Identification of Prognostic and Metastatic Alternative Splicing Signatures in Kidney Renal Clear Cell Carcinoma

Exploring novel biomarkers and immunotherapeutic targets for biofeedback therapies to reveal the tumor-associated immune microenvironment through a multimetric analysis of kidney renal clear cell carcinoma

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) constitutes the primary subtype of renal cell carcinoma, representing 75% to 80% of cases and carrying a substantial cancer-specific mortality rate of up to 24%. Despite advancements in treatment options, KIRC displays notable resistance to conventional therapies, emphasizing the need for innovative targeted immunotherapeutic strategies. Chromatin regulators (CRs), pivotal proteins controlling gene expression and critical biological processes, play a crucial role in the initiation and progression of KIRC. This study employed a multi-omics approach to evaluate the impact of CR-associated genes on KIRC prognosis.

METHODS

The study utilized the TCGA-KIRC dataset and employed LASSO Cox regression to construct and validate a prognostic model that focuses on genes influencing KIRC prognosis. The research investigated interactions among gene characteristics, clinical parameters, the tumor microenvironment, targeted immunotherapy, and drug responsiveness. Experimental validation, encompassing various techniques such as cell culture, transient transfection, qPCR, Transwell assays, confirmed the robust predictive capability of the BRD9 gene.

RESULTS

The analysis identified the risk score of CRs as an independent factor determining KIRC prognosis. Furthermore, the study introduced a predictive Nomogram model that integrates clinical attributes and risk assessment. Significantly, BRD9 exhibited substantially elevated expression within KIRC cells, underscoring its role in driving cancer cell proliferation, invasion, and migration. These findings suggest the potential for tailored immunotherapy targeting BRD9 in the treatment of KIRC.

CONCLUSION

This study presents an innovative prognostic framework for KIRC based on multi-omics approaches, seamlessly incorporating CRs. This model holds promise for improving the accuracy of prognosis prediction for KIRC patients, laying a robust foundation for the development of targeted immunotherapies.

Systematic exploration of prognostic alternative splicing events related to tumor immune microenvironment of Clear Cell Renal Cell Carcinoma

BackgroundPathologically, clear cell renal cell carcinoma (ccRCC) is the most common type of renal carcinoma, with high heterogeneity and poor prognosis. There is increasing evidence that alternative splicing (AS) is involved in tumor evolution and tumor immune microenvironment (TIME). However, studies on the exploration of AS events and TIME in ccRCC are still few but needed.MethodsThe transcriptional data and clinicopathological information of patients with ccRCC in The Cancer Genome Atlas (TCGA) database were extracted completely. Patients were grouped according to the ESTIMATE algorithm and differentially expressed AS events (DEASs) were identified. The relationship between AS events and features of TIME were investigated by functional enrichment analysis and unsupervised consensus analysis. Finally, hub splicing factors (SFs) was identified by the regulatory network of survival-related AS events and intersection SFs, and its biological function was further verified in vitro.ResultsIn total, the data of 515 patients with ccRCC were extracted and analyzed. Patients with low immune-score presented longer overall survival (OS) than high immune-score. 861 AS events were identified as DEASs, and they were enriched in immune-related pathways. 3 AS-based clusters were identified and found to have different prognoses and unique immune features. Finally, MBNL1 was identified as a hub SF, and it was shown to inhibit proliferation and metastasis, promote apoptosis, and block cells in G2/M phase in 786O and A498 cells. Mechanistically, MBNL1 regulates QKI expression through AS.ConclusionThe prognostic risk model constructed base on immune-related AS events has good predictive ability for ccRCC. The hub SF MBNL1 identied in the present study could inhibit the progression of ccRCC. This effect is likely due to the regulation of QKI expression through AS.

Constructing and validating pan-apoptosis-related features for predicting prognosis and immunotherapy response in hepatocellular carcinoma

The study aimed to develop a prognostic model for Hepatocellular Carcinoma (HCC) based on pan-apoptosis-related genes, a novel inflammatory programmed cell death form intricately linked to HCC progression. Utilizing transcriptome sequencing and clinical data from the TCGA database, we identified six crucial pan-apoptosis-related genes through statistical analyses. These genes were then employed to construct a prognostic model that accurately predicts overall survival rates in HCC patients. Our findings revealed a strong correlation between the model's risk scores and tumor microenvironment (TME) status, immune cell infiltration, and immune checkpoint expression. Furthermore, we screened for drugs with potential therapeutic efficacy in high- and low-risk HCC groups. Notably, PPP2R5B gene knockdown was found to inhibit HCC cell proliferation and clonogenic capacity, suggesting its role in HCC progression. In conclusion, this study presents a novel pan-apoptosis gene-based prognostic risk model for HCC, providing valuable insights into patient TME status and guiding the selection of targeted therapies and immunotherapies.

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data

The stage prediction of kidney renal clear cell carcinoma (KIRC) is important for the diagnosis, personalized treatment, and prognosis of patients. Many prediction methods have been proposed, but most of them are based on unimodal gene data, and their accuracy is difficult to further improve. Therefore, we propose a novel multi-weighted dynamic cascade forest based on the bilinear feature extraction (MLW-BFECF) model for stage prediction of KIRC using multimodal gene data (RNA-seq, CNA, and methylation). The proposed model utilizes a dynamic cascade framework with shuffle layers to prevent early degradation of the model. In each cascade layer, a voting technique based on three gene selection algorithms is first employed to effectively retain gene features more relevant to KIRC and eliminate redundant information in gene features. Then, two new bilinear models based on the gated attention mechanism are proposed to better extract new intra-modal and inter-modal gene features; Finally, based on the idea of the bagging, a multi-weighted ensemble forest classifiers module is proposed to extract and fuse probabilistic features of the three-modal gene data. A series of experiments demonstrate that the MLW-BFECF model based on the three-modal KIRC dataset achieves the highest prediction performance with an accuracy of 88.9 %.

Anoikis-Related Long Non-Coding RNA Signatures to Predict Prognosis and Immune Infiltration of Gastric Cancer

Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and its immunological infiltration. The ar-lncRNAs were derived from RNA sequencing data and associated clinical information obtained from The Cancer Genome Atlas. Pearson correlation analysis, differential screening, LASSO and Cox regression were utilized to identify the typical ar-lncRNAs with prognostic significance, and the corresponding risk model was constructed, respectively. Comprehensive methods were employed to assess the clinical characteristics of the prediction model, ensuring the accuracy of the prediction results. Further analysis was conducted on the relationship between immune microenvironment and risk features, and sensitivity predictions were made about anticancer medicines. A risk model was built according to seven selected ar-lncRNAs. The model was validated and the calibration plots were highly consistent in validating nomogram predictions. Further analyses revealed the great accuracy of the model and its ability to serve as a stand-alone GC prognostic factor. We subsequently disclosed that high-risk groups display significant enrichment in pathways related to tumors and the immune system. Additionally, in tumor immunoassays, notable variations in immune infiltrates and checkpoints were noted between different risk groups. This study proposes, for the first time, that prognostic signatures of ar-lncRNA can be established in GC. These signatures accurately predict the prognosis of GC and offer potential biomarkers, suggesting new avenues for basic research, prognosis prediction and personalized diagnosis and treatment of GC.

Critical Cellular Functions and Mechanisms of Action of the RNA Helicase UAP56

Posttranscriptional maturation and export from the nucleus to the cytoplasm are essential steps in the normal processing of many cellular RNAs. The RNA helicase UAP56 (U2AF associated protein 56; also known as DDX39B) has emerged as a critical player in facilitating and co-transcriptionally linking these steps. Originally identified as a helicase involved in pre-mRNA splicing, UAP56 has been shown to facilitate formation of the A complex during spliceosome assembly. Additionally, it has been found to be critical for interactions between components of the exon junction and transcription and export complexes to promote the loading of export receptors. Although it appears to be structurally similar to other helicase superfamily 2 members, UAP56's ability to interact with multiple different protein partners allows it to perform its various cellular functions. Herein, we describe the structure-activity relationship studies that identified protein interactions of UAP56 and its human paralog URH49 (UAP56-related helicase 49; also known as DDX39A) and are beginning to reveal molecular mechanisms by which interacting proteins and substrate RNAs may regulate these helicases. We also provide an overview of reports that have demonstrated less well-characterized roles for UAP56, including R-loop resolution and telomere maintenance. Finally, we discuss studies that indicate a potential pathogenic effect of UAP56 in the development of autoimmune diseases and cancer, and identify the association of somatic and genetic mutations in UAP56 with neurodevelopmental disorders.

Deciphering the role of alternative splicing in neoplastic diseases for immune-oncological therapies

Alternative splicing (AS) is an important molecular biological mechanism regulated by complex mechanisms involving a plethora of cis and trans-acting elements. Furthermore, AS is tissue specific and altered in various pathologies, including infectious, inflammatory, and neoplastic diseases. Recently developed immuno-oncological therapies include monoclonal antibodies (mAbs) and chimeric antigen receptor (CAR) T cells targeting, among others, immune checkpoint (ICP) molecules. Despite therapeutic successes have been demonstrated, only a limited number of patients showed long-term benefit from these therapies with tumor entity-related differential response rates were observed. Interestingly, splice variants of common immunotherapeutic targets generated by AS are able to completely escape and/or reduce the efficacy of mAb- and/or CAR-based tumor immunotherapies. Therefore, the analyses of splicing patterns of targeted molecules in tumor specimens prior to therapy might help correct stratification, thereby increasing therapy success by antibody panel selection and antibody dosages. In addition, the expression of certain splicing factors has been linked with the patients' outcome, thereby highlighting their putative prognostic potential. Outstanding questions are addressed to translate the findings into clinical application. This review article provides an overview of the role of AS in (tumor) diseases, its molecular mechanisms, clinical relevance, and therapy response.

Development of a prognostic model to predict BLCA based on anoikis-related gene signature: preliminary findings

BACKGROUND

The prevalence of bladder urothelial carcinoma (BLCA) is significant on a global scale. Anoikis is a type of procedural cell death that has an important role in tumor invasion and metastasis. The advent of single-cell RNA sequencing (scRNA-seq) approaches has revolutionized the genomics field by providing unprecedented opportunities for elucidating cellular heterogeneity. Understanding the mechanisms associated with anoikis in BLCA is essential to improve its survival rate.

