Aggregate analysis based on TCGA: TTN missense mutation correlates with favorable prognosis in lung squamous cell carcinoma

Identification of the clinical value and biological effects of TTN mutation in liver cancer

Liver cancer, a malignant tumor of the digestive system, is a leading cause of cancer‑related mortality globally. Numerous genetic mutations associated with tumorigenesis have been identified, stemming from genomic instability. However, the clinical implications and therapeutic relevance of these mutations remain poorly understood. The present study evaluated the prognostic significance of titin (TTN) mutations in liver cancer by analyzing the mutation landscape of liver cancer tissues from The Cancer Genome Atlas (TCGA) database. The association between TTN mutations and drug susceptibility was subsequently examined using the OncoPredict algorithm and Cell Counting Kit‑8 (CCK‑8) assays. Furthermore, the impact of TTN mutations on hepatoma cell biology both in vivo and in vitro were assessed by reverse transcription‑quantitative PCR, protein stability assays, colony formation assays, tumor spheroid formation assays and subcutaneous tumor transplantation in BALB/c nude mice. Genetic analysis of the TCGA database revealed that TTN mutations are among the most frequent mutations in liver cancer. Patients with TTN mutations exhibited worse prognoses compared with those with the wild‑type allele. The OncoPredict algorithm and CCK‑8 assays revealed that TTN mutations are associated with altered drug sensitivity, particularly to GSK1904529A, nilotinib, 5‑fluorouracil (5‑FU) and sapitinib. Additionally, TTN mutations were shown to enhance TTN protein stability, decrease intracellular ferrous ion levels and significantly decrease liver cancer sensitivity to 5‑FU both in vitro and in vivo. The findings indicated that TTN mutations increase protein stability and lower intracellular ferrous ion levels, thereby suppressing ferroptosis and contributing to resistance to 5‑FU in hepatoma cells. These results suggest that TTN mutations are associated with poor prognosis in liver cancer and could serve as a predictive biomarker for liver cancer progression, prognosis and drug resistance.

A nicotinamide metabolism-related gene signature for predicting immunotherapy response and prognosis in lung adenocarcinoma patients

Background

Nicotinamide (NAM) metabolism fulfills crucial functions in tumor progression. The present study aims to establish a NAM metabolism-correlated gene (NMRG) signature to assess the immunotherapy response and prognosis of lung adenocarcinoma (LUAD).

Methods

The training set and validation set (the GSE31210 dataset) were collected The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. Molecular subtypes of LUAD were classified by consensus clustering. Mutation landscape of the top 20 somatic genes was visualized by maftools package. Subsequently, differential expression analysis was conducted using the limma package, and univariate, multivariate and LASSO regression analyses were performed on the screened genes to construct a risk model for LUAD. Next, the MCP-counter, TIMER and ESTIMATE algorithms were utilized to comprehensively assess the immune microenvironmental profile of LUAD patients in different risk groups. The efficacy of immunotherapy and chemotherapy drugs was evaluated by TIDE score and pRRophetic package. A nomogram was created by integrating RiskScore and clinical features. The mRNA expressions of independent prognostic NMRGs and the migration and invasion of LUAD cells were measured by carrying out cellular assays.

Results

Two subtypes (C1 and C2) of LUAD were classified, with C1 subtype showing a worse prognosis than C2. The top three genes with a high mutation frequency in C1 and C2 subtypes were TTN (45.25%), FLG (25.25%), and ZNF536 (19.8%). Four independent prognostic NMRGs (GJB3, CPA3, DKK1, KRT6A) were screened and used to construct a RiskScore model, which exhibited a strong predictive performance. High-risk group showed low immune cell infiltration, high TIDE score, and worse prognosis, and the patients in this group exhibited a high drug sensitivity to Cisplatin, Erlotinib, Paclitaxel, Saracatini, and CGP_082996. A nomogram was established with an accurate predictive and diagnostic performance. GJB3, DKK1, CPA3, and KRT6A were all high- expressed in LUAD cells, and silencing GJB3 inhibited the migration and invasion of LUAD cells.

Conclusion

A novel NMRG signature was developed, contributing to the prognostic evaluation and personalized treatment for LUAD patients.

Comprehensive analysis of the prognostic and immunological role of cavins in non-small cell lung cancer

Caveolae, specialized and dynamic subdomains of the plasma membrane, have a crucial role in diverse cellular functions encompassing endocytosis, signal transduction, mechanosensation, lipid storage, and metabolism. Cavin family proteins are indispensable for caveolar formation and function. An increasing number of studies have found that cavins are involved in tumor growth, invasion, metastasis, and angiogenesis and may have dual roles in the regulation of cancer. However, the expression and prognostic value of cavins in non-small cell lung cancer (NSCLC) remain unexplored. In this study, the expression, survival data, immune infiltration, and functional enrichment of cavins in patients with NSCLC were investigated using multiple databases. Furthermore, different subtypes of cavin-binding proteins were identified through protein-protein interaction networks and k-means clustering. The results showed that the expression of Cavin-1-3 in NSCLC tissues was significantly lower than that in normal tissues, and that Cavin-2 is the major subtype of cavin that inhibits NSCLC progression. It regulates downstream signaling pathways, modulates the infiltration of immune cells and influences the prognosis of NSCLC. Related experiments also confirmed that Cavin-2 promotes the proliferation and metastasis of NSCLC cells. These findings suggest that cavins and their binding proteins may be novel biomarkers for NSCLC prognosis and immunotherapy, providing new treatment options for NSCLC.

Construction and validation of a TTN mutation associated immune prognostic model for evaluating immune microenvironment and outcomes of gastric cancer: An observational study

Gastric cancer (GC) is a prevalent form of cancer worldwide, and TTN (titin) mutations are frequently observed in GC. However, the association between TTN mutations and immunotherapy for GC remains unclear, necessitating the development of novel prognostic models. The prognostic value and potential mechanisms of TTN in stomach adenocarcinoma were evaluated by TCGA (The Cancer Genome Atlas)-stomach adenocarcinoma cohort analysis, and an immune prognostic model was constructed based on TTN status. We validated it using the GSE84433 dataset. We performed Gene Set Enrichment Analysis and screened for differentially expressed genes, and used lasso (least absolute shrinkage and selection operator) regression analysis to screen for survival genes to construct a multifactorial survival model. In addition, we evaluated the relative proportions of 22 immune cells using the CIBERSORT algorithm for immunogenicity analysis. Finally, we constructed the nomogram integrating immune prognostic model and other clinical factors. GESA showed enrichment of immune-related phenotypes in patients with TTN mutations. We constructed an immune prognostic model based on 16 genes could identify gastric cancer patients with higher risk of poor prognosis. Immuno-microenvironmental analysis showed increased infiltration of naive B cells, plasma cells, and monocyte in high-risk patients. In addition, Nomo plots predicted the probability of 1-year, 3-year, and 5-year OS (overall survival) in GC patients, showing good predictive performance. In this study, we identified that TTN gene may be a potential clinical biomarker for GC and TTN mutations may be a predictor of immunotherapy in patients. We constructed and validated a new model for prognosis of GC patients based on immune characteristics associated with TTN mutations. This study may provide potential therapeutic strategies for gastric cancer.

Neutrophil in the suppressed immune microenvironment: Critical prognostic factor for lung adenocarcinoma patients with KEAP1 mutation

Purpose

It is still unclear whether KEAP1 mutation is detrimental to immunotherapy of lung adenocarcinoma (LUAD) patients, we try to analyse the exact changes in the TME in LUAD patients with KEAP1 mutations and to identify key factors influencing prognosis.

Experimental design

A total of 1,029 patients with lung squamous carcinoma (LUSC) or LUAD with data obtained from The Cancer Genome Atlas were included in this study. The TME and OS of patients with LUAD stratified by mutant versus wild-type KEAP1 status were comprehensively measured. Moreover, we classified LUAD patients with KEAP1 mutations into three subtypes, by unsupervised consensus clustering. We further analysed the TME, OS, commutated genes and metabolic pathways of different subgroups. A total of 40 LUAD patients underwent immunotherapy were collected and classified into mutant KEAP1 group and wild-type KEAP1 group. We also conducted immunohistochemical staining in KEAP1-MT groups.

Result

Suppressed TME was observed not only in LUAD patients but also in LUSC patients. LUAD patients with mutant KEAP1 underwent immunotherapy had worse PFS than wild-type KEAP1. Unsupervised consensus clustering analysis suggested that the three subtypes of patients exhibited different densities of neutrophil infiltration and had different OS results: cluster 2 patients had significantly higher levels of neutrophils had significantly worse prognoses than those of patients in clusters 1 and 3 and patients with wild-type KEAP1. Univariate and multivariate Cox analyses proved that a high density of neutrophils was significantly associated with worse OS and immunohistochemical staining proved that shorter PFS showed high density of neutrophils.

Conclusion

KEAP1 mutation significantly suppresses the tumour immune microenvironment in LUAD patients. LUAD patients with mutant KEAP1 underwent immunotherapy had worse PFS than with wild-type KEAP1. Neutrophils may play an important role in the prognosis of LUAD patients with KEAP1 mutations and may provide a promising therapeutic target.

Development of prognostic signatures and risk index related to lipid metabolism in ccRCC

Background

Clear cell renal cell carcinoma (ccRCC) is a metabolic disorder characterized by abnormal lipid accumulation in the cytoplasm. Lipid metabolism-related genes may have important clinical significance for prognosis prediction and individualized treatment.

Methods

We collected bulk and single-cell transcriptomic data of ccRCC and normal samples to identify key lipid metabolism-related prognostic signatures. qPCR was used to confirm the expression of signatures in cancer cell lines. Based on the identified signatures, we developed a lipid metabolism risk score (LMRS) as a risk index. We explored the potential application value of prognostic signatures and LMRS in precise treatment from multiple perspectives.

