (DEGs) analysis, and weighted gene co-expression network analysis were used to screen necroptosis related genes. Variables were screened according to random survival forest analysis, and combinations with significant p-values and a low number of genes were defined as prognostic signatures by using log-rank test after gene combination. Based on the sensitivity data of PRISM and CTRP2.0 datasets, we predicted the potential therapeutic agents for high-NRS patients.

Results

Seven genes such as TOP2A were used to define necroptosis-related risk score (NRS). The prognostic value of risk score was further validated, where high NRS was identified as a poor prognostic factor and tended to have higher grades of histologic grade, pathologic stage, T stage, BCLC, CLIP, and higher AFP. Higher NRS was also negatively correlated with the abundance of DCs, Neutrophils, Th17 cells, Macrophages, Endothelial, and positively correlated with Th2 cells. Necroptosis is often accompanied by the release of multiple cytokines, and we found that some cytokines were significantly correlated with both NRS and immune cells, suggesting that necroptosis may affect the infiltration of immune cells through cytokines. In addition, we found that TP53 mutations were more common in samples with high NRS, and these mutations may be associated with changes in NRS. Patients with high NRS may be more sensitive to gemcitabine, and gemcitabine may be an effective drug to improve the prognosis of patients with high NRS, which may play a role by inhibiting the expression of TOP2A.

Conclusions

We constructed a necroptosis-related scoring model to predict OS in HCC patients, and NRS was associated with immune response, TP53 mutation, and poor clinical classification in HCC patients. In addition, gemcitabine may be an effective drug for high-NRS patients.

A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer

The tumor microenvironment (TME) interacting with the malignant cells plays a vital role in cancer development. Herein, we aim to establish and verify a scoring system based on the characteristics of TME cells for prognosis prediction and personalized treatment guidance in patients with triple-negative breast cancer (TNBC). 158 TNBC samples from The Cancer Genome Atlas (TCGA) were included as the training cohort, and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (N = 297), as well as GSE58812 (N = 107), were included as the validation cohort. The enrichment scores of 64 immune and stromal cells were estimated by the xCell algorithm. In the training cohort, cells with prognostic significance were found out using univariate Cox regression analysis and further applied to the random survival forest (RSF) model. Based on the scores of M2 macrophages, CD8+ T cells, and CD4+ memory T cells, a risk scoring system was constructed, which divided TNBC patients into 4 phenotypes (M2low, M2highCD8+ThighCD4+Thigh, M2highCD8+ThighCD4+Tlow, and M2highCD8+Tlow). Furthermore, types 1 and 2 patients were merged into the low-risk group, while types 3 and 4 patients were in the high-risk group. The low-risk group had superior survival outcomes than the high-risk one, which was further confirmed in the validation cohort. Moreover, in the low-risk group, immune-related pathways were significantly enriched, and a higher level of antitumoral immune cells and immune checkpoint molecules, including PD-L1, PD-1, and CTLA-4, could be observed. Additionally, consistent results were achieved in the SYSUCC cohort when the scoring system was applied. In summary, this novel scoring system might predict the survival and immune activity of patients and might serve as a potential index for immunotherapy.

An Integrative Bioinformatic Analysis of Microbiome and Transcriptome for Predicting the Risk of Colon Adenocarcinoma

The abundance of gut microbiota is significantly decreased in patients with colorectal tumors compared to healthy groups. However, few studies have been conducted to correlate the differences in gut microbiota in colon cancer patients with different prognosis. In this study, we analysed the gut microbiota among patients with colon cancer and determined the microbial characteristics of COAD and divided the overall survival of COAD data into the high- and low-risk groups. In addition, we established a microbiome-related gene map and determined the association between microbial features and immune cell infiltration in COAD. In comparison with the low-risk group, the high risk group of COAD samples exhibited a decreased proportion of activated CD4 T cells as well as an increased proportion of M2 macrophages. The current data suggested that different gut flora backgrounds lead to different gene expression profiles, which in turn affect immune cell typing and colorectal tumor prognosis.