METHODS

Data on BLCA and clinical information were acquired from the databases of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). ARGs were obtained from Genecards and Harmonizome databases. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). A multivariate Cox regression analysis was carried out to identify eight prognostic ARGs, leading to the establishment of a risk model. The OS rate of BLCA patients was evaluated using Kaplan-Meier survival analysis. To explore the molecular mechanism in low- and high-risk groups, we employed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSVA). Immune infiltration landscape estimation was performed using ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms. Patients were categorized into different subgroups through consensus clustering analysis. We employed biological functional enrichment analysis and conducted immune infiltration analysis to examine the disparities in potential biological functions, infiltration of immune cells, immune activities, and responses to immunotherapy.

RESULTS

We identified 647 ARGs and 37 survival-related genes. We further developed a risk scoring model to quantitatively assess the predictive capacity of ARGs. The high-risk score group exhibited an unfavorable prognosis, whereas the low-risk score group demonstrated a converse effect. We also found that the two groups of patients might respond differently to immune targets and anti-tumor drugs.

CONCLUSION

The nomogram with 8 ARGs may help guide treatment of BLCA. The systematic assessment of risk scores can help to design more individualized and precise treatment strategies for BLCA patients.

Construction of Metastasis-Specific Regulation Network in Ovarian Cancer Based on Prognostic Stemness-Related Signatures

WE aimed to reveal the correlation between ovarian cancer (OV) metastasis and cancer stemness in OV. RNA-seq data and clinical information of 591 OV samples (551 without metastasis and 40 with metastasis) were obtained from TCGA. The edgeR method was used to determine differentially expressed genes (DEGs) and transcription factors (DETFs). Then, mRNA expression-based stemness index was calculated using one-class logistic regression (OCLR). Weighted gene co-expression network analysis (WGCNA) was used to define stemness-related genes (SRGs). Univariate and multivariate Cox proportional hazard regression were conducted to identify the prognostic SRGs (PSRGs). PSRGs, DETFs, and 50 hallmark pathways quantified by gene set variation analysis (GSVA) were integrated into Pearson co-expression analysis. Significant co-expression interactions were utilized to construct an OV metastasis-specific regulation network. Cell communication analysis was carried out based on single cell RNA sequencing data to explore the molecular regulation mechanism of OV. Eventually, assay for targeting accessible-chromatin with high throughout sequencing (ATAC), chromatin immunoprecipitation sequencing (ChIP-seq) validation, and multiple data sets were used to validate the expression levels and prognostic values of key stemness-related signatures. Moreover, connectivity map (CMap) was used to identify potential inhibitors of stemness-related signatures. Based on edgeR, WGCNA, and Cox proportional hazard regression, 22 PSRGs were defined to construct a prognostic prediction model for metastatic OV. In the metastasis-specific regulation network, key TF-PSRS interaction pair was NR4A1-EGR3 (correlation coefficient = 0.81, p < 0.05, positive), and key PSRG-hallmark pathway interaction pair was EGR3-TNFα signaling via NFκB (correlation coefficient = 0.44, p < 0.05, positive), which were validated in multi-omics databases. Thioridazine was postulated to be the most significant compound in treatment of OV metastasis. PSRGs played critical roles in OV metastasis. Specifically, EGR3 was the most significant PSRG, which was positively regulated by DETF NR4A1, inducing metastasis via TNFα signaling.

Comprehensive analysis of cuproptosis-related lncRNAs signature to predict prognosis in bladder urothelial carcinoma

BACKGROUND

Cuproptosis-related genes (CRGs) have been recently discovered to regulate the occurrence and development of various tumors by controlling cuproptosis, a novel type of copper ion-dependent cell death. Although cuproptosis is mediated by lipoylated tricarboxylic acid cycle proteins, the relationship between cuproptosis-related long noncoding RNAs (crlncRNAs) in bladder urothelial carcinoma (BLCA) and clinical outcomes, tumor microenvironment (TME) modification, and immunotherapy remains unknown. In this paper, we tried to discover the importance of lncRNAs for BLCA.

METHODS

The BLCA-related lncRNAs and clinical data were first obtained from The Cancer Genome Atlas (TCGA). CRGs were obtained through Coexpression, Cox regression and Lasso regression. Besides, a prognosis model was established for verification. Meanwhile, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene ontology (GO) analysis, principal component analysis (PCA), half-maximal inhibitory concentration prediction (IC50), immune status and drug susceptibility analysis were carried out.

RESULTS

We identified 277 crlncRNAs and 16 survival-related lncRNAs. According to the 8-crlncRNA risk model, patients could be divided into high-risk group and low-risk group. Progression-Free-Survival (PFS), independent prognostic analysis, concordance index (C-index), receiver operating characteristic (ROC) curve and nomogram all confirmed the excellent predictive capability of the 8-lncRNA risk model for BLCA. During gene mutation burden survival analysis, noticeable differences were observed in high- and low-risk patients. We also found that the two groups of patients might respond differently to immune targets and anti-tumor drugs.

CONCLUSION

The nomogram with 8-lncRNA may help guide treatment of BLCA. More clinical studies are necessary to verify the nomogram.

Identification of the enhancer RNAs related to tumorgenesis of pituitary neuroendocrine tumors

Background

Pituitary neuroendocrine tumors (PitNETs), which originate from the pituitary gland, account for 10%-15% of all intracranial neoplasms. Recent studies have indicated that enhancer RNAs (eRNAs) exert regulatory effects on tumor growth. However, the mechanisms underlying the eRNA-mediated tumorigenesis of PitNETs have not been elucidated.

Methods

Normal pituitary and PitNETs tissues were used to identify the differentially expressed eRNAs (DEEs). Immune gene sets and hallmarks of cancer gene sets were quantified based on single sample gene set enrichment analysis (ssGSEA) algorithm using GSVA. The perspective of immune cells among all samples was calculated by the CIBERSORT algorithm. Moreover, the regulatory network composed of key DEEs, target genes of eRNAs, hallmarks of cancer gene sets, differentially expressed TF, immune cells and immune gene sets were constructed by Pearson correlation analysis. Small molecular anti-PitNETs drugs were explored by CMap analysis and the accuracy of the study was verified by in vitro and in vivo experiments, ATAC-seq and ChIP-seq.

Results

In this study, data of 134 PitNETs and 107 non-tumorous pituitary samples were retrieved from a public database to identify differentially expressed genes. In total, 1128 differentially expressed eRNAs (DEEs) (494 upregulated eRNAs and 634 downregulated eRNAs) were identified. Next, the correlation of DEEs with cancer-related and immune-related gene signatures was examined to establish a co-expression regulatory network comprising 18 DEEs, 50 potential target genes of DEEs, 5 cancer hallmark gene sets, 2 differentially expressed transcription factors, 4 immune cell types, and 4 immune gene sets. Based on this network, the following four therapeutics for PitNETs were identified using Connectivity Map analysis: ciclopirox, bepridil, clomipramine, and alexidine. The growth-inhibitory effects of these therapeutics were validated using in vitro experiments. Ciclopirox exerted potential growth-inhibitory effects on PitNETs. Among the DEEs, GNLY, HOXB7, MRPL33, PRDM16, TCF7, and ZNF26 were determined to be potential diagnostic and therapeutic biomarkers for PitNETs.

Conclusion

This study illustrated the significant influence of eRNAs on the occurrence and development of PitNETs. By constructing the co-expression regulation network, GNLY, HOXB6, MRPL33, PRDM16, TCF7, and ZNF26 were identified as relatively significant DEEs which were considered as the novel biomarkers of diagnosis and treatment of PitNETs. This study demonstrated the roles of eRNAs in the occurrence and development of PitNETs and revealed that ciclopirox was a potential therapeutic for pituitary adenomas.

Necrotic related-lncRNAs: Prediction of prognosis and differentiation between cold and hot tumors in head and neck squamous cell carcinoma

Treatment of head and neck squamous cell carcinoma (HNSCC) is a substantial clinical challenge due to the high local recurrence rate and chemotherapeutic resistance. This project seeks to identify new potential biomarkers of prognosis prediction and precision medicine to improve this condition. A synthetic data matrix for RNA transcriptome datasets and relevant clinical information on HNSCC and normal tissues was downloaded from the Genotypic Tissue Expression Project and The Cancer Genome Atlas (TCGA). The necrosis-associated long-chain noncoding RNAs (lncRNAs) were identified by Pearson correlation analysis. Then 8-necrotic-lncRNA models in the training, testing and entire sets were established through univariate Cox (uni-Cox) regression and Lasso-Cox regression. Finally, the prognostic ability of the 8-necrotic-lncRNA model was evaluated via survival analysis, nomogram, Cox regression, clinicopathological correlation analysis, and receiver operating characteristic (ROC) curve. Gene enrichment analysis, principal component analysis, immune analysis and prediction of risk group semi-maximum inhibitory concentration (IC50) were also conducted. Correlations between characteristic risk score and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anti-cancer drug sensitivity were analyzed. Eight necrosis-associated lncRNAs (AC099850.3, AC243829.2, AL139095.4, SAP30L-AS1, C5orf66-AS1, LIN02084, LIN00996, MIR4435-2HG) were developed to improve the prognosis prediction of HNSCC patients. The risk score distribution, survival status, survival time, and relevant expression standards of these lncRNAs were compared between low- and high-risk groups in the training, testing and entire sets. Kaplan-Meier analysis showed the low-risk patients had significantly better prognosis. The ROC curves revealed the model had an acceptable predictive value in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 8 necrosis-associated lncRNAs were risk factors independent of various clinical parameters. We recombined the patients into 2 clusters through Consensus ClusterPlus R package according to the expressions of necrotic lncRNAs. Significant differences were found in immune cell infiltration, immune checkpoint molecules, and IC50 between clusters, suggesting these characteristics can be used to evaluate the clinical efficacy of chemotherapy and immunotherapy. This risk model may serve as a prognostic signature and provide clues for individualized immunotherapy for HNSCC patients.

