Construction and verification of a histone deacetylases-related prognostic signature model for colon cancer

Histone deacetylases (HDACs) contribute significantly to the initiation, progression, and prognosis of colorectal adenocarcinoma (COAD). Additionally, HDACs regulate the tumor microenvironment, immune escape, and tumor stem cells, and are closely linked to COAD prognosis. We developed a prognostic model for COAD that incorporates HDACs to evaluate their specific roles. The COAD dataset containing clinical and mutation data was collected using the TCGA and GEO databases to obtain genes associated with HDAC. LASSO analysis and univariate and multivariate Cox regression analysis were used to determine the presence of prognostic genes. Multivariate Cox analysis was also used to determine risk scores for HDAC-related features. Furthermore, genomic alterations, immune infiltration, and drug response were compared between high- and low-risk groups. Cellular experiments validated the potential regulatory role of BRD3 on COAD proliferation, migration, and apoptosis. The median risk scores, calculated based on the characteristics, demonstrated a more significant prognostic improvement in patients in the low-risk group. Furthermore, HDAC-related features were identified as important independent prognostic factors for patients with COAD. Additionally, genomic mutation status, immune infiltration, and function, as well as response to immunotherapy and chemotherapy, were found to be associated with risk scores. Subgroup analyses indicate that anti-PD-1 therapy may be beneficial for patients in the low-risk group. Additionally, a decrease in risk score was associated with a decrease in immune infiltration. Finally, HCT116 and HT29 cells exhibited inhibition of BRD3 gene proliferation and migration, as well as promotion of apoptosis. In patients with COAD, HDAC-related characteristics may be useful in predicting survival and selecting treatment.

Identification of a prognosis-related gene signature and ceRNA regulatory networks in lung adenocarcinoma

The ceRNA network, consisting of both noncoding RNA and protein-coding RNA, governs the occurrence, progression, metastasis, and infiltration of lung adenocarcinoma. Signatures comprising multiple genes can effectively determine survival stratification and prognosis of patients with lung adenocarcinoma. To explore the mechanisms of lung adenocarcinoma progression and identify potential biological targets, we carried out systematic bioinformatics analyses of the genetic profiles of lung adenocarcinoma, such as weighted gene co-expression network analysis (WGCNA), differential expression (DE) assessment, univariate and multivariate Cox proportional hazard regression models, ceRNA modulatory networks generated using the ENCORI and miRcode databases, nomogram models, ROC curve assessment, and Kaplan-Meier survival curve analysis. The ceRNA network encompassed 37 nodes, comprising 12 mRNAs, 22 lncRNAs, and three miRNAs. Simultaneously, we performed integration analysis using the 12 genes from the ceRNA network. Our findings revealed that the signature established by these 12 genes serves as an adverse element in lung adenocarcinoma, contributing to unfavorable patient prognosis. To ensure the credibility of our results, we used in vitro experiments for further verification. In conclusion, our study delved into the potential mechanisms underlying lung adenocarcinoma via the ceRNA regulatory network, specifically focusing on the PIF1 and has-miR-125a-5p axis. Additionally, a signature comprising 12 genes was identified as a biomarker related to the prognosis of lung adenocarcinoma.

Identification of immune-related signature with prognosis in children with stage 4 and 4S neuroblastoma

OBJECTIVE

Spontaneous regression of tumors is an attractive phenomenon that most commonly occurs in stage 4S neuroblastoma (NB). However, the mechanism underlying this phenomenon remains unclear.

METHODS

Datasets correlated with NB were downloaded from online public databases, the differentially expressed genes (DEGs) between stage 4 and 4S associated with immunity were identified, and functional enrichment analysis was utilized to explore the potential functions and signaling pathways of these DEGs. In addition, based on these DEGs, a prognostic signature was constructed and validated, and differences in immune cell infiltration were analyzed.

RESULTS

A total of 13 DEGs were finally identified, and functional enrichment analysis revealed that these DEGs were primarily enriched in the positive regulation of neuron differentiation and TGF-β signaling pathway. The signature successfully stratifies patients into two risk score groups and performs well in judging prognosis and predicting overall survival time. In addition, the prognostic value of the risk score calculated by the signature was independent of clinical factors. The results of immune cell infiltration showed that patients with a high infiltration of resting CD4 + memory T cells had a better prognosis, while plasma cells had a worse prognosis.

CONCLUSION

The results of the functional enrichment analysis of these identified DEGs suggested that these DEGs may be related to spontaneous regression of NB. In addition, the prognostic signature has the potential to create new risk stratification in patients with NB.

Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer

Background

One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC.

Methods

In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The "ConsensusClusterPlus" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors.

Results

We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.

Identification of molecular subtypes and a prognostic signature based on m6A/m5C/m1A-related genes in lung adenocarcinoma

Lung cancer, specifically the histological subtype lung adenocarcinoma (LUAD), has the highest global occurrence and fatality rate. Extensive research has indicated that RNA alterations encompassing m6A, m5C, and m1A contribute actively to tumorigenesis, drug resistance, and immunotherapy responses in LUAD. Nevertheless, the absence of a dependable predictive model based on m6A/m5C/m1A-associated genes hinders accurately predicting the prognosis of patients diagnosed with LUAD. In this study, we collected patient data from The Cancer Genome Atlas (TCGA) and identified genes related to m6A/m5C/m1A modifications using the GeneCards database. The "ConsensusClusterPlus" R package was used to produce molecular subtypes by utilizing genes relevant to m6A/m5C/m1A identified through differential expression and univariate Cox analyses. An independent prognostic factor was identified by constructing a prognostic signature comprising six genes (SNHG12, PABPC1, IGF2BP1, FOXM1, CBFA2T3, and CASC8). Poor overall survival and elevated expression of human leukocyte antigens and immune checkpoints were correlated with higher risk scores. We examined the associations between the sets of genes regulated by m6A/m5C/m1A and the risk model, as well as the immune cell infiltration, using algorithms such as ESTIMATE, CIBERSORT, TIMER, ssGSEA, and exclusion (TIDE). Moreover, we compared tumor stemness indices (TSIs) by considering the molecular subtypes related to m6A/m5C/m1A and risk signatures. Analyses were performed based on the risk signature, including stratification, somatic mutation analysis, nomogram construction, chemotherapeutic response prediction, and small-molecule drug prediction. In summary, we developed a prognostic signature consisting of six genes that have the potential for prognostication in patients with LUAD and the design of personalized treatments that could provide new versions of personalized management for these patients.

Predicting drug response of small cell lung cancer cell lines based on enrichment analysis of complex gene signatures

Advances in the field of genomics and transcriptomics have enabled researchers to identify gene signatures related to development and treatment of Small Cell Lung Cancer. In most cases, complex gene expression patterns are identified, comprising of genes with differential behavior. Most tools use single-genes as predictors of drug response, with only limited options for multi-gene use. Here we examine the potential of predicting drug response using these complex gene expression signatures by employing clustering and signal enrichment in Small Cell Lung Cancer. Our results demonstrate clustering genes from complex expression patterns helps identify differential activity of gene groups with alternate function which can then be used to predict drug response.

Identification and validation of a novel six-gene signature based on mucinous adenocarcinoma-related gene molecular typing in colorectal cancer

BACKGROUND AND OBJECTIVES

Colorectal mucinous adenocarcinoma (MAC) is a particular pathological type that has yet to be thoroughly studied. This study aims to investigate the characteristics of colorectal MAC-related genes in colorectal cancer (CRC), explore the role of MAC-related genes in accurately classifying CRC, and further construct a prognostic signature.

METHODS

CRC samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). MAC-related differentially expressed genes (DEGs) were analyzed in TCGA samples. Based on colorectal MAC-related genes, TCGA CRC samples were molecularly typed by the non-negative matrix factorization (NMF). According to the molecular subtype characteristics, the RiskScore signature was constructed through univariate Cox, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Clinical significance in CRC of the RiskScore signature was analyzed. A nomogram was further built based on the RiskScore signature.

RESULTS

From the colorectal MAC-related genes, three distinct molecular subtypes were identified. A RiskScore signature composed of six CRC subtype-related genes (CALB1, MMP1, HOXC6, ZIC2, SFTA2, and HYAL1) was constructed. Patients with high-RiskScores had the worse prognoses. RiskScores led to differences in gene mutation characteristics, antitumor drug sensitivity, and tumor microenvironment of CRC. A nomogram based on the signature was developed to predict the one-, three-, and five-year survival of CRC patients.

CONCLUSION

MAC-related genes were able to classify CRC. A RiskScore signature based on the colorectal MAC-related molecular subtype was constructed, which had important clinical significance for guiding the accurate stratification of CRC patients.

