Spontaneous mutations in the single TTN gene represent high tumor mutation burden

CIBRA identifies genomic alterations with a system-wide impact on tumor biology

MOTIVATION

Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that determines the system-wide impact of genomic alterations on tumor biology by integrating two distinct omics data types: one indicating genomic alterations (e.g. genomics), and another defining a system-wide expression response (e.g. transcriptomics). CIBRA was evaluated with genome-wide screens in 33 cancer types using primary and metastatic cancer data from the Cancer Genome Atlas and Hartwig Medical Foundation.

RESULTS

We demonstrate the capability of CIBRA by successfully confirming the impact of point mutations in experimentally validated oncogenes and tumor suppressor genes (0.79 AUC). Surprisingly, many genes affected by structural variants were identified to have a strong system-wide impact (30.3%), suggesting that their role in cancer development has thus far been largely under-reported. Additionally, CIBRA can identify impact with only 10 cases and controls, providing a novel way to prioritize genomic alterations with a prominent role in cancer biology. Our findings demonstrate that CIBRA can identify cancer drivers by combining genomics and transcriptomics data. Moreover, our work shows an unexpected substantial system-wide impact of structural variants in cancer. Hence, CIBRA has the potential to preselect and refine current definitions of genomic alterations to derive more nuanced biomarkers for diagnostics, disease progression, and treatment response.

AVAILABILITY AND IMPLEMENTATION

The R package CIBRA is available at https://github.com/AIT4LIFE-UU/CIBRA.

Spatial whole exome sequencing reveals the genetic features of highly-aggressive components in lung adenocarcinoma

In invasive lung adenocarcinoma (LUAD), patients with micropapillary (MIP) or solid (SOL) components had a significantly poorer prognosis than those with only lepidic (LEP), acinar (ACI) or papillary (PAP) components. It is interesting to explore the genetic features of different histologic subtypes, especially the highly aggressive components. Based on a cohort of 5,933 patients, this study observed that in different tumor size groups, LUAD with MIP/SOL components showed a different prevalence, and patients with ALK alteration or TP53 mutations had a higher probability of developing MIP/SOL components. To control individual differences, this research used spatial whole-exome sequencing (WES) via laser-capture microdissection of five patients harboring these five coexistent components and identified genetic features among different histologic components of the same tumor. In tracing the evolution of components, we found that titin (TTN) mutation might serve as a crucial intratumor potential driver for MIP/SOL components, which was validated by a cohort of 146 LUAD patients undergoing bulk WES. Functional analysis revealed that TTN mutations enriched the complement and coagulation cascades, which correlated with the pathway of cell adhesion, migration, and proliferation. Collectively, the histologic subtypes of invasive LUAD were genetically different, and certain trunk genotypes might synergize with branching TTN mutation to develop highly aggressive components.

Proteogenomic Characterization Reveals Estrogen Signaling as a Target for Never-Smoker Lung Adenocarcinoma Patients without EGFR or ALK Alterations

Never-smoker lung adenocarcinoma (NSLA) is prevalent in Asian populations, particularly in women. EGFR mutations and anaplastic lymphoma kinase (ALK) fusions are major genetic alterations observed in NSLA, and NSLA with these alterations have been well studied and can be treated with targeted therapies. To provide insights into the molecular profile of NSLA without EGFR and ALK alterations (NENA), we selected 141 NSLA tissues and performed proteogenomic characterization, including whole genome sequencing (WGS), transcriptomic, methylation EPIC array, total proteomic, and phosphoproteomic analyses. Forty patients with NSLA harboring EGFR and ALK alterations and seven patients with NENA with microsatellite instability were excluded. Genome analysis revealed that TP53 (25%), KRAS (22%), and SETD2 (11%) mutations and ROS1 fusions (14%) were the most frequent genetic alterations in NENA patients. Proteogenomic impact analysis revealed that STK11 and ERBB2 somatic mutations had broad effects on cancer-associated genes in NENA. DNA copy number alteration analysis identified 22 prognostic proteins that influenced transcriptomic and proteomic changes. Gene set enrichment analysis revealed estrogen signaling as the key pathway activated in NENA. Increased estrogen signaling was associated with proteogenomic alterations, such as copy number deletions in chromosomes 14 and 21, STK11 mutation, and DNA hypomethylation of LLGL2 and ST14. Finally, saracatinib, an Src inhibitor, was identified as a potential drug for targeting activated estrogen signaling in NENA and was experimentally validated in vitro. Collectively, this study enhanced our understanding of NENA NSLA by elucidating the proteogenomic landscape and proposed saracatinib as a potential treatment for this patient population that lacks effective targeted therapies.

SIGNIFICANCE

The proteogenomic landscape in never-smoker lung cancer without known driver mutations reveals prognostic proteins and enhanced estrogen signaling that can be targeted as a potential therapeutic strategy to improve patient outcomes.

OH2 oncolytic virus: A novel approach to glioblastoma intervention through direct targeting of tumor cells and augmentation of anti-tumor immune responses

Glioblastoma (GBM), the deadliest central nervous system cancer, presents a poor prognosis and scant therapeutic options. Our research spotlights OH2, an oncolytic viral therapy derived from herpes simplex virus 2 (HSV-2), which demonstrates substantial antitumor activity and favorable tolerance in GBM. The extraordinary efficacy of OH2 emanates from its unique mechanisms: it selectively targets tumor cells replication, powerfully induces cytotoxic DNA damage stress, and kindles anti-tumor immune responses. Through single-cell RNA sequencing analysis, we discovered that OH2 not only curtails the proliferation of cancer cells and tumor-associated macrophages (TAM)-M2 but also bolsters the infiltration of macrophages, CD4+ and CD8+ T cells. Further investigation into molecular characteristics affecting OH2 sensitivity revealed potential influencers such as TTN, HMCN2 or IRS4 mutations, CDKN2A/B deletion and IDO1 amplification. This study marks the first demonstration of an HSV-2 derived OV's effectiveness against GBM. Significantly, these discoveries have driven the initiation of a phase I/II clinical trial (ClinicalTrials.gov: NCT05235074). This trial is designed to explore the potential of OH2 as a therapeutic option for patients with recurrent central nervous system tumors following surgical intervention.

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets

Background

Lung adenocarcinoma (LUAD) has a complex tumor heterogeneity. Our research attempts to clearness LUAD subtypes and build a reliable prognostic signature according to the activity changes of the hallmark and immunologic gene sets.

Methods

According to The Cancer Genome Atlas (TCGA) - LUAD dataset, changes in marker and immune gene activity were analyzed, followed by identification of prognosis-related differential gene sets (DGSs) and their related LUAD subtypes. Survival analysis, correlation with clinical characteristics, and immune microenvironment assessment for subtypes were performed. Moreover, the differentially expressed genes (DEGs) between different subtypes were identified, followed by the construction of a prognostic risk score (RS) model and nomogram model. The tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) of different risk groups were compared.

Results

Two LUAD subtypes were determined according to the activity changes of the hallmark and immunologic gene sets. Cluster 2 had worse prognosis, more advanced tumor and clinical stages than cluster 1. Moreover, a prognostic RS signature was established using two LUAD subtype-related DEGs, which could stratify patients at different risk levels. Nomogram model incorporated RS and clinical stage exerted good prognostic performance in LUAD patients. A shorter survival time and higher TMB were observed in the high-risk patients.

Conclusions

Our findings revealed that our constructed prognostic signature could exactly predict the survival status of LUAD cases, which was helpful in predicting the prognosis and guiding personalized therapeutic strategies for LUAD.

TTN Mutation in Endometrial Endometrioid Carcinoma Is Associated with Poor Clinical Outcomes and High Tumor Mutation Burden

Endometrioid endometrial carcinoma (EEC) stands as a prevalent gynecologic malignancy in developed regions. However, predicting relapse cases remains challenging, necessitating the identification of a novel biomarker for EEC relapse. The assessment of tumor mutational burden (TMB) is pivotal for immunotherapy in EEC patients. However, both whole-exome sequencing (WES) and targeted sequencing encountered application-related difficulties. In light of this, standardized and simplified techniques for TMB measurement are imperative. In this study, we employed WES on 25 EEC patients (12 relapsed cases and 13 non-relapsed cases) who accepted hysterectomy surgery (CHCAMS cohort). We additionally obtained a total of 391 tumor samples with clinicopathological features from TCGA website to broaden the study cohort. In the CHCAMS cohort, the TTN mutant group showed shorter progression-free survival (p < 0.001) and overall survival (p < 0.001) than TTN wild-type group. Additionally, we discovered that the number of TTN mutations per sample was significantly linked with TMB-WES in CHCAMS cohort and TCGA cohort (p < 0.05). And the number of TTN mutations per sample in POLE mutant group was greater than in the POLE wild-type group (p < 0.0001). In conclusion, TTN mutation may serve as a biomarker for EEC prognosis. TTN mutation is also associated with WES-TMB, and could be a simplified TMB measurement technique.

The Impact of Mutational Hotspots on Cancer Survival

BACKGROUND

Cofactors, biomarkers, and the mutational status of genes such as TP53, EGFR, IDH1/2, or PIK3CA have been used for patient stratification. However, many genes exhibit recurrent mutational positions known as hotspots, specifically linked to varying degrees of survival outcomes. Nevertheless, few hotspots have been analyzed (e.g., TP53 and EGFR). Thus, many other genes and hotspots remain unexplored.

