Germinal center B-cell subgroups in the tumor microenvironment cannot be overlooked: Their involvement in prognosis, immunotherapy response, and treatment resistance in head and neck squamous carcinoma.

Background: More than 60 % of patients with head and neck squamous carcinoma (HNSCC) are diagnosed at advanced stages and miss radical treatment. This has prompted the need to find new biomarkers to achieve early diagnosis and predict early recurrence and metastasis of tumors. Methods: Single-cell RNA sequencing (scRNA-seq) data from HNSCC tissues and peripheral blood samples were obtained through the Gene Expression Omnibus (GEO) database (GSE164690) to characterize the B-cell subgroups, differentiation trajectories, and intercellular communication networks in HNSCC and to construct a prognostic model of the associated risks. In addition, this study analyzed the differences in clinical features, immune cell infiltration, functional enrichment, tumor mutational burden (TMB), and drug sensitivity between the high- and low-risk groups. Results: Using scRNA-seq of HNSCC, we classified B and plasma cells into a total of four subgroups: naive B cells (NBs), germinal center B cells (GCBs), memory B cells (MBs), and plasma cells (PCs). Pseudotemporal trajectory analysis revealed that NBs and GCBs were at the early stage of B cell differentiation, while MBs and PCs were at the end. Cellular communication revealed that GCBs acted on tumor cells through the CD99 and SEMA4 signaling pathways. The independent prognostic value, immune cell infiltration, TMB and drug sensitivity assays were validated for the MEF2B+ GCB score groups. Conclusions: We identified GCBs as B cell-specific prognostic biomarkers for the first time. The MEF2B+ GCB score fills the research gap in the genetic prognostic prediction model of HNSCC and is expected to provide a theoretical basis for finding new therapeutic targets for HNSCC.

Purine metabolism in lung adenocarcinoma: A single-cell analysis revealing prognostic and immunotherapeutic insights.

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer, yet the contribution of purine metabolism (PM) to its pathogenesis remains poorly elucidated. PM, a critical component of intracellular nucleotide synthesis and energy metabolism, is hypothesized to exert a significant influence on LUAD development. Herein, we employed single-cell analysis to investigate the role of PM within the tumour microenvironment (TME) of LUAD. PM scoring (PMS) across distinct cell types was determined using AUCell, UCell, singscore and AddModuleScore algorithms. Subsequently, we explored communication networks among cells within high- and low-PMS groups, establishing a robust PM-associated signature (PAS) utilizing a comprehensive dataset comprising LUAD samples from TCGA and five GEO datasets. Our findings revealed that the high-PMS group exhibited intensified cell interactions, while the PAS, constructed using PM-related genes, demonstrated precise prognostic predictive capability. Notably, analysis across the TCGA dataset and five GEO datasets indicated that low-PAS patients exhibited a superior prognosis. Furthermore, the low-PAS group displayed increased immune cell infiltration and elevated CD8A expression, coupled with reduced PD-L1 expression. Moreover, data from eight publicly available immunotherapy cohorts suggested enhanced immunotherapy outcomes in the low-PAS group. These results underscore a close association between PAS and tumour immunity, offering predictive insights into genomic alterations, chemotherapy drug sensitivity and immunotherapy responses in LUAD. The newly established PAS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature.

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. However, there is currently a lack of reliable signatures for identifying patients who would benefit from immunotherapy. We conducted a comparative analysis of two immunotherapy cohorts (OAK and POPLAR) and utilized single-factor COX regression to identify genes that significantly impact the prognosis of LUAD. Based on the TCGA-LUAD dataset, we employed a combination of 101 machine learning algorithms to construct a model and selected the optimal model. The model was validated on five GEO datasets and compared with 144 previously published signatures to assess its performance. Subsequently, we explored the underlying biological mechanisms through tumor mutation burden analysis, enrichment analysis, and immune infiltration analysis. An immunotherapy prognostic prediction signature (IPPS) was constructed based on 13 genes, showing robust performance in the TCGA-LUAD dataset. IPPS exhibited consistent predictive accuracy in the validation cohorts. Compared to 144 previously published signatures, IPPS consistently ranked among the top in terms of C-index values. Further exploration revealed differences between high and low-IPPS groups in terms of tumor mutation burden, pathway enrichment, and immune infiltration. IPPS demonstrates strong predictive capabilities for the prognosis of LUAD patients, offering the potential to identify suitable candidates for immunotherapy and contribute to precision treatment strategies for LUAD.

