BACKGROUND: Studies have indicated a close association between genes linked to liquid-liquid phase separation (LLPS) and the progression of prostate cancer (PCa). However, the interplay among long non-coding RNAs (lncRNAs) linked to LLPS in PCa remains elusive. Therefore, we constructed a prediction model based on LLPS-related LncRNA in PCa to explore its relationship with the prognosis and drug treatment of PCa.
METHODS: We obtained clinical and sequencing data from TCGA and LLPS genes from the Phase Separation Protein Database. By analyzing the differential expression of LLPS-related genes and lncRNAs in prostate cancer, and using Poisson correlation, we identified LLPS-related lncRNAs. Prognostic LLPS-lncRNAs were found through prognostic correlation analysis and included in a Cox model to compute regression coefficients. Patients were scored and divided into high- and low-risk groups. Independent prognostic factors were integrated into a prognostic nomogram with risk and Gleason scores. We also conducted drug sensitivity analyses, GSEA, and validated the impact of key lncRNAs through functional experiments.
RESULTS: Our study identified five LLPS-associated lncRNAs that are of prognostic importance. And found notable disparities in biochemical recurrence rates and survival outcomes between these risk groups, with the low-risk cohort exhibiting superior prognostic indicators. Moreover, our prediction nomogram demonstrated robust predictive accuracy and significant clinical utility. Furthermore, our model exhibited promising capabilities in forecasting patient sensitivity to various conventional therapeutic drugs, thereby highlighting its potential in personalized treatment strategies. GSEA showed that these lncRNAs may influence PCa prognosis and sensitivity to therapeutic agents by affecting pathways such as cell cycle. Knockdown of AC009812.4 could inhibit the ability of PCa cells to proliferate, migrate and invade, and compare to paracancerous tissue, AC009812.4 in PCa tissue has significantly higher expression.
CONCLUSIONS: Our research uncovers the prognostic significance of lncRNAs associated with LLPS in PCa and established a model exhibiting excellent predictive accuracy for prognosis. Those lncRNAs may influence progress of PCa as well as sensitivity to therapy drugs through pathways such as cell cycle.

UNASSIGNED: Colorectal cancer (CRC) is the third-most prevalent cancer globally. The biological significance of telomeres in CRC carcinogenesis and progression is underscored by accumulating data. Nevertheless, not much is known about how telomere-related genes (TRGs) affect CRC prognosis. Therefore, the aim of this study was to investigate the role of TRGs in CRC prognosis.
UNASSIGNED: We retrospectively obtained the expression profiles and clinical data of CRC patients from public databases. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, we created a telomere-related risk model to predict survival outcomes, identifying ten telomere-related differentially expressed genes (TRDEGs). Based on TRDEGs, we stratified patients from The Cancer Genome Atlas (TCGA) into low- and high-risk subsets. Subsequently, we conducted comprehensive analyses, including survival assessment, immune cell infiltration, drug sensitivity, and prediction of molecular interactions using Kaplan-Meier curves, ESTIMATE, CIBERSORT, OncoPredict, and other approaches.
UNASSIGNED: The model showed exceptional predictive accuracy for survival. Significant differences in survival were observed between the two groups of participants grouped according to the model (P<0.001), and this difference was further confirmed in the external validation set (GSE39582) (P=0.004). Additionally, compared to the low-risk group, the high-risk group exhibited significantly advanced tumor node metastasis (TNM) stages, lower proportions of activated CD4+ T cells, effector memory CD4+ T cells, and memory B cells, but increased ratios of M2 macrophages and regulatory T cells (Tregs), elevated tumor immune dysfunction and exclusion (TIDE) scores, and diminished sensitivity to dabrafenib, lapatinib, camptothecin, docetaxel, and telomerase inhibitor IX, reflecting the signature\'s capacity to distinguish clinical pathological characteristics, immune environment, and drug efficacy. Finally, we validated the expression of the ten TRDEGs (ACACB, TPX2, SRPX, PPARGC1A, CD36, MMP3, NAT2, MMP10, HIGD1A, and MMP1) through quantitative real-time polymerase chain reaction (qRT-PCR) and found that compared to normal cells, the expression levels of ACACB, HIGD1A, NAT2, PPARGC1A, and TPX2 in CRC cells were elevated, whereas those of CD36, SRPX, MMP1, MMP3, and MMP10 were reduced.
UNASSIGNED: Overall, we constructed a telomere-related biomarker capable of predicting prognosis and treatment response in CRC individuals, offering potential guidance for drug therapy selection and prognosis prediction.

