Xiaoqiang Feng

Renal clear cell carcinoma (ccRCC) is a highly aggressive and common form of kidney cancer, with limited treatment options for advanced stages. Recent studies have highlighted the importance of the ubiquitin‐proteasome system in tumor progression, particularly the role of ubiquitin‐conjugating enzyme E2 (UBE2) family members. However, the prognostic significance of UBE2‐related genes (UBE2RGs) in ccRCC remains unclear. In this study, bulk RNA‐sequencing and single‐cell RNA‐sequencing data from ccRCC patients were retrieved from the Cancer Genome Atlas and Gene Expression Omnibus databases. Differential expression analysis was performed to identify UBE2RGs associated with ccRCC. A combination of 10 machine learning methods was applied to develop an optimal prognostic model, and its predictive performance was evaluated using area under the curve (AUC) values for 1‐, 3‐, and 5‐year overall survival (OS) in both training and validation cohorts. Functional enrichment analyses of gene ontology and Kyoto Encyclopedia of Genes and Genomes were conducted to explore the biological pathways involved. Correlation analysis was conducted to investigate the association between the risk score and tumor mutational burden (TMB) and immune cell infiltration. Immunotherapy and chemotherapy sensitivity were assessed by immunophenoscore and tumor immune, dysfunction, and exclusion scores to identify potential predictive significance. In vitro, knockdown of the key gene UBE2C in 786‐O cells by specific small interfering RNA to validate its impact on apoptosis, migration, cell cycle, migration, invasion of tumor cells, and induction of regulatory T cells (Tregs). Analysis of sc‐RNA revealed that UBE2 activity was significantly upregulated in malignant cells, suggesting its role in tumor progression. A three‐gene prognostic model comprising UBE2C, UBE2D3, and UBE2T was constructed by Lasoo Cox regression and demonstrated robust predictive accuracy, with AUC values of 0.745, 0.766, and 0.771 for 1‐, 3‐, and 5‐year survival, respectively. The model was validated as an independent prognostic factor in ccRCC. Patients in the high‐risk group had a worse prognosis, higher TMB scores, and low responsiveness to immunotherapy. Additionally, immune infiltration and chemotherapy sensitivity analyses revealed that UBE2RGs are associated with various immune cells and drugs, suggesting that UBE2RGs could be a potential therapeutic target for ccRCC. In vitro experiments confirmed that the reduction of UBE2C led to an increase in apoptosis rate, as well as a decrease in tumor cell invasion and metastasis abilities. Additionally, si‐UBE2C cells reduced the release of the cytokine Transforming Growth Factor‐beta 1 (TGF‐β1), leading to a decreased ratio of Tregs in the co‐culture system. This study presents a novel three‐gene prognostic model based on UBE2RGs that demonstrates significant predictive value for OS, immunotherapy, and chemotherapy in ccRCC patients. The findings underscore the potential of UBE2 family members as biomarkers and therapeutic targets in ccRCC, warranting further investigation in prospective clinical trials.

Triple-negative breast Cancer (TNBC) is an aggressive subtype lacking estrogen, progesterone, and HER2 receptors. Known for limited targeted therapies, it poses challenges and requires personalized treatment strategies. Differential analysis revealed a significant decrease in keratin 81 (KRT81) expression in non-TNBC samples and an increase in TNBC samples, lower KRT81 expression correlated with better TNBC patient outcomes. It emerged as an independent predictive factor for TNBC, with associations found between its expression and clinically relevant features. We further developed a nomogram for survival probability assessment based on Cox regression results, demonstrating its accuracy through calibration curves. Gene annotation analysis indicated that KRT81 is involved in immune-related pathways and tumor cell adhesion. KRT81 is associated with immune cell infiltration of Follicular helper T cells (Tfh) and CD8 + T cells, suggesting its potential impact on the immunological microenvironment. The study delved into KRT81's predictive value for immunotherapy responses, high expression of KRT81 was associated with greater potential for immune evasion. Single-cell RNA sequencing analysis pinpointed KRT81 expression within a specific malignant subtype which was a risk factor for TNBC. Furthermore, KRT81 promoted TNBC cell proliferation, migration, invasion, and adhesion was confirmed by gene knockout or overexpression assay. Co-culture experiments further indicated KRT81's potential role in inhibiting CD8 + T cells, and correlation analysis implied KRT81 was highly correlated with immune checkpoint CD276, providing insights into its involvement in the immune microenvironment via CD276. In conclusion, this comprehensive study positions KRT81 as a promising prognostic marker for predicting tumor progression and immunotherapy responses in TNBC.

Hepatocellular carcinoma (HCC) is a significant contributor to cancer-related deaths in the world. The development and progression of HCC are closely correlated with the abnormal regulation of non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). Important biological pathways in cancer biology, such as cell proliferation, death, and metastasis, are impacted by these ncRNAs, which modulate gene expression. The abnormal expression of non-coding RNAs in HCC raises the possibility that they could be applied as new biomarkers for diagnosis, prognosis, and treatment targets. Furthermore, by controlling the expression of cancer-related genes, miRNAs can function as either tumor suppressors or oncogenes. On the other hand, lncRNAs play a role in the advancement of cancer by interacting with other molecules within the cell, which, in turn, affects processes such as chromatin remodeling, transcription, and post-transcriptional processes. The importance of ncRNA-driven regulatory systems in HCC is being highlighted by current research, which sheds light on tumor behavior and therapy response. This research highlights the great potential of ncRNAs to improve patient outcomes in this difficult disease landscape by augmenting the present methods of HCC care through the use of precision medicine approaches.