A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis.

Background: Breast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients. Methods: BRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the 'limma' package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function. Results: Our study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells. Conclusion: Our 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.

Senescence-related genes analysis in breast cancer reveals the immune microenvironment and implications for immunotherapy.

Despite the advent of precision therapy for breast cancer (BRCA) treatment, some individuals are still unable to benefit from it and have poor survival prospects as a result of the disease's high heterogeneity. Cell senescence plays a crucial role in the tumorigenesis, progression, and immune regulation of cancer and has a major impact on the tumor microenvironment. To find new treatment strategies, we aimed to investigate the potential significance of cell senescence in BRCA prognosis and immunotherapy. We created a 9-gene senescence-related signature. We evaluated the predictive power and the role of signatures in the immune microenvironment and infiltration. In vitro tests were used to validate the expression and function of the distinctive critical gene ACTC1. Our risk signature allows BRCA patients to receive a Predictive Risk Signature (PRS), which may be used to further categorize a patient's response to immunotherapy. Compared to conventional clinicopathological characteristics, PRS showed strong predictive efficacy and precise survival prediction. Moreover, PRS subgroups were examined for altered pathways, mutational patterns, and possibly useful medicines. Our research offers suggestions for incorporating senescence-based molecular classification into risk assessment and ICI therapy decision-making.

Digging out the biology properties of tRNA-derived small RNA from black hole.

An unique subclass of functional non-coding RNAs generated by transfer RNA (tRNA) under stress circumstances is known as tRNA-derived small RNA (tsRNA). tsRNAs can be divided into tRNA halves and tRNA-derived fragments (tRFs) based on the different cleavage sites. Like microRNAs, tsRNAs can attach to Argonaute (AGO) proteins to target downstream mRNA in a base pairing manner, which plays a role in rRNA processing, gene silencing, protein expression and viral infection. Notably, tsRNAs can also directly bind to protein and exhibit functions in transcription, protein modification, gene expression, protein stabilization, and signaling pathways. tsRNAs can control the expression of tumor suppressor genes and participate in the initiation of cancer. It can also mediate the progression of diseases by regulating cell viability, migration ability, inflammatory factor content and autophagy ability. Precision medicine targeting tsRNAs and drug therapy of plant-derived tsRNAs are expected to be used in clinical practice. In addition, liquid biopsy technology based on tsRNAs indicates a new direction for the non-invasive diagnosis of diseases.

ACLY promotes gastric tumorigenesis and accelerates peritoneal metastasis of gastric cancer regulated by HIF-1A.

Mounting evidence indicates the potential involvement of ATP-citrate lyase (ACLY) in the modulation of various cancer types. Nevertheless, the precise biological significance of ACLY in gastric cancer (GC) remains elusive. This study sought to elucidate the biological function of ACLY and uncover its influence on peritoneal metastasis in GC. The expression of ACLY was assessed using both real-time quantitative PCR and western blot techniques. To investigate the impact of ACLY on the proliferation of gastric cancer (GC) cells, colony formation and 5-ethynyl-2'-deoxyuridine (EdU) assays were performed. The migratory and invasive abilities of GC were evaluated using wound healing and transwell assays. Additionally, a bioinformatics analysis was employed to predict the correlation between ACLY and HIF-1A. This interaction was subsequently confirmed through a chromatin immunoprecipitation (ChIP) assay. ACLY exhibited upregulation in gastric cancer (GC) as well as in peritoneal metastasis. Its overexpression was found to facilitate the proliferation and metastasis of GC cells in both in vitro and in vivo experiments. Moreover, ACLY was observed to play a role in promoting angiogenesis and epithelial-mesenchymal transition (EMT). Notably, under hypoxic conditions, HIF-1A levels were elevated, thereby acting as a transcription factor to upregulate ACLY expression. Under the regulatory influence of HIF-1A, ACLY exerts a significant impact on the progression of gastric cancer, thereby facilitating peritoneal metastasis.

Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy.