Gap junction protein beta 3 (GJB3) has been reported as a tumor suppressor in most tumors. However, its role in lung adenocarcinoma (LUAD) remains unknown. The purpose of this study is to explore the role of GJB3 in the prognosis and tumor microenvironment of LUAD patients. The data used in this study were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and imvigor210 cohorts. We found that GJB3 expression was increased in LUAD patients and correlated with LUAD stages. LUAD patients with high GJB3 expression exhibited a worse prognosis. A total of 164 pathways were significantly activated in the GJB3 high group. GJB3 expression was positively associated with nine transcription factors and might be negatively regulated by hsa-miR-6511b-5p. Finally, we found that immune cell infiltration and immune checkpoint expression were different between the GJB3 high and GJB3 low groups. In summary. GJB3 demonstrated high expression levels in LUAD patients, and those with elevated GJB3 expression displayed unfavorable prognoses. Additionally, there was a correlation between GJB3 and immune cell infiltration, as well as immune checkpoint expression in LUAD patients.

BACKGROUND: Organoids are approved by the US FDA as an alternative to animal experiments to guide drug development and for sensitivity screening. Stable organoids models of gastric cancer are desirable for personalized medicine and drug screening.
METHODS: Tumor tissues from a primary cancer of the stomach and metastatic cancer of the lymph node were collected for 3D culture. By long-term culture for over 50 generations in vitro, we obtained stably growing organoid lines. We analyzed short tandem repeats (STRs) and karyotypes of cancer cells, and tumorigenesis of the organoids in nude mice, as well as multi-omics profiles of the organoids. A CCK8 method was used to determine the drugs sensitivity to fluorouracil (5-Fu), platinum and paclitaxel.
RESULTS: Paired organoid lines from primary cancer (SPDO1P) and metastatic lymph node (SPDO1LM) were established with unique STRs and karyotypes. The organoid lines resulted in tumorigenesis in vivo and had clear genetic profiles. Compared to SPDO1P from primary cancer, upregulated genes of SPDO1LM from the metastatic lymph node were enriched in pathways of epithelial-mesenchymal transition and angiogenesis with stronger abilities of cell migration, invasion, and pro-angiogenesis. Based on drug sensitivity analysis, the SOX regimen (5-Fu plus oxaliplatin) was used for chemotherapy with an optimal clinical outcome.
CONCLUSIONS: The organoid lines recapitulate the drug sensitivity of the parental tissues. The paired organoid lines present a step-change toward living biobanks for further translational usage.

UNASSIGNED: The prognosis and therapeutic response of patients with liver hepatocellular carcinoma (LIHC) can be predicted based on programmed cell death (PCD) as PCD plays a crucial role during tumor progression. We developed a PCD-related gene signature to evaluate the therapeutic response and prognosis for patients with LIHC.
UNASSIGNED: Molecular subtypes of LIHC were classified using ConsensusClusterPlus according to the gene biomarkers related to PCD. To predict the prognosis of high- and low-risk LIHC patients, a risk model was established by LASSO regression analysis based on the prognostic genes. Functional enrichment analysis was conducted using clusterProfiler package, and relative abundance of immune cells was quantified applying CIBERSORT package. Finally, to determine drug sensitivity, oncoPredict package was employed.
UNASSIGNED: PCD was correlated with the clinicopathologic features of LIHC. Then, we defined four molecular subtypes (C1-C4) of LIHC using PCD-related prognostic genes. Specifically, subtype C1 had the worst prognosis with enriched T cells regulatory (Tregs) and Macrophage_M0 and higher expression of T cell exhaustion markers, meanwhile, C1 also had a relatively higher TIDE score and metastasis potential. A risk model was established using 5 prognostic genes. High-risk patients tended to have higher expression of T cell exhaustion markers and TIDE score and unfavorable outcomes, and they were more sensitive to small molecule drug 5.Fluorouracil.
UNASSIGNED: A PCD-related gene signature was developed and verified to be able to accurately predict the prognosis and drug sensitivity of LIHC patients.