GDI2 is a novel diagnostic and prognostic biomarker in hepatocellular carcinoma

Background: GDP Dissociation inhibitor 2 (GDI2) gene has been correlated with some important biological processes in a variety of cancers, whereas the role of GDI2 in hepatocellular carcinoma (HCC) is ill-defined. We aimed to demonstrate the relationship between GDI2 and HCC based on The Cancer Genome Atlas (TCGA) data mining.

Methods: The expression of GDI2 was compared between cancer and normal tissues of 371 HCC patients collected from TCGA-LIHC, and verified in HCC cell lines. Gene set enrichment analysis (GSEA) was applied to annotate biological function of GDI2. Furthermore, Wilcoxon rank sum test, Logistics regression, as well as Cox regression and Kaplan-Meier survival analysis, were employed to evaluate the association of GDI2 expression with clinicopathological characteristics, and survival status of HCC patients, respectively.

Results: It showed that the expression of GDI2 was much higher in tumor tissues than in normal tissues (P < 0.001) of HCC patients. And the elevated expression of GDI2 was correlated with more aggressive HCC tumor status, including severe primary tumor extent, advanced pathological stage, serious histologic grade, and mutated TP53 status (P < 0.05). Moreover, high GDI2 expression was strongly associated with a poor survival rate (P < 0.001). Both enrichment and immune infiltration analyses implied that GDI2-associated signaling mainly involve lipid metabolism and extracellular matrix (ECM) constructing pathways related to tumor microenvironment (TME) (P < 0.05).

Conclusions: The elevated expression of GDI2 predicts poor prognosis in HCC patients, indicating that GDI2 could be applied as a predictive biomarker for diagnosis and prognosis of HCC.

The Identification of a Tumor Infiltration CD8+ T-Cell Gene Signature That Can Potentially Improve the Prognosis and Prediction of Immunization Responses in Papillary Renal Cell Carcinoma

Background

CD8+ T cells, vital effectors pertaining to adaptive immunity, display close relationships to the immunization responses to kill tumor cells. Understanding the effect exerted by tumor infiltration CD8+ T cells in papillary renal cell carcinoma (papRCC) is critical for assessing the prognosis process and responses to immunization therapy in cases with this disease.

Materials and Approaches

The single-cell transcriptome data of papRCC were used for screening CD8+ T-cell-correlated differentially expressed genes to achieve the following investigations. On that basis, a prognosis gene signature associated with tumor infiltration CD8+ T cell was built and verified with The Cancer Genome Atlas data set. Risk scores were determined for papRCC cases and categorized as high- or low-risk groups. The prognosis significance for risk scores was assessed with multiple-variate Cox investigation and Kaplan–Meier survival curves. In addition, the possible capability exhibited by the genetic profiles of cases to assess the response to immunization therapy was further explored.

Results

Six hundred twenty-one cell death-inhibiting RNA genes were screened using single-cell RNA sequencing. A gene signature consisting of seven genes (LYAR, YBX1, PNRC1, TCF25, MYL12B, MINOS1, and LINC01420) was then identified, and this collective was considered to be an independent prognosis indicator that could strongly assess overall survival in papRCC. In addition, the data allowed papRCC cases to fall to cohorts at high and low risks, exhibiting a wide range of clinically related features as well as different CD8+ T-cell immunization infiltration and immunization therapy responses.

Conclusions

Our work provides a possible explanation for the limited response of current immunization checkpoint-inhibiting elements for combating papRCC. Furthermore, the researchers built a novel genetic signature that was able to assess the prognosis and immunotherapeutic response of cases. This may also be considered as a promising therapeutic target for the disease.

Silencing BLNK protects against interleukin-1β-induced chondrocyte injury through the NF-κB signaling pathway

BACKGROUND

Osteoarthritis (OA) is the most common joint disease in the elderly and is characterized by the progressive degeneration of articular cartilage. It is necessary to study the molecular pathology of OA. This study aimed to explore the role and mechanism of BLNK in regulating interleukin-1β (IL-1β)-induced chondrocyte injury and OA progression.