METHODS

We systematically screened over 1400 hotspots across 33 TCGA cancer types. We compared the patients carrying a hotspot against (i) all cases, (ii) gene-mutated cases, (iii) other mutated hotspots, or (iv) specific hotspots. Due to the limited number of samples in hotspots and the inherent group imbalance, besides Cox models and the log-rank test, we employed VALORATE to estimate their association with survival precisely.

RESULTS

We screened 1469 hotspots in 6451 comparisons, where 314 were associated with survival. Many are discussed and linked to the current literature. Our findings demonstrate associations between known hotspots and survival while also revealing more potential hotspots. To enhance accessibility and promote further investigation, all the Kaplan-Meier curves, the log-rank tests, Cox statistics, and VALORATE-estimated null distributions are accessible on our website.

CONCLUSIONS

Our analysis revealed both known and putatively novel hotspots associated with survival, which can be used as biomarkers. Our web resource is a valuable tool for cancer research.

Clear-cell papillary renal cell tumour: New insights into clinicopathological features and molecular landscape after renaming by 5th WHO classification

OBJECTIVE

Clear cell papillary renal cell tumour (CCPRCT) is a kind of renal epithelial cell tumor, and was renamed by the 5th WHO due to its specific epidemiology and clinicopathological characteristics. However, the biological mechanism and molecular basis of CCPRCT still need to be further clarified. This study aims to comprehensively evaluate clinicopathologic and molecular characteristics of CCPRCC, and particularly compare it with other more prevalent subtypes of renal cell carcinoma.

METHODS

12 cases of CCPRCT were collected for analyzing the clinicopathological characteristics. Then, whole-exome sequencing (WES) was employed to reveal the genetic profiles, followed by comparison with the molecular genetic alterations identified in ccRCC (341) and pRCC (200) datasets obtained from the TCGA database.

RESULTS

Of the 12 CCPRCT cases, the male-to-female ratio was 4:1 with a mean age of 49.5 years (48.5 ± 10.5) at diagnosis. All patients were diagnosed accidentally during routine physical examinations. All tumors (12/12, 100%）had a solid-cystic appearance with a well-defined fibrous capsule. The median size of the tumors was 3 cm (2.98 ± 1.2). Histologically, the cystic papillary structures were considered to be prominent, lined with cuboidal tumor cells away from basement membrane. The tumor cells were moderately atypia equivalent to grade 1 or grade 2 according to the ISUP nuclear grading system. Typically, the tumor cell diffusely positive for CK7 and CAIX in a "cup-like" pattern. The results of WES revealed recurrent gene alterations (mainly missense mutation) of TTN and FLT in 4 cases (4/12, 33.3%), respectively, of which, the alteration of FLT was not observed in ccRCC and pRCC of the TCGA database. Other gene alterations including POTEC (1 cases), PRADC1 (1 cases), ZZZ3 (1 case) and PTPRZ1 (1 case), etc. Moreover, all of the CCPRCT cases displayed a lower tumor mutation burden (TMB) compared to ccRCC and pRCC with median TMB of 1.04 (range: 1.94 ± 2.74). None of the patients experienced tumor metastasis, recurrence, or tumor-related deaths.

CONCLUSION

CCPRCT is a renal epithelial cell tumor characterized by specific clinical and pathological features. Our study provides additional evidence supporting the favorable prognosis of CCPRCT. Furthermore, the potential molecular alterations were uncovered by this study in CCPRCT such as the FLT family and TTN. However, due to the limited sample size, larger studies are required to validate these findings.

A novel endoplasmic reticulum stress-related lncRNA signature for prognosis prediction and immune response evaluation in Stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is a significant contributor to cancer-related mortality worldwide. Although previous research has identified endoplasmic reticulum stress (ERS) as a regulator of various tumor-promoting properties of cancer cells, the impact of ERS-related long non-coding RNAs (lncRNAs) on STAD prognosis has not yet been investigated. Therefore, our study aims to develop and validate an ERS-related lncRNA signature that can accurately predict the prognosis of STAD patients.

METHODS

We collected RNA expression profiles and clinical data of STAD patients from The Cancer Genome Atlas (TCGA) and identified ERS-related genes from the Molecular Signature Database (MSigDB). Co-expression analysis enabled us to identify ERS-related lncRNAs, and we applied univariate Cox, least absolute shrinkage, and selection operator (LASSO), and multivariate Cox regression analyses to construct a predictive signature comprising of 9 ERS-related lncRNAs. We assessed the prognostic accuracy of our signature using Kaplan-Meier survival analysis, and validated our predictive signature in an independent gene expression omnibus (GEO) cohort. We also performed tumor mutational burden (TMB) and tumor immune microenvironment (TIME) analyses. Enrichment analysis was used to investigate the functions and biological processes of the signature, and we identified two distinct STAD patient subgroups through consensus clustering. Finally, we performed drug sensitivity analysis and immunologic efficacy analysis to explore further insights.

RESULTS

The 9 ERS related-lncRNAs signature demonstrated satisfactory predictive performance as an independent prognostic marker and was significantly associated with STAD clinicopathological characteristics. Furthermore, patients in the high-risk group displayed a worse STAD prognosis than those in the low-risk group. Notably, gene set enrichment analysis (GSEA) revealed significant enrichment of extracellular matrix pathways in the high-risk group, indicating their involvement in STAD progression. Additionally, the high-risk group exhibited significantly lower TMB expression levels than the low-risk group. Consensus clustering revealed two distinct STAD patient subgroups, with Cluster 1 exhibiting higher immune cell infiltration and more active immune functions. Drug sensitivity analysis suggested that the low-risk group was more responsive to oxaliplatin, epirubicinl, and other drugs.

CONCLUSION

Our study highlights the crucial regulatory roles of ERS-related lncRNAs in STAD, with significant clinical implications. The 9-lncRNA signature we have constructed represents a reliable prognostic indicator that has the potential to inform more personalized treatment decisions for STAD patients. These findings shed new light on the pathogenesis of STAD and its underlying molecular mechanisms, offering opportunities for novel therapeutic strategies to be developed for STAD patients.

Disulfidptosis-related signatures for prognostic and immunotherapy reactivity evaluation in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and a nonnegligible health concern on a worldwide scale. Disulfidptosis is a novel mode of cell death, which is mainly caused by the collapse of the actin skeleton. Although many studies have demonstrated that various types of cell death are associated with cancer treatment, the relationship between disulfidptosis and HCC has not been elucidated.

METHODS

Here, we mainly applied bioinformatics methods to construct a disulfidptosis related risk model in HCC patients. Specifically, transcriptome data and clinical information were downloaded from the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database. A total of 45 co-expressed genes were extracted between the disulfidptosis-related genes (DRGs) and the differential expression genes (DEGs) of liver hepatocellular carcinoma (LIHC) in the TCGA database. The LIHC cohort was divided into two subgroups with different prognosis by k-mean consensus clustering and functional enrichment analysis was performed. Subsequently, three hub genes (CDCA8, SPP2 and RDH16) were screened by Cox regression and LASSO regression analysis. In addition, a risk signature was constructed and the HCC cohort was divided into high risk score and low risk score subgroups to compare the prognosis, clinical features and immune landscape between the two subgroups. Finally, the prognostic model of independent risk factors was constructed and verified.

CONCLUSIONS

High DRGs-related risk score in HCC individuals predict poor prognosis and are associated with poor immunotherapy response, which indicates that risk score assessment model can be utilized to guide clinical treatment strategy.

Development and validation of a disulfidptosis and M2 TAM-related classifier for bladder cancer to explore tumor subtypes, immune landscape and drug treatment

BACKGROUND

Disulfidptosis, as a new mode of programmed cell death, is closely associated with tumorigenesis. Meanwhile, M2 tumor-associated macrophage (TAM) plays an important role in tumor progression. Here, we propose to combine these two perspectives to detect novel disulfidptosis and M2 TAM-related biomarkers in bladder cancer (BCa) to identify various tumor subtypes, construct prognostic features, reveal immune and somatic mutational landscapes, and screen for drugs in BCa.

METHODS

We used weighted gene co-expression network analysis (WGCNA) to mine M2 TAM-related genes. Consensus unsupervised clustering was performed to identify potential tumor subtypes. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were utilized to build the risk model. We then explored the immune cell, immune function, immune checkpoint expression patterns and somatic mutational landscape in clusters and risk groups. In addition, we performed sensitivity analysis for anti-cancer drugs.

RESULTS

We identified 3057 M2 TAM-related genes and intersected them with disulfidptosis-related genes to obtain 95 disulfidptosis and M2 TAM-related genes (DMRGs). In terms of tumor subtypes, two molecular clusters were identified. Cluster 1 showed stronger immunogenicity and higher tumor mutational burden (TMB). We also predicted 50 drugs with high sensitivity in cluster 1. On the basis of risk grouping, the high-risk group had poor overall survival in the training, test, and validation groups. Ten screened anti-cancer drugs were more sensitive in the high-risk group. A nomogram predicting survival of BCa patients was also established.