HIGD1A inactivated by DNA hypermethylation promotes invasion of kidney renal clear cell carcinoma

Hypoxia contributes to the tumorigenesis and metastasis of the tumor. However, the detailed mechanisms underlying hypoxia and kidney renal clear cell carcinoma (KIRC) development and progression remain unclear. Here, we investigated the role of the system HIG1 hypoxia inducible domain family member 1 A (HIGD1A) in the proliferation and metastasis of KIRC and elucidated the underlying molecular mechanisms. The expression of HIGD1A is significantly downregulated in KIRC due to promoter hypermethylation. HIGD1A could serve as a valuable diagnostic biomarker in KIRC. In addition, ectopic overexpression of HIGD1A significantly suppressed the growth and invasive capacity of KIRC cells in vitro under normal glucose conditions. Interestingly, the suppressive efficacy in invasion is much more significant when depleted glucose, but not in proliferation. Furthermore, mRNA expression of HIGD1A positively correlates with CDH1 and EPCAM, while negatively correlated with VIM and SPARC, indicating that HIGD1A impedes invasion of KIRC by regulating epithelial-mesenchymal transition (EMT). Our data suggest that HIGD1A is a potential diagnostic biomarker and tumor suppressor in KIRC.

Integrated analysis identifies oxidative stress-related lncRNAs associated with progression and prognosis in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most common cancers in the world. Oxidative stress reactions have been reportedly associated with oncogenesis and tumor progression. By analyzing mRNA expression data and clinical information from The Cancer Genome Atlas (TCGA), we aimed to construct an oxidative stress-related long noncoding RNA (lncRNA) risk model and identify oxidative stress-related biomarkers to improve the prognosis and treatment of CRC.

RESULTS

Differentially expressed oxidative stress-related genes (DEOSGs) and oxidative stress-related lncRNAs were identified by using bioinformatics tools. An oxidative stress-related lncRNA risk model was constructed based on 9 lncRNAs (AC034213.1, AC008124.1, LINC01836, USP30-AS1, AP003555.1, AC083906.3, AC008494.3, AC009549.1, and AP006621.3) by least absolute shrinkage and selection operator (LASSO) analysis. The patients were then divided into high- and low-risk groups based on the median risk score. The high-risk group had a significantly worse overall survival (OS) (p < 0.001). Receiver operating characteristic (ROC) and calibration curves displayed the favorable predictive performance of the risk model. The nomogram successfully quantified the contribution of each metric to survival, and the concordance index and calibration plots demonstrated its excellent predictive capacity. Notably, different risk subgroups showed significant differences in terms of their metabolic activity, mutation landscape, immune microenvironment and drug sensitivity. Specifically, differences in the immune microenvironment implied that CRC patients in certain subgroups might be more responsive to immune checkpoint inhibitors.

CONCLUSIONS

Oxidative stress-related lncRNAs can predict the prognosis of CRC patients, which provides new insight for future immunotherapies based on potential oxidative stress targets.

Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits

Kidney renal clear cell carcinoma (KIRC) is one of the common malignant tumors of the urinary system. Patients with different risk levels are other in terms of disease progression patterns and disease regression. The poorer prognosis for high-risk patients compared to low-risk patients. Therefore, it is essential to accurately high-risk screen patients and gives accurate and timely treatment. Differential gene analysis, weighted correlation network analysis, Protein-protein interaction network, and univariate Cox analysis were performed sequentially on the train set. Next, the KIRC prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO), and the Cancer Genome Atlas (TCGA) test set and the Gene Expression Omnibus dataset verified the model's validity. Finally, the constructed models were analyzed; including gene set enrichment analysis (GSEA) and immune analysis. The differences in pathways and immune functions between the high-risk and low-risk groups were observed to provide a reference for clinical treatment and diagnosis. A four-step key gene screen resulted in 17 key factors associated with disease prognosis, including 14 genes and 3 clinical features. The LASSO regression algorithm selected the seven most critical key factors to construct the model: age, grade, stage, GDF3, CASR, CLDN10, and COL9A2. In the training set, the accuracy of the model in predicting 1-, 2- and 3-year survival rates was 0.883, 0.819, and 0.830, respectively. The accuracy of the TCGA dataset was 0.831, 0.801, and 0.791, and the accuracy of the GSE29609 dataset was 0.812, 0.809, and 0.851 in the test set. Model scoring divided the sample into a high-risk group and a low-risk group. There were significant differences in disease progression and risk scores between the two groups. GSEA analysis revealed that the enriched pathways in the high-risk group mainly included proteasome and primary immunodeficiency. Immunological analysis showed that CD8 (+) T cells, M1 macrophages, PDCD1, and CTLA4 were upregulated in the high-risk group. In contrast, antigen-presenting cell stimulation and T-cell co-suppression were more active in the high-risk group. This study added clinical characteristics to constructing the KIRC prognostic model to improve prediction accuracy. It provides help to assess the risk of patients more accurately. The differences in pathways and immunity between high and low-risk groups were also analyzed to provide ideas for treating KIRC patients.

Assessment of Diagnosis, Prognosis and Immune Infiltration Response to the Expression of the Ferroptosis-Related Molecule HAMP in Clear Cell Renal Cell Carcinoma

BACKGROUND

Hepcidin antimicrobial peptide (HAMP) is a key factor in maintaining iron metabolism, which may induce ferroptosis when upregulated. However, its prognostic value and relation to immune infiltrating cells remains unclear.

METHODS

This study analyzed the expression levels of HAMP in the Oncomine, Timer and Ualcan databases, and examined its prognostic potential in KIRC with R programming. The Timer and GEPIA databases were used to estimate the correlations between HAMP and immune infiltration and the markers of immune cells. The intersection genes and the co-expression PPI network were constructed via STRING, R programming and GeneMANIA, and the hub genes were selected with Cytoscape. In addition, we analyzed the gene set enrichment and GO/KEGG pathways by GSEA.

RESULTS

Our study revealed higher HAMP expression levels in tumor tissues including KIRC, which were related to poor prognosis in terms of OS, DSS and PFI. The expression of HAMP was positively related to the immune infiltration level of macrophages, Tregs, etc., corresponding with the immune biomarkers. Based on the intersection genes, we constructed the PPI network and used the 10 top hub genes. Further, we performed a pathway enrichment analysis of the gene sets, including Huntington's disease, the JAK-STAT signaling pathway, ammonium ion metabolic process, and so on.

CONCLUSION

In summary, our study gave an insight into the potential prognosis of HAMP, which may act as a diagnostic biomarker and therapeutic target related to immune infiltration in KIRC.

Identification and validation of endocrine resistance-related and immune-related long non-coding RNA (lncRNA) signatures for predicting endocrinotherapy response and prognosis in breast cancer

Background

Endocrine resistance remains a major challenge in breast cancer (BRCA). Increasing evidence has revealed that long non-coding RNA (lncRNA) are closely implicated in tumorigenesis, drug resistance, and the immune-related pathways of cancer. However, the immune-related lncRNA remains to be thoroughly investigated in predicting the endocrine therapeutic response and prognosis of BRCA.

Methods

Based on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, and calculating the correlation of lncRNAs with immune-related genes obtained from ImmPort and InnateDB databases, we finally obtained endocrine resistance-related and immune-related long non-coding RNAs (ERIR-lncRNAs). Univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression were performed to screen prognosis-associated ERIR-lncRNAs and establish signatures, using 2 separate datasets from GEO for external validation. Principal component analysis (PCA), Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and multivariate Cox regression were performed to demonstrate the robustness and predictability of the signature. We investigated tumor immune infiltration and tumor mutation burden (TMB) between high- and low-risk groups, and the role of key lncRNAs in endocrine resistant breast cancer was confirmed by quantitative real-time polymerase chain reaction (qRT-PCR), Cell Counting Kit 8 (CCK 8) and transwell assays.

Results

A total of 781 endocrine resistance related lncRNAs were identified, of which 12 lncRNAs were associated with immunity. Then, three ERIR-lncRNAs with prognostic relevance were screened to successfully construct the risk signature. Compared to sensitive patients, the endocrine resistant patients had higher risk scores in both the training and validation sets (P<0.05). The high-risk group had significantly shorter survival times (P<0.001) with area under the curve (AUC) values of 0.710, 0.649, and 0.672 at 1, 3, and 5 years. Univariate and multivariate Cox regression indicated that our signature was an independent prognostic factor (P<0.001). Through immune infiltration analysis, it was revealed that the high-risk scores were associated with T follicular helper (Tfh) differentiation and exhibited a pro-tumor phenomenon with the Th1/Th2 balance shifting toward Th2. The key lncRNAs promote cell proliferation and migration as confirmed by qRT-PCR, CCK-8 and transwell assays.

Conclusions

The ERIR-lncRNA signature is valuable in predicting endocrine therapeutic response and prognosis of BRCA, revealing a potential relationship between endocrine resistance and TME.

Comprehensive analysis of anoikis-related long non-coding RNA immune infiltration in patients with bladder cancer and immunotherapy

Background

Anoikis is a form of programmed cell death or programmed cell death(PCD) for short. Studies suggest that anoikis involves in the decisive steps of tumor progression and cancer cell metastasis and spread, but what part it plays in bladder cancer remains unclear. We sought to screen for anoikis-correlated long non-coding RNA (lncRNA) so that we can build a risk model to understand its ability to predict bladder cancer prognosis and the immune landscape.

Methods

We screened seven anoikis-related lncRNAs (arlncRNAs) from The Cancer Genome Atlas (TCGA) and designed a risk model. It was validated through ROC curves and clinicopathological correlation analysis, and demonstrated to be an independent factor of prognosis prediction by uni- and multi-COX regression. In the meantime, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration, and half-maximal inhibitory concentration prediction (IC50) were implemented with the model. Moreover, we divided bladder cancer patients into three subtypes by consensus clustering analysis to further study the differences in prognosis, immune infiltration level, immune checkpoints, and drug susceptibility.

Result

We designed a risk model of seven arlncRNAs, and proved its accuracy using ROC curves. COX regression indicated that the model might be an independent prediction factor of bladder cancer prognosis. KEGG enrichment analysis showed it was enriched in tumors and immune-related pathways among the people at high risk. Immune correlation analysis and drug susceptibility results indicated that it had higher immune infiltration and might have a better immunotherapy efficacy for high-risk groups. Of the three subtypes classified by consensus clustering analysis, cluster 3 revealed a positive prognosis, and cluster 2 showed the highest level of immune infiltration and was sensitive to most chemistries. This is helpful for us to discover more precise immunotherapy for bladder cancer patients.