Results

Through comprehensive analysis, we identified five lipid metabolism-related prognostic signatures (ACADM, ACAT1, ECHS1, HPGD, DGKZ). We developed a risk index LMRS, which was significantly associated with poor prognosis in patients. There was a significant correlation between LMRS and the infiltration levels of multiple immune cells. Patients with high LMRS may be more likely to respond to immunotherapy. The different LMRS groups were suitable for different anticancer drug treatment regimens.

Conclusion

Prognostic signatures and LMRS we developed may be applied to the risk assessment of ccRCC patients, which may have potential guiding significance in the diagnosis and precise treatment of ccRCC patients.

Exploration of molecular markers related to chemotherapy efficacy of hepatoid adenocarcinoma of the stomach

PURPOSE

Preoperative neoadjuvant chemotherapy may not improve the prognosis of patients with hepatoid adenocarcinoma of the stomach (HAS), a rare pathological type of gastric cancer. Thus, the study aimed at the genomic and transcriptomic impacts of preoperative chemotherapy on HAS.

METHODS

Patients with HAS who underwent surgical resection at Peking University Cancer Hospital were retrospectively included in this study. Whole exome sequencing and transcriptome sequencing were performed on pre-chemotherapy, non-chemotherapy and post-chemotherapy samples. We then compared the alterations in molecular markers between the post-chemotherapy and non-chemotherapy groups, and between the chemotherapy-effective and chemotherapy-ineffective groups, respectively.

RESULTS

A total of 79 tumor samples from 72 patients were collected. Compared to the non-chemotherapy group, the mutation frequencies of several genes were changed after chemotherapy, including TP53. In addition, there was a significant increase in the frequency of frameshift mutations and cytosine transversion to adenine (C > A), appearance of COSMIC signature 6 and 14, and a reduced gene copy number amplification. Interestingly, the same phenomenon was observed in chemotherapy-ineffective patients. In addition, many HAS patients had ERBB2, FGFR2, MET and HGF gene amplification. Moreover, the expression of immune-related genes, especially those related to lymphocyte activation, was down-regulated after chemotherapy.

CONCLUSION

Chemotherapy is closely associated with changes in the molecular characteristics of HAS. After chemotherapy, at genomic and transcriptome level, many features were altered. These changes may be molecular markers of poor chemotherapeutic efficacy and play an important role in chemoresistance in HAS. In addition, ERBB2, FGFR2, MET and HGF gene amplification may be potential therapeutic targets for HAS.

Prognostic analysis of mutated genes and insight into effects of DNA damage and repair on mutational strand asymmetries in gastric cancer

Gastric cancer (GACA) is a complex and multifaceted disease influenced by a variety of environmental and genetic factors. Somatic mutations play a major role in its development, and their characteristics, including the asymmetry between two DNA strands, are of great interest and appear as a signal of information and guidance, revealing mechanisms of DNA damage and repair. Here, we analyzed the impact of High-frequency mutated genes on patient prognosis and found that the effect of expression levels of tumor protein p53 (TP53) and lysine methyltransferase 2C (KMT2C) genes remained high throughout the development of GACA, with similar expression patterns. After investigating mutation asymmetry across mutagenic processes, we found that transcriptional asymmetry was dominated by T > G mutations under the influence of transcription couples repair and damage. The apolipoprotein B mRNA editing enzyme catalytic polypeptide like (APOBEC) enzyme that induces mutations during DNA replication has been identified here and we identified a replicative asymmetry, which was dominated by C > A mutations in left-replicating. Strand bias in different mutation classes at transcription factor binding sites and enhancer regions were also confirmed, which implies the important role of non-coding regulatory elements in the occurrence of mutations. This work systematically describes mutational strand asymmetries in specific genomic regions, shedding light on the DNA damage and repair mechanisms underlying somatic mutations in cohorts of GACA patients with gastric cancer.

Identification of Novel Stemness-based Subtypes and Construction of a Prognostic Risk Model for Patients with Lung Squamous Cell Carcinoma

BACKGROUND

Although cancer stem cells (CSCs) contribute to tumorigenesis, progression, and drug resistance, stemness-based classification and prognostic signatures of lung squamous cell carcinoma (LUSC) remain unclarified. This study attempted to identify stemness-based subtypes and develop a prognostic risk model for LUSC.

METHODS

Based on RNA-seq data from The Cancer Genome Atlas (TCGA), Gene-Expression Omnibus (GEO) and Progenitor Cell Biology Consortium (PCBC), mRNA expression-based stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR) algorithm. A weighted gene coexpression network (WGCNA) was employed to identify stemness subtypes. Differences in mutation, clinical characteristics, immune cell infiltration, and antitumor therapy responses were determined. We constructed a prognostic risk model, followed by validations in GEO cohort, pan-cancer and immunotherapy datasets.

RESULTS

LUSC patients with subtype C2 had a better prognosis, manifested by higher mRNAsi, higher tumor protein 53 (TP53) and Titin (TTN) mutation frequencies, lower immune scores and decreased immune checkpoints. Patients with subtype C2 were more sensitive to Imatinib, Pyrimethamine, and Paclitaxel therapy, whereas those with subtype C1 were more sensitive to Sunitinib, Saracatinib, and Dasatinib. Moreover, we constructed stemness-based signatures using seven genes (BMI1, CCDC51, CTNS, EIF1AX, FAM43A, THBD, and TRIM68) and found high-risk patients had a poorer prognosis in the TCGA cohort. Similar results were found in the GEO cohort. We verified the good performance of risk scores in prognosis prediction and therapy responses.

CONCLUSION

The stemness-based subtypes shed novel insights into the potential roles of LUSC-stemness in tumor heterogeneity, and our prognostic signatures offer a promising tool for prognosis prediction and guide therapeutic decisions in LUSC.

The Evaluation of Prognostic Value and Immune Characteristics of Ferroptosis-Related Genes in Lung Squamous Cell Carcinoma

Background  The purpose of our study was to construct a prognostic model based on ferroptosis-related gene signature to improve the prognosis prediction of lung squamous carcinoma (LUSC). Methods  The mRNA expression profiles and clinical data of LUSC patients were downloaded. LUSC-related essential differentially expressed genes were integrated for further analysis. Prognostic gene signatures were identified through random forest regression and univariate Cox regression analyses for constructing a prognostic model. Finally, in a preliminary experiment, we used the reverse transcription-quantitative polymerase chain reaction assay to verify the relationship between the expression of three prognostic gene features and ferroptosis. Results  Fifty-six ferroptosis-related essential genes were identified by using integrated analysis. Among these, three prognostic gene signatures (HELLS, POLR2H, and POLE2) were identified, which were positively affected by LUSC prognosis but negatively affected by immune cell infiltration. Significant overexpression of immune checkpoint genes occurred in the high-risk group. In preliminary experiments, we confirmed that the occurrence of ferroptosis can reduce three prognostic gene signature expression. Conclusions  The three ferroptosis-related genes could predict the LUSC prognostic risk of antitumor immunity.

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis

Colon cancer (CC) has a poor 5-year survival rate though the treatment techniques and strategies have been improved. Succinylation and long noncoding RNAs (lncRNAs) have prognostic value for CC patients. We analyzed and obtained succinylation-related lncRNA by co-expression in CC. A novel succinylation-related lncRNA model was developed by univariate and Least absolute shrinkage and selection operator (Lasso) regression analysis and we used principal component analysis (PCA), functional enrichment annotation, tumor immune environment, drug sensitivity and nomogram to verify the model, respectively. Six succinylation-related lncRNAs in our model were finally confirmed to distinguish the survival status of CC and showed statistically significant differences in training set, testing set, and entire set. The prognosis of with this model was associated with age, gender, M0 stage, N2 stage, T3 + T4 stage and Stage III + IV. The high-risk group showed a higher mutation rate than the low-risk group. We constructed a model to predict overall survival for 1-, 3-, and 5-year with AUCs of 0.694, 0.729, and 0.802, respectively. The high-risk group was sensitive to Cisplatin and Temozolomide compounds. Our study provided novel insights into the value of the succinylation-related lncRNA signature as a predictor of prognosis, which had high clinical application value in the future.

Identification, validation and biological characterisation of novel glioblastoma tumour microenvironment subtypes: implications for precision immunotherapy

BACKGROUND

New precision medicine therapies are urgently required for glioblastoma (GBM). However, to date, efforts to subtype patients based on molecular profiles have failed to direct treatment strategies. We hypothesised that interrogation of the GBM tumour microenvironment (TME) and identification of novel TME-specific subtypes could inform new precision immunotherapy treatment strategies.

MATERIALS AND METHODS

A refined and validated microenvironment cell population (MCP) counter method was applied to >800 GBM patient tumours (GBM-MCP-counter). Specifically, partition around medoids (PAM) clustering of GBM-MCP-counter scores in the GLIOTRAIN discovery cohort identified three novel patient clusters, uniquely characterised by TME composition, functional orientation markers and immune checkpoint proteins. Validation was carried out in three independent GBM-RNA-seq datasets. Neoantigen, mutational and gene ontology analysis identified mutations and uniquely altered pathways across subtypes. The longitudinal Glioma Longitudinal AnalySiS (GLASS) cohort and three immunotherapy clinical trial cohorts [treatment with neoadjuvant/adjuvant anti-programmed cell death protein 1 (PD-1) or PSVRIPO] were further interrogated to assess subtype alterations between primary and recurrent tumours and to assess the utility of TME classifiers as immunotherapy biomarkers.

RESULTS

TME^High tumours (30%) displayed elevated lymphocyte, myeloid cell immune checkpoint, programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 transcripts. TME^High/mesenchymal+ patients featured tertiary lymphoid structures. TME^Med (46%) tumours were enriched for endothelial cell gene expression profiles and displayed heterogeneous immune populations. TME^Low (24%) tumours were manifest as an 'immune-desert' group. TME subtype transitions upon recurrence were identified in the longitudinal GLASS cohort. Assessment of GBM immunotherapy trial datasets revealed that TME^High patients receiving neoadjuvant anti-PD-1 had significantly increased overall survival (P = 0.04). Moreover, TME^High patients treated with adjuvant anti-PD-1 or oncolytic virus (PVSRIPO) showed a trend towards improved survival.