METHODS

GSE1919 (5 normal samples and 5 OA samples) was downloaded from the Gene Expression Omnibus (GEO) database. The limma package in R software was used to identify differentially expressed genes (DEGs) between control and OA-affected cartilage. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the differentially expressed genes were also performed. Apoptosis was assessed by flow cytometry. An OA rat model was established, and the relative expression of BLNK was assessed by real time quantitative PCR (qRT-PCR) and immunohistochemical staining. The expression of collagen II, MMP9, p65 and p-p65 was measured by Western blot analysis. Moreover, inflammatory factors (TNF-α and IL-18) were assessed by ELISA. The NF-κB inhibitor JSH-23 was used to assess the impact of BLNK on the NF-κB signaling pathway.

RESULTS

In total, 1318 DEGs were identified between normal and OA-affected cartilage according to the criteria (P-value <0.05 and |logFC > 1|). These DEGs were mainly enriched in the NF-κB pathway. BLNK was highly expressed in OA cartilage tissue and injured chondrocytes. Silencing BLNK significantly downregulated the IL-1β-induced apoptosis of chondrocytes. Silencing BLNK partially increased collagen II expression and downregulated MMP13 expression. Moreover, silencing BLNK partially decreased TNF-α and IL-18 expression. BLNK silencing inhibited the activation of NF-κB in OA. Silencing BLNK delayed OA progression through the NF-κB signaling pathway.

CONCLUSION

Silencing BLNK delayed OA progression and IL-1β-induced chondrocyte injury by regulating the NF-κB pathway.

Tumor stemness and immune infiltration synergistically predict response of radiotherapy or immunotherapy and relapse in lung adenocarcinoma

Cancer stem cells (CSCs) have been shown to accelerate tumor recurrence, radiotherapy, and chemotherapy resistance. Immunotherapy is a powerful anticancer treatment that can significantly prolong the overall survival of patients with lung adenocarcinoma (LUAD). However, little is known about the function of genes related to tumor stemness and immune infiltration in LUAD. After integrating the tumor stemness index based on mRNA expression (mRNAsi), immune score, mRNA expression, and clinical information from the TCGA database, we screened 380 tumor stemness and immune (TSI)-related genes and constructed a five TSI-specific-gene (CPS1, CCR2, NT5E, ANLN, and ABCC2) signature (TSISig) using a machine learning method. Survival analysis indicated that TSISig could stably predict the prognosis of patients with LUAD. Comparison of mRNAsi and immune score between high- and low-TSISig groups suggested that TSISig characterized tumor stemness and immune infiltration. In addition, enrichment of immune subpopulations showed that the low-TSISig group held more immune subpopulations. GSEA revealed that TSISig had a strong association with the cell cycle and human immune response. Further analysis revealed that TSISig not only had a good predictive ability for prognosis but could also serve as an excellent predictor of tumor recurrence and response to radiotherapy and immunotherapy in LUAD patients. TSISig might regulate the development of LUAD by coordinating tumor stemness and immune infiltration. Finally, a connectivity map (CMap) analysis demonstrated that the HDAC inhibitor could target TSISig.

Comparative transcriptomic analysis reveals the gonadal development-related gene response to environmental temperature in Mauremys mutica