Conclusion

In a nutshell, we found seven arlncRNAs and built a risk model that can identify different bladder cancer subtypes and predict the prognosis of bladder cancer patients. Immune-related and drug sensitivity researches demonstrate it can provide individual therapeutic schedule with greater precision for bladder cancer patients.

Identification of prognostic immune-related lncRNAs in pancreatic cancer

Long noncoding RNAs (lncRNAs) play a critical role in the immune regulation and tumor microenvironment of pancreatic cancer (PaCa). To construct a novel immune-related prognostic risk model for PaCa and evaluate the prognostic prediction of lncRNAs, essential immune-related lncRNAs (IRlncRNAs) were identified by Pearson correlation analysis of differentially expressed immune-related genes (IRGs) and IRlncRNAs in PaCa from The Cancer Genome Atlas (TCGA) and GTEx databases. Least absolute shrinkage and selection operator (LASSO) regression was also applied to construct a prognostic risk model of IRlncRNAs, and gene set enrichment analysis (GSEA) was further applied for functional annotation for these IRlncRNAs. A total of 148 IRlncRNAs were identified in PaCa to construct a prognostic risk model. Among them, lncRNA LINC02325, FNDC1-AS1, and ZEB2-AS1 were significantly upregulated in 69 pairs of PaCa tissues by qRT-PCR. ROC analyses showed that LINC02325 (AUC = 0.80), FNDC1-AS1 (AUC = 0.76), and ZEB2-AS1 (AUC = 0.75) had a good predictive effect on 5-year survival prognosis. We demonstrated that high expression levels of ZEB2-AS1 and LINC02325 were not only positively associated with tumor size and CA199, but elevated levels of ZEB2-AS1 and FNDC1-AS1 were also positively correlated with tumor stage. GSEA further revealed that immune-related pathways were mainly enriched in the high-risk groups. Several immune-related algorithms demonstrated that four IRlncRNAs were related to immune infiltration, immune checkpoints, and immune-related functions. Thus, the prognostic risk model based on IRlncRNAs in Paca indicates that the four IRlncRNA signatures may serve as predictors of survival and potential predictive biomarkers of the pancreatic tumor immune response.

A Novel Oxidative Stress-Related lncRNA Signature That Predicts the Prognosis and Tumor Immune Microenvironment of Breast Cancer

Background

The association between oxidative stress and lncRNAs within the cancer-related researching field has been a controversial subject. At present, the exact function of oxidative stress as well as lncRNAs exert in breast cancer (BC) are still unclear. Therefore, the present study examined the lncRNAs oxidative stress-related in BC.

Methods

Transcriptome data of BC obtained from TCGA (The Cancer Genome Atlas) database were used to generate synthetic matrices. Patients with breast cancer were randomly assigned to training, testing, or combined groups. The prognostic signature of oxidative stress was created using the selection operator Cox regression method, and the difference in prognosis between groups was examined using Kaplan-Meier curves, the accuracy of which was calculated using a receiver-operating characteristic-area through the curve (ROC-AUC) analysis with internal validation. Also, the Gene Set Enrichment Analyses (GSEA) was applied for the analysis of the risk groups. To conclude, the half-maximal inhibitory concentration (IC50) of these groups were investigated by immunoassay assay.

Results

A model based on 7 lncRNAs related to oxidative stress was proposed, and the calibration plots and projected prognosis matched well. For prognosis at 5, 3, and 1 year, the area under the ROC curve (AUC) values were 0.777, 0.777, and 0.759. The functions of target genes identified by GSEA appear to be mainly expressed in metabolism, signal transduction, tumorigenesis, and also the progression. The remarkable differences in IC50 and gene expression between risk groups in this study provide a deep insight for further systemic treatment. Higher macrophage scores were acquired in the high-risk group, of which patients showed more response to conventional chemotherapy drugs, such as AKT inhibitor VIII and Lapatinib, as well as immunotherapy strategies including anti-CD80, TNF SF4, CD276, and NRP1.

Conclusion

The prognosis of breast cancer can be independently predicted by the markers, which sheds light on further research of the specific role of lncRNAs which are oxidative stress-related and clinical treatment of breast cancer.

Alternative splicing events in tumor immune infiltration in renal clear cell carcinomas

Alternative splicing (AS) is a gene regulatory mechanism that drives protein diversity and dysregulation of AS plays a significant role in tumorigenesis. This study aimed to develop a prognostic signature based on AS and elucidate the role in tumor immune microenvironment (TIME) in clear cell renal cell carcinoma (ccRCC). The prognosis-related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan-Meier survival analysis, and proportional hazards model. The context of TIME in ccRCC was also analyzed. Gene and protein expression data of C4orf19 were obtained from ONCOMINE website and Human Protein Altas. Splicing factors (SFs) regulatory networks were visualized. 4431 survival-related AS events in ccRCC were screened. Based on splicing subtypes, eight AS prognostic signatures were constructed. A nomogram with good prognostic prediction was generated. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. C4orf19 was the only gene whose expression levels were downregulated among the prognostic AS-related genes, which is considered as a promising prognostic factor in ccRCC. Potential functions of SFs were determined by splicing regulatory networks. In our study, AS patterns of novel indicators for prognostic prediction of ccRCC were explored. The AS-SF networks provide information of regulatory mechanisms. Players of AS events related to TIME were investigated, which contribute to prognosis monitoring of ccRCC.

Identification of cuproptosis-associated IncRNAs signature and establishment of a novel nomogram for prognosis of stomach adenocarcinoma

Purpose: Stomach adenocarcinoma (STAD) is one of the common cancers globally. Cuproptosis is a newly identified cell death pattern. The role of cuproptosis-associated lncRNAs in STAD is unknown.

Methods: STAD patient data from TCGA were used to identify prognostic lncRNAs by Cox regression and LASSO. A nomogram was constructed to predict patient survival. The biological profiles were evaluated through GO and KEGG.

Results: We identified 298 cuproptosis-related lncRNAs and 13 survival-related lncRNAs. Patients could be categorized into either high risk group or low risk group with 9-lncRNA risk model with significantly different survival time (p < 0.001). ROC curve and nomogram confirmed the 9-lncRNA risk mode had good prediction capability. Patients in the lower risk score had high gene mutation burden. We also found that patients in the two groups might respond differently to immune checkpoint inhibitors and some anti-tumor compounds.

Conclusion: The nomogram with 9-lncRNA may help guide treatment of STAD. Future clinical studies are necessary to verify the nomogram.

Necroptosis-associated long noncoding RNAs can predict prognosis and differentiate between cold and hot tumors in ovarian cancer

Objective

The mortality rate of ovarian cancer (OC) is the highest among all gynecologic cancers. To predict the prognosis and the efficacy of immunotherapy, we identified new biomarkers.

Methods

The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx) databases were used to extract ovarian cancer transcriptomes. By performing the co-expression analysis, we identified necroptosis-associated long noncoding RNAs (lncRNAs). We used the least absolute shrinkage and selection operator (LASSO) to build the risk model. The qRT-PCR assay was conducted to confirm the differential expression of lncRNAs in the ovarian cancer cell line SK-OV-3. Gene Set Enrichment Analysis, Kaplan-Meier analysis, and the nomogram were used to determine the lncRNAs model. Additionally, the risk model was estimated to evaluate the efficacy of immunotherapy and chemotherapy. We classified necroptosis-associated IncRNAs into two clusters to distinguish between cold and hot tumors.

Results

The model was constructed using six necroptosis-associated lncRNAs. The calibration plots from the model showed good consistency with the prognostic predictions. The overall survival of one, three, and five-year areas under the ROC curve (AUC) was 0.691, 0.678, and 0.691, respectively. There were significant differences in the IC50 between the risk groups, which could serve as a guide to systemic treatment. The results of the qRT-PCR assay showed that AL928654.1, AL133371.2, AC007991.4, and LINC00996 were significantly higher in the SK-OV-3 cell line than in the Iose-80 cell line (P < 0.05). The clusters could be applied to differentiate between cold and hot tumors more accurately and assist in accurate mediation. Cluster 2 was more vulnerable to immunotherapies and was identified as the hot tumor.

Conclusion

Necroptosis-associated lncRNAs are reliable predictors of prognosis and can provide a treatment strategy by screening for hot tumors.

N7-methylguanosine-related lncRNAs: Predicting the prognosis and diagnosis of colorectal cancer in the cold and hot tumors

Background: 7-Methylguanosine(m7G) contributes greatly to its pathogenesis and progression in colorectal cancer. We proposed building a prognostic model of m7G-related LncRNAs. Our prognostic model was used to identify differences between hot and cold tumors.

Methods: The study included 647 colorectal cancer patients (51 cancer-free patients and 647 cancer patients) from The Cancer Genome Atlas (TCGA). We identified m7G-related prognostic lncRNAs by employing the univariate Cox regression method. Assessments were conducted using univariate Cox regression, multivariate Cox regression, receiver operating characteristics (ROC), nomogram, calibration curves, and Kaplan-Meier analysis. All of these procedures were used with the aim of confirming the validity and stability of the model. Besides these two analyses, we also conducted half-maximal inhibitory concentration (IC50), immune analysis, principal component analysis (PCA), and gene set enrichment analysis (GSEA). The entire set of m7G-related (lncRNAs) with respect to cold and hot tumors has been divided into two clusters for further discussion of immunotherapy.

Results: The risk model was constructed with 17 m7G-related lncRNAs. A good correlation was found between the calibration plots and the prognosis prediction in the model. By assessing IC50 in a significant way across risk groups, systemic treatment can be guided. By using clusters, it may be possible to distinguish hot and cold tumors effectively and to aid in specific therapeutic interventions. Cluster 1 was identified as having the highest response to immunotherapy drugs and thus was identified as the hot tumor.

Conclusion: This study shows that 17 m7G-related lncRNA can be used in clinical settings to predict prognosis and use them to determine whether a tumor is cold or hot in colorectal cancer and improve the individualization of treatment.