CONCLUSIONS

We have established a novel TME-based classification system for application in intracranial malignancies. TME subtypes represent canonical 'termini a quo' (starting points) to support an improved precision immunotherapy treatment approach.

Whole-genome characterization of large-cell lung carcinoma: A comparative analysis based on the histological classification

Background: According to the 2015 World Health Organization classification, large cell neuroendocrine carcinoma (LCNEC) was isolated from Large-cell lung cancer (LCLC) tumors, which constitutes 2%-3% of non-small cell lung cancer (NSCLC). However, LCLC tumors are still fairly vaguely defined at the molecular level compared to other subgroups. Materials and Methods: In this study, whole-genome sequencing (WGS) was performed on 23 LCLC and 15 LCNEC tumor specimens. Meanwhile, data from the TCGA (586 LUADs and 511 LUSCs) and U Cologne (120 SCLCs) were analyzed and compared. Results: The most common driver mutations were found in TP53 (13/23, 57%), FAM135B (8/23, 35%) and FAT3 (7/23, 30%) in LCLC, while their counterparts in LCNEC were TP53 (13/15, 87%), LRP1B (6/15, 40%) and FAT1 (6/15, 40%). Notably, FAM135B mutations only occurred in LCLC (P = 0.013). Cosmic signature analysis revealed widespread defective DNA mismatch repair and tobacco-induced mutations in both LCLC and LCNEC. Additionally, LCNEC had a higher incidence of chromosomal copy number variations (CNVs) and structural variations (SVs) compared with LCLC, although the differences were not statistically significant. Particularly, chromothripsis SVs was significantly associated with CNVs. Furthermore, mutational landscape of different subtypes indicated differences between subtypes, and there seems to be more commonalty between our cohort and SCLC than with other subtypes. SMARCA4 mutations may be specific driver gene alteration in our cohort. Conclusion: Our results support that LCLC and LCNEC tumors follow distinct tumorigenic pathways. To our knowledge, this is the first genome-wide profiling comparison of LCLC and LCNEC.

Heterogeneous pattern of gene expression driven by TTN mutation is involved in the construction of a prognosis model of lung squamous cell carcinoma

Objective

To investigate the impact that TTN mutation had on the gene heterogeneity expression and prognosis in patients with lung adenocarcinoma.

Methods

In this study, the Cancer Genome Atlas (TCGA) dataset was used to analyze the TTN mutations in lung adenocarcinoma. Lung adenocarcinoma data was collected from the TCGA database, clinical information of patients was analyzed, and bioinformatics statistical methods were applied for mutation analysis and prognosis survival analysis. The results were verified using the GEO dataset.

Results

The incidence of TTN mutations in lung adenocarcinoma was found to be 73%, and it was related to the prognosis of lung adenocarcinoma. Ten genes were screened with significant contributions to prognosis. A prognosis model was constructed and verified by LASSO COX analysis in the TCGA and GEO datasets based on these ten beneficial factors. The independent prognostic factor H2BC9 for TTN mutation-driven gene heterogeneity expression was screened through multi-factor COX regression analysis.

Conclusion

Our data showed that the gene heterogeneity expression, which was driven by TTN mutations, prolonged the survival of lung adenocarcinoma patients and provided valuable clues for the prognosis of TTN gene mutations in lung adenocarcinoma.

Identification of key somatic oncogenic mutation based on a confounder-free causal inference model

Abnormal cell proliferation and epithelial-mesenchymal transition (EMT) are the essential events that induce cancer initiation and progression. A fundamental goal in cancer research is to develop an efficient method to detect mutational genes capable of driving cancer. Although several computational methods have been proposed to identify these key mutations, many of them focus on the association between genetic mutations and functional changes in relevant biological processes, but not their real causality. Causal effect inference provides a way to estimate the real induce effect of a certain mutation on vital biological processes of cancer initiation and progression, through addressing the confounder bias due to neutral mutations and unobserved latent variables. In this study, integrating genomic and transcriptomic data, we construct a novel causal inference model based on a deep variational autoencoder to identify key oncogenic somatic mutations. Applied to 10 cancer types, our method quantifies the causal effect of genetic mutations on cell proliferation and EMT by reducing both observed and unobserved confounding biases. The experimental results indicate that genes with higher mutation frequency do not necessarily mean they are more potent in inducing cancer and promoting cancer development. Moreover, our study fills a gap in the use of machine learning for causal inference to identify oncogenic mutations.

Identifying key mutations of cancers is helpful to better understand the mechanisms of cancer cell transformation and is critical for therapeutic approaches. Besides sequence and structure-based computational approaches, some functional impact-based methods which consider the association between mutation events and the activity of cancer-related biological processes have also been developed to detect key mutations. However, these methods mainly consider the correlation but ignore that the correlation is far from causality due to the existence of observed and unobserved confounding factors. We develop a confounder-free machine learning-based causal inference framework to estimate the causal effect of mutations on abnormal cell proliferation and epithelial-mesenchymal transition (EMT). It fills a gap in the use of causal mechanisms to discover potential driver mutations in cancer biological systems. Applying our method to 10 cancer types, the identified key mutations are highly consistent with public well-verified ones. Additionally, some new key mutations have also been discovered.

TTN mutations predict a poor prognosis in patients with thyroid cancer

OBJECTIVE

We aimed to investigate the relationship between titin (TTN) gene mutations and thyroid cancer (THCA) and to explore the feasibility of the TTN gene as a potential prognostic indicator of THCA.

METHODS

From TCGA-THCA cohort, we performed a series of analyses to evaluate the prognostic value and potential mechanism of TTN in THCA. These patients were divided into the mutant-type (MUT) group and the wild-type (WT) group. Differentially expressed genes (DEGs) in the two groups were screened using the 'DESeq2' R package. Functional enrichment analysis was performed, and the protein-protein interaction (PPI) network, transcription factor (TF)-target interaction networks, and competitive endogenous RNA (ceRNA) regulatory networks were established for the DEGs. The TIMER database was applied for immune cell infiltration. Survival analysis and Cox regression analysis were used to analyze the potential prognostic value of the TTN gene.

RESULTS

Differential expression analysis showed that 409 genes were significantly up-regulated and 36 genes were down-regulated. Functional enrichment analysis revealed that TTN gene mutations played a potential role in the development of THCA. Analysis of the immune microenvironment indicated that TTN gene mutations were significantly associated with enrichment of M0 macrophages. Survival analysis showed that the MUT group predicted poorer prognosis than the WT group. Cox regression analysis demonstrated that TTN gene mutations were an independent risk factor for THCA. Nomograms also confirmed the prognostic values of the TTN gene in THCA. Conclusions In summary, our results demonstrated that TTN gene mutations predict poor prognosis in patients with THCA. This is the first study to research TTN gene mutations in THCA and to investigate their prognostic value in THCA.

Comparative Analysis of Mutation Status and Immune Landscape for Squamous Cell Carcinomas at Different Anatomical sites

Objective

It has been controversial whether tumor mutation burden (TMB) affects the prognosis and the efficacy of immunotherapy in different tumor types. We provided a comprehensive analysis of mutation status and immune landscape of squamous cell carcinomas (SCCs) from four sites in order to investigate the relationship of TMB with prognosis and immune cell infiltration in different SCCs.

Methods

The transcriptome profiles and somatic mutation data of SCCs downloaded from the Cancer Genome Atlas (the Cancer Genome Atlas) database were analyzed and visualized. Then, TMB was calculated to analyze its correlations with prognosis and clinical features. Differentially expressed genes (DEGs) between the high and low TMB groups were screened for functional enrichment analysis. CIBERSORT algorithm was used to compare differences of immune cell infiltration between two groups in different SCCs. In addition, immune DEGs associated with prognosis were identified and risk prediction model was constructed via Cox regression analysis.

Results

Missense mutation was the most dominant mutation type in SCCs. The difference was that the top10 mutated genes varied widely among different SCCs. High TMB group had better prognosis in lung squamous cell carcinoma (LUSC) and cervical squamous cell carcinoma (CESC), while the result was reverse in head and neck squamous cell carcinoma (HNSCC) and esophageal squamous cell carcinoma (ESCC). In addition, patients with older age, smoking history, earlier pathological stage and no lymphatic invasion had higher TMB. The identified DEGs were mainly enriched in the regulation of immune system, muscular system and the activity of epidermal cells. The proportions of CD8+T cells, CD4+ memory T cells, follicular helper T cells, macrophages were distinct between two groups. The prognosis-related hub genes (CHGB, INHBA, LCN1 and VEGFC) screened were associated with poor prognosis.

Conclusion

This study reveals the mutation status and immune cell infiltration of SCCs at different anatomical sites. TMB is closely related to the prognosis of SCCs, and its effects on prognosis are diverse in different SCCs, which might result from the situation of immune cell infiltration. These findings contribute to the exploration of biomarkers for predicting the efficacy of immunotherapy in SCCs and providing innovative insights for accurate application of immunotherapy.

Deciphering Prognostic Value of TTN and Its Correlation With Immune Infiltration in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common type of lung cancer, accounting for around 40%. Despite achievements in the treatment approach, the prognosis is still dismal, with overall survival of fewer than five years. Thus, novel prognostic biomarkers are needed to predict the clinical outcomes of individual patients better. TTN has a high mutation rate in the LUAD, which encodes a large abundant protein of striated muscle. However, the value of TTN in prognosis and the immune environment are poorly understood.

Methods

We investigated the clinicopathological characteristics, transcriptional and protein level, prognostic value, biological function, and its relationship with immune infiltration of TTN gene in LUAD patients through bioinformatics analysis.