The Asian yellow pond turtle (Mauremys mutica) displays temperature-dependent sex determination (TSD), in which incubation temperature during embryonic development determines the sexual fate of the individual. However, the mechanism of the sex determination/differentiation of Mauremys mutica remains a mystery. Here, we first analyzed the temperature-specific gonadal transcriptomes of Mauremys mutica prior to gonad formation and gonads during the thermosensitive period. We uncovered a list of candidates that respond to temperature stimuli enriched in several categories, such as heat shock protein family members dnajb6a, dnaja4, hspa8 and hsp90aa1, temperature sensor genes mmp17 and mmp28, and putative novel temperature-responsive genes tmco6, gria3 and eif3f. Notably, striking differences were identified in the expression profiles of genes underlying sexual development, such as tex15, insr, igf1r, cirbp, esr1, dmrt2 and Serpinh1. Moreover, we analyzed the similarity and divergence of the timecourse of gene expression among Mauremys mutica and two other reported TSD turtles (Trachemys scripta and Chrysemys picta). The shared genes revealed the common gonad-specific regulatory mechanisms existing in these three TSD turtles that initiate their sexual development. Therefore, our findings could provide basic data to elucidate the mechanisms of sex determination/differentiation of M. mutica, even contributing to further understanding of these mechanisms in other TSD turtles.

The Landscape of the Tumor Microenvironment in Skin Cutaneous Melanoma Reveals a Prognostic and Immunotherapeutically Relevant Gene Signature

The tumorigenesis of skin cutaneous melanoma (SKCM) remains unclear. The tumor microenvironment (TME) is well known to play a vital role in the onset and progression of SKCM. However, the dynamic mechanisms of immune regulation are insufficient. We conducted a comprehensive analysis of immune cell infiltration in the TME. Based on the differentially expressed genes (DEGs) in clusters grouped by immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration was explored. Methods: We analyzed immune cell infiltration in two independent cohorts and assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways, and gene mutations. Genes related to the infiltration pattern of TME immune cells were determined. Furthermore, the unsupervised clustering method (k-means) was used to divide samples into three different categories according to TME, which were defined as TME cluster-A, -B, and -C. DEGs among three groups of samples were analyzed as signature genes. We further distinguished common DEGs between three groups of samples according to whether differences were significant and divided DEGs into the Signature gene-A group with significant differences and the Signature gene-B group with insignificant differences. The Signature gene-A gene set mainly had exon skipping in SKCM, while the Signature gene-B gene set had no obvious alternative splicing form. Subsequently, we analyzed genetic variations of the two signatures and constructed a competing endogenous RNA (ceRNA) regulatory network. LASSO Cox regression was used to determine the immune infiltration signature and risk score of SKCM. Finally, we obtained 13 hub genes and calculated the risk score based on the coefficient of each gene to explore the impact of the high- and low-risk scores on biologically related functions and prognosis of SKCM patients further. The correlation between the risk score and clinicopathological characteristics of SKCM patients indicated that a low-risk score was associated with TME cluster-A classification (p < 0.001) and metastatic SKCM (p < 0.001). Thirteen hub genes also showed different prognostic effects in pan-cancer. The results of univariate and multivariate Cox analyses revealed that risk score could be used as an independent risk factor for predicting the prognosis of SKCM patients. The nomogram that integrated clinicopathological characteristics and immune characteristics to predict survival probability was based on multivariate Cox regression. Finally, 13 hub genes that showed different prognostic effects in pan-cancers were obtained. According to immunohistochemistry staining results, Ube2L6, SRPX2, and IFIT2 were expressed at higher levels, while CLEC4E, END3, and KIR2DL4 were expressed at lower levels in 25 melanoma specimens. Conclusion: We performed a comprehensive assessment of the immune-associated TME. To elucidate the potential development of immune-genomic features in SKCM, we constructed an unprecedented set of immune characteristic genes (EDN3, CLEC4E, SRPX2, KIR2DL4, UBE2L6, and IFIT2) related to the immune landscape of TME. These genes are related to different prognoses and drug responses of SKCM. The immune gene signature constructed can be used as a robust prognostic biomarker of SKCM and a predictor of an immunotherapy effect.

Comprehensive Analysis of Pyroptosis-Related Genes and Tumor Microenvironment Infiltration Characterization in Breast Cancer

Background

Immunotherapy has emerged as a significant strategy to treat numerous tumors. The positive response to immunotherapy depends on the dynamic interaction between tumor cells and infiltrating lymphocytes in the tumor microenvironment (TME). Pyroptosis, inflammation-induced cell death, is intricately associated with several tumors. However, the relationship between pyroptosis and clinical prognosis, immune cell infiltration, and immunotherapy effect is unclear in breast cancer (BRCA).