An in-silico analysis reveals further evidence of an aggressive subset of lung carcinoids sharing molecular features of high-grade neuroendocrine neoplasms

Little is known as to whether there may be any pathogenetic link between pulmonary carcinoids and neuroendocrine carcinomas (NECs). A gene signature we previously found to cluster pulmonary carcinoids, large cell neuroendocrine carcinoma (LCNEC) and small cell lung carcinoma (SCLC), and which encompassed MEN1, MYC, MYCL1, RICTOR, RB1, SDHA, SRC and TP53 mutations or copy number variations (CNVs), was used to reclassify an independent cohort of 54 neuroendocrine neoplasms (NENs) [31 typical carcinoids (TC), 11 atypical carcinoids (AC) and 12 SCLC], by means of transcriptome and mutation data. Unsupervised clustering analysis identified two histology-independent clusters, namely CL1 and CL2, where 17/42 (40.5%) carcinoids and all the SCLC samples fell into the latter. CL2 carcinoids affected survival adversely, were enriched in T to G transversions or T > C/C > T transitions in the context of specific mutational signatures, presented with at least 1.5-fold change (FC) increase of gene mutations including TSC2, SMARCA2, SMARCA4, ERBB4 and PTPRZ1, differed for gene expression and showed epigenetic changes in charge of MYC and MTORC1 pathways, cellular senescence, inflammation, high-plasticity cell state and immune system exhaustion. Similar results were also found in two other independent validation sets comprising 101 lung NENs (24 carcinoids, 21 SCLC and 56 LCNEC) and 30 carcinoids, respectively. We herein confirmed an unexpected sharing of molecular traits along the spectrum of lung NENs, with a subset of genomically distinct aggressive carcinoids sharing molecular features of high-grade neuroendocrine neoplasms.

PDGFRA exhibits potential as an indicator of angiogenesis within the tumor microenvironment and is up-regulated in BLCA

Bladder cancer (BLCA) is a common type of urogenital malignancy worldwide. The recurrence and metastasis of bladder cancer are closely related to angiogenesis, but the underlying mechanisms are unclear. In this study, we developed a method to predict survival outcomes among BLCA patients, which could be used to guide immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and identified angiogenesis-related genes from the GeneCards database. First, we used differential expression analysis and univariate Cox analysis to identify angiogenesis-related genes and used correlation analysis to generate molecular subtypes based on M2 macrophages. Next, we constructed a prognostic signature consisting of four genes (ECM1, EFEMP1, SLIT2, and PDGFRΑ), which was found to be an independent prognostic factor. Higher risk scores were associated with worse overall survival and higher expression of immune checkpoints. We also evaluated immune cell infiltration using the CIBERSORT and ssGSEA algorithms. Additionally, we performed stratification analyses, constructed a nomogram, and predicted chemotherapeutic responses based on the risk signature. Finally, we validated our findings by using qRT-PCR as well as IHC data to detect the expression levels of the four genes at mRNA and protein levels in BLCA patients and obtained results that were consistent with our predictions. Our study demonstrates the utility of a four-gene prognostic signature for prognostication in bladder cancer patients and designing personalized treatments, which could provide new avenues for personalized management of these patients.

Risk factors analysis and survival prediction model establishment of patients with lung adenocarcinoma based on different pyroptosis-related gene subtypes

BACKGROUND

Lung adenocarcinoma (LUAD) is a common cancer with a poor prognosis. Pyroptosis is an important process in the development and progression of LUAD. We analyzed the risk factors affecting the prognosis of patients and constructed a nomogram to predict the overall survival of patients based on different pyroptosis-related genes (PRGs) subtypes.

METHODS

The genomic data of LUAD were downloaded from the TCGA and GEO databases, and all data were filtered and divided into TCGA and GEO cohorts. The process of data analysis and visualization was performed via R software. The data were classified based on different PRGs subtypes using the K-means clustering method. Then, the differentially expressed genes were identified between two different subtypes, and risk factors analysis, survival analysis, functional enrichment analysis, and immune cells infiltration landscape analysis were conducted. The COX regression analysis was used to construct the prediction model.

RESULTS

Based on the PRGs of LUAD, the patients were divided into two subtypes. We found the survival probability of patients in subtype 1 is higher than that in subtype 2. The results of the logistics analysis showed that gene risk score was closely associated with the prognosis of LUAD patients. The results of GO analysis and KEGG analysis revealed important biological processes and signaling pathways involved in the differentially expressed proteins between the two subtypes. Then we constructed a prediction model of patients' prognosis based on 13 genes, including IL-1A, P2RX1, GSTM2, ESYT3, ZNF682, KCNF1, STK32A, HHIPL2, GDF10, NDC80, GSTA1, BCL2L10, and CCR2. This model was strongly related to the overall survival (OS) and also reflects the immune status in patients with LUAD.

CONCLUSION

In our study, we examined LUAD heterogeneity with reference to pyroptosis and found different prognoses between the two subtypes. And a novel prediction model was constructed to predict the OS of LUAD patients based on different PRGs signatures. The model has shown excellent predictive efficiency through validation.

Identification and validation of eight lysosomes-related genes signatures and correlation with immune cell infiltration in lung adenocarcinoma

Lung cancer is the leading cause of cancer-related death. Lysosomes are key degradative compartments that maintain protein homeostasis. In current study, we aimed to construct a lysosomes-related genes signature to predict the overall survival (OS) of patients with Lung Adenocarcinoma (LUAD). Differentially expressed lysosomes-related genes (DELYs) were analyzed using The Cancer Genome Atlas (TCGA-LUAD cohort) database. The prognostic risk signature was identified by Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression and multivariate Cox analysis. The predictive performance of the signature was assessed by Kaplan-Meier curves and Time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the potential molecular biological function and signaling pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) were applied to estimate the difference of tumor microenvironment (TME) between the different risk subtypes. An eight prognostic genes (ACAP3, ATP8B3, BTK, CAV2, CDK5R1, GRIA1, PCSK9, and PLA2G3) signature was identified and divided patients into high-risk and low-risk groups. The prognostic signature was an independent prognostic factor for OS (HR > 1, p < 0.001). The molecular function analysis suggested that the signature was significantly correlated with cancer-associated pathways, including angiogenesis, epithelial mesenchymal transition, mTOR signaling, myc-targets. The low-risk patients had higher immune cell infiltration levels than high-risk group. We also evaluated the response to chemotherapeutic, targeted therapy and immunotherapy in high- and low-risk patients with LUAD. Furthermore, we validated the expression of the eight gene expression in LUAD tissues and cell lines by qRT-PCR. LYSscore signature provide a new modality for the accurate diagnosis and targeted treatment of LUAD and will help expand researchers' understanding of new prognostic models.

A liquid biopsy signature of circulating extracellular vesicles-derived RNAs predicts response to first line chemotherapy in patients with metastatic colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most threatening tumors in the world, and chemotherapy remains dominant in the treatment of metastatic CRC (mCRC) patients. The purpose of this study was to develop a biomarker panel to predict the response of the first line chemotherapy in mCRC patients.

METHODS

Totally 190 mCRC patients treated with FOLFOX or XEOLX chemotherapy in 3 different institutions were included. We extracted the plasma extracellular vesicle (EV) RNA, performed RNA sequencing, constructed a model and generated a signature through shrinking the number of variables by the random forest algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort (n = 80). We validated it in an internal validation cohort (n = 62) and a prospective external validation cohort (n = 48).

RESULTS

We established a signature consisted of 22 EV RNAs which could identify responders, and the area under the receiver operating characteristic curve (AUC) values was 0.986, 0.821, and 0.816 in the training, internal validation, and external validation cohort respectively. The signature could also identify the progression-free survival (PFS) and overall survival (OS). Besides, we constructed a 7-gene signature which could predict tumor response to first-line oxaliplatin-containing chemotherapy and simultaneously resistance to second-line irinotecan-containing chemotherapy.

CONCLUSIONS

The study was first to develop a signature of EV-derived RNAs to predict the response of the first line chemotherapy in mCRC with high accuracy using a non-invasive approach, indicating that the signature could help to select the optimal regimen for mCRC patients.

A ubiquitin-proteasome system-related signature to predict prognosis, immune infiltration, and therapy efficacy for breast cancer

The ubiquitin-proteasome system (UPS) is an essential regulatory system for maintaining homeostasis, and its dysfunction may cause various diseases. The activity of proteasome and ubiquitin-conjugating enzymes has been found to be greatly increased in breast cancer (BC), indicating that the heterogeneity of UPS may be related to the progression of BC. Gene data was obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and performed in multiple algorithms to construct a UPS-related signature for BC. Patients in the UPS low-risk group had greater overall and recurrence-free survival probability than those in the UPS high-risk group. This signature was closely associated with functional enrichment. Some high metabolism-related pathways were more active in the UPS high-risk group. The UPS low-risk group had more abundant anti-tumor immune cells, while in the UPS high-risk group, immunosuppressive cells were dominant. More importantly, we found that the UPS low-risk group was more sensitive to immunotherapy, while the UPS high-risk group responded better to radiotherapy. Drug sensitivity analysis identified more effective chemotherapy drugs in different UPS-related risk groups. This UPS-related signature may serve as a novel biomarker and independent prognostic factor for BC. It can effectively predict prognosis, immune infiltration, and therapy efficacy, providing new strategies for individualized treatment.

Construction of an anoikis‒related prognostic signature to predict immunotherapeutic response and prognosis in hepatocellular carcinoma

PURPOSE

Anoikis resistance is an important inducer of tumor metastasis. The role of anoikis-related genes (ARGs) in hepatocellular carcinoma (HCC) remains unclear.