Results

TTN expression was significantly lower in LUAD than that in normal lung tissue. Lower TTN expression was associated with worse survival. Besides, TTN is highly expressed in alveolar type 2 cells which were surmised as the origin of LUAD.

Conclusion

Our findings indicated the potential prognostic value of TTN and its role as a biomarker for determining the immune infiltration levels in patients with LUAD.

Construction of an immune-related lncRNA signature pair for predicting oncologic outcomes and the sensitivity of immunosuppressor in treatment of lung adenocarcinoma

Background

Although immunotherapy has shown clinical activity in lung adenocarcinoma (LUAD), LUAD prognosis has been a perplexing problem. We aimed to construct an immune-related lncRNA pairs (IRLPs) score for LUAD and identify what immunosuppressor are appropriate for which group of people with LUAD.

Methods

Based on The Cancer Genome Atlas (TCGA)-LUAD cohort, IRLPs were identified to construct an IRLPs scoring system by Cox regression and validated in the Gene Expression Omnibus (GEO) dataset using log-rank test and the receiver operating characteristic curve (ROC). Next, we used spearman’s correlation analysis, t-test, signaling pathways analysis and gene mutation analysis to explore immune and molecular characteristics in different IRLP subgroups. The “pRRophetic” package was used to predict the sensitivity of immunosuppressant.

Results

The IRLPs score was constructed based on eight IRLPs calculated as 2.12 × (MIR31HG|RRN3P2) + 0.43 × (NKX2-1-AS1|AC083949.1) + 1.79 × (TMPO-AS1|LPP-AS2) + 1.60 × (TMPO-AS1|MGC32805) + 1.79 × (TMPO-AS1|PINK1-AS) + 0.65 × (SH3BP5-AS1|LINC01137) + 0.51 × (LINC01004|SH3PXD2A-AS1) + 0.62 × (LINC00339|AGAP2-AS1). Patients with a lower IRLPs risk score had a better overall survival (OS) (Log-rank test P_{TCGA train dataset} < 0.001, P_{TCGA test dataset} = 0.017, P_{GEO dataset} = 0.027) and similar results were observed in the AUCs of TCGA dataset and GEO dataset (AUC _{TCGA train dataset} = 0.777, AUC _{TCGA test dataset} = 0.685, AUC _{TCGA total dataset} = 0.733, AUC _{GEO dataset} = 0.680). Immune score (Cor = -0.18893, P < 0.001), stoma score (Cor = -0.24804, P < 0.001), and microenvironment score (Cor = -0.22338, P < 0.001) were significantly decreased in the patients with the higher IRLP risk score. The gene set enrichment analysis found that high-risk group enriched in molecular changes in DNA and chromosomes signaling pathways, and in this group the tumor mutation burden (TMB) was higher than in the low-risk group (P = 0.0015). Immunosuppressor methotrexate sensitivity was higher in the high-risk group (P = 0.0052), whereas parthenolide (P < 0.001) and rapamycin (P = 0.013) sensitivity were lower in the high-risk group.

Conclusions

Our study established an IRLPs scoring system as a biomarker to help in the prognosis, the identification of molecular and immune characteristics, and the patient-tailored selection of the most suitable immunosuppressor for LUAD therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02043-4.

Multi-omics data identified TP53 and LRP1B as key regulatory gene related to immune phenotypes via EPCAM in HCC

Background

Many studies showed that the prognosis of hepatocellular carcinoma (HCC) was significantly associated with the expressions of TP53 and LRP1B. However, the potential influence of the two genes on the malignant progression of HCC is still to be expounded.

Methods

According to the correlation analysis between immune cells and expression levels of TP53 and LRP1B, we filtered the immune cells to perform unsupervised clustering analysis. Integration of multi‐omic data analysis identified genetic alteration and epigenetic alteration. In addition, pathway analysis was used to explore the potential function of the differentially expressed mRNAs. According to the differentially expressed genes, we established an interaction network to seek the hub gene. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to build a prognosis model.

Results

The unsupervised clustering analysis showed that the cluster A1 showed the highest immune cell levels and the cluster B2 showed the lowest immune cell levels. Multi‐omics data analysis identified that somatic mutations, copy number variations, and DNA methylation levels had significant differences between cluster A1 and cluster B2. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis found that the upregulated mRNAs in the cluster A1 were mainly concentrated in T cell activation, external side of plasma membrane, receptor ligand activity, and cytokine−cytokine receptor interaction. Importantly, the EPCAM was identified as a critical node in the lncRNAs–miRNAs–mRNAs regulatory network correlated with the immune phenotypes. In addition, based on differentially expressed genes between cluster A1 and cluster B2, the prognostic model established by LASSO could predict the overall survival (OS) of HCC accurately.

Conclusions

The results indicated that the TP53 and LRP1B acted as the key genes in regulating the immune phenotypes of HCC via EPCAM.

Identification of a Five-Gene Panel to Assess Prognosis for Gastric Cancer

Methods

Two datasets were used as training and validation cohorts to establish the predictive model. We used three types of screening criteria: background analysis, pathway analysis, and functional analysis provided by the cBioportal website. Fisher's exact test and multivariable logistic regression were performed to screen out related genes. Furthermore, we performed receiver operating characteristic (ROC) and Kaplan–Meier curve analyses to evaluate the correlation between the selected genes and overall survival.

Result

We screened five genes (KNL1, NRXN1, C6, CCDC169-SOHLH2, and TTN) that were highly related to recurrence of GC. The area under the receiver operating characteristic (ROC) curve was 0.813, which was much higher than that of the baseline model (AUC = 0.699). This result suggested that the mutation of five selected genes had a significant effect on the prediction of recurrence compared with other factors (age, stages, history, etc.). Furthermore, the Kaplan-Meier estimator also revealed that the mutation of five genes positively correlated with patient survival.

Conclusions

The patients who have mutations in these five genes may experience longer survival than those who do not have mutations. This five-gene panel will likely be a practical tool for prognostic evaluation and will provide another possible way for clinicians to determine therapy.

Unraveling the Expression Patterns of Immune Checkpoints Identifies New Subtypes and Emerging Therapeutic Indicators in Lung Adenocarcinoma

Immune checkpoint genes (ICGs) play pivotal roles in tumor immune microenvironment (TIME), and thus, targeting them represents a promising strategy for cancer immunotherapy. However, the genetic landscape of ICGs in lung adenocarcinoma (LUAD) is still unknown. Herein, we comprehensively evaluated the ICG expression profiles of 1439 LUAD samples and linked ICG expression patterns with infiltration of immune cells, clinical features, and response to immune checkpoint blockade (ICB). The ICGscore was developed to quantify ICG expression patterns of individual patient by principal component analysis algorithms. Three distinct ICG expression patterns and three ICG-related genomic clusters were determined, which were implicated in different clinical outcomes, level of immune infiltrates, and biological process. LUAD patients were subdivided into high- and low-ICGscore subgroups. Patients with higher ICGscore were characterized by favorable survival outcomes, increased immune cell infiltration, and enhanced expression of ICGs. Further analysis revealed that lower ICGscore was associated with greater tumor mutation loads and higher mutation rates of TTN, KEAP1, and ZFHX4. High ICGscore has the potential to be a robust indicator in clinical benefit of immunotherapy. Taken together, unraveling the ICG expression patterns will advance our understanding of heterogeneity of TIME and guides more effective immunotherapeutic strategies in LUAD.

Patient-derived organoids for personalized gallbladder cancer modelling and drug screening

BACKGROUND

Gallbladder carcinoma (GBC) is a relatively rare but highly aggressive cancer with late clinical detection and a poor prognosis. However, the lack of models with features consistent with human gallbladder tumours has hindered progress in pathogenic mechanisms and therapies.

METHODS

We established organoid lines derived from human GBC as well as normal gallbladder and benign gallbladder adenoma (GBA) tissues. The histopathology signatures of organoid cultures were identified by H&E staining, immunohistochemistry and immunofluorescence. The genetic and transcriptional features of organoids were analysed by whole-exome sequencing and RNA sequencing. A set of compounds targeting the most active signalling pathways in GBCs were screened for their ability to suppress GBC organoids. The antitumour effects of candidate compounds, CUDC-101 and CUDC-907, were evaluated in vitro and in vivo.

RESULTS

The established organoids were cultured stably for more than 6 months and closely recapitulated the histopathology, genetic and transcriptional features, and intratumour heterogeneity of the primary tissues at the single-cell level. Notably, expression profiling analysis of the organoids revealed a set of genes that varied across the three subtypes and thus may participate in the malignant progression of gallbladder diseases. More importantly, we found that the dual PI3K/HDAC inhibitor CUDC-907 significantly restrained the growth of various GBC organoids with minimal toxicity to normal gallbladder organoids.

CONCLUSIONS

Patient-derived organoids are potentially a useful platform to explore molecular pathogenesis of gallbladder tumours and discover personalized drugs.