Methods

We comprehensively evaluated 33 pyroptosis-related genes and systematically assessed the relationship between pyroptosis and tumor progression, prognosis, and immune cell infiltration. The PyroptosisScore was used to quantify the pyroptosis pattern of a single tumor patient. We then assessed their values for predicting prognoses and therapeutic responses in BRCA.

Results

Three different modes of PyroptosisClusters were determined. The characteristics of TME cell infiltration in these three PyroptosisClusters were highly consistent with three immunophenotypes of tumors, including immune-excluded, immune-inflamed, and immune-desert phenotypes. Comprehensive bioinformatics analysis revealed that patients with a low PyroptosisScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, and were more sensitive to immunotherapy than those with a high PyroptosisScore.

Conclusions

Our findings revealed the crucial role of pyroptosis in maintaining the diversity and complexity of TME. Pyroptosis is closely related to tumor progression, tumor prognosis, and immunotherapy response. Evaluating the PyroptosisScore of a single tumor can assist in understanding the characteristics of TME infiltration and lead to the development of more effective immunotherapy strategies.

A Tumor-Infiltration CD8+ T Cell-Based Gene Signature for Facilitating the Prognosis and Estimation of Immunization Responses in HPV+ Head and Neck Squamous Cell Cancer

Background

CD8+ T cells, which play a vital role in response to adaptive immunity, are closely related to the immunization responses to kill tumor cells. Understanding the effects exerted by tumor-infiltrated CD8+ T cells in HPV+ and HPV- head and neck squamous cell carcinoma (HNSCC) patients is critical for predicting their prognosis as well as their responses towards immunization-related therapy.

Materials and Methods

HNSCC single cell transcriptome was used to screen for differentially expressed genes (DEGs) based on CD8+ T cells. A gene signature associated with CD8+ T cells was built and verified with the cancer genome atlas dataset with a view to predicting the prognosis of HNSCC patients. Risk scores were calculated for HNSCC cases and categorized into either high- or low-risk cohorts. The prognosis-correlated data of the risk scores were analyzed by using Kaplan-Meier survival curves and multi-variate Cox regression plots. In addition, the possibility of using the genetic profiles to predict responses toward immunization-related therapy was explored.

Results

From the DEGs screened from the sequencing of single-cell RNA, a gene signature of 4 genes (ACAP1, ANKRD28, C12orf75, and M6PR) were identified. It was seen that these genes could predict overall survival in HPV+ HNSCC patients. In addition, high- and low-risk HPV+ HNSCC patients showed marked differences in their CD8+ T-cell infiltration due to immunization when clinical characteristics were taken into consideration. This correlated with their immunization therapy responses.

Conclusions

Our work provides insights into explaining the restricted responses of current immunization checkpoint inhibiting substances in HPV+ HNSCC patients. A novel genetic signature to predict the prognosis and immunization-correlated therapeutic responses is presented. This will provide potential new therapeutic opportunities for HPV+ HNSCC patients.

Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics

Background

Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis.

Methods

The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes.

Results

In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database.

Conclusion

Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00837-6.

Identification of the miRNA signature and key genes in colorectal cancer lymph node metastasis

BACKGROUND

Because its metastasis to the lymph nodes are closely related to poor prognosis, miRNAs and mRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of colorectal cancer (CRC). This study aimed to identify novel gene signatures in the lymph node metastasis of CRC.

METHODS

GSE56350, GSE70574, and GSE95109 datasets were downloaded from the Gene Expression Omnibus (GEO) database, while data from 569 colorectal cancer cases were also downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DE-miRNAs) were calculated using R programming language (Version 3.6.3), while gene ontology and enrichment analysis of target mRNAs were performed using FunRich ( http://www.funrich.org ). Furthermore, the mRNA-miRNA network was constructed using Cytoscape software (Version 3.8.0). Gene expression levels were verified using the GEO datasets. Similarly, quantitative real-time PCR (qPCR) was used to examine expression profiles from 20 paired non-metastatic and metastatic lymph node tissue samples obtained from patients with CRC.