METHODS

A list of ARGs was obtained and regression analysis was employed to assemble an anoikis-related prognostic signature and risk score formula from mRNA data and clinical prognostic data downloaded from The Cancer Genome Atlas database. Quantitative real-time PCR (qRT-PCR) was performed on clinical samples to validate the selected ARGs expressions. Kaplan‒Meier curves, ROC curves, and Cox regression analyses were used to demonstrated the prognostic value of this signature. Biological functional enrichment analysis and immune infiltration analysis were utilized to analyze the differences in potential biological functions, immune cell infiltration, immune functions, and immunotherapeutic responses.

RESULTS

A prognostic signature based on 6 ARGs and corresponding prognostic nomogram were established. Our qRT-PCR results showed a higher expression of 6 ARGs in HCC tissues (p value < 0.05). Kaplan‒Meier curves, ROC curves, and Cox regression analyses demonstrated good prognostic value of the signature in HCC (p value < 0.05). Significant differences between the enriched biological functions and immune landscapes of patients in different risk groups were discovered (p value < 0.05). In addition, patients with higher risk scores possibly had poor therapeutic response to transhepatic arterial chemotherapy and embolization or sorafenib, but their responses to immunotherapy might be more effective.

CONCLUSION

A successful anoikis-related prognostic signature and corresponding clinical nomogram were established, which might facilitate better predictions of prognosis and therapeutic responses for HCC patients.

Recognition of the Subtypes Classification and Diagnostic Signature Based on RNA N6-Methyladenosine Regulators in Recurrent Spontaneous Abortion

Recurrent spontaneous abortion (RSA) is a common reproductive disease in female patients that seriously affects the quality of life of patients. N6-methyladenosine (m6A), as the most common modification, plays an important role in various biological behaviors; however, the relationship between m6A and RSA is still unknown. In the present study, we utilized RNA sequencing data and clinical information of RSA patients and normal women in the GEO database to identify the expression profiles of m6A regulators in RSA. Based on the m6A regulators' expression profiles, we constructed a random forest model consisting of 4 genes to predict the prevalence of RSA patients, including FMR1, METTL14, LRPPRC, and RBMX. The predictive performance of the nomogram was constructed and validated. Not only that, consensus clustering was performed to divide RSA patients into 3 clusters based on the expression of m6A regulators and calculated the m6A scores and immune infiltration of patients in different clusters. It was found that the TH1-type immune response was dominant in the A cluster, the B-type immune activity was poor, and the C cluster was the strongest. In addition, on the basis of m6A typing, we further used the differentially expressed genes between clusters to perform consensus clustering verification, and the results were consistent with the previous findings. In conclusion, the m6A regulators played an indispensable role in the occurrence of RSA, and the m6A-based typing could effectively identify the immune characteristics of different RSA patients to a certain extent, providing a new direction and strategy for the diagnosis and treatment of RSA patients.

Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer

BACKGROUND

Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients.

METHODS

We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated.

RESULTS

The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful.

CONCLUSIONS

We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.

A novel necroptosis signature for predicting survival in lung adenocarcinoma

BACKGROUND

To explore the necroptosis-related genes (NRGs) signature and its predictive values in lung adenocarcinoma (LUAD).

METHODS

The training cohort consisted of tumor samples from The Cancer Genome Atlas, and the validation set comprised data from the Gene Expression Omnibus. Univariate and multivariate Cox regression analyses were applied to identify the prognostic NRG signature as an independent molecular indicator. Correlation analysis was used for the association assessment between the NRG signature and immune checkpoint molecules.

RESULTS

NRGs involved in necroptosis and immune NOD-like receptor signaling. The NRG signature based on eight NRGs can divide tumors into high-risk and low-risk groups, which was significantly associated with worse survival. Multivariate Cox regression analysis showed that this NRG signature remained an independent prognostic indicator. Stratification analyses demonstrated that this NRG signature was still effective for predicting survival in each stratum of age, gender, and tumor stage. The ROC curve showed a good predictive ability using the NRG signature in the validation cohort (AUC = 0.81). The NRG signature was related to immune checkpoint molecules PD - 1, PD-L1, and PD-L2.

CONCLUSIONS

The NRG signature could be a novel predictor of the prognosis and may become a potential therapeutic target in LUAD.

Leveraging a disulfidptosis/ferroptosis-based signature to predict the prognosis of lung adenocarcinoma

BACKGROUND

Disulfidptosis and Ferroptosis are two novel forms of cell death. Although their mechanisms differ, research has shown that there is a relationship between the two. Investigating the connection between these two forms of cell death can further deepen our understanding of the development and progression of cancer, and provide better prediction models for accurate prognosis.

METHODS

In this study, RNA sequencing (RNA-seq) data, clinical data, single nucleotide polymorphism (SNP) data, and single-cell sequencing data were obtained from public databases. We used weighted gene co-expression network analysis (WGCNA) and unsupervised clustering to identify new Disulfidptosis/Ferroptosis-Related Genes (DFRG), and constructed a LASSO COX prognosis model that was externally validated. To further explore this novel signature, pathway and function analysis was performed, and differences in gene mutation frequency between high- and low-risk groups were studied. Importantly, we also conducted research on immune checkpoint, immune cell infiltration levels and immune resistance indicators, in addition to analyzing real clinical immunotherapy data.

RESULTS

We have identified four optimal disulfidptosis/ferroptosis-related genes (ODFRGs) that are differentially expressed and associated with the prognosis of Lung Adenocarcinoma (LUAD). These genes include GMPR, MCFD2, MRPL13, and SALL2. Based on these ODFRGs, we constructed a robust prognostic model in this study, and the high-risk group showed significantly lower overall survival (OS) compared to the low-risk group. Furthermore, this model can also predict the immunotherapy outcomes of LUAD patients to some extent.

Does cognitive decline influence signing?

OBJECTIVE

The study explored the change in handwritten signature in neurodegenerative diseases by using of a rater-based approach.

METHODS

Four independent observers were required to compare a pair of signatures (on average, 5 years elapsed between the two signatures) made by 103 patients (mean age 72 years) with Alzheimer's disease (AD) or frontotemporal dementia (FTD) and by 31 healthy participants (HC; mean age 73 years), judging their change according to a 0-1 rating scale (0 = similar or 1 = different). If a signature change was detected, the rater had also to report which signature features (spatial layout, omitted/added/switched letters or names, shape of letter, pen-flow) changed on the same 0-1 scale. For the AD and FTD groups, one signature was collected prior to the diagnosis of dementia, the other subsequent.

RESULTS

A signature change was reported by raters in 36% of AD patients, 44% of FTD, and 17% of HC, with significant differences between both clinical groups and HC (vs. AD, p = .01; vs. FTD, p = .001). There was not a distinctive marker of the signature change (i.e., feature change) in patients with dementia. Moreover, the signature changes in neurological patients were unrelated to their clinical and demographic characteristics (age, sex, education, time elapsed between the two signatures, Mini-mental State Examination score).

CONCLUSION

The findings suggest a resistance of handwritten signature in neurodegenerative diseases and in physiological aging, also suggesting that the signature may be an unreliable indicator of the cognitive status in AD and FTD, at least if subjectively evaluated.

Metabolite signature in acute ischemic stroke thrombi: a systematic review

Metabolites are reliable biomarkers for many diseases. However, their role in acute ischemic stroke (AIS) pathogenesis is not well understood. In this systematic review we aim to evaluate the current literature on the presence of metabolites in thrombi retrieved by mechanical thrombectomy from AIS patients. Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guidelines, we searched OVID Medline, PubMed, OVID Embase, Scopus, and Web of Science until July 13, 2022. Metabolites lists were extracted, and pathway analysis was performed in MetaboAnalyst database. Four articles listing metabolites were included in this systematic review. D-Glucose, diacylglycerol, phytosphingosine, galabiosylceramide, glucosylceramide and 4-hydroxynonenal were reported to be associated with clots. Metabolomics data analysis showed that glycolysis, lactose, and sphingolipid metabolism pathways were enriched. In conclusion, results of the present study show that the thrombi niche has a glycolytic phenotype. Future studies should work to better understand the metabolic properties of AIS thrombi.

Leveraging a disulfidptosis-related signature to predict the prognosis and immunotherapy effectiveness of cutaneous melanoma based on machine learning

BACKGROUND

Disulfidptosis is a recently discovered programmed cell death pathway. However, the exact molecular mechanism of disulfidptosis in cutaneous melanoma remains unclear.

METHODS

In this study, clustering analysis was performed using data from public databases to construct a prognostic model, which was subsequently externally validated. The biological functions of the model genes were then investigated through various experimental techniques, including qRT-PCR, Western blotting, CCK-8 assay, wound healing assay, and Transwell assay.

RESULTS

We constructed a signature using cutaneous melanoma (CM) data, which accurately predicts the overall survival (OS) of patients. The predictive value of this signature for prognosis and immune therapy response was validated using multiple external datasets. High-risk CM subgroups may exhibit decreased survival rates, alterations in the tumor microenvironment (TME), and increased tumor mutation burden. We initially verified the expression levels of five optimum disulfidptosis-related genes (ODRGs) in normal tissues and CM. The expression levels of these genes were further confirmed in HaCaT cells and three melanoma cell lines using qPCR and protein blotting analysis. HLA-DQA1 emerged as the gene with the highest regression coefficient in our risk model, highlighting its role in CM. Mechanistically, HLA-DQA1 demonstrated the ability to suppress CM cell growth, proliferation, and migration.

