Glioblastoma (GBM) is a highly aggressive brain tumor with a complex tumor microenvironment (TME) that includes immune cell infiltration, notably macrophages. The role of macrophages in GBM progression is influenced by their polarization state, which can be either pro-inflammatory (M1) or immunosuppressive (M2). This study investigates the macrophage polarization in GBM, identifying key macrophage-related genes and their impact on tumor progression. Analysis of TCGA-GBM data revealed that macrophage infiltration correlates with poor prognosis, with 41 risk-associated genes identified. DSP dataset analysis highlighted 378 differentially expressed genes between CD68+ macrophages and GFAP+ controls, including immune-related genes like SPP1, CD74, and C3. Cross-validation with single-cell RNA-seq confirmed the expression of 9 key genes, with 7 genes being macrophage-specific. In vitro experiments using conditioned media from GBM cell lines demonstrated that GBM cells promote macrophage polarization towards an M2-like phenotype. Overexpression of CD74, CLEC7A, and IFI30 in macrophages further enhanced M2 polarization, which was associated with increased tumor-promoting functions, including enhanced invasion and reduced apoptosis in GBM cells. Together, these findings highlight the role of M2 macrophage polarization in promoting GBM progression and suggest that targeting macrophage polarization pathways may offer therapeutic potential.

PURPOSE:The aim of this study was to evaluate the efficacy of neoadjuvant chemotherapy (albumin-bound paclitaxel + carboplatin) combined with the PD-1 inhibitor tislelizumab in patients with potentially resectable stage II to IVb head and neck squamous cell carcinoma (HNSCC) and explore immune and circulating tumor-related features potentially associated with treatment response. PATIENTS AND METHODS:This was a single-arm phase II trial involving 33 patients with potentially resectable stage II to IVb HNSCC. Participants received two cycles of neoadjuvant therapy followed by surgery. Primary endpoints were pathologic complete response (pCR) rate and major pathologic response (MPR) rate. Safety and exploratory analyses, including circulating tumor cells (CTC), PD-L1 expression, and T-cell senescence, were also assessed. RESULTS:The study demonstrated an objective response rate of 72.7% (24/33) and an R0 resection rate of 93.1% (27/29) in surgical patients. The pCR rate was 44.8%, and the MPR rate was 62.1%. The laryngeal preservation rate was 70.4% (19/27). With a median follow-up of 20.5 months, the 12- and 24-month event-free survival rates were 93.1% (95% confidence interval, 84.3%-100%). Five of six patients with decreased CTC levels achieved MPR (5/6, 83.3%). Among patients whose T-cell senescence decreased, four of six achieved MPR, whereas only two of five with increased T-cell senescence achieved MPR. The pCR rate was significantly higher in patients with PD-L1 combined positive score ≥ 1 compared with those with combined positive score < 1 (55.6% vs. 12.5%). CONCLUSIONS:Neoadjuvant immunochemotherapy improves pathologic response and organ preservation in stage II to IVb HNSCC. Dynamic changes in CTCs and T-cell senescence are associated with treatment efficacy, suggesting their potential as early, noninvasive indicators of response, supporting precision treatment strategies for locally advanced HNSCC.

Proton therapy, an emerging radiation therapy technique, has gained significant attention owing to its unique physical properties and biological effects. This review aims to explore the impact of proton therapy on the tumor immune system and of its combination with other therapies. This review outlines the fundamental principles and oncological applications of proton therapy, followed by an analysis of its impact on the immune system-encompassing draining lymph nodes, peripheral blood immune cells, tumor cell immune responses, and the tumor microenvironment. We further discuss current combinatorial strategies with immunotherapy and/or chemotherapy, which point toward promising clinical directions.