Glycolysis Changes the Microenvironment and Therapeutic Response Under the Driver of Gene Mutation in Esophageal Adenocarcinoma

Background: Esophageal cancer is one of the most leading and lethal malignancies. Glycolysis and the tumor microenvironment (TME) are responsible for cancer progressions. We aimed to study the relationships between glycolysis, TME, and therapeutic response in esophageal adenocarcinoma (EAC). Materials and Methods: We used the ESTIMATE algorithm to divide EAC patients into ESTIMATE ^high and ESTIMATE ^low groups based on the gene expression data downloaded from TCGA. Weighted gene co-expression network analysis (WGCNA) and Gene Set Enrichment Analysis (GSEA) were performed to identify different glycolytic genes in the TME between the two groups. The prognostic gene signature for overall survival (OS) was established through Cox regression analysis. Impacts of glycolytic genes on immune cells were assessed and validated. Next, we conducted the glycolytic gene mutation analysis and drug therapeutic response analysis between the two groups. Finally, the GEO database was employed to validate the impact of glycolysis on TME in patients with EAC. Results: A total of 78 EAC patients with gene expression profiles and clinical information were included for analysis. Functional enrichment results showed that the genes between ESTIMATE ^high and ESTIMATE ^low groups (N = 39, respectively) were strongly related with glycolytic and ATP/ADP metabolic pathways. Patients in the low-risk group had probabilities to survive longer than those in the high-risk group (p < 0.001). Glycolytic genes had significant impacts on the components of immune cells in TME, especially on the T-cells and dendritic cells. In the high-risk group, the most common mutant genes were TP53 and TTN, and the most frequent mutation type was missense mutation. Glycolysis significantly influenced drug sensitivity, and high tumor mutation burden (TMB) was associated with better immunotherapeutic response. GEO results confirmed that glycolysis had significant impacts on immune cell contents in TME. Conclusion: We performed a comprehensive study of glycolysis and TME and demonstrated that glycolysis could influence the microenvironment and drug therapeutic response in EAC. Evaluation of the glycolysis pattern could help identify the individualized therapeutic regime.

Development of an Oxidative Phosphorylation-Related and Immune Microenvironment Prognostic Signature in Uterine Corpus Endometrial Carcinoma

Background: As the fourth most common malignant tumors in women, uterine corpus endometrial carcinoma (UCEC) requires novel and reliable biomarkers for prognosis prediction to improve the overall survival. Oxidative phosphorylation (OXPHOS) is found to be strongly correlated with the progression of tumor. Here, we aimed to construct an OXPHOS-related and immune microenvironment prognostic signature to stratify UCEC patients for optimization of treatment strategies.

Method: Prognosis-associated OXPHOS-related differentially expressed genes were identified by multivariable Cox regression from TCGA–UCEC cohort. Based on the candidate genes, an OXPHOS-related prognostic signature was constructed by the train set data and verified by the entire set. When integrated with relevant clinical characteristics, a nomogram was also created for clinical application. Through comparison of tumor microenvironment between different risk groups, the underlying mechanism of the model and the inner correlation between immune microenvironment and energy metabolism were further investigated.

Results: An OXPHOS-related signature containing ATP5IF1, COX6B1, FOXP3, and NDUFB11 was constructed and had better predictive ability compared with other recently published signatures in UCEC. Patients with lower risk score showed higher immune cell infiltration, higher ESTIMATE score (p = 2.808E−18), lower tumor purity (p = 2.808E−18), higher immunophenoscores (IPSs) (p < 0.05), lower expression of mismatch repair (MMR) proteins (p < 0.05), higher microsatellite instability (MSI), lower expression of markers of N6-methyladenosine (m6A) mRNA methylation regulators, higher tumor mutation burden (TMB) (p = 1.278E−9), and more sensitivity to immune checkpoint blockade (ICB) (p < 0.001) and chemotherapy drugs, thus, possessing improved prognosis.

Conclusion: An OXPHOS-related and immune microenvironment prognostic signature classifying EC patients into different risk subsets was constructed in our study, which could be used to predict the prognosis of patients and help to select a specific subset of patients who might benefit from immunotherapy and chemotherapy, thus, improving the overall survival rate of UCEC. These findings may contribute to the discovery of novel and robust biomarkers or target therapy in UCEC and give new insights into the molecular mechanism of tumorigenesis and progression of UCEC.

TCGA database analysis of the tumor mutation burden and its clinical significance in colon cancer

Background

Colon cancer is one of the most common malignant tumors, with high rates of incidence and death. The tumor mutational burden (TMB), which is characterized by microsatellite instability, has been becoming a powerful predictor which can show tumor behavior and response to immunotherapy.

Methods

In this study, we analyzed 437 mutation data of colon cancer samples obtained from The Cancer Genome Atlas (TCGA) and divided patients into low- and high-TMB groups according to the TMB value. Then we identified differentially-expressed genes (DEGs), conducted immune cell infiltration and survival analyses between groups.

Results

The higher TMB of the patients with colon cancer predicts a poorer prognosis. Functional analysis was performed to assess the prognostic value of the top 30 core genes. The CIBER-SORT algorithm was used to investigate the correlation between the immune cells and TMB subtypes. An immune prognosis model was constructed to screen out immune genes related to prognosis, and the tumor immunity assessment resource (TIMER) was then used to determine the correlation between gene expression and the abundance of tumor-infiltrating immune cell subsets in colon cancer. We observed that APC, TP53, TTN, KRAS, MUC16, SYNE1, PIK3CA have higher somatic mutations. DEGs enrichment analysis showed that they are involved in the regulation of neuroactive ligand-receptor interaction, the Cyclic adenosine monophosphate (cAMP) signaling pathway, the calcium signaling pathway, and pantothenate and Coenzyme A (CoA) biosynthesis. The difference in the abundance of various white blood cell subtypes showed that Cluster of Differentiation 8 (CD8) T cells (P=0.008), activated CD4 memory T cells (P=0.019), M1 macrophages (P=0.002), follicular helper T cells (P=0.034), activated Natural killer (NK cell) cells (P=0.017) increased remarkably, while M0 macrophages significantly reduced (P=0.025). The two immune model genes showed that secretin (SCT) was negatively correlated with survival, while Guanylate cyclase activator 2A (GUCA2A) was positively correlated.

Conclusions

This study conducted a systematically comprehensive analysis of the prediction and clinical significance of TMB in colon cancer in identification, monitoring, and prognosis of colon cancer, and providing reference information for immunotherapy.

Integration Analysis of m6A Regulators and m6A-Related Genes in Hepatocellular Carcinoma

Abstract Background: N6-Methyladenosine (m6A) RNA methylation of eukaryotic mRNA is involved in the progression of various tumors. We aimed to investigate m6A-related genes and m6A regulators in hepatocellular carcinoma (HCC) and their association with prognosis in HCC.Methods: We downloaded liver cancer sample data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium database. A total of 21 m6A regulators and 1258 m6A-related genes were then analyzed by consensus clustering, Spearman’s correlation, GO, KEGG, LASSO Cox regression, and univariate Cox regression analyses. Finally, we constructed a risk prognostic model.Results: We obtained 192 candidate m6A-related genes and 3 m6A regulators, including YTHDF1, YTHDF2, and YTHDC1. The expression of these genes and regulators differed significantly in different stages of HCC. Based on Cox regression analysis, 19 of 98 m6A-related prognostic genes were obtained to construct a risk score model. The 1- and 3-year area under the curves (AUCs) among HCC patients were greater than 0.7. Finally, based on analysis of mutation differences between high- and low-risk score groups, we determined that TP53 had the highest mutation frequency in the high-risk HCC patient group, whereas titin (TTN) had the highest mutation frequency in the low-risk HCC patient group.Conclusion: This study comprehensively analyzed m6A regulators and m6A-related genes through an integrated bioinformatic analysis, including expression, clustering, protein‐protein interaction, and prognosis, thus providing novel insights into the roles of m6A regulators and m6A-related genes in HCC.

Differences in genetics and microenvironment of lung adenocarcinoma patients with or without TP53 mutation

Background

Differences in genetics and microenvironment of LUAD patients with or without TP53 mutation were analyzed to illustrate the role of TP53 mutation within the carcinogenesis of LUAD, which will provide new concepts for the treatment of LUAD.

Methods

In this study, we used genetics and clinical info from the TCGA database, including somatic mutations data, RNA-seq, miRNA-seq, and clinical data. More than one bioinformatics tools were used to analyze the unique genomic pattern of TP53-related LUAD.

Results

According to TP53 gene mutation status, we divided the LUAD patients into two groups, including 265 in the mutant group (MU) and 295 in the wild-type group (WT). 787 significant somatic mutations were detected between the groups, including mutations in titin (TTN), type 2 ryanodine receptor (RYR2) and CUB and Sushi multiple domains 3(CSMD3), which were up-regulated in the MU. However, no significant survival difference was observed. At the RNA level, we obtained 923 significantly differentially expressed genes; in the MU, α-defensin 5(DEFA5), pregnancy-specific glycoprotein 5(PSG5) and neuropeptide Y(NPY) were the most up-regulated genes, glucose-6-phosphatase (G6PC), alpha-fetoprotein (AFP) and carry gametocidal (GC) were the most down-regulated genes. GSVA analysis revealed 30 significant pathways. Compared with the WT, the expression of 12 pathways in the mutant group was up-regulated, most of which pointed to cell division. There were significant differences in tumor immune infiltrating cells, such as Macrophages M1, T cells CD4 memory activated, Mast cells resting, and Dendritic cells resting. In terms of immune genes, a total of 35 immune-related genes were screened, of which VGF (VGF nerve growth factor inducible) and PGC (peroxisome proliferator-activated receptor gamma coactivator) were the most significant up-regulated and down-regulated genes, respectively. Research on the expression pattern of immunomodulators found that 9 immune checkpoint molecules and 6 immune costimulatory molecules were considerably wholly different between the two groups.

Conclusions

Taking the mutant group as a reference, LUAD patients in the mutant group had significant differences in somatic mutations, mRNA-seq, miRNA-seq, immune infiltration, and immunomodulators, indicating that TP53 mutation plays a crucial role in the occurrence and development of LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01671-8.

Establishment of an Endocytosis-Related Prognostic Signature for Patients With Low-Grade Glioma

Background

Low-grade glioma (LGG) is a heterogeneous tumor that might develop into high-grade malignant glioma, which markedly reduces patient survival time. Endocytosis is a cellular process responsible for the internalization of cell surface proteins or external materials into the cytosol. Dysregulated endocytic pathways have been linked to all steps of oncogenesis, from initial transformation to late invasion and metastasis. However, endocytosis-related gene (ERG) signatures have not been used to study the correlations between endocytosis and prognosis in cancer. Therefore, it is essential to develop a prognostic model for LGG based on the expression profiles of ERGs.