BACKGROUND: PDAC, also known as pancreatic ductal adenocarcinoma, is often diagnosed at a late stage due to nonspecific symptoms and a distinct lack of reliable biomarkers for timely diagnosis. Ferroptosis, a novel non-apoptotic cell death mode discovered in recent years, is strongly linked to the progression of PDAC and the evasion of the immune system. The objective of this study is to discover a novel ceRNA biomarker associated with ferroptosis and investigate its possible molecular mechanisms and therapeutic potential in PDAC.
METHODS: Based on the FerrDb and TCGA databases, the R survival package was used to screen for ferroptosis-related mRNAs associated with PDAC prognosis. The ferroptosis-related ceRNA network was identified by miRTarBase, miRNet, and starBase and visualized using Cytoscape. The LASSO regression analysis was used to build a risk model associated with ceRNA. Additionally, we investigated the correlation between the ceRNA axis and the infiltration of immune cells in PDAC by employing the ssGSEA algorithm. Spearman correlation analysis was used to investigate the association between the ceRNA network and the expression levels of immune checkpoint genes in PDAC. The prediction of potential medications for PAAD patients with high risk scores was conducted using the R package oncoPredict and the Genomics of Drug Sensitivity in Cancer (GDSC) repository. Expression levels of LINC02535 in clinical specimens and PDAC cell lines were determined using qRT-PCR. CCK-8, colony formation, EdU, wound healing, and transwell assays were performed to assess the impact of reducing LINC02535 on the growth, migration, and invasion of PDAC cell lines BxPC3 and PANC1.
RESULTS: We first discovered a new LINC02535/miR-30c-5p/EIF2S1 axis associated with ferroptosis and created a prognostic nomogram for predicting overall survival. Meanwhile, the risk scores of the LINC02535/miR-30c-5p/EIF2S1 axis associated with ferroptosis were linked to immune subtypes in PDAC. The high immune infiltration subtype exhibited elevated ceRNA risk scores and EIF2S1 expression. The correlation analysis revealed a positive correlation between ceRNA risk scores and four immune cells, namely Activated CD4 T cell, Memory B cell, Neutrophil, and Type 2 T helper cell, as well as four immune checkpoint genes, namely CD274, HAVCR2, PDCD1LG2, and TIGIT. The analysis of drug sensitivity indicated that individuals with a high-risk score may exhibit greater sensitivity to inhibitors targeting MEK1/2 compared to those with a low-risk score. In our validation experiments, it was observed that the expression of LINC02535 was increased in both PDAC tissues and cell lines. Additionally, the inhibition of LINC02535 resulted in decreased proliferation, migration, and invasion of PDAC cells. Rescue experiments demonstrated that LINC02535 promoted PDAC cell growth and metastasis by upregulating EIF2S1 expression.
CONCLUSIONS: To summarize, a novel ferroptosis-associated LINC02535/miR-30c-5p/EIF2S1 ceRNA network for PDAC patients was established. The analysis of this network\'s functionality offers potential insights for clinical decision-making and the advancement of precision medicine.

OBJECTIVE: To analyze the isolation rate, prevalence trends, species distribution, and drug sensitivity of non-tuberculous mycobacteria (NTM) in Anhui Province, providing a reference for diagnosis and treatment strategies.
METHODS: Specimens from suspected mycobacterial infection patients at Anhui Chest Hospital (including outpatients and inpatients) from January 2021 to December 2023 were cultured. Identified NTM strains were analyzed for species distribution and drug sensitivity.
RESULTS: Of 10,519 mycobacteria strains cultured, 1,589 were NTM (15.11%). The top four species were Mycobacterium intracellulare (75.36%), Mycobacterium abscessus (11.78%), Mycobacterium kansasii (7.09%), and Mycobacterium avium (2.85%). NTM strains showed high sensitivity to amikacin and clarithromycin (≥90%) and significant sensitivity to rifabutin, moxifloxacin, and rifampicin (89.03%-79.61%). They exhibited high resistance to imipenem/cilastatin, sulfamethoxazole, minocycline, and doxycycline (≥95%).
CONCLUSIONS: NTM isolation rates in Anhui have remained stable, with the predominant species being M. intracellulare, M. kansasii, M. abscessus, and M. avium. NTM strains are highly sensitive to amikacin, clarithromycin, rifabutin, moxifloxacin, and rifampicin. These findings can guide diagnosis, treatment strategies, and drug selection for NTM disease in Anhui Province.