CONCLUSION

In this study, a novel signature related to disulfidptosis was constructed, which accurately predicts the survival rate and treatment sensitivity of CM patients. Additionally, HLA-DQA1 is expected to be a feasible therapeutic target for effective clinical treatment of CM.

Identification of a basement membrane-related genes signature with immune correlation in bladder urothelial carcinoma and verification in vitro

BACKGROUND

Bladder urothelial carcinoma (BLCA) is the most common genitourinary cancer and the prognosis of patients is often poor. However, studies of basement membrane-related genes (BM-related genes) in BLCA are less reported. Therefore, we established a BM-related genes signature to explore their functional and prognostic value in BLCA.

METHODS

In this study, a BM-related genes signature was constructed by LASSO-Cox regression analysis, and then a series of bioinformatics methods was used to assess the accuracy and validity of the signature. We constructed a nomogram for clinical application and also screened for possible therapeutic drugs. To investigate the functions and pathways affected by BM-related genes in BLCA, we performed functional enrichment analyses. In addition, we analyzed the immune cell infiltration landscape and immune checkpoint-related genes in the high and low-risk groups. Finally, we confirmed the prognostic value of BM-related genes in BLCA in vitro.

RESULTS

Combining multiple bioinformatics approaches, we identified a seven-gene signature. The accuracy and validity of this signature in predicting BLCA patients were confirmed by the test cohort. In addition, the risk score was strongly correlated with prognosis, immune checkpoint genes, drug sensitivity, and immune cell infiltration landscape. The risk score is an independent prognostic factor for BLCA patients. Further experiments revealed that all seven signature genes were differentially expressed between BLCA cell lines and normal bladder cells. Finally, overexpression of LAMA2 inhibited the migration and invasion ability of BLCA cell lines.

CONCLUSIONS

In summary, the BM-related genes signature was able to predict the prognosis of BLCA patients accurately, indicating that the BM-related genes possess great clinical value in the diagnosis and treatment of BLCA. Moreover, LAMA2 could be a potential therapeutic target, which provides new insights into the application of the BM-related genes in BLCA patients.

Mutational signatures and their association with survival and gene expression in urological carcinomas

Different sources of mutagenesis cause consistently identifiable patterns of mutations and mutational signatures that mirror the various carcinogenetic processes. We used publicly available data from the Cancer Genome Atlas to evaluate the associations between the activity of the mutational signatures and various survival endpoints in six types of urological cancers after adjusting for established prognostic factors. The predictive power of the signatures was evaluated with dynamic area under curve models. In addition, links between mutational signature activities and differences in gene expression patterns were analysed. APOBEC-related signature SBS2 was associated with improved overall survival (OS) and disease-specific survival (DSS) in bladder carcinomas in the multivariate analysis, while clock-like signature SBS1 predicted shortened DSS and progression-free interval (PFI) in clear cell renal cell carcinomas (ccRCC). In papillary renal cell carcinomas (pRCC), SBS45 was a predictor of improved outcomes, and APOBEC-related SBS13 was a predictor of worse outcomes. Gene expression analyses revealed various enriched pathways between the low- and high-signature groups. Interestingly, in both the ccRCC and pRCC cohorts, the genes of several members of the melanoma antigen (MAGE) family were highly upregulated in the signatures, which predicted poor outcomes, and downregulated in signatures, which were associated with improved survival. To summarize, SBS signatures provide substantial prognostic value compared with just the traditional prognostic factors in certain cancer types. APOBEC-related SBS2 and SBS13 seem to provide robust prognostic information for particular urological cancers, maybe driven by the expression of specific groups of genes, including the MAGE gene family.

Prognostic modelling of colorectal cancer based on oxidative stress-related genes

BACKGROUND

Colon cancer is one of the most prevalent cancers of the digestive tract. There is mounting evidence that genes associated with oxidative stress might affect the tumour immune microenvironment during tumour growth, maintenance, and treatment response. However, how oxidative stress-related genes affect prognostic importance, tumour microenvironment features, and treatment outcomes in colon cancer patients has not been fully elucidated.

METHODS

The Cancer Genome Atlas (TCGA) dataset was used to construct a signature model and nomogram using step and Cox regression approaches to investigate how gene expression affected immunological responses to colon cancer, including the degree of immune infiltration, MSI, and drug sensitivity.

RESULTS AND CONCLUSIONS

The nomogram and the signature model had strong prognostic potential for colon cancer, with gene expression highly correlated with multiple immune cells. The first signature model and nomogram including oxidative stress-related genes were constructed for use in clinical decision-making. In addition, SRD5A1, GSR, TXN, TRAF2 and TRAP1 were identified as potential biomarkers for colon cancer diagnosis and indicators for immunotherapy.

Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients

BACKGROUND

Glioma is the most common and devastating form of malignant brain tumor, with a poor prognosis. Extracellular matrix (ECM) organization is a crucial determinant of glioma invasion and progression. However, the clinical significance of ECM organization in glioma patients remains unclear.

OBJECTIVE

To evaluate the prognostic value of ECM organization-related genes in glioma patients and identify potential therapeutic targets.

METHODS

Bulk RNA-sequencing and corresponding clinical data for patients with glioma were downloaded from TCGA and GEO databases. Differentially expressed ECM organization genes were identified, and an ECM organization-related gene prognostic model was then generated. Furthermore, the prognostic model has validated in the Chinese Glioma Genome Atlas (CGGA) dataset. The role of TIMP1 in glioma cells by using various functional assays revealed their underlying mechanism in vitro.

RESULTS

We identified and validated a nine-gene signature (TIMP1, SERPINE1, PTX3, POSTN, PLOD3, PDPN, LOXL1, ITGA2, and COL8A1) related to ECM organization as a robust prognostic biomarker for glioma. Time-dependent ROC curve analysis confirmed the specificity and sensitivity of the signature. The signature was closely related to an immunosuppressive phenotype, and its combination with immune checkpoints served as a good predictor for patients' clinical outcomes. Notably, single-cell RNA sequencing analysis revealed high expression of TIMP1 in astrocytes and oligodendrocyte progenitor cells in glioma patients. Last, we show that TIMP1 regulates glioma cell growth and invasion via the AKT/GSK3β signaling pathway.

CONCLUSION

This study provides promising insights into predicting glioma prognosis and identifying a potential therapeutic target in TIMP1.

Identification of a novel stemness-related signature with appealing implications in discriminating the prognosis and therapy responses for prostate cancer

PURPOSE

Cancer stemness represents the tumor-initiation and self-renewal potentials of cancer stem cells. It is involved in prostate cancer progression and resistance to therapy. Herein, we aimed to unveil the stemness features, establish a novel prognostic model, and identify potential therapeutic targets.

METHODS

26 stemness-related signatures were obtained from StemChecker. The expression profiles and clinical traits of TCGA-PRAD were obtained from TCGA and cBioPortal, respectively. GSE5446 and GSE70769 cohorts were acquired from GEO. PRAD_MSKCC cohort was also retrieved via the cBioPortal. The consensus clustering method was used for stemness subclusters classification. WGCNA was used to identify hub genes related to the stemness subcluster. The most important feature was explored in vitro.

RESULTS

Prostate cancer patients of TCGA-PRAD were divided into two subclusters (C1 and C2) based on the enrichment scores of the 26 stemness-related signatures. C1 was characterized by decreased survival, rich infiltrations of M0 macrophages and regulatory T cells, minimum sensitivity to chemotherapy, and a low response to immunotherapy. Hub genes of the red module with the highest correlation with C1 were subsequently identified by WGCNA and subjected to stemness-related risk model construction based on the machine-learning framework. Prostate cancer patients with high stemness scores had unfavorable prognosis, immunosuppressive tumor microenvironment, minimum sensitivity to chemotherapy, and a low response to immunotherapy. MXD3, the most important factor of the model, can regulate the stemness traits of prostate cancer cells.

CONCLUSIONS

Our study depicted the stemness landscapes of prostate cancer and characterized two subclusters with diverse prognoses and tumor immune microenvironments. A stemness-risk signature was developed and demonstrated prospective implications in predicting prognosis and precision medicine.

Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer

Background

RNA-binding proteins (RBPs) are closely related to tumors, but little is known about the mechanism of RBPs in tumorigenesis and progression of gastric cancer (GC). As genes do not usually act alone in the pathway deregulation, gene pair combinations are more likely to become stable and accurate biomarkers. The purpose of our research is to establish a novel signature based on RBP gene pairs to predict the prognosis of gastric cancer patients.

Methods

We downloaded genetic and clinical information from the TCGA and GEO database. TCGA and GSE13911 were used for screening differentially expressed genes (DEGs). The RBP genes were gathered from previous studies and employed to screen out DE-RBP genes after intersecting with DEGs. Samples were classified according to the relative expression of each pair of DE-RBP genes. The univariate Cox regression analysis and random forest were used to identify hub gene pairs to construct signature for predicting the prognosis of gastric cancer. Time-dependent ROC curves and KM survival curves were performed to evaluate the signature. GSEA was performed in TCGA training cohort and GSE62254 testing cohort to analyze enrichment pathways. Finally, the influence of these gene pairs on the prognosis of GC patients was further elucidated respectively through the combination of high and low expression of the two genes in each hub gene pair.