Methods

The Cancer Genome Atlas and the Genotype-Tissue Expression database were used to identify differentially expressed ERGs in LGG patients. Gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene set enrichment analysis methodologies were adopted for functional analysis. A protein-protein interaction (PPI) network was constructed and hub genes were identified based on the Search Tool for the Retrieval of Interacting Proteins database. Univariate and multivariate Cox regression analyses were used to develop an ERG signature to predict the overall survival (OS) of LGG patients. Finally, the association between the ERG signature and gene mutation status was further analyzed.

Results

Sixty-two ERGs showed distinct mRNA expression patterns between normal brain tissues and LGG tissues. Functional analysis indicated that these ERGs were strikingly enriched in endosomal trafficking pathways. The PPI network indicated that EGFR was the most central protein. We then built a 29-gene signature, dividing patients into high-risk and low-risk groups with significantly different OS times. The prognostic performance of the 29-gene signature was validated in another LGG cohort. Additionally, we found that the mutation scores calculated based on the TTN, PIK3CA, NF1, and IDH1 mutation status were significantly correlated with the endocytosis-related prognostic signature. Finally, a clinical nomogram with a concordance index of 0.881 predicted the survival probability of LGG patients by integrating clinicopathologic features and ERG signatures.

Conclusion

Our ERG-based prediction models could serve as an independent prognostic tool to accurately predict the outcomes of LGG.

DNA Methylation Regulator-Meditated Modification Patterns Define the Distinct Tumor Microenvironment in Lung Adenocarcinoma

Background

Epigenetic changes of lung adenocarcinoma (LUAD) have been reported to be a relevant factor in tumorigenesis and cancer progression. However, the molecular mechanisms responsible for DNA methylation patterns in the tumor immune-infiltrating microenvironment and in cancer immunotherapy remain unclear.

Methods

We conducted a global analysis of the DNA methylation modification pattern (DMP) and immune cell-infiltrating characteristics of LUAD patients based on 21 DNA methylation regulators. A DNA methylation score (DMS) system was constructed to quantify the DMP model in each patient and estimate their potential benefit from immunotherapy.

Results

Two DNA methylation modification patterns able to distinctly characterize the immune microenvironment characterization were identified among 513 LUAD samples. A lower DMS, characterized by increased CTLA-4/PD-1/L1 gene expression, greater methylation modifications, and tumor mutation burden, characterized a noninflamed phenotype with worse survival. A higher DMS, characterized by decreased methylation modification, a greater stromal-relevant response, and immune hyperactivation, characterized an inflamed phenotype with better prognosis. Moreover, a lower DMS indicated an increased mutation load and exhibited a poor immunotherapeutic response in the anti-CTLA-4/PD-1/PD-L1 cohort.

Conclusion

Evaluating the DNA methylation modification pattern of LUAD patients could enhance our understanding of the features of tumor microenvironment characterization and may promote more favorable immunotherapy strategies.

Establishment of a novel CNV-related prognostic signature predicting prognosis in patients with breast cancer

BACKGROUND

Copy number variation (CNVs) is a key factor in breast cancer development. This study determined prognostic molecular characteristics to predict breast cancer through performing a comprehensive analysis of copy number and gene expression data.

METHODS

Breast cancer expression profiles, CNV and complete information from The Cancer Genome Atlas (TCGA) dataset were collected. Gene Expression Omnibus (GEO) chip data sets (GSE20685 and GSE31448) containing breast cancer samples were used as external validation sets. Univariate survival COX analysis, multivariate survival COX analysis, least absolute shrinkage and selection operator (LASSO), Chi square, Kaplan-Meier (KM) survival curve and receiver operating characteristic (ROC) analysis were applied to build a gene signature model and assess its performance.

RESULTS

A total of 649 CNV related-differentially expressed gene obtained from TCGA-breast cancer dataset were related to several cancer pathways and functions. A prognostic gene sets with 9 genes were developed to stratify patients into high-risk and low-risk groups, and its prognostic performance was verified in two independent patient cohorts (n = 327, 246). The result uncovered that 9-gene signature could independently predict breast cancer prognosis. Lower mutation of PIK3CA and higher mutation of TP53 and CDH1 were found in samples with high-risk score compared with samples with low-risk score. Patients in the high-risk group showed higher immune score, malignant clinical features than those in the low-risk group. The 9-gene signature developed in this study achieved a higher AUC.

CONCLUSION

The current research established a 5-CNV gene signature to evaluate prognosis of breast cancer patients, which may innovate clinical application of prognostic assessment.

Significance of Tumor Mutation Burden Combined With Immune Infiltrates in the Progression and Prognosis of Advanced Gastric Cancer

Gastric cancer (GC) is a serious malignant tumor with high mortality and poor prognosis. The prognosis and survival are much worse for advanced gastric cancer (AGC). Recently, immunotherapy has been widely promoted for AGC patients, and studies have shown that tumor mutation burden (TMB) is closely related to immunotherapy response. Here, RNA-seq data, matched clinical information, and MAF files were downloaded from the cancer genome atlas (TCGA)-STAD project in the TCGA database. The collation and visual analysis of mutation data were implemented by the “maftools” package in R. We calculated the TMB values for AGC patients and divided the patients into high- and low-TMB groups according to the median value of TMB. Then, the correlation between high or low TMB and clinicopathological parameters was calculated. Next, we examined the differences in gene expression patterns between the two groups by using the “limma” R package and identified the immune-related genes among the DEGs. Through univariate Cox regression analysis, 15 genes related to prognosis were obtained. Furthermore, the two hub genes (APOD and SLC22A17) were used to construct a risk model to evaluate the prognosis of AGC patients. ROC and survival curves and GEO data were used as a validation set to verify the reliability of this risk model. In addition, the correlation between TMB and tumor-infiltrating immune cells was examined. In conclusion, our results suggest that AGC patients with high TMB have a better prognosis. By testing the patient’s TMB, we could better guide immunotherapy and understand patient response to immunotherapy.

Significance of Tumor Mutation Burden Combined With Immune Infiltrates in the Progression and Prognosis of Advanced Gastric Cancer

Gastric cancer (GC) is a serious malignant tumor with high mortality and poor prognosis. The prognosis and survival are much worse for advanced gastric cancer (AGC). Recently, immunotherapy has been widely promoted for AGC patients, and studies have shown that tumor mutation burden (TMB) is closely related to immunotherapy response. Here, RNA-seq data, matched clinical information, and MAF files were downloaded from the cancer genome atlas (TCGA)-STAD project in the TCGA database. The collation and visual analysis of mutation data were implemented by the “maftools” package in R. We calculated the TMB values for AGC patients and divided the patients into high- and low-TMB groups according to the median value of TMB. Then, the correlation between high or low TMB and clinicopathological parameters was calculated. Next, we examined the differences in gene expression patterns between the two groups by using the “limma” R package and identified the immune-related genes among the DEGs. Through univariate Cox regression analysis, 15 genes related to prognosis were obtained. Furthermore, the two hub genes (APOD and SLC22A17) were used to construct a risk model to evaluate the prognosis of AGC patients. ROC and survival curves and GEO data were used as a validation set to verify the reliability of this risk model. In addition, the correlation between TMB and tumor-infiltrating immune cells was examined. In conclusion, our results suggest that AGC patients with high TMB have a better prognosis. By testing the patient’s TMB, we could better guide immunotherapy and understand patient response to immunotherapy.

A Novel Immune-Related Seventeen-Gene Signature for Predicting Early Stage Lung Squamous Cell Carcinoma Prognosis

With the increasingly early stage lung squamous cell carcinoma (LUSC) being discovered, there is an urgent need for a comprehensive analysis of the prognostic characteristics of early stage LUSC. Here, we developed an immune-related gene signature for outcome prediction of early stage LUSC based on three independent cohorts. Differentially expressed genes (DEGs) were identified using CIBERSORT and ESTMATE algorithm. Then, a 17-immune-related gene (RPRM, APOH, SSX1, MSGN1, HPR, ISM2, FGA, LBP, HAS1, CSF2, RETN, CCL2, CCL21, MMP19, PTGIS, F13A1, C1QTNF1) signature was identified using univariate Cox regression, LASSO regression and stepwise multivariable Cox analysis based on the verified DEGs from 401 cases in The Cancer Genome Atlas (TCGA) database. Subsequently, a cohort of GSE74777 containing 107 cases downloaded from Gene Expression Omnibus (GEO) database and an independent data set consisting of 36 frozen tissues collected from National Cancer Center were used to validate the predictive value of the signature. Seventeen immune-related genes were identified from TCGA cohort, which were further used to establish a classification system to construct cases into high- and low-risk groups in terms of overall survival. This classifier was still an independent prognostic factor in multivariate analysis. In addition, another two independent cohorts and different clinical subgroups validated the significant predictive value of the signature. Further mechanism research found early stage LUSC patients with high risk had special immune cell infiltration characteristics and gene mutation profiles. In conclusion, we characterized the tumor microenvironment and established a highly predictive model for evaluating the prognosis of early stage LUSC, which may provide a lead for effective immunotherapeutic options tailored for each subtype.