RESULTS

In total, five DE-miRNAs were selected, and 34 mRNAs were identified after filtering the results. Moreover, two key miRNAs (hsa-miR-99a, hsa-miR-100) and one gene (heparan sulfate-glucosamine 3-sulfotransferase 2 [HS3ST2]) were identified. The GEO datasets analysis and qPCR results showed that the expression of key miRNA and genes were consistent with that obtained from the bioinformatic analysis. A novel miRNA-mRNA network capable of predicting the prognosis and confirmed experimentally, hsa-miR-99a-HS3ST2-hsa-miR-100, was found after expression analysis in metastasized lymph node tissue from CRC samples.

CONCLUSION

In summary, miRNAs and genes with potential as biomarkers were found and a novel miRNA-mRNA network was established for CRC lymph node metastasis by systematic bioinformatic analysis and experimental validation. This network may be used as a potential biomarker in the development of lymph node metastatic CRC.

Construction of a Promising Tumor-Infiltrating CD8+ T Cells Gene Signature to Improve Prediction of the Prognosis and Immune Response of Uveal Melanoma

Background

CD8+ T cells work as a key effector of adaptive immunity and are closely associated with immune response for killing tumor cells. It is crucial to understand the role of tumor-infiltrating CD8+ T cells in uveal melanoma (UM) to predict the prognosis and response to immunotherapy.

Materials and Methods

Single-cell transcriptomes of UM with immune-related genes were combined to screen the CD8+ T-cell-associated immune-related genes (CDIRGs) for subsequent analysis. Next, a prognostic gene signature referred to tumor-infiltrating CD8+ T cells was constructed and validated in several UM bulk RNA sequencing datasets. The risk score of UM patients was calculated and classified into high- or low-risk subgroup. The prognostic value of risk score was estimated by using multivariate Cox analysis and Kaplan–Meier survival analysis. Moreover, the potential ability of gene signature for predicting immunotherapy response was further explored.

Results

In total, 202 CDIRGs were screened out from the single-cell RNA sequencing of GSE139829. Next, a gene signature containing three CDIRGs (IFNGR1, ANXA6, and TANK) was identified, which was considered as an independent prognostic indicator to robustly predict overall survival (OS) and metastasis-free survival (MFS) of UM. In addition, the UM patients were classified into high- and low-risk subgroups with different clinical characteristics, distinct CD8+ T-cell immune infiltration, and immunotherapy response. Gene set enrichment analysis (GSEA) showed that immune pathways such as allograft rejection, inflammatory response, interferon alpha and gamma response, and antigen processing and presentation were all positively activated in low-risk phenotype.

Conclusion

Our work gives an inspiration to explain the limited response for the current immune checkpoint inhibitors to UM. Besides, we constructed a novel gene signature to predict prognosis and immunotherapy responses, which may be regarded as a promising therapeutic target.

A five-m6A regulatory gene signature is a prognostic biomarker in lung adenocarcinoma patients

We analyzed the prognostic value of N6-methyladenosine (m6A) regulatory genes in lung adenocarcinoma (LADC) and their association with tumor immunity and immunotherapy response. Seventeen of 20 m6A regulatory genes were differentially expressed in LDAC tissue samples from the TCGA and GEO databases. We developed a five-m6A regulatory gene prognostic signature based on univariate and Lasso Cox regression analysis. Western blot analysis confirmed that the five prognostic m6A regulatory proteins were highly expressed in LADC tissues. We constructed a nomogram with five-m6A regulatory gene prognostic risk signature and AJCC stages. ROC curves and calibration curves showed that the nomogram was well calibrated and accurately distinguished high-risk and low-risk LADC patients. Weighted gene co-expression analysis showed significant correlation between prognostic risk signature genes and the turquoise module enriched with cell cycle genes. The high-risk LADC patients showed significantly higher PD-L1 levels, increased tumor mutational burden, and a lower proportion of CD8⁺ T cells in the tumor tissues and improved response to immune checkpoint blockade therapy. These findings show that this five-m6A regulatory gene signature is a prognostic biomarker in LADC and that immune checkpoint blockade is a potential therapeutic option for high-risk LADC patients.