CONCLUSION

By combining two hotspot perspectives, disulfidptosis and M2 TAM, we provide a valuable risk score signature for establishing individualized treatment regimens and drug choices. The risk score may serve as an independent risk factor for BCa patients.

Cuproptosis-related lncRNAs predict the prognosis and immune response in hepatocellular carcinoma

Cuproptosis has been recently used to indicate unique biological processes triggered by Cu action as a new term. This study aimed to explore the relationship between cuproptosis-related lncRNA and hepatocellular carcinoma (HCC) with regard to immunity and prognosis. RNA sequencing and the clinical data were downloaded from the TCGA database. The cuproptosis-related genes were sorted out through literature study. The cuproptosis-related IncRNA signature was identified by Cox regression analysis and the least absolute shrinkage and selection operator analysis. The K-M survival analysis, receiver operating characteristic analysis, and C-index analysis were adopted to evaluate the prognostic prediction performance of the signature. The functional enrichment, immune infiltration and tumor mutation analysis were further analyzed. Subsequently, we predicted the differences in chemosensitivity from tumor gene expression levels for some chemotherapy drugs. The prognostic signature consisting of 5 overall survival-related CUPlncRNAs. It showed an extraordinary ability to predict the prognoses of patients with HCC. The signature can predict the abundance of immune cell infiltration, immune functions, expression of immune checkpoint inhibitors, m6A genes, which was supported by the GO biological process and KEGG analysis. And it may also have a guiding effect in the sensitivity of different chemotherapeutic drugs and tumor mutation burden. We constructed a new cuproptosis-related lncRNA signature for HCC patients. The model can be used for prognostic prediction and immune evaluation, providing a reference for immunotherapies and targeted therapies.

A disulfidptosis-related lncRNA prognostic model to predict survival and response to immunotherapy in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and has a poor prognosis. Disulfidptosis is a novel regulated cell death form characterized by aberrant disulfide stress and actin network collapse. This study aimed to identify disulfidptosis-related lncRNAs, and predict LUAD patients' prognosis and response to antitumor therapies by establishing a disulfidptosis-related lncRNA model. Methods: Transcriptome and clinical data of LUAD patients were obtained from the TCGA database. Pearson correlation and Cox regression analysis was used to identify disulfidptosis-related lncRNAs associated with overall survival. LASSO regression analysis was adopted to construct the prognostic model. GO, KEGG and GSEA analysis was used to identify cellular pathways related to this model. Immune cell infiltration was investigated by ESTIMATE and CIBERSORT algorithms. Tumor mutational burden (TMB) and its association with model-derived risk score were analyzed using simple nucleotide variation data. Patients' response to immunotherapy and other antineoplastic drugs was predicted by the TIDE algorithm and GDSC tool, respectively. Results: We identified 127 disulfidptosis-related lncRNAs, and a prognostic model that consists eight of them (KTN1-AS1, AL365181.3, MANCR, LINC01352, AC090559.1, AC093673.1, AP001094.3, and MHENCR) was established and verified. The prognostic model could stratify LUAD patients into two distinct risk-score groups. A high risk score was an independent prognosis factor indicating poor overall survival, and correlated with reduced immune cell infiltration, high TMB, and lower activity of tumor immune response. Immune checkpoint blockade might bring more survival benefits to the high-risk LUAD patients, whereas low-risk patients might be more responsive to targeted therapy and diverse kinase inhibitors. Conclusion: We established a disulfidptosis-related lncRNA model that can be exploited to predict the prognosis, tumor mutational burden, immune cell infiltration landscape, and response to immunotherapy and targeted therapy in LUAD patients.

Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature

BACKGROUND

The tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression.

METHODS

The TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts.

RESULTS

The TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples.

CONCLUSION

Our study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy.

Integrated single cell and bulk sequencing analysis identifies tumor reactive CXCR6+ CD8 T cells as a predictor of immune infiltration and immunotherapy outcomes in hepatocellular carcinoma

Background

Various immune cell types in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) have been identified as important parameters associated with prognosis and responsiveness to immunotherapy. However, how various factors influence immune cell infiltration remains incompletely understood. Hence, we investigated the single cell multi-omics landscape of immune infiltration in HCC, particularly key gene and cell subsets that influence immune infiltration, thus potentially linking the immunotherapy response and immune cell infiltration.

Methods

We grouped patients with HCC according to immune cell infiltration scores calculated by single sample gene set enrichment analysis (ssGSEA). Differential expression analysis, functional enrichment, clinical trait association, gene mutation analysis, tumor immune dysfunction and exclusion (TIDE) and prognostic model construction were used to investigate the immune infiltration landscape through multi-omics. Stepwise regression was further used to identify key genes regulating immune infiltration. Single cell analysis was performed to explore expression patterns of candidate genes and investigate associated cellular populations. Correlation analysis, ROC analysis, Immunotherapy cohorts were used to explore and confirm the role of key gene and cellular population in predicting immune infiltration state and immunotherapy response. Immunohistochemistry and multiplexed fluorescence staining were used to further validated our results.

Results

Patients with HCC were clustered into high and low immune infiltration groups. Mutations of CTNNB1 and TTN were significantly associated with immune infiltration and altered enrichment of cell populations in the TME. TIDE analysis demonstrated that T cell dysfunction and the T cell exclusion score were elevated in the high and low infiltration groups, respectively. Six risk genes and five risk immune cell types were identified and used to construct risk scores and a nomogram model. CXCR6 and LTA, identified by stepwise regression, were highly associated with immune infiltration. Single cell analysis revealed that LTA was expressed primarily in tumor infiltrating T lymphocytes and partial B lymphocytes, whereas CXCR6 was enriched predominantly in T and NK cells. Notably, CXCR6+ CD8 T cells were characterized as tumor enriched cells that may be potential predictors of high immune infiltration and the immune-checkpoint blockade response, and may serve as therapeutic targets.

Conclusion

We constructed a comprehensive single cell and multi-omics landscape of immune infiltration in HCC, and delineated key genes and cellular populations regulating immune infiltration and immunotherapy response, thus providing insights into the mechanisms of immune infiltration and future therapeutic control.

Joint analysis of WES and RNA-Seq identify signature genes related to metastasis in prostate cancer

Prostate cancer (PCa) has a certain degree of heritability, and metastasis occurs as cancer progresses. However, its underlying mechanism remains largely unknown. We sequenced four cases of cancer without metastasis, four metastatic cancer, and four benign hyperplasia tissues as controls. A total of 1839 damaging mutations were identified. Pathway analysis, gene clustering, and weighted gene co-expression network analysis were employed to find characteristics associated with metastasis. Chr19 had the most mutation density and 1p36 had the highest mutation frequency across the genome. These mutations occurred in 1630 genes, including the most frequently mutated genes TTN and PLEC, and dozens of metastasis-related genes, such as FOXA1, NCOA1, CD34, and BRCA2. Ras signalling and arachidonic acid metabolism were uniquely enriched in metastatic cancer. Gene programmes 10 and 11 showed the signatures indicating the occurrence of metastasis better. A module (135 genes) was specifically associated with metastasis. Of them, 67.41% reoccurred in program 10, with 26 genes further retained as the signature genes related to PCa metastasis, including AGR3, RAPH1, SOX14, DPEP1, and UBL4A. Our study provides new molecular perspectives on PCa metastasis. The signature genes and pathways could be served as potential therapeutic targets for metastasis or cancer progression.

Social, Genetics and Histopathological Factors Related to Titin (TTN) Gene Mutation and Survival in Women with Ovarian Serous Cystadenocarcinoma: Bioinformatics Analysis

Several factors may increase the risk of development of ovarian cancer. In this study, we investigated the relationship between social, genetic, and histopathologic factors in women with ovarian serous cystadenocarcinoma and titin (TTN) mutations, whether the TTN gene mutation may be a predictor, and its impact on mortality and survival in these patients. A total of 585 samples from patients with ovarian serous cystadenocarcinoma were collected from The Cancer Genome Atlas and PanCancer Atlas through the cBioPortal for analysis of social, genetic, and histopathological factors. Logistic regression was used to investigate whether TTN mutation could be a predictor, and the Kaplan-Meier method was applied to analyze survival time. TTN mutation frequency did not differ between age at diagnosis, tumor stage, and race, and was related to increased Buffa hypoxia score (p = 0.004), mutation count (p < 0.0001), Winter hypoxia Score (p = 0.030), nonsynonymous tumor mutation burden (TMB) (p < 0.0001), and reduced microsatellite instability sensor score (p = 0.010). The number of mutations (p < 0.0001) and winter hypoxia score (p = 0.008) were positively associated with TTN mutations, and nonsynonymous TMB (p < 0.0001) proved to be a predictor. Mutated TTN affects the score of genetic variables involved in cancer cell metabolism in ovarian cystadenocarcinoma.

Machine learning-based glycolysis-associated molecular classification reveals differences in prognosis, TME, and immunotherapy for colorectal cancer patients

Background

Aerobic glycolysis is a process that metabolizes glucose under aerobic conditions, finally producing pyruvate, lactic acid, and ATP for tumor cells. Nevertheless, the overall significance of glycolysis-related genes in colorectal cancer and how they affect the immune microenvironment have not been investigated.