A Novel Signature of Necroptosis-Associated Genes as a Potential Prognostic Tool for Head and Neck Squamous Cell Carcinoma

Background: Head and neck squamous cell carcinoma (HNSCC) arises from squamous cells in the oral cavity, pharynx and larynx. Although HNSCC is sensitive to radiotherapy, patient prognosis is poor. Necroptosis is a novel programmed form of necrotic cell death. The prognostic value of necroptosis-associated gene expression in HNSCC has not been explored. Material and Methods: We downloaded mRNA expression data of HNSCC patients from TCGA databases and Gene Expression Omnibus (GEO) databases, and compared gene expression between tumor tissues and adjacent normal tissues to identify differentially expressed genes (DEGs) and necroptosis-related prognostic genes. A model with necroptosis-related genes was established to predict patient prognosis via LASSO method and Kaplan-Meier analysis. GSE65858 data set (n = 270) from GEO was used to verify the model's predictive ability. Gene set enrichment analyses, immune microenvironment analysis, principal component analysis, and anti-tumor compound IC₅₀ prediction were also performed. Results: We identified 49 DEGs and found 10 DEGs were associated with patient survival (p < 0.05). A risk model of 6-gene signature was constructed using the TCGA training data set and further validated with the GEO data set. Patients in the low-risk group survived longer than those in the high-risk group (p < 0.05) in the GEO validation sets. Functional analysis showed the two patient groups were associated with distinct immunity conditions and IC₅₀. Conclusion: We constructed a prognostic model with 6 necroptosis-associated genes for HNSCC. The model has potential usage to guide treatment because survival was different between the two groups.

A Novel Necroptosis-Associated IncRNAs Signature for Prognosis of Head and Neck Squamous Cell Carcinoma

Purpose: The prognosis of head and neck squamous cell carcinoma (HNSCC) is poor. Necroptosis is a novel programmed form of necrotic cell death. The prognostic value of necroptosis-associated lncRNAs expression in HNSCC has not been explored. Methods: We downloaded mRNA expression data of HNSCC patients from TCGA databases. Prognostic lncRNAs were identified by univariate Cox regression. LASSO was used to establish a model with necroptosis-related lncRNAs. Kaplan-Meier analysis and ROC were applied to verify the model. Finally, functional studies including gene set enrichment analyses, immune microenvironment analysis, and anti-tumor compound IC50 prediction were performed. Results: We identified 1,117 necroptosis-related lncRNAs. The Cox regression showed 55 lncRNAs were associated with patient survival (p < 0.05). The risk model of 24- lncRNAs signature categorized patients into high and low risk groups. The patients in the low-risk group survived longer than the high-risk group (p < 0.001). Validation assays including ROC curve, nomogram and correction curves confirmed the prediction capability of the 24-lncRNA risk mode. Functional studies showed the two patient groups had distinct immunity conditions and IC50. Conclusion: The 24-lncRNA model has potential to guide treatment of HNSCC. Future clinical studies are needed to verify the model.

Bulk RNA Sequencing With Integrated Single-Cell RNA Sequencing Identifies BCL2A1 as a Potential Diagnostic and Prognostic Biomarker for Sepsis

BACKGROUND

Sepsis is one of the leading causes of morbidity and mortality worldwide in the intensive care unit (ICU). The prognosis of the disease strongly depends on rapid diagnosis and appropriate treatment. Thus, some new and accurate sepsis-related biomarkers are pressing needed and their efficiency should be carefully demonstrated.

METHODS

Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to detect sepsis and monocyte/macrophage-related genes. Least absolute shrinkage and selection operator (LASSO) and random forest regression analyses were used in combination to screen out prognostic genes. Single-cell RNA sequence profiling was utilized to further verify the expression of these genes on a single cell level. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were also applied to verify the diagnostic value of the target biomarkers.

RESULTS

The intersections of the genes detected by differential expression and WGCNA analyses identified 141 overlapping candidate genes that were closely related to sepsis and macrophages. The LASSO and random forest regression analyses further screened out 17 prognostic genes. Single-cell RNA sequencing analysis detected that FCGR1A and BCL2A1 might be potential biomarkers for sepsis diagnosis and the diagnostic efficacy of BCL2A1 was further validated by ROC curve and DCA.

CONCLUSIONS

It was revealed that BCL2A1 had good diagnostic and prognostic value for sepsis, and that it can be applied as a potential and novel biomarker for the management of the disease.

Prognostic Ability of Enhancer RNAs in Metastasis of Non-Small Cell Lung Cancer

(1) Background: Non-small cell lung cancer (NSCLC) is the most common lung cancer. Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription factors (DETFs), enhancer RNAs (DEEs), and target genes (DETGs) between primary NSCLC and metastatic NSCLC were identified. Prognostic DEEs (PDEEs) were screened by Cox regression analyses and a predicting model for metastatic NSCLC was constructed. We identified DEE interactions with DETFs, DETGs, reverse phase protein arrays (RPPA) protein chips, immunocytes, and pathways to construct a regulation network using Pearson correlation. Finally, the mechanisms and clinical significance were explained using multi-dimensional validation unambiguously; (3) Results: A total of 255 DEEs were identified, and 24 PDEEs were selected into the multivariate Cox regression model (AUC = 0.699). Additionally, the NSCLC metastasis-specific regulation network was constructed, and six key PDEEs were defined (ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1 and TNS4); (4) Conclusions: This study focused on the exploration of the prognostic value of eRNAs in the metastasis of NSCLC. Finally, six eRNAs were identified as potential markers for the prediction of metastasis of NSCLC.

PhAc-ALGP-Dox, a Novel Anticancer Prodrug with Targeted Activation and Improved Therapeutic Index

Clinical use of doxorubicin (Dox) is limited by cumulative myelo- and cardiotoxicity. This research focuses on the detailed characterization of PhAc-ALGP-Dox, a targeted tetrapeptide prodrug with a unique dual-step activation mechanism, designed to circumvent Dox-related toxicities and is ready for upcoming clinical investigation. Coupling Dox to a phosphonoacetyl (PhAc)-capped tetrapeptide forms the cell-impermeable, inactive compound, PhAc-ALGP-Dox. After extracellular cleavage by tumor-enriched thimet oligopeptidase-1 (THOP1), a cell-permeable but still biologically inactive dipeptide-conjugate is formed (GP-Dox), which is further processed intracellularly to Dox by fibroblast activation protein-alpha (FAPα) and/or dipeptidyl peptidase-4 (DPP4). In vitro, PhAc-ALGP-Dox is effective in various 2D- and 3D-cancer models, while showing improved safety toward normal epithelium, hematopoietic progenitors, and cardiomyocytes. In vivo, these results translate into a 10-fold higher tolerability and 5-fold greater retention of Dox in the tumor microenvironment compared with the parental drug. PhAc-ALGP-Dox demonstrates 63% to 96% tumor growth inhibition in preclinical models, an 8-fold improvement in efficacy in patient-derived xenograft (PDX) models, and reduced metastatic burden in a murine model of experimental lung metastasis, improving survival by 30%. The current findings highlight the potential clinical benefit of PhAc-ALGP-Dox, a targeted drug-conjugate with broad applicability, favorable tissue biodistribution, significantly improved tolerability, and tumor growth inhibition at primary and metastatic sites in numerous solid tumor models.

Prognostic Bone Metastasis-Associated Immune-Related Genes Regulated by Transcription Factors in Mesothelioma

Mesothelioma (MESO) is a mesothelial originate neoplasm with high morbidity and mortality. Despite advancement in technology, early diagnosis still lacks effectivity and is full of pitfalls. Approaches of cancer diagnosis and therapy utilizing immune biomarkers and transcription factors (TFs) have attracted more and more attention. But the molecular mechanism of these features in MESO bone metastasis has not been thoroughly studied. Utilizing high-throughput genome sequencing data and lists of specific gene subsets, we performed several data mining algorithm. Single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to identify downstream immune cells. Potential pathways involved in MESO bone metastasis were identified using Gene Oncology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Cox regression analysis. Ultimately, a model to help early diagnosis and to predict prognosis was constructed based on differentially expressed immune-related genes between bone metastatic and nonmetastatic MESO groups. In conclusion, immune-related gene SDC2, regulated by TFs TCF7L1 and POLR3D, had an important role on immune cell function and infiltration, providing novel biomarkers and therapeutic targets for metastatic MESO.

DDX39B contributes to the proliferation of colorectal cancer through direct binding to CDK6/CCND1

DDX39B (also called UAP56 or BAT1) which is a kind of DEAD-box family helicase plays pivotal roles in mRNA binding, splicing, and export. It has been found upregulated in many kinds of tumors as an oncogene. Nevertheless, the underlying molecular mechanisms of DDX39B in the proliferation of human colorectal cancer (CRC) remain fairly elusive. In our study, function experiments including the CCK8 and colony formation assay revealed that DDX39B facilitates CRC proliferation in vitro. DDX39B knockdown cells were administered for the orthotopic CRC tumor xenograft mouse model, after which tumor growth was monitored and immunohistochemistry (IHC) was performed to prove that DDX39B can also facilitates CRC proliferation in vivo. Flow cytometry demonstrated that DDX39B promotes the proliferation of CRC cells by driving the cell cycle from G0/G1 phase to the S phase. Mechanistically, RNA-binding protein immunoprecipitation-sequencing (RIP-seq) confirmed that DDX39B binds directly to the first exon of the CDK6/CCND1 pre-mRNA and upregulates their expression. Splicing experiments in vitro using a RT-PCR and gel electrophoresis assay confirmed that DDX39B promotes CDK6/CCND1 pre-mRNA splicing. Rescue experiments indicated that CDK6/CCND1 is a downstream effector of DDX39B-mediated CRC cell proliferation. Collectively, our results demonstrated that DDX39B and CDK6/CCND1 direct interactions serve as a CRC proliferation promoter, which can accelerate the G1/S phase transition to enhance CRC proliferation, and can offer novel and emerging treatment strategies targeting this cell proliferation-promoting gene.

Tumor microenvironment characterization in esophageal cancer identifies prognostic relevant immune cell subtypes and gene signatures

Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been fully elucidated. In this study, we comprehensively estimated the TME infiltration patterns of 164 ESCA patients using Gene Set Variation Analysis (GSVA) and identified 4 key immune cells (natural killer T cell, immature B cell, natural killer cell, and type 1 T helper cell) associated with the prognosis of ESCA patients. Besides, two TME groups were defined based on the TME patterns with different clinical outcomes. According to the expression gene set between two TME groups, we built a model to calculate TMEscore based on the single-sample gene-set enrichment analysis (ssGSEA) algorithm. TMEscore systematically correlated the TME groups with genomic characteristics and clinicopathologic features. In conclusion, our data provide a novel TMEscore which can be regarded as a reliable index for predicting the clinical outcomes of ESCA.