Results

We screened out 6 hub RBP gene pairs (COL5A2/FEN1, POP1/GFRA1, EXO1/PLEKHS1, SLC39A10/CHI3L1, MMP7/PPP1R1 B and SLC5A6/BYSL) to predict the prognosis of patients with gastric cancer. Using the optimal cut-off value to divide patients into high-risk and low-risk groups in the training and testing cohort, we found that the overall survival (OS) of the low-risk group was higher than that of the high-risk group (P < 0.05). The area under the ROC curves for 1, 3, and 5 years were (0.659, 0.744, 0.758) and (0.624, 0.650, 0.653) in two cohorts. Univariate and multivariate Cox regression analysis showed that 6 RBP gene pairs signature were independent prognostic factors for gastric cancer (P < 0.05). In addition, the prognostic survival analysis showed that COL5A2-high/FEN1-low, POP1-low/GFRA1-high, EXO1-low/PLEKHS1-low,SLC39A10-high/CHI3L1-low, MMP7-high/PPP1R1 B-low, SLC5A6-low/BYSL-low had worse OS (P < 0.05). And the gene correlation analysis showed that there was no obvious correlation between the genes in each gene pairs except SLC5A6/BYSL and POP1/GFRA1. Finally, GSEA analysis showed that the high-risk group was enriched in tumor migration, invasion and growth-related pathways.

Conclusion

Our study identified a novel 6 RBP gene pairs signature to predict the prognosis of gastric cancer patients and provide potential targets for clinical gene therapy.

Comprehensive analysis of candidate signatures of long non-coding RNA LINC01116 and related protein-coding genes in patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a long-term malignancy that causes high morbidities and mortalities worldwide. Notably, long non-coding RNAs (LncRNAs) have been identified as candidate targets for malignancy treatments.

METHODS

LncRNA LINC01116 and its Pearson-correlated genes (PCGs) were identified and analyzed in HCC patients. The diagnostic and prognostic value of the lncRNA was evaluated using data from The Cancer Genome Atlas (TCGA). Further, we explored the target drugs of LINC01116 for clinical application. Relationships between immune infiltration and PCGs, methylation and PCGs were explored. The diagnostic potentials were then validated by Oncomine cohorts.

RESULTS

LINC01116 and the PCG OLFML2B are differentially and highly expressed in tumor tissues (both P ≤ 0.050). We found that LINC01116, TMSB15A, PLAU, OLFML2B, and MRC2 have diagnostic potentials (all AUC ≥ 0.700, all P ≤ 0.050) while LINC01116 and TMSB15A have prognostic significance (both adjusted P ≤ 0.050). LINC01116 was enriched in the vascular endothelial growth factor (VEGF) receptor signaling pathway, mesenchyme morphogenesis, etc. After that, candidate target drugs with potential clinical significance were identified: Thiamine, Cromolyn, Rilmenidine, Chlorhexidine, Sulindac_sulfone, Chloropyrazine, and Meprylcaine. Analysis of immune infiltration revealed that MRC2, OLFML2B, PLAU, and TMSB15A are negatively associated with the purity but positively associated with the specific cell types (all P < 0.050). Analysis of promoter methylation demonstrated that MRC2, OLFML2B, and PLAU have differential and high methylation levels in primary tumors (all P < 0.050). Validation results of the differential expressions and diagnostic potential of OLFML2B (Oncomine) were consistent with those obtained in the TCGA cohort (P < 0.050, AUC > 0.700).

CONCLUSIONS

Differentially expressed LINC01116 could be a candidate diagnostic and an independent prognostic signature in HCC. Besides, its target drugs may work for HCC therapy via the VEGF receptor signaling pathway. Differentially expressed OLFML2B could be a diagnostic signature involved in HCC via immune infiltrates.

An immune related signature inhibits the occurrence and development of serous ovarian cancer by affecting the abundance of dendritic cells

Serous ovarian cancer is one of the major causes of cancer related death among women worldwide. The advanced diagnosis worsens the prognosis of patients with serous ovarian cancer. The immune system has an important impact on the progression of ovarian cancer. Herein, we aimed to establish an immune related prognostic signature to assist in the early diagnosis, treatment, and prognostic evaluation of patients with serous ovarian cancer. Multiple public data sets and immune related genes were obtained from various online public databases, and immune related prognostic signatures were developed through differential expression analysis, univariate Cox proportional hazard regression analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model. The nomogram model, Kaplan-Meier survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis showed that this signature had a good prediction potential. In conclusion, an immune related signature with good prediction efficiency was established through systematic bioinformatics analysis, which may play a tumor inhibition role by affecting the abundance of activated dendritic cells.

An ICD-Associated DAMP Gene signature predicts survival and immunotherapy response of patients with lung adenocarcinoma

BACKGROUND

While some lung adenocarcinoma (LUAD) patients benefit long-term from treatment with immune checkpoint inhibitors, the sad reality is that a considerable proportion of patients do not. The classification of the LUAD tumor microenvironment (TME) can be used to conceptually comprehend primary resistance mechanisms. In addition, the most recent research demonstrates that the release of damage-associated molecular pattern (DAMP) in TME by immunogenic cell death (ICD) may contribute to the adaptive immune response. Currently, however, there is no such comprehensive research on this topic in LUAD patients. Therefore, we set out to investigate how to reverse the poor infiltration characteristics of immune cells and boost antitumor immunity by identifying DAMP model.

METHODS

In this study, ICD-related DAMP genes were selected to investigate their effects on the prognosis of LUAD. To create a risk signature using the TCGA-LUAD cohort, the univariate COX regression and the least absolute shrinkage and selection operator regression were carried out, and the results were verified in a GEO dataset. Subsequently, the multivariate COX regression was applied to establish a prognostic nomogram. And the ESTIMATE and ssGSEA algorithms were utilized to analyze immune activity and the TIDE algorithm was for responsiveness to immunotherapy. Moreover, clinical tissue samples were used to verify the differential expression of 9 DAMP genes in the signature.

RESULTS

We identified two distinct DAMP molecular subtypes, and there are remarkable differences in survival probability between the two subtypes, and patients with higher levels of DAMP-related genes are "hot tumors" with increased immune activity. In addition, 9 DAMP genes were selected as prognostic signature genes, and clinical outcomes and immunotherapy response were better for participants in the low-risk group. Importantly, according to the area under the curve (AUC) value in evaluating the efficacy of immunotherapy, this signature is superior to existing predictors, such as PD-L1 and TIDE.

CONCLUSIONS

Our study suggests ICD plays an important part in modeling the TME of LUAD patients. And this signature could be utilized as a reliable predictor to estimate clinical outcomes and predict immunotherapy efficacy among LUAD patients.

Cuproptosis-Related Ferroptosis genes for Predicting Prognosis in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a main subtype of kidney cancers. Cuproptosis and ferroptosis are correlated with immune infiltration and prognosis in tumors. However, the role of Cuproptosis-related Ferroptosis genes (CRFGs) in KIRC has rarely been fully understood. Therefore, we constructed a prognostic signature based on different expression of CRFGs in KIRC. All raw data of this study were extracted from public TCGA datasets. Cuproptosis and Ferroptosis genes were collected from the previous research. Finally, a total of 36 significantly different CRFGs were identified from TCGA-KIRC cohort. Six-gene signature (TRIB3, SLC2A3, PML, CD44, CDKN2A and MIOX) was identified by LASSO Cox regression based on the significantly different CRFGs. The CRFGs signature was correlated with worse overall survival and the AUC was 0.750. Functional enrichment indicated that CRFGs were mainly enriched in metabolism, drug resistance, tumor immunity pathways. Besides, the IC50 and immune checkpoint differentially expressed between different groups. The proposed 6-CRFGs signature is a promising biomarker to predict clinical outcomes and therapeutic responses for KIRC patient.

CRISPR-cas9 screening identified lethal genes enriched in Hippo kinase pathway and of predictive significance in primary low-grade glioma

BACKGROUND

Low-grade gliomas (LGG) are a type of brain tumor that can be lethal, and it is essential to identify genes that are correlated with patient prognosis. In this study, we aimed to use CRISPR-cas9 screening data to identify key signaling pathways and develop a genetic signature associated with high-risk, low-grade glioma patients.

METHODS

The study used CRISPR-cas9 screening data to identify essential genes correlated with cell survival in LGG. We used RNA-seq data to identify differentially expressed genes (DEGs) related to cell viability. Moreover, we used the least absolute shrinkage and selection operator (LASSO) method to construct a genetic signature for predicting overall survival in patients. We performed enrichment analysis to identify pathways mediated by DEGs, overlapping genes, and genes shared in the Weighted correlation network analysis (WGCNA). Finally, the study used western blot, qRT-PCR, and IHC to detect the expression of hub genes from signature in clinical samples.

RESULTS

The study identified 145 overexpressed oncogenes in low-grade gliomas using the TCGA database. These genes were intersected with lethal genes identified in the CRISPR-cas9 screening data from Depmap database, which are enriched in Hippo pathways. A total of 19 genes were used to construct a genetic signature, and the Hippo signaling pathway was found to be the predominantly enriched pathway. The signature effectively distinguished between low- and high-risk patients, with high-risk patients showing a shorter overall survival duration. Differences in hub gene expression were found in different clinical samples, with the protein and mRNA expression of REP65 being significantly up-regulated in tumor cells. The study suggests that the Hippo signaling pathway may be a critical regulator of viability and tumor proliferation and therefore is an innovative new target for treating cancerous brain tumors, including low-grade gliomas.