Methods

By combining the transcriptome and single-cell analysis, we summarize the various expression patterns of glycolysis-related genes in colorectal cancer. Three glycolysis-associated clusters (GAC) were identified with distinct clinical, genomic, and tumor microenvironment (TME). By mapping GAC to single-cell RNA sequencing analysis (scRNA-seq), we next discovered that the immune infiltration profile of GACs was similar to that of bulk RNA sequencing analysis (bulk RNA-seq). In order to determine the kind of GAC for each sample, we developed the GAC predictor using markers of single cells and GACs that were most pertinent to clinical prognostic indications. Additionally, potential drugs for each GAC were discovered using different algorithms.

Results

GAC1 was comparable to the immune-desert type, with a low mutation probability and a relatively general prognosis; GAC2 was more likely to be immune-inflamed/excluded, with more immunosuppressive cells and stromal components, which also carried the risk of the poorest prognosis; Similar to the immune-activated type, GAC3 had a high mutation rate, more active immune cells, and excellent therapeutic potential.

Conclusion

In conclusion, we combined transcriptome and single-cell data to identify new molecular subtypes using glycolysis-related genes in colorectal cancer based on machine-learning methods, which provided therapeutic direction for colorectal patients.

Comprehensive Analysis of Purine-Metabolism-Related Gene Signature for Predicting Ovarian Cancer Prognosis, Immune Landscape, and Potential Treatment Options

Purine metabolism is an important branch of metabolic reprogramming and has received increasing attention in cancer research. Ovarian cancer is an extremely dangerous gynecologic malignancy for which there are no adequate tools to predict prognostic risk. Here, we identified a prognostic signature consisting of nine genes related to purine metabolism, including ACSM1, CACNA1C, EPHA4, TPM3, PDIA4, JUNB, EXOSC4, TRPM2, and CXCL9. The risk groups defined by the signature are able to distinguish the prognostic risk and the immune landscape of patients. In particular, the risk scores offer promising personalized drug options. By combining risk scores with clinical characteristics, we have created a more detailed composite nomogram that allows for a more complete and individualized prediction of prognosis. In addition, we demonstrated metabolic differences between platinum-resistant and platinum-sensitive ovarian cancer cells. In summary, we have performed the first comprehensive analysis of genes related to purine metabolism in ovarian cancer patients and created a feasible prognostic signature that will aid in risk prediction and support personalized medicine.

Whole-Exome Sequencing Revealed the Mutational Profiles of Primary Central Nervous System Lymphoma

BACKGROUND

Primary central nervous system lymphoma (PCNSL) is a highly aggressive type of extranodal non-Hodgkin lymphoma, of which approximately 90% of the cases are diffuse large B-cell lymphoma (DLBCL). In recent years, the incidence of PCNSL has significantly increased in women and older men. Although advanced treatments such as high-dose methotrexate (HD-MTX) and targeted agents have been introduced, the prognosis of these patients remains poorer than those with other forms of non-Hodgkin's lymphoma.

METHODS

Twelve cases of Chinese PCNSL were analyzed to detect their genetic alterations using whole-exome sequencing (WES). We identified 448 potential somatic single nucleotide variants (SNVs) with a median of 12 SNVs per PCNSL sample and 35 small indels with potentially protein-changing features in 9 PCNSL samples.

RESULTS

We found that myeloid differentiation factor 88 (MYD88) had the highest mutation frequency, which affected the activity of the nuclear factor-κB (NF-κB) pathway. PCNSL samples with low-density lipoprotein receptor-related protein 1B (LRP1B) mutations had a higher mutation rate than samples with wild-type LRP1B. Polycystic kidney and hepatic disease 1 (PKHD1), the causal gene of autosomal recessive polycystic kidney disease (ARPKD), was identified in 2 PCNSL cases and exhibited missense mutations. Pathway analysis revealed enrichment in pathways associated with central carbon metabolism in cancer, renal cell carcinoma, nicotine addiction, bladder cancer, and long-term depression.

CONCLUSIONS

WES revealed significantly mutated genes associated with the molecular mechanisms of PCNSL, which could serve as therapeutic targets to improve patient outcomes.

Risk scoring based on DNA methylation-driven related DEGs for colorectal cancer prognosis with systematic insights

Colorectal cancer is a common malignant tumor of the digestive tract. Despite advances in diagnostic techniques and medications. Its prognosis remains challenging. DNA methylation-driven related circulating tumor cells have attracted enormous interest in diagnosing owing to their non-invasive nature and early recognition properties. However, the mechanism through which risk biomarkers act remains elusive. Here, we designed a risk model based on differentially expressed genes, DNA methylation, robust, and survival-related factors in the framework of Cox regression. The model has satisfactory performance and is independently verified by an external and isolated dataset in terms of C-index value, ROC, and tROC. The model was applied to Colorectal cancer patients who were subsequently divided into high- and low-risk groups. Functional annotations, genomic alterations, tumor immune environment, and drug sensitivity were analyzed. We observed that up-regulated genes are associated with epithelial cell differentiation and MAPK signaling pathways. The down-regulated genes are related to IL-7 signaling and apoptosis-induced DNA fragmentation. Interestingly, the immune system was inhibited in high-risk groups. High-frequency mutation genes tend to co-occur. High-risk score patients are related to copy number amplification events. To address the challenges, we suggested eleven and twenty-one drugs that are sensitive to low- and high-risk patients. Finally, an artificial neural network was provided to evaluate the immunotherapeutic efficiency. Taken together, the findings demonstrated that our risk score model is robust and reliable for evaluating the prognosis with novel diagnostic and treatment targets. It also yields benefits for the treatment and provides unique insights into developing therapeutic strategies.

Identification and validation of a TTN-associated immune prognostic model for skin cutaneous melanoma

TTN is the most commonly mutated gene in skin cutaneous melanoma (SKCM). Tumor mutational burden (TMB) can generate new antigens that regulate the recognition of T cells, which will significantly affect the prognosis of patients. The TTN gene has a long coding sequence and a high number of mutant sites, which allows SKCM patients to produce higher TMB and may influence the immune response. It has been found that the overall survival (OS) of SKCM patients with TTN mutation was significantly higher than that of wild-type patients. However, the effect of TTN mutation on the immune microenvironment of SKCM has not been fully investigated. Here, we systematically explored the relationship and potential mechanisms between TTN mutation status and the immune response. We first revealed that TTN mutated SKCM were significantly associated with four immune-related biological processes. Next, 115 immune genes differentially expressed between TTN mutation and wild-type SKCM patients were found to significantly affect the OS of SKCM patients. Then, we screened four immune-related genes (CXCL9, PSMB9, CD274, and FCGR2A) using LASSO regression analysis and constructed a TTN mutation-associated immune prognostic model (TM-IPM) to distinguish the SKCM patients with a high or low risk of poor prognosis, independent of multiple clinical characteristics. SKCM in the low-risk group highly expressed a large number of immune-related genes, and functional enrichment analysis of these genes showed that this group was involved in multiple immune processes and pathways. Furthermore, the nomogram constructed by TM-IPM with other clinicopathological parameters can provide a predictive tool for clinicians. Moreover, we found that CD8+ T cells were significantly enriched in the low-risk group. The expression level of immune checkpoints was higher in the low-risk group than in the high-risk group. Additionally, the response to chemotherapeutic agents was higher in the low-risk group than in the high-risk group, which may be related to the long survival in the low-risk group. Collectively, we constructed and validated a TM-IPM using four immune-related genes and analyzed the potential mechanisms of TM-IPM to predict patient prognosis and response to immunotherapy from an immunological perspective.

Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis

One of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop a novel LMRG prognostic model for HCC patients and to study its utility for predictive, preventive, and personalized medicine. We used the single-cell RNA sequencing (scRNA-seq) dataset and TCGA dataset of HCC samples and discovered differentially expressed LMRGs between primary and metastatic HCC patients. By using the least absolute selection and shrinkage operator (LASSO) regression machine learning algorithm, we constructed a risk prognosis model with six LMRGs (AKR1C1, CYP27A1, CYP2C9, GLB1, HMGCS2, and PLPP1). The risk prognosis model was further validated in an external cohort of ICGC. We also constructed a nomogram that could accurately predict overall survival in HCC patients based on cancer status and LMRGs. Further investigation of the association between the LMRG model and somatic tumor mutational burden (TMB), tumor immune infiltration, and biological function was performed. We found that the most frequent somatic mutations in the LMRG high-risk group were CTNNB1, TTN, TP53, ALB, MUC16, and PCLO. Moreover, naïve CD8+ T cells, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, hematopoietic stem cells, M2 macrophages, and plasmacytoid dendritic cells were significantly correlated with the LMRG high-risk group. Finally, gene set enrichment analysis showed that RNA degradation, spliceosome, and lysosome pathways were associated with the LMRG high-risk group. For the first time, we used scRNA-seq and bulk RNA-seq to construct an LMRG-related risk score model, which may provide insights into more effective treatment strategies for predictive, preventive, and personalized medicine of HCC patients.