Background: Uveal melanoma (UVM) is a primary intraocular malignancy that poses a significant threat to patients' visual function and life. The basement membrane (BM) is critical for establishing and maintaining cell polarity, adult function, embryonic and organ morphogenesis, and many other biological processes. Some basement membrane protein genes have been proven to be prognostic biomarkers for various cancers. This research aimed to develop a novel risk assessment system based on BMRGs that would serve as a theoretical foundation for tailored and accurate treatment. Methods: We used gene expression profiles and clinical data from the TCGA-UVM cohort of 80 UVM patients as a training set. 56 UVM patients from the combined cohort of GSE84976 and GSE22138 were employed as an external validation dataset. Prognostic characteristics of basement membrane protein-related genes (BMRGs) were characterized by Lasso, stepwise multifactorial Cox. Multivariate analysis revealed BMRGs to be independent predictors of UVM. The TISCH database probes the crosstalk of BMEGs in the tumor microenvironment at the single-cell level. Finally, we investigated the function of ITGA5 in UVM using multiple experimental techniques, including CCK8, transwell, wound healing assay, and colony formation assay. Results: There are three genes in the prognostic risk model (ADAMTS10, ADAMTS14, and ITGA5). After validation, we determined that the model is quite reliable and accurately forecasts the prognosis of UVM patients. Immunotherapy is more likely to be beneficial for UVM patients in the high-risk group, whereas the survival advantage may be greater for UVM patients in the low-risk group. Knockdown of ITGA5 expression was shown to inhibit the proliferation, migration, and invasive ability of UVM cells in vitro experiments. Conclusion: The 3-BMRGs feature model we constructed has excellent predictive performance which plays a key role in the prognosis, informing the individualized treatment of UVM patients. It also provides a new perspective for assessing pre-immune efficacy.

Significance of liquid-liquid phase separation (LLPS)-related genes in breast cancer: a multi-omics analysis.

Currently, the role of liquid-liquid phase separation (LLPS) in cancer has been preliminarily explained. However, the significance of LLPS in breast cancer is unclear. In this study, single cell sequencing datasets GSE188600 and GSE198745 for breast cancer were downloaded from the GEO database. Transcriptome sequencing data for breast cancer were downloaded from UCSC database. We divided breast cancer cells into high-LLPS group and low-LLPS group by down dimension clustering analysis of single-cell sequencing data set, and obtained differentially expressed genes between the two groups. Subsequently, weighted co-expression network analysis (WGCNA) was performed on transcriptome sequencing data, and the module genes most associated with LLPS were obtained. COX regression and Lasso regression were performed and the prognostic model was constructed. Subsequently, survival analysis, principal component analysis, clinical correlation analysis, and nomogram construction were used to evaluate the significance of the prognostic model. Finally, cell experiments were used to verify the function of the model's key gene, PGAM1. We constructed a LLPS-related prognosis model consisting of nine genes: POLR3GL, PLAT, NDRG1, HMGB3, HSPH1, PSMD7, PDCD2, NONO and PGAM1. By calculating LLPS-related risk scores, breast cancer patients could be divided into high-risk and low-risk groups, with the high-risk group having a significantly worse prognosis. Cell experiments showed that the activity, proliferation, invasion and healing ability of breast cancer cell lines were significantly decreased after knockdown of the key gene PGAM1 in the model. Our study provides a new idea for prognostic stratification of breast cancer and provides a novel marker: PGAM1.

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.

Background: Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs). Methods: We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD. Results: We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells. Conclusion: Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

Mast cell marker gene signature: prognosis and immunotherapy response prediction in lung adenocarcinoma through integrated scRNA-seq and bulk RNA-seq.

Background: Mast cells, comprising a crucial component of the tumor immune milieu, modulate neoplastic progression by secreting an array of pro- and antitumorigenic factors. Numerous extant studies have produced conflicting conclusions regarding the impact of mast cells on the prognosis of patients afflicted with lung adenocarcinoma (LUAD). Methods: Employing single-cell RNA sequencing (scRNA-seq) analysis, mast cell-specific marker genes in LUAD were ascertained. Subsequently, a mast cell-related genes (MRGs) signature was devised to stratify LUAD patients into high- and low-risk cohorts based on the median risk value. Further investigations were conducted to assess the influence of distinct risk categories on the tumor microenvironment. The prognostic import and capacity to prognosticate immunotherapy benefits of the MRGs signature were corroborated using four external cohorts. Ultimately, the functional roles of SYAP1 were validated through in vitro experimentation. Results: After scRNA-seq and bulk RNA-seq data analysis, we established a prognostic signature consisting of nine MRGs. This profile effectively distinguished favorable survival outcomes in both the training and validation cohorts. In addition, we identified the low-risk group as a population more effective for immunotherapy. In cellular experiments, we found that silencing SYAP1 significantly reduced the proliferation, invasion and migratory capacity of LUAD cells while increasing apoptosis. Conclusion: Our MRGs signature offers valuable insights into the involvement of mast cells in determining the prognosis of LUAD and may prove instrumental as a navigational aid for immunotherapy selection, as well as a predictor of immunotherapy response in LUAD patients.