A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma

Increasing evidences show the clinical significance of the interaction between hypoxia and immune in clear cell renal cell carcinoma (ccRCC) microenvironment. However, reliable prognostic signatures based on a combination of hypoxia and immune have not been well established. Moreover, many studies have only used RNA-seq profiles to screen the prognosis feature of ccRCC. Presently, there is no comprehensive analysis of multiomics data to mine a better one. Thus, we try and get it. First, t-SNE and ssGSEA analysis were used to establish tumor subtypes related to hypoxia-immune, and we investigated the hypoxia-immune-related differences in three types of genetic or epigenetic characteristics (gene expression profiles, somatic mutation, and DNA methylation) by analyzing the multiomics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct a satisfying prognostic model, with average 1-year, 3-year and 5-year areas under the curve (AUCs) equal to 0.806, 0.776 and 0.837. Comparing it with other nine known prognostic biomarkers and clinical prognostic scoring algorithms, the multiomics-based signature performs better. Then, we verified the gene expression differences in two external databases (ICGC and SYSU cohorts). Next, eight hub genes were singled out and seven hub genes were validated as prognostic genes in SYSU cohort. Furthermore, it was indicated high-risk patients have a better response for immunotherapy in immunophenoscore (IPS) analysis and TIDE algorithm. Meanwhile, estimated by GDSC and cMAP database, the high-risk patients showed sensitive responses to six chemotherapy drugs and six candidate small-molecule drugs. In summary, the signature can accurately predict the prognosis of ccRCC and may shed light on the development of novel hypoxia-immune biomarkers and target therapy of ccRCC.

Chemical complementarity between immune receptors and cancer mutants, independent of antigen presentation protein binding, is associated with increased survival rates

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Establishment of an immunological distinction between endometrioid and serous uterine cancers.

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High priority CDR3s, mutant amino acids (AA) for endometrioid cancer prognosis, therapy tools.

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Further understanding of CDR3-mutant AA complementarity scoring factors, such as HLA binding.

Uterine cancer has been associated with a T-cell immune response that leads to increased survival. Therefore, we used several bioinformatics approaches to explore specific interactions between T-cell receptor (TCR) and tumor mutant peptide sequences. Using endometrioid uterine cancer exome files from the The Cancer Genome Atlas database, we obtained tumor resident V-J recombinations for the T-Cell Receptor alpha gene (TRA). The charged-based, chemical complementarity for each patient's LRP2 or TTN mutant amino acids (AAs) and the recovered, TRA complementarity determining region-3 (CDR3) sequences was calculated, allowing a division of patients into complementary and noncomplementary groups. Complementary groups with TTN mutants had increased disease-free survival and increased expression of complement genes. Furthermore, the survival distinction based on CDR3-mutant peptide complementarity was independent of programmatically assessed HLA class II binding and was not observable based on the CDR3 AA chemical features alone. The above approach provides a potential, highly efficient method for identifying TCR targets in uterine cancer and may aid in the development of novel prognostic tools.

TTN/TP53 mutation might act as the predictor for chemotherapy response in lung adenocarcinoma and lung squamous carcinoma patients

BACKGROUND

Chemotherapy is the preferred treatment in many types of cancer including lung cancer. However, most of patients resist chemotherapy resulting in disease progressive and recurrence. Titin (TTN) mutation is proved as a beneficial role in lung squamous carcinoma (LUSC), but the predictive role on chemotherapy resistance of lung cancer is still limited and discussable.

METHODS

Clinical information and related somatic mutation profiles were obtained from The Cancer Genome Atlas (TCGA) database and analyzed by R-Studio using R-package. Overall survival (OS) curve and the association between gene mutation and clinical features were determined by GraphPad 6.0 software.

RESULTS

Available data including 563 lung adenocarcinoma (LUAD) and 505 LUSC subjects were included in this study. Among all patients, 205 out of 563 LUAD and 326 out of 505 LUSC patients displayed TTN gene mutation. When comparing the clinical features in TTN-mutated patients to patients without TTN mutation who received chemotherapy, the tumors were always located in the upper lung in LUAD patients with TTN mutation and most of TTN-mutated subjects were at low pathological stage, which was not observed in LUSC patients. However, patients with TTN-mutation, particularly missense mutation, had a higher chemosensitivity and longer OS period than that patients without TTN mutation in both LUAD and LUSC. Of note, LUAD and LUSC patients possessed favorable OS and better chemotherapy response benefiting from TTN/tumor protein 53 (TP53) double mutation compared to TTN and TP53 mutation alone, respectively. Additionally, TTN/TP53 double mutation-initiated high rate of chemotherapy response were largely concentrated within LUAD and LUSC patients whose anatomic neoplasm subdivision were located in the upper lung.

CONCLUSIONS

Collectively, TTN/TP53 co-mutation is possibly served as an effective predictor for OS and chemotherapy response in lung cancer.

Identification of Potential Driver Genes Based on Multi-Genomic Data in Cervical Cancer

Background: Cervical cancer became the third most common cancer among women, and genome characterization of cervical cancer patients has revealed the extensive complexity of molecular alterations. However, identifying driver mutation and depicting molecular classification in cervical cancer remain a challenge.

Methods: We performed an integrative multi-platform analysis of a cervical cancer cohort from The Cancer Genome Atlas (TCGA) based on 284 clinical cases and identified the driver genes and possible molecular classification of cervical cancer.

Results: Multi-platform integration showed that cervical cancer exhibited a wide range of mutation. The top 10 mutated genes were TTN, PIK3CA, MUC4, KMT2C, MUC16, KMT2D, SYNE1, FLG, DST, and EP300, with a mutation rate from 12 to 33%. Applying GISTIC to detect copy number variation (CNV), the most frequent chromosome arm-level CNVs included losses in 4p, 11p, and 11q and gains in 20q, 3q, and 1q. Then, we performed unsupervised consensus clustering of tumor CNV profiles and methylation profiles and detected four statistically significant expression subtypes. Finally, by combining the multidimensional datasets, we identified 10 potential driver genes, including GPR107, CHRNA5, ZBTB20, Rb1, NCAPH2, SCA1, SLC25A5, RBPMS, DDX3X, and H2BFM.

Conclusions: This comprehensive analysis described the genetic characteristic of cervical cancer and identified novel driver genes in cervical cancer. These results provide insight into developing precision treatment in cervical cancer.

Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment

Background

Colon cancer is a disease with high malignancy and incidence in the world. Tumor immune microenvironment (TIM) and tumor mutational burden (TMB) have been proved to play crucial roles in predicting clinical outcomes and therapeutic efficacy, but the correlation between them and the underlying mechanism were not completely understood in colon cancer.

Methods

In this study, we used Single-Sample Gene Set Enrichment Analysis (ssGSEA) and unsupervised consensus clustering analysis to divide patients from the TCGA cohort into three immune subgroups. Then we validated their differences in immune cell infiltration, overall survival outcomes, clinical phenotypes and expression levels of HLA and checkpoint genes by Mann–Whitney tests. We performed weighted correlation network analysis (WGCNA) to obtain immunity-related module and hub genes. Then we explored the underlying mechanism of hub genes by gene set enrichment analysis (GSEA) and gene set evaluation analysis (GSVA). Finally, we gave an overall view of gene variants and verified the correlation between TIM and TMB by comparing microsatellite instability (MSI) and gene mutations among three immune subgroups.

Results

The colon cancer patients were clustered into low immunity, median immunity and high immunity groups. The median immunity group had a favorable survival probability compared with that of the low and high immunity groups. Three groups had significant differences in immune cell infiltration, tumor stage, living state and T classification. We got 8 hub genes (CCDC69, CLMP, FAM110B, FAM129A, GUCY1B3, PALLD, PLEKHO1 and STY11) and predicted that immunity may correlated with inflammatory response, KRAS signaling pathway and T cell infiltration. With higher immunity, the TMB was higher. The most frequent mutations in low and median immunity groups were APC, TP53 and KRAS, while TTN and MUC16 showed higher mutational frequency in high immunity group.

Conclusions

We performed a comprehensive evaluation of the immune microenvironment landscape of colon cancer and demonstrated the positive correlation between immunity and TMB. The hub genes and frequently mutated genes were strongly related to immunity and may give suggestion for immunotherapy in the future.

Identification of critical radioresistance genes in esophageal squamous cell carcinoma by whole-exome sequencing

Background

Esophageal squamous cell carcinoma (ESCC) is one of the most lethal cancer due to insufficient actionable molecules. Radiotherapy (RT) plays a vital role in the treatment of ESCC, while radioresistance is a significant challenge to RT and results in locoregional and distant failure.

Methods

Radioresistance is a complex involving confounding factors, and its genetic mechanism is challenging to study. Postoperative recurrence after RT is more likely to be due to genetic causes than recurrence in unoperated patients. Therefore, two independent cohorts of ESCC patients who had received postoperative radiotherapy (PORT) and had opposite prognoses were set up, and whole-exome sequencing (WES) technology was applied. We compared the differences in the mutant spectra between the two groups.

Results

The mutation rate was slightly higher in the relapsed group than in the stable group [average mutation rate, 1.15 vs. 0.73 mutations per megabyte (Mb)], while the mutation types and proportions in the two groups were not significantly different. In particular, three mutated genes (TTN, MUC19, and NPIPA5) and two copy number alterations (CNAs) (1q amplification and 14q deletion) were identified to be associated with poor RT prognosis, while MUC4 was a favorable factor.

Conclusions

These radioresistance biomarkers may supply insight into predicting the radioresponse. Further, these findings offer the first data on the mutational landscape of ESCC radioresistance.

Genome-wide identification of CpG island methylator phenotype related gene signature as a novel prognostic biomarker of gastric cancer

Background

Gastric cancer (GC) is one of the most fatal cancers in the world. Results of previous studies on the association of the CpG island methylator phenotype (CIMP) with GC prognosis are conflicting and mainly based on selected CIMP markers. The current study attempted to comprehensively assess the association between CIMP status and GC survival and to develop a CIMP-related prognostic gene signature of GC.

Methods

We used a hierarchical clustering method based on 2,082 GC-related methylation sites to stratify GC patients from the cancer genome atlas into three different CIMP subgroups according to the CIMP status. Gene set enrichment analysis, tumor-infiltrating immune cells, and DNA somatic mutations analysis were conducted to reveal the genomic characteristics in different CIMP-related patients. Cox regression analysis and the least absolute shrinkage and selection operator were performed to develop a CIMP-related prognostic signature. Analyses involving a time-dependent receiver operating characteristic (ROC) curve and calibration plot were adopted to assess the performance of the prognostic signature.