In silico Screening and Validation of Achyranthes aspera as a Potential Inhibitor of BRAF and NRAS in Controlling Thyroid Cancer

BACKGROUND

Thyroid carcinoma (THCA) is one of the most prevalent endocrine tumors, accounting for 3.4% of all cancers diagnosed annually. Single Nucleotide Polymorphisms (SNPs) are the most prevalent genetic variation associated with thyroid cancer. Understanding thyroid cancer genetics will enhance diagnosis, prognosis, and treatment.

METHODS

This TCGA-based study analyzes thyroid cancer-associated highly mutated genes through highly robust in silico techniques. Pathway, gene expression, and survival studies were performed on the top 10 highly mutated genes (BRAF, NRAS, TG, TTN, HRAS, MUC16, ZFHX3, CSMD2, EIFIAX, SPTA1). Novel natural compounds from Achyranthes aspera Linn were discovered to target two highly mutated genes. The natural compounds and synthetic drugs used to treat thyroid cancer were subjected to comparative molecular docking against BRAF and NRAS targets. The ADME characteristics of Achyranthes aspera Linn compounds were also investigated.

RESULTS

The gene expression analysis revealed that the expression of ZFHX3, MCU16, EIF1AX, HRAS, and NRAS was up-regulated in tumor cells while BRAF, TTN, TG, CSMD2, and SPTA1 were down-regulated in tumor cells. In addition, the protein-protein interaction network demonstrated that HRAS, BRAF, NRAS, SPTA1, and TG proteins have strong interactions with each other as compared to other genes. The ADMET analysis shows that seven compounds have druglike properties. These compounds were further studied for molecular docking studies. The compounds MPHY012847, IMPHY005295, and IMPHY000939 show higher binding affinity with BRAF than pimasertib. In addition, IMPHY000939, IMPHY000303, IMPHY012847, and IMPHY005295 showed a better binding affinity with NRAS than Guanosine Triphosphate.

CONCLUSION

The outcomes of docking experiments conducted on BRAF and NRAS provide insight into natural compounds with pharmacological characteristics. These findings indicate that natural compounds derived from plants as a more promising cancer treatment option. Thus, the results of docking investigations conducted on BRAF and NRAS substantiate the conclusions that the molecule possesses the most suited drug-like qualities. Compared to other compounds, natural compounds are superior, and they are also druggable. This demonstrates that natural plant compounds can be an excellent source of potential anti-cancer agents. The preclinical research will pave the road for a possible anti-cancer agent.

Damage-associated molecular patterns and sensing receptors based molecular subtypes in malignant pleural mesothelioma and implications for immunotherapy

Objectives

Malignant pleural mesothelioma (MPM) is characterized as an incredibly aggressive form of cancer with a dismal diagnosis and a dearth of specific biomarkers and therapeutic options. For MPM patients, the effectiveness of immunotherapy may be influenced by damage-associated molecular pattern (DAMP)-induced immunogenic cell death (ICD).The objective of this work is to create a molecular profile associated with DAMPs to categorize MPM patients and predict their prognosis and response to immunotherapy.

Methods

The RNA-seq of 397 patients (263 patients with clinical data, 57.2% male, 73.0% over 60 yrs.) were gathered from eight public datasets as a training cohort to identify the DAMPs-associated subgroups of MPMs using K-means analysis. Three validation cohorts of patients or murine were established from TCGA and GEO databases. Comparisons were made across each subtype's immune status, gene mutations, survival prognosis, and predicted response to therapy.

Results

Based on the DAMPs gene expression, MPMs were categorized into two subtypes: the nuclear DAMPs subtype, which is classified by the upregulation of immune-suppressed pathways, and the inflammatory DAMPs subtype, which is distinguished by the enrichment of proinflammatory cytokine signaling. The inflammatory DAMPs subgroup had a better prognosis, while the nuclear DAMPs subgroup exhibited a worse outcome. In validation cohorts, the subtyping system was effectively verified. We further identified the genetic differences between the two DAMPs subtypes. It was projected that the inflammatory DAMPs subtype will respond to immunotherapy more favorably, suggesting that the developed clustering method may be implemented to predict the effectiveness of immunotherapy.

Conclusion

We constructed a subtyping model based on ICD-associated DAMPs in MPM, which might serve as a signature to gauge the outcomes of immune checkpoint blockades. Our research may aid in the development of innovative immunomodulators as well as the advancement of precision immunotherapy for MPM.

Construction of a TTN Mutation-Based Prognostic Model for Evaluating Immune Microenvironment, Cancer Stemness, and Outcomes of Colorectal Cancer Patients

Background

Colorectal cancer (CRC) is one of the commonest cancers worldwide. As conventional biomarkers cannot clearly define the heterogeneity of CRC, it is essential to establish novel prognostic models.

Methods

For the training set, data pertaining to mutations, gene expression profiles, and clinical parameters were obtained from the Cancer Genome Atlas. Consensus clustering analysis was used to identify the CRC immune subtypes. CIBERSORT was used to analyze the immune heterogeneity across different CRC subgroups. Least absolute shrinkage and selection operator regression was used to identify the genes for constructing the immune feature-based prognostic model and to determine their coefficients.

Result

A gene prognostic model was then constructed to predict patient outcomes; the model was then externally validated using data from the Gene Expression Omnibus. As a high-frequency somatic mutation, the titin (TTN) mutation has been identified as a risk factor for CRC. Our results demonstrated that TTN mutations have the potential to modulate the tumor microenvironment, converting it into the immunosuppressive type. In this study, we identified the immune subtypes of CRC. Based on the identified subtypes, 25 genes were selected for prognostic model construction; a prediction model was also constructed, and its prediction accuracy was tested using the validation dataset. The potential of the model in predicting immunotherapy responsiveness was then explored.

Conclusion

TTN-mutant and TTN-wild-type CRC demonstrated different microenvironment features and prognosis. Our model provides a robust immune-related gene prognostic tool and a series of gene signatures for evaluating the immune features, cancer stemness, and prognosis of CRC.

Reshaping tumor immune microenvironment by Epstein-Barr virus activation in the stroma of colorectal cancer

The formation of tumor immune microenvironment (TIM) is complicated and poorly understood. Little is known about the effect of a viral infection potentially inducing an additional immune response in the TIM. Here, we identify Epstein-Barr virus (EBV) expression in the TIM in colorectal cancer (CRC) tissue through EBV-encoded RNA in-situ hybridization and RNA sequencing data and investigate the effects of EBV on TIM composition and clinical outcomes. EBV was detected in tumor-infiltrating lymphocytes, but not in cancer cells. EBV positivity was associated with older age, male sex, and SMAD4 mutations. EBV-positive tumors were characterized by enrichment in chemokine/cytokine signaling pathways and altered immune cell composition, including plasma and CD4 T cells, as well as cancer cells intrinsically enriched pathways related to immune tolerance, leading to poor prognosis. In conclusion, we identified EBV expression in TIM and suggested its association with poor prognosis by altering the TIM in CRC.

Repulsion and attraction in searching: A hybrid algorithm based on gravitational kernel and vital few for cancer driver gene prediction

By taking a new perspective to combine a machine learning method with an evolutionary algorithm, a new hybrid algorithm is developed to predict cancer driver genes. Firstly, inspired by the search strategy with the capability of global search in evolutionary algorithms, a gravitational kernel is proposed to act on the full range of gene features. Constructed by fusing PPI and mutation features, the gravitational kernel is capable to produce repulsion effects. The candidate genes with greater mutation effects and PPI have higher similarity scores. According to repulsion, the similarity score of these promising genes is larger than ordinary genes, which is beneficial to search for these promising genes. Secondly, inspired by the idea of elite populations related to evolutionary algorithms, the concept of vital few is proposed. Targeted at a local scale, it acts on the candidate genes associated with vital few genes. Under attraction effect, these vital few driver genes attract those with similar mutational effects to them, which leads to greater similarity scores. Lastly, the model and parameters are optimized by using an evolutionary algorithm, so as to obtain the optimal model and parameters for cancer driver gene prediction. Herein, a comparison is performed with six other advanced methods of cancer driver gene prediction. According to the experimental results, the method proposed in this study outperforms these six state-of-the-art algorithms on the pan-oncogene dataset.

Whole-exome sequencing of epithelial ovarian carcinomas differing in resistance to platinum therapy

Exploration of the prognostic and predictive significance of exome variation in epithelial ovarian carcinoma patients, with TP53, Hippo, homologous recombination genes, and the SBS6 signature as the most interesting results.

Epithelial ovarian carcinoma (EOC) is highly fatal because of the risk of resistance to therapy and recurrence. We performed whole-exome sequencing of blood and tumor tissue pairs of 50 patients with surgically resected EOC. Compared with sensitive patients, platinum-resistant patients had a significantly higher somatic mutational rate in TP53 and lower in several genes from the Hippo pathway. We confirmed the pivotal role of somatic mutations in homologous recombination repair genes in platinum sensitivity and favorable prognosis of EOC patients. Implementing the germline homologous recombination repair profile significantly improved the prediction. In addition, distinct mutational signatures, for example, SBS6, and overall mutational load, somatic mutations in PABPC1, PABPC3, and TFAM co-segregated with the resistance status, high-grade serous carcinoma subtype, or overall survival of patients. We generated germline and somatic genetic landscapes of prognostically different subgroups of EOC patients for further follow-up studies focused on utilizing the observed associations in precision oncology.