Liquid-liquid phase separation throws novel insights into treatment strategies for skin cutaneous melanoma.

BACKGROUND: In recent years, there has been growing evidence indicating a relationship between liquid-liquid phase separation (LLPS) and cancer development. However, to date, the clinical significance of LLPS in skin cutaneous melanoma (SKCM, hereafter referred to as melanoma) remains to be elucidated. In the current study, the impact of LLPS-related genes on melanoma prognosis has been explored. METHODS: LLPS-related genes were retrieved from the DrLLPS database. The prognostic feature for LLPS in melanoma was developed in The Cancer Genome Atlas (TCGA) dataset and verified in the GSE65904 cohort. Based on risk scores, melanoma patients were categorized into high- and low-risk groups. Thereafter, the differences in clinicopathological correlation, functional enrichment, immune landscape, tumor mutational burden, and impact of immunotherapy between the two groups were investigated. Finally, the role of key gene TROAP in melanoma was validated by in vitro and in vivo experiments. RESULTS: The LLPS-related gene signature was developed based on MLKL, PARVA, PKP1, PSME1, RNF114, and TROAP. The risk score was a crucial independent prognostic factor for melanoma and patients with high-risk scores were related to a worse prognosis. Approximately, all immune-relevant characteristics, such as immune cell infiltration and immune scores, were extremely evident in patients with low-risk scores. The findings from the in vitro and in vivo experiments indicated that the viability, proliferation, and invasion ability of melanoma cells were drastically decreased after the knockdown of TROAP. CONCLUSION: Our gene signature can independently predict the survival of melanoma patients. It provides a basis for the exploration of the relationship between LLPS and melanoma and can offer a fresh perspective on the clinical diagnosis and treatment of the disease.

The integrated single-cell analysis developed a lactate metabolism-driven signature to improve outcomes and immunotherapy in lung adenocarcinoma.

Background: It has been suggested that lactate metabolism (LM) is crucial for the development of cancer. Using integrated single-cell RNA sequencing (scRNA-seq) analysis, we built predictive models based on LM-related genes (LMRGs) to propose novel targets for the treatment of LUAD patients. Methods: The most significant genes for LM were identified through the use of the AUCell algorithm and correlation analysis in conjunction with scRNA-seq analysis. To build risk models with superior predictive performance, cox- and lasso-regression were utilized, and these models were validated on multiple external independent datasets. We then explored the differences in the tumor microenvironment (TME), immunotherapy, mutation landscape, and enriched pathways between different risk groups. Finally, cell experiments were conducted to verify the impact of AHSA1 in LUAD. Results: A total of 590 genes that regulate LM were identified for subsequent analysis. Using cox- and lasso-regression, we constructed a 5-gene signature that can predict the prognosis of patients with LUAD. Notably, we observed differences in TME, immune cell infiltration levels, immune checkpoint levels, and mutation landscapes between different risk groups, which could have important implications for the clinical treatment of LUAD patients. Conclusion: Based on LMRGs, we constructed a prognostic model that can predict the efficacy of immunotherapy and provide a new direction for treating LUAD.

Exploring the role of sphingolipid-related genes in clinical outcomes of breast cancer.