Gliomas are highly invasive and aggressive tumors having the highest incidence rate of brain cancer. Identifying effective prognostic and potential therapeutic targets is necessitated. The relationship of pyroptosis, a form of programmed cellular death, with gliomas remains elusive. We constructed and validated a prognostic model for gliomas using pyroptosis-related genes. Differentially expressed pyroptosis-related genes were screened using the “limma” package. Based on LASSO-Cox regression, nine significant genes including CASP1, CASP3, CASP6, IL32, MKI67, MYD88, PRTN3, NOS1, and VIM were employed to construct a prognostic model in the TCGA cohort; the results were validated in the CGGA cohort. According to the median risk score, the patients were classified into two risk groups, namely, high- and low-risk groups. Patients at high risk had worse prognoses relative to those at low risk evidenced by the Kaplan-Meier curve analysis. The two groups exhibited differences in immune cell infiltration and TMB scores, with high immune checkpoint levels, TMB scores, and immune cell infiltration levels in the high-risk group. KEGG and GO analyses suggested enrichment in immune-related pathways. Furthermore, we found that the genes in our signature strongly correlated with oxidative stress-related pathways and the subgroups exhibited different ssGSEA scores. Some small molecules targeted the genes in the model, and we verified their drug sensitivities between the risk groups. The scRNA-seq dataset, GSE138794, was processed using the “Seurat” package to assess the level of risk gene expression in specific cell types. Finally, the MYD88 level was lowered in the U87 glioma cell line using si-RNA constructs. Cellular proliferation was impaired, and fewer pyroptosis-related cytokines were released upon exposure to LPS. In summary, we built a pyroptosis-related gene model that accurately classified glioma patients into high- and low-risk groups. The findings suggest that the signature may be an effective prognostic predictive tool for gliomas.

Gliomas are the most common tumor in the central nervous system with limited prognostic markers making it difficult to research progression. Induction of cellular immunogenic death is a promising treatment for glioma. Pyroptosis is one of the recently discovered programmed immuogenic cell death modes which remains unclear in glioma. We obtained glioma datasets from the CGGA and TCGA websites. Pearson correlation analysis was used to find pyroptosis-related lncRNAs. Subsequently, the univariate, LASSO, and multivariate Cox regression were applied to construct a prognostic signature based on pyroptosis-related lncRNAs. Kaplan-Meier plots, ROC curves, and PCA were utilized for testing the prognostic performance of the signature. We conducted the univariate and multivariate Cox regressions to ascertain if the signature worked as an independent factor for predicting overall survival (OS) for individuals with glioma from other characteristics. For evaluating the immune landscape differences between the subgroups, ESTIMATE, CIBERTSORT, and ssGSEA were adopted. Additionally, biological functions and pathways of DEGs were identified by KEGG and GO. We also screened potential drugs and measured sensitivities of chemotherapeutics between the subgroups by CellMiner and pRRophetic package. Finally, shRNA was conducted to knockdown of COX10-AS1 in U87 cells to determine its relationship with pyroptosis. We successfully created an effective pyroptosis-related lncRNA signature that divided individuals into groups of low- and high-risk, and individuals in the high-risk group were with poor prognosis in comparison to the individuals in the other group. A nomogram including clinical factors and risk scores to predict the OS was built. Furthermore, the two groups appeared to have different immune landscapes; the high-risk group showed greater levels of ESTIMATE scores, immune cell infiltration, and immune checkpoints. Additionally, immune-related pathways and functions were shown to be enriched according to KEGG and GO findings. Knockdown of COX10-AS1 inhibited U87 cell growth, upregulated CASP1 and NLRP3, and released more IL1-β and IL-18 than the negative control. In summary, our study developed an lncRNA signature related to pyroptosis for OS prediction of gliomas and demonstrated its relationship with immune infiltration and drug sensitivity.

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction. Methods. The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature. Results. Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8⁺ T cells, resting mast cells, and nave B cells were significantly correlated with the signature. Conclusion. Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

Acute rejection (AR) after kidney transplant is one of the major obstacles to obtain ideal graft survival. Reliable molecular biomarkers for AR and renal allograft loss are lacking. This study was performed to identify novel long noncoding RNAs (lncRNAs) for diagnosing AR and predicting the risk of graft loss. The several microarray datasets with AR and nonrejection specimens of renal allograft downloaded from Gene Expression Omnibus database were analyzed to screen differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). Univariate and multivariate Cox regression analyses were used to identify optimal prognosis-related DElncRNAs for constructing a risk score model. 39 common DElncRNAs and 185 common DEmRNAs were identified to construct a lncRNA-mRNA regulatory relationship network. DElncRNAs were revealed to regulate immune cell activation and proliferation. Then, 4 optimal DElncRNAs, ATP1A1-AS1, CTD-3080P12.3, EMX2OS, and LINC00645, were selected from 17 prognostic DElncRNAs to establish the 4-lncRNA risk score model. In the training set, the high-risk patients were more inclined to graft loss than the low-risk patients. Time-dependent receiver operating characteristics analysis revealed the model had good sensitivity and specificity in prediction of 1-, 2-, and 3-year graft survival after biopsy ( , respectively). The internal testing set verified the result well. Gene set enrichment analysis which expounded NOD-like receptor, the Toll-like receptor signaling pathways, and other else playing important role in immune response was enriched by the 4 lncRNAs. Allograft-infiltrating immune cells analysis elucidated the expression of 4 lncRNAs correlated with gamma delta T cells and eosinophils, etc. Our study identified 4 novel lncRNAs as potential biomarkers for AR of renal allograft and constructed a lncRNA-based model for predicting the risk of graft loss, which would provide new insights into mechanisms of AR.