Comprehensive Analysis of Gene Expression Changes and Validation in Hepatocellular Carcinoma

Aim

This study aimed to analyze the involvement of hub genes in hepatocellular carcinoma.

Methods

Four series were used in this study: GSE45267, GSE84402, and GSE101685 from GPL570 platform in the Gene Expression Omnibus and the other from The Cancer Genome Atlas. The gene audition was completed using R software and Venn diagrams. The outcome, Gene Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes preliminary analyses of differentially expressed genes were performed using the R software. A string image was obtained using the Search Tool for the Retrieval of Interacting Genes. The protein–protein interaction network was examined using Cytoscape software. The corrplot package was used to analyze the correlation of genes. Human Protein Atlas was used to confirm the protein levels. Univariate Cox regression was used to analyze whether these genes were related to survival. UALCAN was used to confirm the effect of these genes on patient survival.

Results

A total of 107 differentially expressed genes from 491 patients with hepatocellular carcinoma and 119 normal individuals were selected in this study. Cytoscape revealed 25 central nodes from the 107 genes. CCNB1, CDK1, CCNA2, PTTG1, and CDC20 were selected based on the cell cycle pathway. A significant correlation was found among the 6 DEGs. The transcription levels and protein levels of these genes were verified in cells and human tissue samples. The overall survival for these genes was analyzed using univariate Cox regression and UALCAN.

Conclusion

CCNB1, CDK1, CDC20, PTTG1, CCNA2, and TTK were overexpressed and correlated in hepatocellular carcinoma cells and tumors. The results might help explore the prognosis and diagnostic markers of HCC.

Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches

Aims

In the cancer-related research field, there is currently a major need for a greater number of valuable biomarkers to predict the prognosis of hepatocellular carcinoma (HCC). In this study, we aimed to screen hub genes related to immune cell infiltration and explore their prognostic value for HCC.

Methods

We analyzed five datasets (GSE46408, GSE57957, GSE74656, GSE76427, and GSE87630) from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes; then, the hub genes were identified. Functional enrichment of the genes was performed on the Metascape website. Next, the expression of these hub genes was validated in several databases, including Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Human Protein Atlas. We explored the correlations between the hub genes and infiltrated immune cells in the TIMER2.0 database. The survival curves were generated in GEPIA2, and the univariate and multivariate Cox regression analyses were performed using TIMER2.0.

Results

The top ten hub genes [DNA topoisomerase II alpha (TOP2A), cyclin B2 (CCNB2), protein regulator of cytokinesis 1 (PRC1), Rac GTPase-activating protein 1 (RACGAP1), aurora kinase A (AURKA), cyclin-dependent kinase inhibitor 3 (CDKN3), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle-associated 5 (CDCA5), abnormal spindle microtubule assembly (ASPM), and non-SMC condensin I complex subunit G (NCAPG)] were identified in subsequent analysis. These genes are most markedly enriched in cell division, suggesting their close association with tumorigenesis. Multi-database analyses validated that the hub genes were upregulated in HCC tissues. All hub genes positively correlated with several types of immune infiltration, including B cells, CD4⁺ T cells, macrophages, and dendritic cells. Furthermore, these hub genes served as independent prognostic factors, and the expression of these hub genes combing with the macrophage levels could help predict an unfavorable prognosis of HCC.

Conclusion

In sum, these hub genes (TOP2A, CCNB2, PRC1, RACGAP1, AURKA, CDKN3, NUSAP1, CDCA5, ASPM, and NCAPG) may be pivotal markers for prognostic prediction as well as potentially work as targets for immune-based intervention strategies in HCC.