A novel prognostic index of stomach adenocarcinoma based on immunogenomic landscape analysis and immunotherapy options

Stomach adenocarcinoma (STAD) is one of the most common malignant tumors worldwide. In this study, we attempted to construct a valid immune-associated gene prognostic index risk model that can predict the survival of patients with STAD and the efficacy of immune checkpoint inhibitors (ICIs) treatment. Transcriptome, clinical, and gene mutational data were obtained from the TCGA database. Immune-related genes were downloaded from the ImmPort and InnateDB databases. A total of 493 immune-related genes were identified to be enriched in functions associated with immune response, as well as in immune and tumor-related pathways. Further, 36 candidate genes related to the overall survival (OS) of STAD were obtained by weighted gene co-expression network analysis (WGCNA). Next, based on a Cox regression analysis, we constructed an immune-associated gene prognostic index (IAGPI) risk model based on eight genes, which was verified using the GEO STAD cohort. The patients were divided into two subsets according to their risk score. Patients in the low-risk group had better OS than those in the high-risk group. In the low-risk group, there were more CD8, activated memory CD4, and follicular helper T cells, and M1 macrophages, whereas monocytes, M2 macrophages, eosinophils, and neutrophils were more abundant in the high-risk group. The patients in the low-risk group were more sensitive to ICIs therapy. The IAGPI risk model can precisely predict the prognosis, reflect the tumor immune microenvironment, and predict the efficacy of ICIs therapy in patients with STAD.

Autophagy-related prognostic signature characterizes tumor microenvironment and predicts response to ferroptosis in gastric cancer

Background

Gastric cancer (GC) is an important disease and the fifth most common malignancy worldwide. Autophagy is an important process for the turnover of intracellular substances. Autophagy-related genes (ARGs) are crucial in cancer. Accumulating evidence indicates the clinicopathological significance of the tumor microenvironment (TME) in predicting prognosis and treatment efficacy.

Methods

Clinical and gene expression data of GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. A total of 22 genes with differences in expression and prognosis were screened from 232 ARGs. Three autophagy patterns were identified using an unsupervised clustering algorithm and scored using principal component analysis to predict the value of autophagy in the prognosis of GC patients. Finally, the relationship between autophagy and ferroptosis was validated in gastric cancer cells.

Results

The expression of ARGs showed obvious heterogeneity in GC patients. Three autophagy patterns were identified and used to predict the overall survival of GC patients. These three patterns were well-matched with the immunophenotype. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses showed that the biological functions of the three autophagy patterns were different. A scoring system was then set up to quantify the autophagy model and further evaluate the response of the patients to the immunotherapy. Patients with high autophagy scores had a more severe tumor mutation burden and better prognosis. High autophagy scores were accompanied by high microsatellite instability. Patients with high autophagy scores had significantly higher PD-L1 expression and increased survival. The experimental results confirmed that the expression of ferroptosis genes was positively correlated with the expression of autophagy genes in different autophagy clusters, and inhibition of autophagy dramatically reversed the decrease in ferroptotic cell death and lipid accumulation.

Conclusions

Autophagy patterns are involved in TME diversity and complexity. Autophagy score can be used as an independent prognostic biomarker in GC patients and to predict the effect of immunotherapy and ferroptosis-based therapy. This might benefit individualized treatment for GC.

Pan-cancer integrative histology-genomic analysis via multimodal deep learning

The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these data sources can be integrated to develop joint image-omic prognostic models. Additionally, identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We use multimodal deep learning to jointly examine pathology whole-slide images and molecular profile data from 14 cancer types. Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover prognostic features that correlate with poor and favorable outcomes. We present all analyses for morphological and molecular correlates of patient prognosis across the 14 cancer types at both a disease and a patient level in an interactive open-access database to allow for further exploration, biomarker discovery, and feature assessment.

Prognostic analysis of cuproptosis-related gene in triple-negative breast cancer

Background

Cuproptosis is a copper-dependent cell death mechanism that is associated with tumor progression, prognosis, and immune response. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of triple-negative breast cancer (TNBC) remains unclear.

Patients and methods

In total, 346 TNBC samples were collected from The Cancer Genome Atlas database and three Gene Expression Omnibus datasets, and were classified using R software packages. The relationships between the different subgroups and clinical pathological characteristics, immune infiltration characteristics, and mutation status of the TME were examined. Finally, a nomogram and calibration curve were constructed to predict patient survival probability to improve the clinical applicability of the CRG_score.

Results

We identified two CRG clusters with immune cell infiltration characteristics highly consistent with those of the immune-inflamed and immune-desert clusters. Furthermore, we demonstrated that the gene signature can be used to evaluate tumor immune cell infiltration, clinical features, and prognostic status. Low CRG_scores were characterized by high tumor mutation burden and immune activation, good survival probability, and more immunoreactivity to CTLA4, while high CRG_scores were characterized by the activation of stromal pathways and immunosuppression.

Conclusion

This study revealed the potential effects of CRGs on the TME, clinicopathological features, and prognosis of TNBC. The CRGs were closely associated with the tumor immunity of TNBC and are a potential tool for predicting patient prognosis. Our data provide new directions for the development of novel drugs in the future.

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.

Genomic Landscape, Clinical Features and Outcomes of Non-Small Cell Lung Cancer Patients Harboring BRAF Alterations of Distinct Functional Classes

Simple Summary

Non-small cell lung cancer (NSCLC) patients harboring BRAF non-V600 alterations constitute a heterogeneous and poorly studied population orphan of targeted therapies. We conducted a systematic review to detect all BRAF alterations of defined functional class across different cancer types. Then, we searched for NSCLC patients harboring these alterations in the cancer bioportal and in POPLAR and OAK trials using patient-level data, to investigate clinical and genomic differences associated with each BRAF functional class and the prognostic impact of BRAF non-V600 mutations. We found that NSCLC patients harboring distinct classes of BRAF alterations have different clinical characteristics, clinical features and genomic landscape. Moreover, BRAF non-V600 alterations were associated with a poor prognostic impact, apparently regardless of the treatment received. These peculiar features may suggest the use of tailored treatments according to each class of BRAF alteration.

Abstract

Background: In non-small cell lung cancer (NSCLC), BRAF class 1 alterations are effectively targeted by BRAF inhibitors. Conversely, targeted therapies have very low or absent activity in patients carrying class 2 and 3 alterations. The spectrum of BRAF alterations in NSCLC patients, and their accompanying clinical features, genomic landscape and treatment outcomes have been poorly reported. Patients and methods: We identified BRAF alterations of defined functional class across different tumors through a systematic review. Then, we selected NSCLC patients carrying BRAF alterations, according to the systematic review, in the cBioPortal (cBioPortal cohort) to collect and analyze clinical, biomolecular and survival data. Finally, we identified NSCLC patients carrying BRAF non-V600 mutations enrolled in POPLAR and OAK trials (POPLAR/OAK cohort), extracting clinical and survival data for survival analyses. Results: 100 different BRAF non-V600 alterations were identified through the systematic review. In the cBioPortal cohort (n = 139), patients harboring class 2 and 3 alterations were more frequently smokers and had higher tumor mutational burden compared to those carrying class 1 alterations. The spectrum of most frequently co-altered genes was significantly different between BRAF alterations classes, including SETD2, STK11, POM121L12, MUC16, KEAP1, TERT, TP53 and other genes. In the POPLAR/OAK cohort, patients carrying non-V600 BRAF alterations were characterized by poor prognosis compared to BRAF wild-type patients. Conclusions: Different classes of BRAF alterations confer distinctive clinical features, biomolecular signature and disease behavior to NSCLC patients. Non-V600 alterations are characterized by poor prognosis, but key gene co-alterations involved in cancer cell survival and immune pathways may suggest their potential sensitivity to tailored treatments.

Shared Genetic Risk Factors Between Cancer and Cardiovascular Diseases

Cancer and cardiovascular diseases (CVD) account for approximately 27.5 million deaths every year. While they share some common environmental risk factors, their shared genetic risk factors are not yet fully understood. The aim of the present study was to aggregate genetic risk factors associated with the comorbidity of cancer and CVDs. For this purpose, we: (1) created a catalog of genes associated with cancer and CVDs, (2) visualized retrieved data as a gene-disease network, and (3) performed a pathway enrichment analysis. We performed screening of PubMed database for literature reporting genetic risk factors in patients with both cancer and CVD. The gene-disease network was visualized using Cytoscape and the enrichment analysis was conducted using Enrichr software. We manually reviewed the 181 articles fitting the search criteria and included 13 articles in the study. Data visualization revealed a highly interconnected network containing a single subnetwork with 56 nodes and 146 edges. Genes in the network with the highest number of disease interactions were JAK2, TTN, TET2, and ATM. The pathway enrichment analysis revealed that genes included in the study were significantly enriched in DNA damage repair (DDR) pathways, such as homologous recombination. The role of DDR mechanisms in the development of CVDs has been studied in previously published research; however, additional functional studies are required to elucidate their contribution to the pathophysiology to CVDs.