CONCLUSION

Our study identified a novel genetic signature associated with high-risk, LGG patients. We found that the Hippo signaling pathway was significantly enriched in this signature, indicating that it may be a critical regulator of tumor viability and proliferation in LGG. Targeting the Hippo pathway could be an innovative new strategy for treating LGG.

Machine learning-based mRNA signature in early acute myocardial infarction patients: the perspective toward immunological, predictive, and personalized

Patients diagnosed with stable coronary artery disease (CAD) are at continued risk of experiencing acute myocardial infarction (AMI). This study aims to unravel the pivotal biomarkers and dynamic immune cell changes, from an immunological, predictive, and personalized viewpoint, by implementing a machine-learning approach and a composite bioinformatics strategy. Peripheral blood mRNA data from different datasets were analyzed, and CIBERSORT was used for deconvoluting human immune cell subtype expression matrices. Weighted gene co-expression network analysis (WGCNA) in single-cell and bulk transcriptome levels was conducted to explore possible biomarkers for AMI, with a particular emphasis on examining monocytes and their involvement in cell-cell communication. Unsupervised cluster analysis was performed to categorize AMI patients into different subtypes, and machine learning methods were employed to construct a comprehensive diagnostic model to predict the occurrence of early AMI. Finally, RT-qPCR on peripheral blood samples collected from patients validated the clinical utility of the machine learning-based mRNA signature and hub biomarkers. The study identified potential biomarkers for early AMI, including CLEC2D, TCN2, and CCR1, and found that monocytes may play a vital role in AMI samples. Differential analysis revealed that CCR1 and TCN2 exhibited elevated expression levels in early AMI compared to stable CAD. Machine learning methods showed that the glmBoost+Enet [alpha=0.9] model achieved high predictive accuracy in the training set, external validation sets, and clinical samples in our hospital. The study provided comprehensive insights into potential biomarkers and immune cell populations involved in the pathogenesis of early AMI. The identified biomarkers and the constructed comprehensive diagnostic model hold great promise for predicting the occurrence of early AMI and can serve as auxiliary diagnostic or predictive biomarkers.

A novel PD-1/PD-L1 pathway molecular typing-related signature for predicting prognosis and the tumor microenvironment in breast cancer

BACKGROUND

Currently, the development of breast cancer immunotherapy based on the PD-1/PD-L1 pathway is relatively slow, and the specific mechanism affecting the immunotherapy efficacy in breast cancer is still unclear.

METHODS

Weighted correlation network analysis (WGCNA) and the negative matrix factorization (NMF) were used to distinguish subtypes related to the PD-1/PD-L1 pathway in breast cancer. Then univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were used to construct the prognostic signature. A nomogram was established based on the signature. The relationship between the signature gene IFNG and breast cancer tumor microenvironment was analyzed.

RESULTS

Four PD-1/PD-L1 pathway-related subtypes were distinguished. A prognostic signature related to PD-1/PD-L1 pathway typing was constructed to evaluate breast cancer's clinical characteristics and tumor microenvironment. The nomogram based on the RiskScore could be used to accurately predict breast cancer patients' 1-year, 3-year, and 5-year survival probability. The expression of IFNG was positively correlated with CD8+ T cell infiltration in the breast cancer tumor microenvironment.

CONCLUSION

A prognostic signature is constructed based on the PD-1/PD-L1 pathway typing in breast cancer, which can guide the precise treatment of breast cancer. The signature gene IFNG is positively related to CD8+ T cell infiltration in breast cancer.

Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer

BACKGROUND

There has been a recent discovery of a new type of cell death produced by copper-iron ions, called Cuproptosis (copper death). The purpose of this study was to identify LncRNA signatures associated with Cuproptosis in ovarian cancer that could be used as prognostic indicators.

METHODS

RNA sequencing (RNA-seq) profiles with clinicopathological data from TCGA database were used to select prognostic CRLs and then constructed prognostic risk model using multivariate regression analysis and LASSO algorithms. An independent dataset from GEO database was used to validate the prognostic performance. Combined with clinical factors, we further constructed a prognostic nomogram. In addition, tumor immune microenvironment, somatic mutation and drug sensitivity were analyzed using ssGSEA, GSVA, ESTIMATE and CIBERSORT algorithms.

RESULT

A total of 129 CRLs were selected whose expression levels were significantly related to expression levels of 10 cuproptosis-related genes. The univariate Cox regression analysis showed that 12 CRLs were associated with overall survival (OS). Using LASSO algorithms and multivariate regression analysis, we constructed a four-CRLs prognostic signature in the training dataset. Patients in the training dataset could be classified into high- or low-risk subgroups with significantly different OS (log-rank p < 0.001). The prognostic performance was confirmed in TCGA-OC cohort (log-rank p < 0.001) and an independent GEO cohort (log-rank p = 0.023). Multivariate cox regression analysis proved the four-CRLs signature was an independent prognostic factor for OC. Additionally, different risk subtypes showed significantly different levels of immune cells, signal pathways, and drug response.

CONCLUSION

We established a prognostic signature based on cuproptosis-related lncRNAs for OC patients, which will be of great value in predicting the prognosis patients and may provide a new perspective for research and individualized treatment.

Longitudinal large-scale changes in maternal circulating microRNAs associated with gestation-related compartments, fetal sex, and growth during and post-pregnancy

Circulating microRNAs (c-miRNAs) during pregnancy could provide information regarding the functional status of the mother and the fetus. However, it remains unclear which pregnancy-related processes are reflected by c-miRNAs. Here, we used large-scale c-miRNA profiling of maternal plasma during and post-pregnancy, and compared it with that of non-pregnant women. Fetal growth measurements and fetal sex data were used to identify associated changes in c-miRNAs. Surprisingly, c-miRNA subpopulations with prominent expression in maternal/fetal compartments (placenta, amniotic fluid, umbilical cord plasma and breast milk) were found to be under expressed in circulation throughout pregnancy relative to non-pregnant plasma profiles. Furthermore, we found a bias in global c-miRNA expression in association with fetal sex right from the first trimester, in addition to a specific c-miRNA signature of fetal growth. Our results demonstrate the existence of specific temporal changes in c-miRNA populations associated with specific pregnancy-related compartments and processes, including fetal sex, and growth.

Breast cancer prognosis and immunological characteristics are predicted using the m6A/m5C/m1A/m7G-related long noncoding RNA signature

According to statistics, breast cancer (BC) has replaced lung cancer as the most common cancer in the world. Therefore, specific detection markers and therapeutic targets need to be explored as a way to improve the survival rate of BC patients. We first identified m6A/m5C/m1A/m7G-related long noncoding RNAs (MRlncRNAs) and developed a model of 16 MRlncRNAs. Kaplan-Meier survival analysis was applied to assess the prognostic power of the model, while univariate Cox analysis and multivariate Cox analysis were used to assess the prognostic value of the constructed model. Then, we constructed a nomogram to illustrate whether the predicted results were in good agreement with the actual outcomes. We tried to use the model to distinguish the difference in sensitivity to immunotherapy between the two groups and performed some analyses such as immune infiltration analysis, ssGSEA and IC50 prediction. To explore the novel anti-tumor drug response, we reclassified the patients into two clusters. Next, we assessed their response to clinical treatment by the R package pRRophetic, which is determined by the IC50 of each BC patient. We finally identified 11 MRlncRNAs and based on them, a risk model was constructed. In this model, we found good agreement between calibration plots and prognosis prediction. The AUC of ROC curves was 0.751, 0.734, and 0.769 for 1-year, 2-year, and 3-year overall survival (OS), respectively. The results showed that the IC50 was significantly different between the risk groups, suggesting that the risk groups can be used as a guide for systemic treatment. We regrouped patients into two clusters based on 11 MRlncRNAs expression. Next, we conducted immune scores for 2 clusters, which showed that cluster 1 had higher stromal scores, immune scores and higher estimated (microenvironment) scores, demonstrating that TME of cluster 1 was different from cluster 2. The results of this study support that MRlncRNAs can predict tumor prognosis and help differentiate patients with different sensitivities to immunotherapy as a basis for individualized treatment for BC patients.

Identification of biomarkers of hepatocellular carcinoma gene prognosis based on the immune-related lncRNA signature of transcriptome data

BACKGROUND

Long non-coding RNAs (lncRNAs) are well established to have an important role in cancer. The goal of this research was to investigate the prognostic usefulness of putative immune-related lncRNAs in hepatocellular carcinoma (HCC).

METHODS

The developed lncRNA signature was validated using 343 HCC patients from The Cancer Genome Atlas (TCGA) and 81 samples from Gene Expression Omnibus (GEO). Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis were used to analyze immune-related lncRNAs for HCC prognosis. Patients in the low-risk group survived substantially longer than those in the high-risk group (P < 0.05). The discovered signal might be a useful prognostic factor for predicting patient survival. Overall survival predicted some clinical net improvements, according to the nomogram. Numerous enrichment approaches (including gene set enrichment analysis) were utilized to investigate the underlying mechanisms.