Radiation therapy is a crucial adjunct treatment for head and neck tumors, as well as primary or metastatic brain tumors. Radiation-induced brain injury is one of the most severe complications, postirradiation, in patients with head and neck tumors, and significantly impacts their quality of life. Currently, there are no effective treatments for radiation-induced brain injury, making the study of radiation-induced molecular mechanisms and the identification of early damage biomarkers critical for the early diagnosis and treatment of such injuries. In this study, twelve male C57 mice aged 6-8 weeks were randomly divided into a control group, a 15 Gy irradiation group, and a 30 Gy irradiation group. Mice were exposed to 6 MV X rays. The control group underwent the same anesthesia procedure as the irradiated groups but did not receive radiation. General health and weight changes were monitored and recorded. Four months postirradiation, mice were subjected to intracranial magnetic resonance imaging [T2-weighted imaging (T2WI)], open field test (OFT), novel object recognition (NOR), followed by a collection of brain tissues for immunofluorescence, SA-β-gal staining, and transcriptomic and metabolomic analyses. Compared to the control group, the 15 Gy and 30 Gy irradiated mice showed reduced activity and weight loss. The irradiated mice exhibited impaired recognition memory in the NOR test and decreased body weight, but radiation had no significant effect on weight or performance in the OFT. Electron microscopy reveals significant demyelination of mouse cortex after irradiation, and MRI T2-weighted imaging demonstrated varying degrees of brain atrophy and ventricular enlargement in irradiated mice compared to the control group. Immunofluorescence staining showed a significant increase in astrocytes and microglia activated after irradiation. SA-β-gal staining revealed significant increases in the numbers of β-gal+ cells in irradiated mice compared to those in untreated control mice. Bioinformatics analysis identified enriched pathways primarily related to lipid metabolism and neuroinflammatory responses; associated metabolites and genes were variously upregulated or downregulated. The findings suggest that radiation-induced brain injury involves complex biological processes, with lipid metabolism disorders and neuroinflammation being the predominant pathological changes observed. Further studies on these metabolic pathways and genes could enhance our understanding of the pathogenic mechanisms underlying radiation-induced brain injury and identify potential therapeutic targets.

Radiation therapy serves as a fundamental treatment for primary and metastatic brain tumors, whether used alone or combined with surgery and chemotherapy. Despite its oncological efficacy, this treatment paradigm frequently induces radiation-induced brain injury (RBI), a progressive neuropathological condition characterized by structural and functional damage to healthy cerebral parenchyma. Patients with RBI frequently develop affective disorders, particularly major depressive disorder and generalized anxiety disorder, which profoundly impair psychosocial functioning and quality of life. The pathophysiology involves complex mechanisms such as neuroinflammation, oxidative stress, blood–brain barrier disruption, and white matter damage. Current management strategies include antidepressants, corticosteroids, and neuroprotective agents, while emerging therapies targeting neuroinflammation and neural repair show promise. This review comprehensively examines the pathogenesis of RBI-related affective disorders and evaluates both conventional and novel treatment approaches. By synthesizing current evidence, we aim to provide insights for developing more effective interventions to improve patient outcomes and quality of life.

Head and neck lymphoepithelioma-like carcinoma (HNLEC) is a rare malignancy characterized by distinctive histology and strong association with Epstein-Barr virus infection. However, its genome aberration remains systematically undefined. Through whole-exome sequencing of tumor samples from 20 patients with HNLEC and integrated bioinformatic analyses, this study delineates the somatic mutational landscape of HNLEC for the first time. Through OncodriveCLUST clustering and survival modeling, a set of potentially prognosis-related genes, including EPPK1, EVPL, GLIS1, MUC5B, RP1L1, and XIRP2, were identified. In addition, interrogation of the Drug-Gene Interaction Database highlighted putative therapeutic targets such as CDK13, MUC16, and MUC17. While preliminary, these findings establish the first comprehensive mutational blueprint of HNLEC, providing novel insights into its pathogenesis, potential prognostic determinants, and therapeutic vulnerabilities, and laying a foundation for future translational and clinical research.

BACKGROUND:Stomach adenocarcinoma (STAD) is a major contributor to cancer-related mortality worldwide. Alterations in amino acid metabolism, which is integral to protein synthesis, have been observed across various tumor types. However, the prognostic significance of amino acid metabolism-related genes in STAD remains underexplored. METHODS:Transcriptomic gene expression and clinical data for STAD patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Amino acid metabolism-related gene sets were sourced from the Gene Set Enrichment Analysis (GSEA) database. A prognostic model was built using LASSO Cox regression based on the TCGA cohort and validated with GEO datasets (GSE84433, GSE84437, GSE84426). Kaplan-Meier analysis compared overall survival (OS) between high- and low-risk groups, and ROC curves assessed model accuracy. A nomogram predicted 1-, 3-, and 5-year survival. Copy number variations (CNVs) in model genes were visualized using data from the Xena platform, and mutation profiles were analyzed with "maftools" to create a waterfall plot. KEGG and GO enrichment analyses were performed to explore biological mechanisms. Immune infiltration and related functions were evaluated via ssGSEA, and Spearman correlation analyzed associations between risk scores and immune components. The TIDE database predicted immunotherapy efficacy, while FDA-approved drug sensitivity was assessed through CellMiner database. The role of MATN3 in STAD was further examined in vitro and in vivo, including amino acid-targeted metabolomic sequencing to assess its impact on metabolism. Finally, Mendelian randomization (MR) analysis evaluated the causal relationship between the model genes and gastric cancer. RESULTS:In this study, we developed a prognostic risk model for STAD based on three amino acid metabolism-related genes (SERPINE1, NRP1, MATN3) using LASSO regression analysis. CNV amplification was common in SERPINE1 and NRP1, while CNV deletion frequently occurred in MATN3. STAD patients were classified into high- and low-risk groups based on the median risk score, with the high-risk group showing worse prognosis. A nomogram incorporating the risk score and clinical factors was created to estimate 1-, 3-, and 5-year survival rates. Distinct mutation profiles were observed between risk groups, with KEGG pathway analysis showing immune-related pathways enriched in the high-risk group. High-risk scores were significantly associated with the C6 (TGF-β dominant) subtype, while low-risk scores correlated with the C4 (lymphocyte-depleted) subtype. Higher risk scores also indicated increased immune infiltration, enhanced immune functions, lower tumor purity, and poorer immunotherapy response. Model genes were linked to anticancer drug sensitivity. Manipulating MATN3 expression showed that it promoted STAD cell proliferation and migration in vitro and tumor growth in vivo. Metabolomic sequencing revealed that MATN3 knockdown elevated levels of 30 amino acid metabolites, including alpha-aminobutyric acid, glycine, and aspartic acid, while reducing (S)-β-Aminoisobutyric acid and argininosuccinic acid. MR analysis found a significant causal effect of NRP1 on gastric cancer, but no causal relationship for MATN3 or SERPINE1. CONCLUSION:In conclusion, the amino acid metabolism-related prognostic model shows promise as a valuable biomarker for predicting the clinical prognosis, selecting immunotherapy and drug treatment for STAD patients. Furthermore, our study has shed light on the potential value of the MATN3 as a promising strategy for combating the progression of STAD.