A Novel Immune-Prognosis Index Predicts the Benefit of Lung Adenocarcinoma Patients

Background: Constructed an immune-prognosis index (IPI) and divided lung adenocarcinoma (LUAD) patients into different subgroups according to IPI score, describe the molecular and immune characteristics of patients between different IPI subgroups, and explore their response to immune checkpoint blockade (ICB) treatment.

Methods: Based on the transcriptome profile of LUAD patients in TCGA and immune gene sets from ImmPort and InnateDB, 15 hub immune genes were identified through correlation and Bayesian causal network analysis. Then, IPI was constructed with 5 immune genes by using COX regression analysis and verified with external datasets (GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222). Finally, the characteristics and the response to ICB treatment of LUAD patients between two different IPI subgroups were analyzed.

Results: IPI was constructed based on the expression of 5 genes, including A2M, ADRB1, ADRB2, VIPR1 and PTH1R. IPI-high LUAD patients have a better overall survival than IPI-low LUAD patients, consistent with the results in the GEO cohorts. The comprehensive results showed that patients in the IPI-high subgroup were exhibited characters as metabolism-related signaling pathways activation, lower TP53 and TTN mutation rate, more infiltrations of CD8 T cells, dendritic cells and macrophages M1, especially earned more benefit from ICB treatment. In contrast, patients in the IPI-low subgroup were exhibited characters as p53 signaling pathways activation, higher TP53 and TTN mutation rate, more infiltrations of resting memory CD4 T cells, macrophages M2, immune-suppressive response and less benefit from ICB treatment.

Conclusion: IPI is a potentially valuable prognostic evaluation method for LUAD, which works well in the benefit predicting of LUAD patients within ICB treatment.

Genetic Characteristics Associated With Drug Resistance in Lung Cancer and Colorectal Cancer Using Whole Exome Sequencing of Cell-Free DNA

Circulating cell-free DNA (cfDNA) can be used to characterize tumor genomes through next-generation sequencing (NGS)-based approaches. We aim to identify novel genetic alterations associated with drug resistance in lung cancer and colorectal cancer patients who were treated with EGFR-targeted therapy and cytotoxic chemotherapy through whole exome sequencing (WES) of cfDNA. A cohort of 18 lung cancer patients was treated with EGFR TKI or cytotoxic chemotherapy, and a cohort of 37 colorectal cancer patients was treated with EGFR monoclonal antibody or cytotoxic chemotherapy alone. Serum samples were drawn before and after development of drug resistance, and the genetic mutational profile was analyzed with WES data. For 110 paired cfDNA and matched germline DNA WES samples, mean coverage of 138x (range, 52–208.4x) and 47x (range, 30.5–125.1x) was achieved, respectively. After excluding synonymous variants, mutants identified in more than two patients at the time of acquired resistance were selected. Seven genes in lung cancer and 16 genes in colorectal cancer were found, namely, APC, TP53, KRAS, SMAD4, and EGFR. In addition, the GPR155 I357S mutation in lung cancer and ADAMTS20 S1597P and TTN R7415H mutations in colorectal cancer were frequently detected at the time of acquired resistance, indicating that these mutations have an important function in acquired resistance to chemotherapy. Our data suggest that novel genetic variants associated with drug resistance can be identified using cfDNA WES. Further validation is necessary, but these candidate genes are promising therapeutic targets for overcoming drug resistance in lung cancer and colorectal cancer.

Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer

The morbidity of bladder cancer (BLCA) is high and has gradually elevated in recent years. BLCA is also characterized by high recurrence and high invasiveness. Due to the drug resistance and lack of effective prognostic indicators, the prognosis of patients with BLCA is greatly affected. Iron metabolism is considered to be a pivot of tumor occurrence, progression, and tumor microenvironment (TME) in tumors, but there is little research in BLCA. Herein, we used univariate COX regression analysis to screen 95 prognosis-related iron metabolism-related genes (IMRGs) according to transcription RNA sequencing and prognosis information of the Cancer Genome Atlas (TCGA) database. TCGA-BLCA cohort was clustered into four distinct iron metabolism patterns (C1, C2, C3, and C4) by the non-negative matrix factorization (NMF) algorithm. Survival analysis showed that C1 and C3 patterns had a better prognosis. Gene set variant analysis (GSVA) revealed that C2 and C4 patterns were mostly enriched in carcinogenic and immune activation pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) also confirmed the level of immune cell infiltration in C2 and C4 patterns was significantly elevated. Moreover, the immune checkpoint genes in C2 and C4 patterns were observably overexpressed. Studies on somatic mutations showed that the tumor mutation burden (TMB) of C1 and C4 patterns was the lowest. Chemotherapy response assessment revealed that C2 pattern was the most sensitive to chemotherapy, while C3 pattern was the most insensitive. Then we established the IMRG prognosis signature (IMRGscore) by the least absolute shrinkage and selection operator (LASSO), including 13 IMRGs (TCIRG1, CTSE, ATP6V0A1, CYP2C8, RNF19A, CYP4Z1, YPEL5, PLOD1, BMP6, CAST, SCD, IFNG, and ASIC3). We confirmed IMRGscore could be utilized as an independent prognostic indicator. Therefore, validation and quantification of iron metabolism landscapes will help us comprehend the formation of the BLCA immunosuppressive microenvironment, guide the selection of chemotherapeutic drugs and immunotherapy, and predict the prognosis of patients.

Methylation- and homologous recombination deficiency-related mutant genes predict the prognosis of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a lung cancer subtype with poor prognosis. We investigated the prognostic value of methylation‐ and homologous recombination deficiency (HRD)‐associated gene signatures in LUAD.

Methods

Data on RNA sequencing, somatic mutations, and methylation were obtained from TCGA database. HRD scores were used to stratify patients with LUAD into high and low HRD groups and identify differentially mutated and expressed genes (DMEGs). Pearson correlation analysis between DMEGs and methylation yielded methylation‐associated DMEGs. Cox regression analysis was used to construct a prognostic model, and the distribution of clinical features in the high‐ and low‐risk groups was compared.

Results

Patients with different HRD scores showed different DNA mutation patterns. There were 272 differentially mutated genes and 6294 differentially expressed genes. Fifty‐seven DMEGs were obtained; the top 10 upregulated genes were COL11A1, EXO1, ASPM, COL12A1, COL2A1, COL3A1, COL5A2, DIAPH3, CAD, and SLC25A13, while the top 10 downregulated genes were C7, ERN2, DLC1, SCN7A, SMARCA2, CARD11, LAMA2, ITIH5, FRY, and EPHB6. Forty‐two DMEGs were negatively correlated with 259 methylation sites. Gene ontology and pathway enrichment analysis of the DMEGs revealed enrichment of loci involved in extracellular matrix‐related remodeling and signaling. Six out of the 42 methylation‐associated DMEGs were significantly associated with LUAD prognosis and included in the prognostic model. The model effectively stratified high‐ and low‐risk patients, with the high‐risk group having more patients with advanced stage disease.

Conclusion

We developed a novel prognostic model for LUAD based on methylation and HRD. Methylation‐associated DMEGs may function as biomarkers and therapeutic targets for LUAD. Further studies are needed to elucidate their roles in LUAD carcinogenesis.

A novel ferroptosis-related gene prognostic index for prognosis and response to immunotherapy in patients with prostate cancer

BACKGROUND

Prostate cancer (PCa) is among the leading causes of cancer death worldwide. Ferroptosis refers to an iron-dependent form of regulated cell death and is involved in prostate tumorigenesis. A few ferroptosis-related gene signatures have been developed to predict the prognosis for PCa patients. However, previous signatures were typically established based on biochemical recurrence-free survival, which has proven not to be a good surrogate for overall survival (OS). This study aimed to construct a novel ferroptosis-related gene prognostic index (FRGPI) to predict disease-free survival (DFS) and response to immunotherapy for PCa patients after radical prostatectomy.

METHODS

Gene expression and clinicopathological data on PCa patients were obtained from the TCGA database. Ferroptosis-related hub genes associated with DFS of PCa patients were identified by an in-depth bioinformatics analysis using a novel and comprehensive algorithm based on functional enrichment, consensus clustering, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network construction. The FRGPI was established on the basis of the genes selected using multivariate cox regression analysis and further validated in two additional PCa cohorts. Next, the clinicopathological, molecular, and immune profiles were characterized and compared between FRGPI-high and FRGPI-low subgroups. Finally, the predictive role of the FRGPI in response to immunotherapy was estimated using a metastatic urothelial cancer cohort treated with an anti-PD-L1 agent.

RESULTS

The FRGPI was constructed based on four genes (E2F1, CDC20, TYMS, and NUP85), and FRGPI-high patients had worse DFS than FRGPI-low patients. Multivariate cox regression analysis revealed that FRGPI could act as an independent prognostic factor for PCa patients after radical prostatectomy. A prognostic nomogram comprising the FRGPI and other clinicopathological parameters was established to predict the DFS for PCa patients quantitatively. In addition, comprehensive results demonstrated that high FRGPI scores showed a significantly positive correlation with worse clinicopathological features, higher mutation counts, increased frequency of copy number variations (CNVs), higher homologous recombination deficiency (HRD) and immune scores, higher mRNAsi, and more importantly, enhanced sensitivity to immunotherapy.