RESULTS

Drug metabolism, mTOR, and p53 signaling pathways were associated with high-risk groups. When the expression of lncRNA PRRT3-AS1 was silenced in HepG2 cells, the proliferation, migration, and invasion abilities of HepG2 cells were decreased, and apoptosis was enhanced. In the supernatant from HepG2 cells with PRRT3-AS1 knockdown, the anti-inflammatory factors IL-10 and TGF-1 were induced, whereas the pro-inflammatory factors IL-1β, TNF-α, and IL-6 were reduced (P < 0.05). After PRRT3-AS1 knockdown, the protein expression of CD24, THY1, LYN, CD47, and TRAF2 in HepG2 cells was attenuated (P < 0.05).

CONCLUSION

The discovery of five immune-related lncRNA signatures has significant therapeutic significance for predicting patient prognosis and directing personalized treatment for patients with HCC, which requires additional prospective confirmation.

An immune-related signature for optimizing prognosis prediction and treatment decision of hepatocellular carcinoma

BACKGROUND

An immune-related gene signature (IGS) was established for discriminating prognosis, predicting benefit of immunotherapy, and exploring therapeutic options in hepatocellular carcinoma (HCC).

METHODS

Based on Immune-related hub genes and The Cancer Genome Atlas (TCGA) LIHC dataset (n = 363), an immune-related gene signature (IGS) was established by least absolute shrinkage and selection operator (LASSO) analysis. The prognostic significance and clinical implications of IGS were verified in International Cancer Genome Consortium (ICGC) and Chinese HCC (CHCC) cohorts. The molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in IGS-defined subgroups were analyzed. In addition, by leveraging the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing datasets, we determined the potential therapeutic agents for high IGS-risk patients.

RESULTS

The IGS was constructed based on 8 immune-related hub genes with individual coefficients. The IGS risk model could robustly predict the survival of HCC patients in TCGA, ICGC, and CHCC cohorts. Compared with 4 previous established immune genes-based signatures, IGS exhibited superior performance in survival prediction. Additionally, for immunological characteristics and enriched pathways, a low-IGS score was correlated with IL-6/JAK/STAT3 signaling, inflammatory response and interferon α/γ response pathways, low TP53 mutation rate, high infiltration level, and more benefit from ICI therapy. In contrast, high IGS score manifested an immunosuppressive microenvironment and activated aggressive pathways. Finally, by in silico screening potential compounds, vindesine, ispinesib and dasatinib were identified as potential therapeutic agents for high-IGS risk patients.

CONCLUSIONS

This study developed a robust IGS model for survival prediction of HCC patients, providing new insights into integrating tailored risk stratification with precise immunotherapy and screening potentially targeted agents.

Clinical significance and immune landscape of a novel ferroptosis-related prognosis signature in osteosarcoma

BACKGROUND

Osteosarcoma is a malignant tumor that usually occurs in adolescents aged 10-20 years and is associated with poor prognosis. Ferroptosis is an iron-dependent cell death mechanism that plays a vital role in cancer.

METHODS

Osteosarcoma transcriptome data were downloaded from the public database TARGET and from previous studies. A prognostic risk score signature was constructed using bioinformatics analysis, and its efficacy was determined by analyzing typical clinical features. The prognostic signature was then validated with external data. Differences in immune cell infiltration between high- and low-risk groups were analyzed. The potential of the prognostic risk signature as a predictor of immunotherapy response was evaluated using the GSE35640 (melanoma) dataset. Five key genes expression were measured by real-time PCR and western blot in human normal osteoblasts and osteosarcoma cells. Moreover, malignant biological behaviors of osteosarcoma cells were tested by modulating gene expression level.

RESULTS

We obtained 268 ferroptosis-related genes from the online database FerrDb and published articles. Transcriptome data and clinical information of 88 samples in the TARGET database were used to classify genes into two categories using clustering analysis, and significant differences in survival status were identified. Differential ferroptosis-related genes were screened, and functional enrichment showed that they were associated with HIF-1, T cells, IL17, and other inflammatory signaling pathways. Prognostic factors were identified by univariate Cox regression and LASSO analysis, and a 5-factor prognostic risk score signature was constructed, which was also applicable for external data validation. Experimental validation indicated that the mRNA and protein expression level of MAP3K5, LURAP1L, HMOX1 and BNIP3 decreased significantly, though meanwhile MUC1 increased in MG-63 and SAOS-2 cells compared with hFOB1.19 cells. Cell proliferation and migration ability of SAOS-2 were affected based on alterations of signature genes.

CONCLUSIONS

Significant differences in immune cell infiltration between high- and low-risk groups indicated that the five ferroptosis-related prognostic signature was constructed and could be used to predict the response to immunotherapy in osteosarcoma.

DNA-methylation and genomic copy number in primary tumors and corresponding lymph node metastases in prostate cancer from patients with low and high Gleason score

Purpose

In prostate cancer, the indication to irradiate the pelvic lymphatic pathways in clinical node-negative patients is solely based on clinical nomograms. To define biological risk patterns of lymphatic spread, we studied DNA-methylation and genomic copy number in primary tumors and corresponding lymph nodes metastases.

Methods/Patients

DNA-methylation and genomic copy number profiles of primary tumors (PT) and paired synchronous lymph node metastases (LN) from Gleason Score (GS)-6/7a (n = 20 LN-positive, n = 20 LN-negative) and GS-9/10 patients (LN-positive n = 20) after prostatectomy and lymphonodectomy were analyzed.

Results

GS-6/7a pN0 PTs and GS-6/7a pN1 PTs differed in histone H3K27me3/H3K9me3 mediated methylation. PTs compared to LNs, in both, GS-6/7a pN1 and GS-9/10 pN1 patients showed large differences in DNA-methylation mediated by histones H3K4me1/2, in addition to copy number changes of chromosomal regions 11q13.1, 14q11.2 and 15q26.1. Between GS-6/7a pN1 and GS-9/10 pN1 patients, methylation levels differed more when comparing LNs than PTs. 16q21-22.1 was specifically lost in GS-9/10 pN0 PTs. Immune system-related pathways characterized the differences between PTs and LNs in both GS-6/7a pN1 and GS-9/10 pN1 patients. Comparing PTs and LKs between GS-6/7a pN1 and GS-9/10 pN1 patients revealed altered transmembrane and G-protein-coupled receptor signaling.

Conclusions

Our data suggest that progression of prostate cancer, including lymphatic spread, is associated with histone-mediated DNA-methylation and we hypothesize a methylation signature predicting lymphatic spread in GS-6/7a patients from primary tumors. Lymphatic spread in GS-6/7a patients, flanked by DNA-methylation and CNA alterations, appears to be more complex than in GS-9/10 patients, in whom the primary tumors already appear to bear lymph node metastasis-enabling alterations.

Combining multiple cell death pathway-related risk scores to develop neuroblastoma cell death signature

PURPOSE

Cell death plays an important role in tumourigenesis and progression; nevertheless, the clinical significance of cell death-related genes in neuroblastoma remains incompletely understood.

METHODS

We separately constructed the corresponding risk scores for each of the eight cell death pathways separately and assessed their predictive performance. Through Cox regression analysis, these eight risk scores were integrated to obtain final cell death risk scores (CDRS). We evaluated the predictive performance of CDRS in multiple datasets and compared its accuracy with the clinical characteristics of patients and some existing prognostic models for neuroblastoma. We then explored the differences in immune infiltration between the high and low CDRS groups, and the significance of CDRS on EFS and disease progression.

RESULTS

All eight risk scores have high predictive accuracy, with the Immunogenic-RS being the most accurate and the cuproptosis-RS the least accurate. Model genes are mainly enriched in a variety of cancer-related pathways and are closely related to the clinical characteristics. CDRS showed superior and robust predictive performance in multiple datasets and was more accurate than the clinical characteristics of patients and some existing prognostic models for neuroblastoma. High CDRS group featured distinct immune cold tumor profiles and may have poorer immune checkpoint inhibitor efficacy. CDRS had significance in predicting EFS and disease progression.

CONCLUSION

We integrated risk scores associated with multiple cell death pathways to develop a high-performing and robust neuroblastoma signature. CDRS was a promising tool that may help with risk assessment and prediction of overall prognosis, and thus improve clinical outcomes.

Necroptosis-Related LncRNA Signatures for Prognostic Prediction in Uterine Corpora Endometrial Cancer

Necroptosis is one of the common modes of apoptosis, and it has an intrinsic association with cancer prognosis. However, the role of the necroptosis-related long non-coding RNA LncRNA (NRLncRNAs) in uterine corpora endometrial cancer (UCEC) has not yet been fully elucidated at present. Therefore, the present study is designed to investigate the potential prognostic value of necroptosis-related LncRNAs in UCEC. In the present study, the expression profiles and clinical data of UCEC patients were downloaded from TCGA database to identify the differentially expressed NRLncRNAs associated with overall survival. A LncRNA risk model was constructed via Cox regression analysis, and its prognostic value was evaluated. We have also further evaluated the relationships between the LncRNA features and the related cellular function, related pathways, immune status, and immune checkpoints m6A-related genes. Seven signatures, including PCAT19, CDKN2B-AS1, LINC01936, LINC02178, BMPR1B-DT, LINC00237, and TRPM2-AS, were established to assess the overall survival (OS) of the UCEC in the present study. Survival analysis and ROC curves indicated that the correlated signature has good predictable performance. The normogram could accurately predict the overall survival of the patients with an excellent clinical practical value. Enrichment analysis of gene sets indicated that risk signals were enriched in several immune-related pathways. In addition, the risk characteristics were significantly correlated with immune cells, immune function, immune cell infiltration, immune checkpoints, and some m6A-related genes. This study has identified seven necroptosis-related LncRNA signatures for the first time, providing a valuable basis for a more accurate prognostic prediction of UCEC.