A Prediction Model Using Alternative Splicing Events and the Immune Microenvironment Signature in Lung Adenocarcinoma

Background

Alternative splicing (AS) is a gene regulatory mechanism that drives protein diversity. Dysregulation of AS is thought to play an essential role in cancer initiation and development. This study aimed to construct a prognostic signature based on AS and explore the role in the tumor immune microenvironment (TIME) in lung adenocarcinoma.

Methods

We analyzed transcriptome profiling and clinical lung adenocarcinoma data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq. Prognosis-related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan–Meier survival analyses, and proportional hazards model. The context of TIME in lung adenocarcinoma was also analyzed. Gene and protein expression data of Cyclin-Dependent Kinase Inhibitor 2A (CDKN2A) were obtained from ONCOMINE and Human Protein Atlas. Splicing factor (SF) regulatory networks were visualized.

Results

A total of 19,054 survival-related AS events in lung adenocarcinoma were screened in 1,323 genes. Exon skip (ES) and mutually exclusive exons (ME) exhibited the most and fewest AS events, respectively. Based on AS subtypes, eight AS prognostic signatures were constructed. Patients with high-risk scores were associated with poor overall survival. A nomogram with good validity in prognostic prediction was generated. AUCs of risk scores at 1, 2, and 3 years were 0.775, 0.736, and 0.759, respectively. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. Low-risk patients had a higher StromalScore, ImmuneScore, and ESTIMATEScore. AS-based risk score signature was positively associated with CD8+ T cells. CDKN2A was also found to be a prognostic factor in lung adenocarcinoma. Finally, potential functions of SFs were determined by regulatory networks.

Conclusion

Taken together, our findings show a clear association between AS and immune cell infiltration events and patient outcome, which could provide a basis for the identification of novel markers and therapeutic targets for lung adenocarcinoma. SF networks provide information of regulatory mechanisms.

The Transmembrane Receptor TIRC7 Identifies a Distinct Subset of Immune Cells with Prognostic Implications in Cholangiocarcinoma

Cholangiocarcinoma (CCA) is a heterogeneous malignancy with a dismal prognosis. Therapeutic options are largely limited to surgery and conventional chemotherapy offers limited benefit. As immunotherapy has proven highly effective in various cancer types, we have undertaken a quantitative immunohistopathological assessment of immune cells expressing the immunoinhibitory T cell immune response cDNA 7 receptor (TIRC7), an emerging immunoinhibitory receptor, in a cohort of 135 CCA patients. TIRC7+ immune cells were present in both the tumor epithelia and stroma in the majority of CCA cases with the highest levels found in intrahepatic CCA. While intraepithelial density of TIRC7+ immune cells was decreased compared to matched non-neoplastic bile ducts, stromal quantity was higher in the tumor samples. Tumors exhibiting signet ring cell or adenosquamous morphology were exclusively associated with an intraepithelial TIRC7+ phenotype. Survival analysis showed intraepithelial TIRC7+ immune cell density to be a highly significant favorable prognosticator in intrahepatic but not proximal or distal CCA. Furthermore, intraepithelial TIRC7+ immune cell density correlated with the number of intraepithelial CD8+ immune cells and with the total number of CD4+ immune cells. Our results suggest the presence and prognostic relevance of TIRC7+ immune cells in CCA and warrant further functional studies on its pharmacological modulation.

Systematic Genome-Wide Profiles Reveal Alternative Splicing Landscape and Implications of Splicing Regulator DExD-Box Helicase 21 in Aggressive Progression of Adrenocortical Carcinoma

Alternative splicing (AS) in the tumor biological process has provided a novel perspective on carcinogenesis. However, the clinical significance of individual AS patterns of adrenocortical carcinoma (ACC) has been underestimated, and in-depth investigations are lacking. We selected 76 ACC samples from the Cancer Genome Atlas (TCGA) SpliceSeq and SpliceAid2 databases, and 39 ACC samples from Fudan University Shanghai Cancer Center (FUSCC). Prognosis-related AS events (PASEs) and survival analysis were evaluated based on prediction models constructed by machine-learning algorithm. In total, 23,984 AS events and 3,614 PASEs were detected in the patients with ACC. The predicted risk score of each patient suggested that eight PASEs groups were significantly correlated with the clinical outcomes of these patients (p < 0.001). Prognostic models produced AUC values of 0.907 in all PASEs' groups. Eight splicing factors (SFs), including BAG2, CXorf56, DExD-Box Helicase 21 (DDX21), HSPB1, MBNL3, MSI1, RBMXL2, and SEC31B, were identified in regulatory networks of ACC. DDX21 was identified and validated as a novel clinical promoter and therapeutic target in 115 patients with ACC from TCGA and FUSCC cohorts. In conclusion, the strict standards used in this study ensured the systematic discovery of profiles of AS events using genome-wide cohorts. Our findings contribute to a comprehensive understanding of the landscape and underlying mechanism of AS, providing valuable insights into the potential usages of DDX21 for predicting prognosis for patients with ACC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-021-00026-x.

Necroptosis-Related lncRNAs: Predicting Prognosis and the Distinction between the Cold and Hot Tumors in Gastric Cancer

Background

In the face of poor prognosis and immunotherapy failure of gastric cancer (GC), this project tried to find new potential biomarkers for predicting prognosis and precision medication to ameliorate the situation.

Methods

To form synthetic matrices, we retrieved stomach adenocarcinoma transcriptome data from Genotype-Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA). Necroptosis-related prognostic lncRNA was identified by coexpression analysis and univariate Cox regression. Then we performed the least absolute shrinkage and selection operator (LASSO) to construct the necroptosis-related lncRNA model. Next, the Kaplan–Meier analysis, time-dependent receiver operating characteristics (ROC), univariate Cox (uni-Cox) regression, multivariate Cox (multi-Cox) regression, nomogram, and calibration curves were made to verify and evaluate the model. Gene set enrichment analyses (GSEA), principal component analysis (PCA), immune analysis, and prediction of the half-maximal inhibitory concentration (IC50) in risk groups were also analyzed. For further discussing immunotherapy between the cold and hot tumors, we divided the entire set into two clusters based on necroptosis-related lncRNAs.

Results

We constructed a model with 16 necroptosis-related lncRNAs. In the model, we found the calibration plots showed a good concordance with the prognosis prediction. The area's 1-, 2-, and 3-year OS under the ROC curve (AUC) were 0.726, 0.763, and 0.770, respectively. Risk groups could be a guide of systemic treatment because of significantly different IC50 between risk groups. Above all, clusters could help distinguish between the cold and hot tumors effectively and contribute to precise mediation. Cluster 2 was identified as the hot tumor and more susceptible to immunotherapeutic drugs.

Conclusion

The results of this project supported that necroptosis-related lncRNAs could predict prognosis and help make a distinction between the cold and hot tumors for improving individual therapy in GC.

Ral GEF with the PH Domain and SH3 Binding Motif 1 Regulated by Splicing Factor Junction Plakoglobin and Pyrimidine Metabolism Are Prognostic in Uterine Carcinosarcoma

Uterine carcinosarcoma (UCS) is a highly invasive malignant tumor that originated from the uterine epithelium. Many studies suggested that the abnormal changes of alternative splicing (AS) of pre-mRNA are related to the occurrence and metastasis of the tumor. This study investigates the mechanism of alternative splicing events (ASEs) in the tumorigenesis and metastasis of UCS. RNA-seq of UCS samples and alternative splicing event (ASE) data of UCS samples were downloaded from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases, several times. Firstly, we performed the Cox regression analysis to identify the overall survival-related alternative splicing events (OSRASEs). Secondly, a multivariate model was applied to approach the prognostic values of the risk score. Afterwards, a coexpressed network between splicing factors (SFs) and OSRASEs was constructed. In order to explore the relationship between the potential prognostic signaling pathways and OSRASEs, we fabricated a network between these pathways and OSRASEs. Finally, validations from multidimension platforms were used to explain the results unambiguously. 1,040 OSRASEs were identified by Cox regression. Then, 6 OSRASEs were incorporated in a multivariable model by Lasso regression. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve was 0.957. The risk score rendered from the multivariate model was corroborated to be an independent prognostic factor (P < 0.001). In the network of SFs and ASEs, junction plakoglobin (JUP) noteworthily regulated RALGPS1-87608-AT (P < 0.001, R = 0.455). Additionally, RALGPS1-87608-AT (P = 0.006) showed a prominent relationship with distant metastasis. KEGG pathways related to prognosis of UCS were selected by gene set variation analysis (GSVA). The pyrimidine metabolism (P < 0.001, R = -0.470) was the key pathway coexpressed with RALGPS1. We considered that aberrant JUP significantly regulated RALGPS1-87608-AT and the pyrimidine metabolism pathway might play a significant part in the metastasis and prognosis of UCS.

DDX39B Predicts Poor Survival and Associated with Clinical Benefit of Anti-PD-L1 Therapy in ccRCC

BACKGROUND

Immune checkpoint inhibitors (ICI) have been shown to improve overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients. However, less than half of the ccRCC patients have objective response to ICI.

OBJECTIVE

We aim to assess the role of DDX39B in predicting ccRCC patients' OS and ICI therapy response.

METHODS

DDX39B was detected by immunohistochemistry in a tissue microarray of 305 ccRCC patients. DDX39B and its relationship with the prognosis of ccRCC were also evaluated in TCGA set and a RECA-EU set. The expression of DDX39B and patients survival was also analysed in two datasets of ccRCC patients treated with ICI.

RESULTS

Overexpression of DDX39B predicted poor OS of ccRCC patients in SYSU set, TCGA set, and a RECA-EU set. DDX39B expression was significantly positive with the expression of PD-L1 and other immunomodulators., DDX39B negatively correlated with cytotoxic T-lymphocyte and HDAC10 exon 3 inclusion in ccRCC. DDX39B knockdown decreased the expression of PD-L1 and increased the expression of HDAC10 exon 3 in renal cancer ACHN cells. Patients of ccRCC with lower levels of HDAC10 exon 3 inclusion have higher TNM stage, higher Fuhrman grade and poor OS. There was a tendency that patients with DDX39B high expression had longer OS and PFS than patients with DDX39B low expression in ccRCC patients treated with ICI.

CONCLUSION

DDX39B gene is highly expressed in ccRCC and is closely related to patients' OS. DDX39B might increase PD-L1 expression via the enhancement of HDAC10 exon 3 skipping, thereby promoting the ICI therapy response.