Background: Despite tremendous advances in cancer research, breast cancer (BC) remains a major health concern and is the most common cancer affecting women worldwide. Breast cancer is a highly heterogeneous cancer with potentially aggressive and complex biology, and precision treatment for specific subtypes may improve survival in breast cancer patients. Sphingolipids are important components of lipids that play a key role in the growth and death of tumor cells and are increasingly the subject of new anti-cancer therapies. Key enzymes and intermediates of sphingolipid metabolism (SM) play an important role in regulating tumor cells and further influencing clinical prognosis. Methods: We downloaded BC data from the TCGA database and GEO database, on which we performed in depth single-cell sequencing analysis (scRNA-seq), weighted co-expression network analysis, and transcriptome differential expression analysis. Then seven sphingolipid-related genes (SRGs) were identified using Cox regression, least absolute shrinkage, and selection operator (Lasso) regression analysis to construct a prognostic model for BC patients. Finally, the expression and function of the key gene PGK1 in the model were verified by in vitro experiments. Results: This prognostic model allows for the classification of BC patients into high-risk and low-risk groups, with a statistically significant difference in survival time between the two groups. The model is also able to show high prediction accuracy in both internal and external validation sets. After further analysis of the immune microenvironment and immunotherapy, it was found that this risk grouping could be used as a guide for the immunotherapy of BC. The proliferation, migration, and invasive ability of MDA-MB-231 and MCF-7 cell lines were dramatically reduced after knocking down the key gene PGK1 in the model through cellular experiments. Conclusion: This study suggests that prognostic features based on genes related to SM are associated with clinical outcomes, tumor progression, and immune alterations in BC patients. Our findings may provide insights for the development of new strategies for early intervention and prognostic prediction in BC.

Integrating single-cell RNA-seq and bulk RNA-seq to construct prognostic signatures to explore the role of glutamine metabolism in breast cancer.

Background: Although breast cancer (BC) treatment has entered the era of precision therapy, the prognosis is good in the case of comprehensive multimodal treatment such as neoadjuvant, endocrine, and targeted therapy. However, due to its high heterogeneity, some patients still cannot benefit from conventional treatment and have poor survival prognoses. Amino acids and their metabolites affect tumor development, alter the tumor microenvironment, play an increasingly obvious role in immune response and regulation of immune cell function, and are involved in acquired and innate immune regulation; therefore, amino acid metabolism is receiving increasing attention. Methods: Based on public datasets, we carried out a comprehensive transcriptome and single-cell sequencing investigation. Then we used 2.5 Weighted Co-Expression Network Analysis (WGCNA) and Cox to evaluate glutamine metabolism-related genes (GRGs) in BC and constructed a prognostic model for BC patients. Finally, the expression and function of the signature key gene SNX3 were examined by in vitro experiments. Results: In this study, we constituted a risk signature to predict overall survival (OS) in BC patients by glutamine-related genes. According to our risk signature, BC patients can obtain a Prognostic Risk Signature (PRS), and the response to immunotherapy can be further stratified according to PRS. Compared with traditional clinicopathological features, PRS demonstrated robust prognostic power and accurate survival prediction. In addition, altered pathways and mutational patterns were analyzed in PRS subgroups. Our study sheds some light on the immune status of BC. In in vitro experiments, the knockdown of SNX3, an essential gene in the signature, resulted in a dramatic reduction in proliferation, invasion, and migration of MDA-MB-231 and MCF-7 cell lines. Conclusion: We established a brand-new PRS consisting of genes associated with glutamine metabolism. It expands unique ideas for the diagnosis, treatment, and prognosis of BC.

By integrating single-cell RNA-seq and bulk RNA-seq in sphingolipid metabolism, CACYBP was identified as a potential therapeutic target in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is a heterogeneous disease with a dismal prognosis for advanced tumors. Immune-associated cells in the microenvironment substantially impact LUAD formation and progression, which has gained increased attention in recent decades. Sphingolipids have a profound impact on tumor formation and immune infiltration. However, few researchers have focused on the utilization of sphingolipid variables in the prediction of LUAD prognosis. The goal of this work was to identify the major sphingolipid-related genes (SRGs) in LUAD and develop a valid prognostic model based on SRGs. Methods: The most significant genes for sphingolipid metabolism (SM) were identified using the AUCell and WGCNA algorithms in conjunction with single-cell and bulk RNA-seq. LASSO and COX regression analysis was used to develop risk models, and patients were divided into high-and low-risk categories. External nine provided cohorts evaluated the correctness of the models. Differences in immune infiltration, mutation landscape, pathway enrichment, immune checkpoint expression, and immunotherapy were also further investigated in distinct subgroups. Finally, cell function assay was used to verify the role of CACYBP in LUAD cells. Results: A total of 334 genes were selected as being most linked with SM activity for further investigation, and a risk model consisting of 11 genes was established using lasso and cox regression. According to the median risk value, patients were split into high- and low-risk groups, and the high-risk group had a worse prognosis. The low-risk group had more immune cell infiltration and higher expression of immune checkpoints, which illustrated that the low-risk group was more likely to benefit from immunotherapy. It was verified that CACYBP could increase the ability of LUAD cells to proliferate, invade, and migrate. Conclusion: The eleven-gene signature identified in this research may help physicians create individualized care plans for LUAD patients. CACYBP may be a new therapeutic target for patients with advanced LUAD.