It is unclear how telomere-binding protein TPP1 interacts with human telomerase reverse transcriptase (hTERT) and influences cervical cancer development and progression. This study included all eligible 156 cervical cancers diagnosed during 2003-2008 and followed up through 2014, 102 cervical intraepithelial neoplasia (CIN) patients, and 16 participants with normal cervix identified at the same period. Correlation of expression of TPP1 and hTERT in these lesions was assessed using Kappa statistics. TPP1 was knocked down by siRNA in three cervical cancer cell lines. We assessed mRNA expression using quantitative real-time polymerase chain reaction and protein expression using tissue microarray-based immunohistochemical staining. We further analyzed the impact of TPP1 expression on the overall survival of cervical cancer patients by calculating the hazard ratio (HR) with 95% confidence intervals (CIs) using the multivariable-adjusted Cox regression model. Compared to the normal cervix, high TPP1expression was significantly associated with CIN 3 and cervical cancers (P<0.001 for both). Expressions of TPP1 and hTERT were highly correlated in CIN 3 (Kappa statistics = 0.50, P = 0.005), squamous cell carcinoma (Kappa statistics = 0.22, P = 0.011), and adenocarcinoma/adenosquamous carcinoma (Kappa statistics = 0.77, P = 0.001). Mechanistically, knockdown of TPP1 inhibited the expression of hTERT in both mRNA and protein levels. High expression of TPP1 (HR = 2.61, 95% CI 1.23-5.51) and co-high expression of TPP1 and hTERT (HR = 2.38, 95% CI 1.28-4.43) were independently associated with worse survival in cervical cancer patients. TPP1 and hTERT expression was correlated and high expression of TPP1 was associated with high risk of CIN 3 and cervical cancer and could predict a worse survival in cervical cancer.

Low-grade glioma (LGG) is a lower malignancy and slower-growing primary tumor of the nervous system. Methylation of N6-methyl adenosine (m6A) has important roles in the growth of tumors and cellular biological processes. The immune system is involved in tumourigenesis and development and plays a certain role in tumor therapy and resistance to drugs. There have been no in-depth studies on m6A-related immune markers in LGG. We obtained gene mutation data, gene expression, and related clinical information of LGG patients from the Chinese Glioma Genome Atlas (CGGA) database and the Cancer Genome Atlas (TCGA). Then, the prognostic model was calculated using multivariate Cox, LASSO, and univariate Cox analyses. A dynamic nomograph online app was also developed based on this model. In addition, for the screened model genes, we performed correlation analyses in the clinical staging, immunological subtype, and microenvironmental aspects. Finally, we determined the biological role of FBXO4 in glioma cells by quantitative reverse transcription-polymerase chain reaction, cell proliferation assay, and cell migration assay. Our prognostic models can accurately and efficiently help investigators analyze the prognosis of LGG patients. In addition, the correlation analysis between m6Ascore and tumor microenvironment can provide a basis for further exploration.