A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis

BACKGROUND

Immune-related genes (IRGs) remain poorly understood in their function in the onset and progression of sepsis.

METHODS

GSE65682 was obtained from the Gene Expression Omnibus database. The IRGs associated with survival were screened for subsequent modeling using univariate Cox regression analysis and least absolute shrinkage and selection operator in the training cohort. Then, we assessed the reliability of the 7 IRGs signature's independent predictive value in the training and validation cohorts following the creation of a signature applying multivariable Cox regression analysis. After that, we utilized the E-MTAB-4451 external dataset in order to do an independent validation of the prognostic signature. Finally, the CIBERSORT algorithm and single-sample gene set enrichment analysis was utilized to investigate and characterize the properties of the immune microenvironment.

RESULTS

Based on 7 IRGs signature, patients could be separated into low-risk and high-risk groups. Patients in the low-risk group had a remarkably increased 28-day survival compared to those in the high-risk group (P < 0.001). In multivariable Cox regression analyses, the risk score calculated by this signature was an independent predictor of 28-day survival (P < 0.001). The signature's predictive ability was confirmed by receiver operating characteristic curve analysis with the area under the curve reaching 0.876 (95% confidence interval 0.793-0.946). Moreover, both the validation set and the external dataset demonstrated that the signature had strong clinical prediction performance. In addition, patients in the high-risk group were characterized by a decreased neutrophil count and by reduced inflammation-promoting function.

CONCLUSION

We developed a 7 IRGs signature as a novel prognostic marker for predicting sepsis patients' 28-day survival, indicating possibilities for individualized reasonable resource distribution of intensive care unit.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unveils heterogeneity and establishes a novel signature for prognosis and tumor immune microenvironment in ovarian cancer

Ovarian cancer is a highly heterogeneous gynecological malignancy that seriously affects the survival and prognosis of female patients. Single-cell sequencing and transcriptome analysis can effectively characterize tumor heterogeneity to better study the mechanism of occurrence and development. In this study, we identified differentially expressed genes with different differentiation outcomes of tumor cells by analyzing a single-cell dataset. Based on the differentially expressed genes, we explored the differences in function and tumor microenvironment among clusters via consensus clustering. Meanwhile, WGCNA was employed to obtain key genes related to ovarian cancer. On the basis of the TCGA and GEO datasets, we constructed a risk model consisting of 7 genes using the LASSO regression model, and successfully verified that the model was characterized as an independent prognostic factor, efficiently predicting the survival prognosis of patients. In addition, immune signature analysis showed that patients in the high-risk group exhibited lower anti-tumor immune cell infiltration and immunosuppressive status, and had poorer responsiveness to chemotherapeutic drugs and immunotherapy. In conclusion, our study provided a 7-gene prognostic model based on the heterogeneity of OC cells for ovarian cancer patients, which could effectively predict the prognosis of patients and identify the immune microenvironment status of patients.

Identification of a four-miRNA signature predicts the prognosis of papillary thyroid cancer

BACKGROUND

In recently diagnosed patients with thyroid cancer, papillary thyroid cancer (PTC), as the most common histological subtype, accounts for 90% of all cases. Although PTC is known as a relatively adolescent malignant disease, there still is a high possibility of recurrence in PTC patients with a poor prognosis. Therefore, new biomarkers are necessary to guide more effective stratification of PTC patients and personalize therapy to avoid overtreatment or inadequate treatment. Accumulating evidence demonstrates that microRNAs (miRNAs) have broad application prospects as diagnostic biomarkers in cancer.

AIM

To explore novel markers consisting of miRNA-associated signatures for PTC prognostication.

METHODS

We obtained and analyzed the data of 497 PTC patients from The Cancer Genome Atlas. The patients were randomly assigned to either a training or testing cohort.

RESULTS

We discovered 237 differentially expressed miRNAs in tumorous thyroid tissues compared with normal tissues, which contained 172 up-regulated and 65 down-regulated miRNAs. The evaluation of differently expressed miRNAs was conducted using our risk score model. We then successfully generated a four-miRNA potential prognostic signature [risk score = (-0.001 × hsa-miR-181a-2-3p) + (0.003 × hsa-miR-138-5p) + (-0.018 × hsa-miR-424-3p) + (0.284 × hsa-miR-612)], which reliably distinguished patients from high and low risk with a significant difference in the overall survival (P < 0.01) and was effective in predicting the five-year disease survival rate with the area under the receiver operating characteristic curve of 0.937 and 0.812 in the training and testing cohorts, respectively. Additionally, there was a trend indicated that high-risk patients had shorter relapse-free survival, although statistical significance was not reached (P = 0.082) in our sequencing cohort.

CONCLUSION

Our results indicated a four-miRNA signature that has a robust predictive effect on the prognosis of PTC. Accordingly, we would recommend more radical therapy and closer follow-ups for high-risk groups.

Identification and validation of an m6A-related gene signature to predict prognosis and evaluate immune features of breast cancer

Breast cancer is the most prevalent cancer, and it is accompanied by high heterogeneity. N6-methyladenosine (m6A) modification significantly contributes to breast cancer tumorigenesis and progression. However, how m6A-related genes affect the clinical outcomes and tumor immune microenvironment (TIME) of breast cancer is largely unknown. Our study developed an m6A-related gene signature on the basis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The m6A-related gene signature was constructed using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Breast cancer patients were classified into low- and high-risk groups depending on the median risk score. The reliability and efficiency of the signature were validated using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). The risk score was validated as an independent indicator associated with overall survival, and a nomogram model was created to estimate the overall survival of patients with breast cancer. Functional annotation suggested that the risk score had a strong relationship with immune-related pathways. Different proportions of immune cell infiltration between the two groups were evaluated using various algorithms. The high-risk group had higher immune checkpoint expression levels. We discovered that one of the 6 prognostic genes, TMEM71, was downregulated in breast cancer tissues. In vitro experiments indicated that overexpression of TMEM71 suppressed breast cancer cell proliferation and migration. In conclusion, the m6A-related gene signature may be a sensitive biomarker for overall survival prediction and guide the individualized treatment for breast cancer patients.

A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures

Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.

Implication of IL-7 receptor alpha chain expression by CD8+ T cells and its signature in defining biomarkers in aging

CD8⁺ T cells play an important role in host defense against infections and malignancies as well as contribute to the development of inflammatory disorders. Alterations in the frequency of naïve and memory CD8⁺ T cells are one of the most significant changes in the immune system with age. As the world population rapidly ages, a better understanding of aging immune function or immunosenescence could become a basis for discovering treatments of illnesses that commonly occur in older adults. In particular, biomarkers for immune aging could be utilized to identify individuals at high risk of developing age-associated conditions and help monitor the efficacy of therapeutic interventions targeting such conditions. This review details the possible role of CD8⁺ T cell subsets expressing different levels of the cytokine receptor IL-7 receptor alpha chain (IL-7Rα) and the gene signature associated with IL-7Rα as potential biomarkers for immune aging given the association of CD8⁺ T cells in host defense, inflammation, and immunosenescence.

High expression of GMNN predicts malignant progression and poor prognosis in ACC

BACKGROUND

Adrenocortical carcinoma (ACC) is a rare endocrine neoplasm, which is characterized by poor prognosis and high recurrence rate. Novel and reliable prognostic and metastatic biomarkers are lacking for ACC patients. This study aims at screening potential prognostic biomarkers and therapeutic targets of ACC through bioinformatic methods and immunohistochemical (IHC) analysis.

METHODS

In the present study, by using the Gene Expression Omnibus (GEO) database we identified differentially expressed genes (DEGs) in ACC and validated these DEGs in The Cancer Genome Atlas (TCGA) ACC cohort. A DEGs-based signature was additionally constructed and we assessed its prognosis and prescient worth for ACC by survival analysis and nomogram. Immunohistochemistry (IHC) was used to verify the relationship between hub gene-GMNN expressions and clinicopathologic outcomes in ACC patients.

RESULTS

A total of 24 DEGs correlated with the prognosis of ACC were screened from the TCGA and GEO databases. Five DEGs were subsequently selected in a signature which was closely related to the survival rates of ACC patients and GMNN was identified as the core gene in this signature. Univariate and multivariate Cox regression showed that the GMNN was an independent prognostic factor for ACC patients (P < 0.05). Meanwhile, GMNN was closely related to the OS and PFI of ACC patients treated with mitotane (P < 0.001). IHC confirmed that GMNN protein was overexpressed in ACC tissues compared with normal adrenal tissues and significantly correlated with stage (P = 0.011), metastasis (P = 0.028) and Ki-67 index (P = 0.014).

CONCLUSIONS

GMNN is a novel tumor marker for predicting the malignant progression, metastasis and prognosis of ACC, and may be a potential therapeutic target for ACC.