Comparative analysis of transient receptor potential channel 5 opposite strand-induced gene expression patterns and protein-protein interactions in triple-negative breast cancer.

In patients with triple-negative breast cancer (TNBC), high tumour mutation burden and aberrant oncogene expression profiles are some of the causes of poor prognosis. Therefore, it is necessary to identify aberrantly expressed oncogenes, since they have the potential to serve as therapeutic targets. Transient receptor potential channel 5 opposite strand (TRPC5OS) has been previously shown to function as a novel tumour inducer. However, the underlying mechanism of TRPC5OS function in TNBC remain to be elucidated. Therefore, in the present study TRPC5OS expression was first measured in tissue samples of patients with TNBC and a panel of breast cancer cell lines (ZR-75-1, MDA-MB-453, SK-BR-3, JIMT-1, BT474 and HCC1937) by using qRT-PCR and Western blotting. Subsequently, the possible effects of TRPC5OS on MDA-MB-231 cells proliferation were determined using Cell Counting Kit-8 and 5-Ethynyl-2'-deoxyuridine assays after Lentiviral transfection of MDA-MB-231. In addition, potential interaction partners of TRPC5OS were explored using liquid chromatography-mass spectrometry (LC-MS)/MS. Gene expression patterns following TRPC5OS overexpression were also detected in MDA-MB-231 cells by using High-throughput sequencing. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis were then used to systematically verify the potential interactions among the TRPC5OS-regulated genes. The potential relationship between TRPC5OS-interacting proteins and gene expression patterns were studied using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) analysis. TRPC5OS expression was found to be significantly higher in TNBC tumour tissues and breast cancer cell lines compared with luminal tumour tissues and ZR-75-1. In addition, the overexpression of TRPC5OS significantly increased cell proliferation. High-throughput sequencing results revealed that 5,256 genes exhibited differential expression following TRPC5OS overexpression, including 3,269 upregulated genes and 1,987 downregulated genes. GO analysis results indicated that the functions of these differentially expressed genes were enriched in the categories of 'cell division' and 'cell proliferation' regulation. KEGG analysis showed that the TRPC5OS-regulated genes were associated with processes of 'homologous recombination' and 'TNF signalling pathways'. Subsequently, 17 TRPC5OS-interacting proteins were found using LC-MS/MS and STRING analysis. The most important protein among interacting proteins was ENO1 which was associated with glycolysis and regulated proliferation of cancer. In summary, data from the present study suggest that TRPC5OS overexpression can increase TNBC cell proliferation and ENO1 may be a potential target protein mediated by TRPC5OS. Therefore, TRPC5OS may serve as a novel therapeutic target for TNBC.

Cuproptosis-related lncRNA signatures: Predicting prognosis and evaluating the tumor immune microenvironment in lung adenocarcinoma.

Background: Cuproptosis, a unique kind of cell death, has implications for cancer therapy, particularly lung adenocarcinoma (LUAD). Long non-coding RNAs (lncRNAs) have been demonstrated to influence cancer cell activity by binding to a wide variety of targets, including DNA, RNA, and proteins. Methods: Cuproptosis-related lncRNAs (CRlncRNAs) were utilized to build a risk model that classified patients into high-and low-risk groups. Based on the CRlncRNAs in the model, Consensus clustering analysis was used to classify LUAD patients into different subtypes. Next, we explored the differences in overall survival (OS), the tumor immune microenvironment (TIME), and the mutation landscape between different risk groups and molecular subtypes. Finally, the functions of LINC00592 were verified through in vitro experiments. Results: Patients in various risk categories and molecular subtypes showed statistically significant variations in terms of OS, immune cell infiltration, pathway activity, and mutation patterns. Cell experiments revealed that LINC00592 knockdown significantly reduced LUAD cell proliferation, invasion, and migration ability. Conclusion: The development of a trustworthy prediction model based on CRlncRNAs may significantly aid in the assessment of patient prognosis, molecular features, and therapeutic modalities and may eventually be used in clinical applications.