Multi-omics integration of methyltransferase-like protein family reveals clinical outcomes and functional signatures in human cancer

Human methyltransferase-like (METTL) proteins transfer methyl groups to nucleic acids, proteins, lipids, and other small molecules, subsequently playing important roles in various cellular processes. In this study, we performed integrated genomic, transcriptomic, proteomic, and clinicopathological analyses of 34 METTLs in a large cohort of primary tumor and cell line data. We identified a subset of METTL genes, notably METTL1, METTL7B, and NTMT1, with high frequencies of genomic amplification and/or up-regulation at both the mRNA and protein levels in a spectrum of human cancers. Higher METTL1 expression was associated with high-grade tumors and poor disease prognosis. Loss-of-function analysis in tumor cell lines indicated the biological importance of METTL1, an m⁷G methyltransferase, in cancer cell growth and survival. Furthermore, functional annotation and pathway analysis of METTL1-associated proteins revealed that, in addition to the METTL1 cofactor WDR4, RNA regulators and DNA packaging complexes may be functionally interconnected with METTL1 in human cancer. Finally, we generated a crystal structure model of the METTL1–WDR4 heterodimeric complex that might aid in understanding the key functional residues. Our results provide new information for further functional study of some METTL alterations in human cancer and might lead to the development of small inhibitors that target cancer-promoting METTLs.

Integrated Analysis of the Roles of RNA Binding Proteins and Their Prognostic Value in Clear Cell Renal Cell Carcinoma

Methods

We downloaded the RNA sequencing data of ccRCC from the Cancer Genome Atlas (TCGA) database and identified differently expressed RBPs in different tissues. In this study, we used bioinformatics to analyze the expression and prognostic value of RBPs; then, we performed functional analysis and constructed a protein interaction network for them. We also screened out some RBPs related to the prognosis of ccRCC. Finally, based on the identified RBPs, we constructed a prognostic model that can predict patients' risk of illness and survival time. Also, the data in the HPA database were used for verification.

Results

In our experiment, we obtained 539 ccRCC samples and 72 normal controls. In the subsequent analysis, 87 upregulated RBPs and 38 downregulated RBPs were obtained. In addition, 9 genes related to the prognosis of patients were selected, namely, RPL36A, THOC6, RNASE2, NOVA2, TLR3, PPARGC1A, DARS, LARS2, and U2AF1L4. We further constructed a prognostic model based on these genes and plotted the ROC curve. This ROC curve performed well in judgement and evaluation. A nomogram that can judge the patient's life span is also made.

Conclusion

In conclusion, we have identified differentially expressed RBPs in ccRCC and carried out a series of in-depth research studies, the results of which may provide ideas for the diagnosis of ccRCC and the research of new targeted drugs.

Low Expression Levels of SLC22A12 Indicates a Poor Prognosis and Progresses Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) accounts for approximately 4/5 of all kidney cancers. Accumulation of minor changes in the cellular homeostasis may be one cause of ccRCC. Therefore, we downloaded the RNA sequencing and survival data of the kidney renal cell carcinoma (KIRC) cohort from the Cancer Genome Atlas (TCGA) database. After the univariate and multivariate Cox regression analyses, 19 kidney-specific differentially expressed genes (DEGs) were found. Solute Carrier Family 22 Member 12 (SLC22A12) resulted in an independent prognostic predictor for both overall survival (OS) and disease-free survival (DFS). SLC22A12 expression was lower in tumoral tissue compared to normal tissue. Moreover, patients in the SLC22A12 low expression group had a higher pathological stage and worse survival than the high expression group. Additionally, qRT-PCR assay, immunoblotting test (IBT), and immunohistochemical (IHC) analyses of cancer tissues/cells and the corresponding normal controls verified that SLC22A12 is downregulated in ccRCC. Receiver operator characteristic (ROC) curves showed that the low expression level of SLC22A12 could be a good diagnostic marker for ccRCC (AUC=0.7258; p <0.0001). Gene set enrichment analysis (GSEA) showed that SLC22A12 expression levels are related to metabolism, cell cycle, and tumor-related signaling pathways. GO and KEGG analyses revealed that SLC22A12 transports multiple organic compounds, ions, and hormones and participates in the extracellular structure organization. Furthermore, SLC22A12 over-expression in vitro inhibited the proliferation, migration, and invasion of renal cancer cells by regulating PI3K/Akt pathways. Such effects were reversed when knocking out SLC22A12. In summary, as a transporter for many vital metabolites, SLC22A12 may affect tumor cell survival through its impacts on the mentioned metabolites. In conclusion, this study uncovered that SLC22A12 is a promising prognostic and diagnostic biomarker for ccRCC.

Identification of prognostic and bone metastasis​-related alternative splicing signatures in mesothelioma

Mesothelioma (MESO) is an infrequent tumor derived from mesothelial cells of pleura, peritoneum, pericardium, and tunica vaginalis testis. Despite advancement in technologies and better understanding of tumor progression mechanism, the prognosis of MESO remains poor. The role of alternative splicing events (ASEs) in the oncogenesis, tumor metastasis and drug resistance has been widely discussed in multiple cancers. But the prognosis and potential therapeutic value of ASEs in MESO were not clearly studied by now. We constructed a prognostic model using RNA sequencing data and matched ASE data of MESO patients obtained from the TCGA and TCGASpliceSeq database. A total of 3,993 ASEs were identified associated with overall survival using Cox regression analysis. Eight of them were finally figured out to institute the model by lasso regression analysis. The risk score of the model can predict the prognosis independently. Among the identified 390 splicing factors (SF), HSPA1A and DDX3Y was significantly associated with 43 OS-SEs. Among these OS-SEs, SNX5-58744-AT (p = 0.048) and SNX5-58745-AT (p = 0.048) were significantly associated with bone metastasis. Co-expression analysis of signal pathways and SNX5-58744-AT, SNX5-58745-AT was also depicted using GSVA. Finally, we proposed that splicing factor (SF) HSPA1A could regulate SNX5-58744-AT (R = -0.414) and SNX5-58745-AT (R = 0.414) through the pathway "Class I MHC mediated antigen processing and presentation" (R = 0.400). In this way, tumorigenesis and bone metastasis of MESO were controlled.

Screening and Identification of an Immune-Associated lncRNA Prognostic Signature in Ovarian Carcinoma: Evidence from Bioinformatic Analysis

Backgrounds

The dysregulated long noncoding RNAs (lncRNAs) have been described to be crucial regulators in the progression of ovarian carcinoma. The infiltration status of immune cells is also related to the clinical outcomes in ovarian carcinoma. The present research is aimed at constructing an immune-associated lncRNA signature with potential prognostic value for ovarian carcinoma patients.

Methods

We obtained 379 ovarian carcinoma cases with available clinical data and transcriptome data from The Cancer Genome Atlas database to evaluate the infiltration status of immune cells, thereby generating high and low immune cell infiltration groups. According to the expression of the immune-associated lncRNA signature, the risk score of each case was calculated. The high- and low-risk groups were classified using the median risk score as threshold.

Results

A total of 169 immune-associated lncRNAs that differentially expressed in ovarian carcinoma were included. According to the Lasso regression analysis and Cox univariate and multivariate analyses, 5 immune-associated lncRNAs, including AC134312.1, AL133467.1, CHRM3-AS2, LINC01722, and LINC02207, were identified as a predictive signature with significant prognostic value in ovarian carcinoma. The following Kaplan-Meier analysis, ROC analysis, and Cox univariate and multivariate analyses further suggested that the predicted signature may be an independent prognosticator for patients with ovarian carcinoma. The following gene set enrichment analysis showed that this 5 immune-associated lncRNAs signature was significantly related to the hedgehog pathway, basal cell carcinoma, Wnt signaling pathway, cytokine receptor interaction, antigen processing and presentation, and T cell receptor pathway.

Conclusion

: This study suggested a predictive model with 5 immune-associated lncRNAs that has an independent prognostic value for ovarian carcinoma patients.

Five-gene signature associating with Gleason score serve as novel biomarkers for identifying early recurring events and contributing to early diagnosis for Prostate Adenocarcinoma

Background: Compared to non-recurrent type, recurrent prostate adenocarcinoma (PCa) is highly fatal, and significantly shortens the survival time of affected patients. Early and accurate laboratory diagnosis is particularly important in identifying patients at high risk of recurrence, necessary for additional systemic intervention. We aimed to develop efficient and accurate diagnostic and prognostic biomarkers for new PCa following radical therapy. Methods: We identified differentially expressed genes (DEGs) and clinicopathological data of PCa patients from Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) repositories. We then uncovered the most relevant clinical traits and genes modules associated with PCa prognosis using the Weighted gene correlation network analysis (WGCNA). Univariate Cox regression analysis and multivariate Cox proportional hazards (Cox-PH) models were performed to identify candidate gene signatures related to Disease-Free Interval (DFI). Data for internal and external cohorts were utilized to test and validate the accuracy and clinical utility of the prognostic models. Results: We constructed and validated an accurate and reliable model for predicting the prognosis of PCa using 5 Gleason score-associated gene signatures (ZNF695, CENPA, TROAP, BIRC5 and KIF20A). The ROC and Kaplan-Meier analysis revealed the model was highly accurate in diagnosing and predicting the recurrence and metastases of PCa. The accuracy of the model was validated using the calibration curves based on internal TCGA cohort and external GEO cohort. Using the model, patients could be prognostically stratified in to various groups including TNM classification and Gleason score. Multivariate analysis revealed the model could independently predict the prognosis of PCa patients and its utility was superior to that of clinicopathological characteristics. In addition, we fund the expression of the 5 gene signatures strongly and positively correlated with tumor purity but negatively correlated with infiltration CD8+ T cells to the tumor microenvironment. Conclusions: A 5 gene signatures can accurately be used in the diagnosis and prediction of PCa prognosis. Thus this can guide the treatment and management prostate adenocarcinoma.