The Ten-Eleven Translocation (TET) family genes are implicated in a wide array of biological functions across various human cancers. Nonetheless, there is a scarcity of studies that comprehensively analyze the correlation between TET family members and the molecular phenotypes and clinical characteristics of different cancers. Leveraging updated public databases and employing several bioinformatics analysis methods, we assessed the expression levels, somatic variations, methylation levels, and prognostic values of TET family genes. Additionally, we explored the association between the expression of TET family genes and pathway activity, tumor microenvironment (TME), stemness score, immune subtype, clinical staging, and drug sensitivity in pan-cancer. Molecular biology and cytology experiments were conducted to validate the potential role of TET3 in tumor progression. Each TET family gene displayed distinct expression patterns across at least ten detected tumors. The frequency of Single Nucleotide Variant (SNV) in TET genes was found to be 91.24%, primarily comprising missense mutation types, with the main types of copy number variant (CNV) being heterozygous amplifications and deletions. TET1 gene exhibited high methylation levels, whereas TET2 and TET3 genes displayed hypomethylation in most cancers, which correlated closely with patient prognosis. Pathway activity analysis revealed the involvement of TET family genes in multiple signaling pathways, including cell cycle, apoptosis, DNA damage response, hormone AR, PI3K/AKT, and RTK. Furthermore, the expression levels of TET family genes were shown to impact the clinical staging of tumor patients, modulate the sensitivity of chemotherapy drugs, and thereby influence patient prognosis by participating in the regulation of the tumor microenvironment, cellular stemness potential, and immune subtype. Notably, TET3 was identified to promote cancer progression across various tumors, and its silencing was found to inhibit tumor malignancy and enhance chemotherapy sensitivity. These findings shed light on the role of TET family genes in cancer progression and offer insights for further research on TET3 as a potential therapeutic target for pan-cancer.

BACKGROUND: Ovarian cancer is a female-specific malignancy with high morbidity and mortality. The metabolic reprogramming of tumor cells is closely related to the biological behavior of tumors.
METHODS: The prognostic signature of the metabolism-related gene (MRGs) was established by LASSO-Cox regression analysis. The prognostic signature of MRGs was also prognosticated in each clinical subgroup. These genes were subjected to functional enrichment analysis and tissue expression exploration. Analysis of the MRG prognostic signature in terms of immune cell infiltration and antitumor drug susceptibility was also performed.
RESULTS: A MRG prognostic signature including 21 genes was established and validated. Most of the 21 MRGs were expressed at different levels in ovarian cancer than in normal ovarian tissue. The enrichment analysis suggested that MRGs were involved in lipid metabolism, membrane organization, and molecular binding. The MRG prognostic signature demonstrated the predictive value of overall survival time in various clinical subgroups. The monocyte, NKT, Tgd and Tex cell scores showed differences between the groups with high- and low-risk score. The antineoplastic drug analysis we performed provided information on ovarian cancer drug therapy and drug resistance. In vitro experiments verified that PLCH1 in 21 MRGs can regulate the apoptosis and proliferation of ovarian cancer cells.
CONCLUSIONS: This metabolism-related prognostic signature was a potential prognostic factor in patients with ovarian cancer, demonstrating high stability and accuracy.

OBJECTIVE: The tonsoku-like DNA repair protein (TONSL) encoded by the TONSL gene, located on chromosome 8q24.3, is crucial for repairing DNA double-strand breaks through homologous recombination. However, TONSL overexpression in lung adenocarcinoma (LUAD) promotes tumor development, leading to a poor prognosis.
METHODS: TONSL was verified as a reliable prognostic marker for LUAD using bioinformatics, and clinical features related to LUAD prognosis were screened from the TCGA database to establish the relationship between risk factors and TONSL expression. In addition, TONSL expression in normal and LUAD tissues was verified using real-time quantitative polymerase chain reaction and immunohistochemistry. To elucidate the possible functions of TONSL, TONSL-related differentially expressed genes were screened, and functional enrichment analysis was performed. Subsequently, siRNA was used to knock down TONSL expression in lung cancer cells for cytobehavioral experiments. The effects of TONSL expression on tumor immune escape were analyzed using the ESTIMATE algorithm and tumor immune-infiltration analysis. In addition, the half-maximal inhibitory concentration of LUAD with varying TONSL expression levels in response to first-line chemotherapeutic drugs and epidermal growth factor receptor-tyrosine kinase inhibitors was analyzed for drug sensitivity.
RESULTS: Up-regulation of TONSL in LUAD promotes the proliferation, migration, and invasion of lung cancer cells, thereby contributing to a poor prognosis. Furthermore, TONSL overexpression promotes immune escape and drug sensitivity in LUAD.
CONCLUSIONS: TONSL serves as a reliable prognostic marker for LUAD, and its up-regulation is associated with increased immune escape and drug sensitivity. These findings suggest that TONSL holds potential as a novel therapeutic target for LUAD.