CONCLUSIONS

FRGPI is not only a promising and robust prognostic biomarker, but also a potential indicator of immunotherapeutic outcomes for PCa patients after radical prostatectomy.

De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of the notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies that were highly varied across patients, showing the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.

GDPLichi: a DNA Damage Repair-Related Gene Classifier for Predicting Lung Adenocarcinoma Immune Checkpoint Inhibitors Response

Lung cancer is one of the most common and mortal malignancies, usually with a poor prognosis in its advanced or recurrent stages. Recently, immune checkpoint inhibitors (ICIs) immunotherapy has revolutionized the treatment of human cancers including lung adenocarcinoma (LUAD), and significantly improved patients' prognoses. However, the prognostic and predictive outcomes differ because of tumor heterogeneity. Here, we present an effective method, GDPLichi (Genes of DNA damage repair to predict LUAD immune checkpoint inhibitors response), as the signature to predict the LUAD patient's response to the ICIs. GDPLichi utilized only 7 maker genes from 8 DDR pathways to construct the predictive model and classified LUAD patients into two subgroups: low- and high-risk groups. The high-risk group was featured by worse prognosis and decreased B cells, CD8+ T cells, CD8+ central memory T cells, hematopoietic stem cells (HSC), myeloid dendritic cells (MDC), and immune scores as compared to the low-risk group. However, our research also suggests that the high-risk group was more sensitive to ICIs, which might be explained by increased TMB, neoantigen, immune checkpoint molecules, and immune suppression genes' expression, but lower TIDE score as compared to the low-risk group. This conclusion was verified in three other LUAD cohort datasets (GSE30219, GSE31210, GSE50081).

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.

M6A Classification Combined With Tumor Microenvironment Immune Characteristics Analysis of Bladder Cancer

Background

Studies have shown that N6-methyl adenosine (m6A) plays an important role in cancer progression; however, the underlying mechanism of m6A modification in tumor microenvironment (TME) cell infiltration of bladder cancer remains unclear. This study aimed to investigate the role of m6A modification in TME cell infiltration of bladder cancer.

Methods

The RNA expression profile and clinical data of bladder cancer were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. We assessed the m6A modification patterns of 664 bladder cancer samples based on 20 m6A regulators through unsupervised clustering analysis and systematically linked m6A modification patterns to TME cell infiltration characteristics. Gene ontology and gene set variation analyses were conducted to analyze the underlying mechanism based on the assessment of m6A methylation regulators. Principal component analysis was used to construct the m6A score to quantify m6A modification patterns of bladder cancer.

Results

The genetic and expression alterations in m6A regulators were highly heterogeneous between normal and bladder tissues. Three m6A modification patterns were identified. The cell infiltration characteristics were highly consistent with the three immune phenotypes, including immune rejection, immune inflammation, and immune desert. The biological functions of three m6A modification patterns were different. Cox regression analyses revealed that the m6A score was an independent signature with patient prognosis (HR = 1.198, 95% CI: 1.031-1.390). Patients with a low-m6A score were characterized by increased tumor mutation burden, PD-L1 expression, and poorer survival. Patients in the low-m6A score group also showed significant immune responses and clinical benefits in the CTLA-4 immunotherapy cohort (p =0.0069).

Conclusions

The m6A methylation modification was related to the formation of TME heterogeneity and complexity. Assessing the m6A modification pattern of individual bladder cancer will improve the understanding of TME infiltration characteristics.

Identification of Mutation Landscape and Immune Cell Component for Liver Hepatocellular Carcinoma Highlights Potential Therapeutic Targets and Prognostic Markers

Liver hepatocellular carcinoma (LIHC) is a primary malignancy, and there is a lack of effective treatment for advanced patients. Although numerous studies exist to reveal the carcinogenic mechanism of LIHC, few studies have integrated multi-omics data to systematically analyze pathogenesis and reveal potential therapeutic targets. Here, we integrated genomic variation data and RNA-seq profiles obtained by high-throughput sequencing to define high- and low-genomic instability samples. The mutational landscape was reported, and the advanced patients of LIHC were characterized by high-genomic instability. We found that the tumor microenvironment underwent metabolic reprograming driven by mutations accumulate to satisfy tumor proliferation and invasion. Further, the co-expression network identifies three mutant long non-coding RNAs as potential therapeutic targets, which can promote tumor progression by participating in specific carcinogenic mechanisms. Then, five potential prognostic markers (RP11-502I4.3, SPINK5, CHRM3, SLC5A12, and RP11-467L13.7) were identified by examining the association of genes and patient survival. By characterizing the immune landscape of LIHC, loss of immunogenicity was revealed as a key factor of immune checkpoint suppression. Macrophages were found to be significantly associated with patient risk scores, and high levels of macrophages accelerated patient mortality. In summary, the mutation-driven mechanism and immune landscape of LIHC revealed by this study will serve precision medicine.

Integrating Genetic and Transcriptomic Data to Reveal Pathogenesis and Prognostic Markers of Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is one of the deadliest malignancies and mortality for PAAD have remained increasing under the conditions of substantial improvements in mortality for other major cancers. Although multiple of studies exists on PAAD, few studies have dissected the oncogenic mechanisms of PAAD based on genomic variation. In this study, we integrated somatic mutation data and gene expression profiles obtained by high-throughput sequencing to characterize the pathogenesis of PAAD. The mutation profile containing 182 samples with 25,470 somatic mutations was obtained from The Cancer Genome Atlas (TCGA). The mutation landscape was generated and somatic mutations in PAAD were found to have preference for mutation location. The combination of mutation matrix and gene expression profiles identified 31 driver genes that were closely associated with tumor cell invasion and apoptosis. Co-expression networks were constructed based on 461 genes significantly associated with driver genes and the hub gene FAM133A in the network was identified to be associated with tumor metastasis. Further, the cascade relationship of somatic mutation-Long non-coding RNA (lncRNA)-microRNA (miRNA) was constructed to reveal a new mechanism for the involvement of mutations in post-transcriptional regulation. We have also identified prognostic markers that are significantly associated with overall survival (OS) of PAAD patients and constructed a risk score model to identify patients’ survival risk. In summary, our study revealed the pathogenic mechanisms and prognostic markers of PAAD providing theoretical support for the development of precision medicine.

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.

TP53 and CDKN2A mutations in patients with early-stage lung squamous cell carcinoma: an analysis of the correlations and prognostic outcomes

Background

Lung squamous cell carcinoma (LUSC) is characterized by frequent mutations of tumor protein p53 (TP53) and cyclin dependent kinase inhibitor 2A (CDKN2A). However, to date, the impact of TP53/CDKN2A status on the clinical outcome of patients with early-stage LUSC is unclear.

Methods

Tissue samples from 16 early-stage, surgically resected LUSCs were analyzed by next-generation sequencing (NGS). Information regarding TP53 and CDKN2A alterations and patient survival time was downloaded from The Cancer Genome Atlas (TCGA) database. The associations between TP53 and CDKN2A status and tumor characteristics, outcomes including overall survival (OS) and disease-free survival (DFS), and mutation counts were investigated.

Results

TP53 and CDKN2A exhibited a high frequency of somatic mutations in early-stage LUSC in our center. Data for 1,176 samples were collected from TCGA. CDKN2A mutation status was associated with TP53 mutation status (P=0.040). TP53 mutation was a favorable prognostic factor for early-stage LUSC. The OS times of patients with wild-type and mutated TP53 were 28.94 and 60.48 months, respectively (P=0.002). In contrast, CDKN2A mutations were significantly associated with a shorter survival time in early-stage LUSC. The OS times for wild-type and mutated CDKN2A patients were 62.81 and 37.55 months, respectively (P=0.026). Patients with TP53 mutations had higher total mutation counts compared to patients with wild-type TP53. Furthermore, OS was significantly shorter in patients with a low mutation count compared to patients with a median or high mutation count.

Conclusions

Early-stage LUSC patients with TP53 mutations had a longer OS, while those with CDKN2A mutations had a shorter OS. Furthermore, patients with TP53 mutation/CDKN2A wild-type status had a longer OS. CDKN2A mutation is a vital indicator for prognostic assessment according to TP53 status. The prolonged survival of patients with TP53 mutations may be due to their high mutation counts. Larger datasets are required to validate these observations.

Multi-omic analyses of hepatocellular carcinoma to determine immunological characteristics and key nodes in gene-expression network

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide, but effective immunotherapy is still limited for those affected. Therefore, there is an urgent need to explore the specific mechanisms governing tumor immunity to improve the survival rate for those diagnosed with HCC. In the present study, we performed a new immune stratification of HCC samples into two subclasses (A and B) from The Cancer Genome Atlas and the International Cancer Genome Consortium databases, and comprehensive multi-omic analyses of major histocompatibility complex genes, gene copy-number variations, somatic mutations, DNA methylation, and non-coding RNAs. Subclass A was found to have a higher survival rate compared with subclass B, and there were significant immunological differences between the two clusters. Based on these differences, we identified DRD1 and MYCN as key hub genes in the immune-phenotype gene expression regulatory network. These results provide novel ideas and evidence for HCC regulatory mechanisms that may improve immunotherapy for this cancer.