In silico identification of the prognostic biomarkers and therapeutic targets associated with cancer stem cell characteristics of glioma

Glioma is the common histological subtype of malignancy in central nervous system, with a high morbidity and mortality. Cancer stem cells (CSCs) play an important role in regulating the tumorigenesis and progression of glioma; however, the prognostic biomarkers and therapeutic targets associated with CSC characteristics have not been fully acknowledged in glioma. In order to identify the prognostic stemness-related genes (SRGs) of glioma in silico, the RNA sequencing data of patients with glioma were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) was significantly associated with the glioma histologic grade, isocitrate dehydrogenase 1 (IDH1) mutation and overall survival of glioma patients by the nonparametric test and Kaplan–Meier survival analysis. A total of 340 SRGs were identified as the overlapped stemness-related differential expressed genes (DEGs) of different histologic grade screened by the univariate Cox analysis. Based on 11 prognostic SRGs, the predict nomogram was constructed with the AUC of 0.832. Moreover, the risk score of the nomogram was an independent prognostic factor, indicating its significant applicability. Besides other eight reported biomarkers of glioma, we found that F2RL2, CLCNKA and LOXL4 were first identified as prognostic biomarkers for glioma. In conclusion, this bioinformatics study demonstrates the mRNAsi as a reliable index for the IDH1 mutation, histologic grade and OS of glioma patients and provides a well-applied model for predicting the OS for patients with glioma based on prognostic SRGs. Additionally, this in silico study also identifies three novel prognostic biomarkers (F2RL2, CLCNKA and LOXL4) for glioma patients.

Identification and Validation of the Prognostic Stemness Biomarkers in Bladder Cancer Bone Metastasis

Background

Bladder urothelial carcinoma (BLCA) is one of the most common urinary system malignancies with a high metastasis rate. Cancer stem cells (CSCs) play an important role in the occurrence and progression of BLCA, however, its roles in bone metastasis and the prognostic stemness biomarkers have not been identified in BLCA.

Method

In order to identify the roles of CSC in the tumorigenesis, bone metastasis and prognosis of BLCA, the RNA sequencing data of patients with BLCA were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) and the differential expressed genes (DEGs) were evaluated and identified. The associations between mRNAsi and the tumorigenesis, bone metastasis, clinical stage and overall survival (OS) were also established. The key prognostic stemness-related genes (PSRGs) were screened by Lasso regression, and based on them, the predict model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic (ROC) curve and the risk score. Additionally, in order to explore the key regulatory network, the relationship among differentially expressing TFs, PSRGs, and absolute quantification of 50 hallmarks of cancer were also identified by Pearson correlation analysis. To verify the identified key TFs and PSRGs, their expression levels were identified by our clinical samples via immunohistochemistry (IHC).

Results

A total of 8,647 DEGs were identified between 411 primary BLCAs and 19 normal solid tissue samples. According to the clinical stage, mRNAsi and bone metastasis of BLCA, 2,383 stage-related DEGs, 3,680 stemness-related DEGs and 716 bone metastasis-associated DEGs were uncovered, respectively. Additionally, compared with normal tissue, mRNAsi was significantly upregulated in the primary BLCA and also associated with the prognosis (P = 0.016), bone metastasis (P < 0.001) and AJCC clinical stage (P < 0.001) of BLCA patients. A total of 20 PSRGs were further screened by Lasso regression, and based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.699). Moreover, we found two key TFs (EPO, ARID3A), four key PRSGs (CACNA1E, LINC01356, CGA and SSX3) and five key hallmarks of cancer gene sets (DNA repair, myc targets, E2F targets, mTORC1 signaling and unfolded protein response) in the regulatory network. The tissue microarray of BLCA and BLCA bone metastasis also revealed high expression of the key TFs (EPO, ARID3A) and PRSGs (SSX3) in BLCA.

Conclusion

Our study identifies mRNAsi as a reliable index in predicting the tumorigenesis, bone metastasis and prognosis of patients with BLCA and provides a well-applied model for predicting the OS for patients with BLCA based on 20 PSRGs. Besides, we also identified the regulatory network between key PSRGs and cancer gene sets in mediating the BLCA bone metastasis.

Tumor Mutation Burden, Immune Cell Infiltration, and Construction of Immune-Related Genes Prognostic Model in Head and Neck Cancer

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, and the prognosis of HNSCC remains bleak. Numerous studies revealed that the tumor mutation burden (TMB) could predict the survival outcomes of a variety of tumors. Objectives: This study aimed to investigate the TMB and immune cell infiltration in these patients and construct an immune-related genes (IRGs) prognostic model. Methods: The expression data of 546 HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) database. All patients were divided into high- and low- TMB groups, and the relationship between TMB and clinical relevance was further analyzed. The differentially expressed genes (DEGs) were identified using the R software package, limma. Functional enrichment analyses were conducted to identify the significantly enriched pathways between two groups. CIBERSORT algorithm was adopted to calculate the abundance of 22 leukocyte subtypes. The IRGs prognostic model was constructed via the multivariate Cox regression analysis. Results: Missense mutation and single nucleotide variants (SNV) were the most predominant mutation types in HNSCC. TP53, TTN, and FAT1 were the most frequently mutated genes. Patients with high TMB were observed with worse survival outcomes. The functional analysis of TMB associated DEGs showed that the identified DEGs mainly involved in spliceosome, RNA degradation, proteasome, and RNA polymerase pathways. We observed that macrophages, T cells CD8, and T cells CD4 memory were the most commonly infiltrated subtypes of immune cells in HNSCC. Finally, an IRGs prognostic model was constructed, and the AUC of the ROC curve was 0.635. Conclusions: Our results suggest that high TMB is associated with poor prognosis in HNSCC patients. The constructed model has potential prognostic value for the prognosis of these individuals, and it needs to be further validated in large-scale and prospective studies.

Aberrant RNA Splicing Events Driven by Mutations of RNA-Binding Proteins as Indicators for Skin Cutaneous Melanoma Prognosis

The worldwide incidence of skin cutaneous melanoma (SKCM) is increasing at a more rapid rate than other tumors. Aberrant alternative splicing (AS) is found to be common in cancer; however, how this process contributes to cancer prognosis still remains largely unknown. Mutations in RNA-binding proteins (RBPs) may trigger great changes in the splicing process. In this study, we comprehensively analyzed DNA and RNA sequencing data and clinical information of SKCM patients, together with widespread changes in splicing patterns induced by RBP mutations. We screened mRNA expression-related and prognosis-related mutations in RBPs and investigated the potential affections of RBP mutations on splicing patterns. Mutations in 853 RBPs were demonstrated to be correlated with splicing aberrations (p < 0.01). Functional enrichment analysis revealed that these alternative splicing events (ASEs) may participate in tumor progress by regulating the modification process, cell-cycle checkpoint, metabolic pathways, MAPK signaling, PI3K-Akt signaling, and other important pathways in cancer. We also constructed a prediction model based on overall survival-related AS events (OS-ASEs) affected by RBP mutations, which exhibited a good predict efficiency with the area under the curve of 0.989. Our work highlights the importance of RBP mutations in splicing alterations and provides effective biomarkers for prediction of prognosis of SKCM.

Comprehensive analysis and establishment of a prediction model of alternative splicing events reveal the prognostic predictor and immune microenvironment signatures in triple negative breast cancer

Background

Triple-negative breast cancer (TNBC) is widely concerning because of high malignancy and poor prognosis. There is increasing evidence that alternative splicing (AS) plays an important role in the development of cancer and the formation of the tumour microenvironment. However, comprehensive analysis of AS signalling in TNBC is still lacking and urgently needed.

Methods

Transcriptome and clinical data of 169 TNBC tissues and 15 normal tissues were obtained and integrated from the cancer genome atlas (TCGA), and an overview of AS events was downloaded from the SpliceSeq database. Then, differential comparative analysis was performed to obtain cancer-associated AS events (CAAS). Metascape was used to perform parent gene enrichment analysis based on CAAS. Unsupervised cluster analysis was performed to analyse the characteristics of immune infiltration in the microenvironment. A splicing network was established based on the correlation between CAAS events and splicing factors (SFs). We then constructed prediction models and assessed the accuracy of these models by receiver operating characteristic (ROC) curve and Kaplan–Meier survival analyses. Furthermore, a nomogram was adopted to predict the individualized survival rate of TNBC patients.

Results

We identified 1194 cancer-associated AS events (CAAS) and evaluated the enrichment of 981 parent genes. The top 20 parent genes with significant differences were mostly related to cell adhesion, cell component connection and other pathways. Furthermore, immune-related pathways were also enriched. Unsupervised clustering analysis revealed the heterogeneity of the immune microenvironment in TNBC. The splicing network also suggested an obvious correlation between SFs expression and CAAS events in TNBC patients. Univariate and multivariate Cox regression analyses showed that the survival-related AS events were detected, including some significant participants in the carcinogenic process. A nomogram incorporating risk, AJCC and radiotherapy showed good calibration and moderate discrimination.

Conclusion

Our study revealed AS events related to tumorigenesis and the immune microenvironment, elaborated the potential correlation between SFs and CAAS, established a prognostic model based on survival-related AS events, and created a nomogram to better predict the individual survival rate of TNBC patients, which improved our understanding of the relationship between AS events and TNBC.

Systematic Profiling of Alternative Splicing for Sarcoma Patients Reveals Novel Prognostic Biomarkers Associated with Tumor Microenvironment and Immune Cells

Background

Alternative splicing (AS) events is a novel biomarker of tumor prognosis, but the role of AS events in sarcoma patients remains unclear.

Material/Methods

RNA-seq and clinicopathologic data of the sarcoma cohort were extracted from the TCGA database and data on AS events were downloaded from the TCGASpliceSeq database. Univariate Cox analysis, LASSO regression analysis, and multivariate Cox analysis were performed to determine the overall survival (OS)- and disease-free survival (DFS)-related AS events. Two nomograms were developed based on the independent variables, and subgroup analysis was performed. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomograms. Then, we used the CIBERSORT and ESTIMATE package to determine the immune cell proportion and tumor microenvironment (TME) score, respectively. The associations between AS events-based clusters and TME and immune cells were studied.

Results

We identified 1945 and 1831 AS events as OS- and DFS-related AS events, respectively. Two nomograms based on the AS events and clinical data were established and the AUCs of nomograms ranged from 0.807 to 0.894. The calibration curve and DCA showed excellent performance of nomograms. In addition, the results indicated the distinct relationships between AS events-based clusters and OS, DFS, immune score, stromal score, and 10 immune cells.

Conclusions

Our study indicated that AS events are novel prognostic biomarkers for sarcoma patients that may be associated with the TME and immune cells.