Results

We found a positive relationship between CIMP and prognosis in GC. Gene set enrichment analysis indicated that cancer-progression-related pathways were enriched in the CIMP-L group. High abundances of CD8+ T cells and M1 macrophages were found in the CIMP-H group, meanwhile more plasma cells, regulatory T cells and CD4+ memory resting T cells were detected in the CIMP-L group. The CIMP-H group showed higher tumor mutation burden, more microsatellite instability-H, less lymph node metastasis, and more somatic mutations favoring survival. We then established a CIMP-related prognostic gene signature comprising six genes (CST6, SLC7A2, RAB3B, IGFBP1, VSTM2L and EVX2). The signature was capable of classifying patients into high‐and low‐risk groups with significant difference in overall survival (OS; p < 0.0001). To assess performance of the prognostic signature, the area under the ROC curve (AUC) for OS was calculated as 0.664 at 1 year, 0.704 at 3 years and 0.667 at 5 years. When compared with previously published gene-based signatures, our CIMP-related signature was comparable or better at predicting prognosis. A multivariate Cox regression analysis indicated the CIMP-related prognostic gene signature was an independent prognostic indicator of GC. In addition, Gene ontology analysis indicated that keratinocyte differentiation and epidermis development were enriched in the high-risk group.

Conclusion

Collectively, we described a positive association between CIMP status and prognosis in GC and proposed a CIMP-related gene signature as a promising prognostic biomarker for GC.

Multi-omics Data Analyses Construct TME and Identify the Immune-Related Prognosis Signatures in Human LUAD

Lung cancer has been the focus of attention for many researchers in recent years for the leading contribution to cancer-related death worldwide, in which lung adenocarcinoma (LUAD) is the most common histological type. However, the potential mechanism behind LUAD initiation and progression remains unclear. Aiming to dissect the tumor microenvironment of LUAD and to discover more informative prognosis signatures, we investigated the immune-related differences in three types of genetic or epigenetic characteristics (expression status, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and both the immune-related metabolic and neural systems by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct the prognostic prediction model. For the prognostic predictions on the independent test set, the performance of the trained models (average concordance index [C-index] = 0.839) is satisfied, with average 1-year, 3-year, and 5-year areas under the curve (AUCs) equal to 0.796, 0.786, and 0.777. Finally, the overall model was constructed based on all samples, which comprised 27 variables and achieved a high degree of accuracy on the 1-year (AUC = 0.861), 3-year (AUC = 0.850), and 5-year (AUC = 0.916) survival predictions.

The Expressions and Mechanisms of Sarcomeric Proteins in Cancers

The sarcomeric proteins control the movement of cells in diverse species, whereas the deregulation can induce tumours in model organisms and occurs in human carcinomas. Sarcomeric proteins are recognized as oncogene and related to tumor cell metastasis. Recent insights into their expressions and functions have led to new cancer therapeutic opportunities. In this review, we appraise the evidence for the sarcomeric proteins as cancer genes and discuss cancer-relevant biological functions, potential mechanisms by which sarcomeric proteins activity is altered in cancer.

Identification of the Mutational Landscape of Gynecological Malignancies

Background: Cancer is a complex disease that arises from the accumulation of multiple genetic and non-genetic changes. Advances in sequencing technologies have allowed unbiased and global analysis of patient-derived tumor samples and the discovery of genetic and transcriptional changes in key genes and oncogenic pathways. That in turn has facilitated a better understanding of the underlying causes of cancer initiation and progression, resulting in new therapeutic targets. Methods: In our study, we have analyzed the mutational landscape of gynecological malignancies using datasets from The Cancer Genome Atlas (TCGA). We have also analyzed Oncomine datasets to establish the impact of their alteration on disease recurrence and survival of patients. Results: In this study, we analyzed a series of different gynecological malignancies for commonly occurring genetic and non-genetic alterations. These studies show that white women have higher incidence of gynecological malignancies. Furthermore, our study identified 16 genes that are altered at a frequency >10% among all of the gynecological malignancies and tumor suppressor TP53 is the most altered gene in these malignancies (>50% of the cases). The top 16 genes fall into the categories of either tumor suppressor or oncogenes and a subset of these genes are associated with poor prognosis, some affecting recurrence and survival of ovarian cancer patients. Conclusion: In sum, our study identified 16 major genes that are broadly mutated in a large majority of gynecological malignancies and in some cases predict survival and recurrence in patients with gynecological malignancies. We predict that the functional studies will determine their relative role in the initiation and progression of gynecological malignancies and also establish if some of them represents drug targets for anti-cancer therapy.

Evaluating the Potential of T Cell Receptor Repertoires in Predicting the Prognosis of Resectable Non-Small Cell Lung Cancers

For resectable cancer patients, a method that could precisely predict the risk of postoperative recurrence would be crucial for guiding adjuvant treatment. Since T cell receptor (TCR) repertoires had been shown to be closely related to the dynamics of cancers, here we enrolled a cohort of patients to evaluate the potential of TCR repertoires in predicting the prognosis of resectable non-small cell lung cancers. Specifically, TCRβ repertoires were analyzed in surgical tumor tissues and matched adjacent non-tumor tissues from 39 patients enrolled with resectable non-small cell lung cancer, through target enrichment and high-throughput sequencing. As a result, there are significant differences between the TCR repertories of tumor samples and those of matched adjacent non-tumor samples as evaluated by criteria like the number of clonotypes. In addition, TCR repertoires were significantly associated with a few clinical features, as well as somatic mutations. Finally, certain TCRβ variable-joining (V-J) pairings were featured to build a logistic regression model in predicting postoperative recurrence of resectable non-small cell lung cancers with a testing area under the receiver operating characteristic curve (AUC) of around 0.9. Thus, we hypothesize that TCR repertoires could be potentially used to predict prognosis after curative surgery for non-small cell lung cancer patients.

Scriptaid/exercise-induced lysine acetylation is another type of posttranslational modification occurring in titin

Titin serves important functions in skeletal muscle during exercise, and posttranslational modifications of titin participate in the regulation of titin-based sarcomeric functions. Scriptaid has exercise-like effects through the inhibition of HDAC and regulatory acetylation of proteins. However, it remains mostly unclear if exercise could result in titin’s acetylation and whether Scriptaid could regulate acetylation of titin. We treated C57BL/6 mice with 6-wk treadmill exercise and 6-wk Scriptaid administration to explore Scriptaid’s effects on mice exercise capacity and whether Scriptaid administration/exercise could induce titin’s acetylation modification. An exercise endurance test was conducted to explore their effects on mice exercise capacity, and proteomic studies were conducted with gastrocnemius muscle tissue of mice from different groups to explore titin’s acetylation modification. We found that Scriptaid and exercise did not change titin’s protein expression, but they did induce acetylation modification changes of titin. In total, 333 acetylated lysine sites were identified. Exercise changed the acetylation levels of 33 lysine sites of titin, whereas Scriptaid changed acetylation levels of 31 titin lysine sites. Exercise treatment and Scriptaid administration shared 11 lysine sites. In conclusion, Scriptaid increased exercise endurance of mice by increasing the time mice spent running to fatigue. Acetylation is a common type of posttranslational modification of titin, and exercise/Scriptaid changed the acetylation levels of titin and titin-interacting proteins. Most importantly, titin may be a mediator through which Scriptaid and exercise modulate the properties and functions of exercise-induced skeletal muscle at the molecular level.

NEW & NOTEWORTHY Scriptaid administration increased mouse exercise endurance. Acetylation is another type of posttranslational modification of titin. Scriptaid/exercise changed acetylation levels of titin and titin-interacting proteins. Titin may mediate exercise-induced skeletal muscle properties and functions.

Slow skeletal muscle troponin T, titin and myosin light chain 3 are candidate prognostic biomarkers for Ewing's sarcoma

Ewing's sarcoma (ES) is a common malignant bone tumor in children and adolescents. Although great efforts have been made to understand the pathogenesis and development of ES, the underlying molecular mechanism remains unclear. The present study aimed to identify new key genes as potential biomarkers for the diagnosis, targeted therapy or prognosis of ES. mRNA expression profile chip data sets GSE17674, GSE17679 and GSE45544 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened using the R software limma package, and functional and pathway enrichment analyses were performed using the enrichplot package and GSEA software. The NetworkAnalyst online tool, as well as Cytoscape and its plug-ins cytoHubba and NetworkAnalyzer, were used to construct a protein-protein interaction network (PPI) and conduct module analysis to screen key (hub) genes. LABSO COX regression and overall survival (OS) analysis of the Hub genes were performed. A total of 211 DEGs were obtained by integrating and analyzing the three data sets. The functions and pathways of the DEGs were mainly associated with the regulation of small-molecule metabolic processes, cofactor-binding, amino acid, proteasome and ribosome biosynthesis in eukaryotes, as well as the Rac1, cell cycle and P53 signaling pathways. A total of one important module and 20 hub genes were screened from the PPI network using the Maximum Correlation Criteria algorithm of cytoHubba. LASSO COX regression results revealed that titin (TTN), fast skeletal muscle troponin T, skeletal muscle actin α-actin, nebulin, troponin C type 2 (fast), myosin light-chain 3 (MYL3), slow skeletal muscle troponin T (TNNT1), myosin-binding protein C1 slow-type, tropomyosin 3 and myosin heavy-chain 7 were associated with prognosis in patients with ES. The Kaplan-Meier curves demonstrated that high mRNA expression levels of TNNT1 (P<0.001), TTN (P=0.049), titin-cap (P=0.04), tropomodulin 1 (P=0.011), troponin I2 fast skeletal type (P=0.021) and MYL3 (P=0.017) were associated with poor OS in patients with ES. In conclusion, the DEGs identified in the present study may be key genes in the pathogenesis of ES, three of which, namely TNNT1, TTN and MYL3, may be potential prognostic biomarkers for ES.