Background Radiation-induced brain injury (RBI) represents a major challenge for cancer patients undergoing cranial radiotherapy. However, the molecular mechanisms and therapeutic strategies of RBI remain inconclusive. With the continuous exploration of the mechanisms of RBI, an increasing number of studies have implicated cerebrovascular dysfunction as a key factor in RBI-related cognitive impairment. As pericytes are a component of the neurovascular unit, there is still a lack of understanding in current research about the specific role and function of pericytes in RBI.Methods We constructed a mouse model of RBI-associated cognitive dysfunction in vivo and an in vitro radiation-induced pericyte model to explore the effects of senescent pericytes on the blood-brain barrier (BBB) and normal central nervous system cells, even glioma cells. To further clarify the effects of pericyte autophagy on senescence, molecular mechanisms were explored at the animal and cellular levels. Finally, we validated the clearance of pericyte senescence by using a senolytic drug and all-trans retinoic acid to investigate the role of radiation-induced pericyte senescence.Results Our findings indicated that radiation-induced pericyte senescence plays a key role in BBB dysfunction, leading to RBI and subsequent cognitive decline. Strikingly, pericyte senescence also contributed to the growth and invasion of glioma cells. We further demonstrated that defective autophagy in pericytes is a vital regulatory mechanism for pericyte senescence. Moreover, autophagy activated by rapamycin could reverse pericyte senescence. Notably, the elimination of senescent cells by senolytic drugs significantly mitigated radiation-induced cognitive dysfunction.Conclusions Our results demonstrated that pericyte senescence may be a promising therapeutic target for RBI and glioma progression. Graphical Abstract

Radiotherapy (RT) remains a primary treatment modality for glioblastoma (GBM), but it induces cellular senescence and is strongly implicated in GBM progression and RT-related injury. Recently, eliminating senescent cells has emerged as a promising strategy for treating cancer and for mitigating radiation-induced brain injury (RBI). Here, we investigated the impact of all-trans retinoic acid (RA) on radiation-induced senescence. The findings of this study revealed that RA effectively eliminated astrocytes, which are particularly prone to senescence after radiation, and that the removal of senescence-associated secretory phenotype factor-producing astrocytes inhibited GBM cell proliferation in vitro. Moreover, RA-mediated clearance of senescent cells improved survival in GBM-bearing mice and alleviated radiation-induced cognitive impairment. Through RNA sequencing, we found that the AKT/mTOR/PPARγ/Plin4 signaling pathway is involved in RA-mediated clearance of senescent cells. In summary, these results suggest that RA could be a potential senolytic drug for preventing GBM progression and improving RBI.

Abstract Background: Methylation of N6-methylandenosine (m6A) has important roles in the growth of tumors and cellular biological processes. The immune system is involved in tumourigenesis and development, and plays a certain role in tumour therapy and in resistance to drugs. There have been no in-depth studies on m6A-related immune markers in Low Grade Glioma (LGG). Methods: LGG patients' mutation data and gene expression and related clinical information were obtained from the China Glioma Genome Atlas (CGGA) database and The Cancer Genome Atlas (TCGA). The prognostic model was calculated using multivariate Cox, LASSO, univariate Cox and other analytical approaches. All data was classified by two-cluster typing. Finally, we determined the biological role of FBXO4 in glioma cells by quantitative reverse transcription-polymerase chain reaction, cell proliferation assay and cell migration assay. Results: The prognostic model for LGG worked well. It has an area under the curve over 0.9. The survival curve for the cluster typing and the Sankey diagram showed that high m6A levels corresponded to high expression of m6A regulatory genes and immune genes, and were associated with a higher degree of immune infiltration and lower survival rates. Finally, silencing FBXO4in glioma cell lines can significantly inhibit their proliferation and migration ability. Conclusion: Prognostic models can accurately and efficiently help investigators analysis the prognosis of LGG patients. And the correlation analysis between m6Ascore and tumor microenvironment can provide a basis for further exploratio. Finally, FBXO4 is an important biomarker for the diagnosis and prognosis of Low Grade Glioma.

Background:Despite receiving standard treatment, the prognosis of glioblastoma (GBM) patients is still poor. Considering the heterogeneity of each patient, it is imperative to identify reliable risk model that can effectively predict the prognosis of each GBM patient to guide the personalized treatment.Methods:Transcriptomic gene expression profiles and corresponding clinical data of GBM patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Inflammatory response-related genes were extracted from Gene Set Enrichment Analysis (GSEA) website. Univariate Cox regression analysis was used for prognosis-related inflammatory genes (P<0.05). A polygenic prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Validation was performed through CGGA cohort. Overall survival (OS) was compared by Kaplan-Meier analysis. A nomogram was plotted to accurately predict the prognosis for each patient. GSEA was used for the pathway enrichment analysis. The single sample GSEA (ssGSEA) algorithm was implemented to conduct the immune infiltration analysis. The potential role of oncostatin M receptor (OSMR) in GBM was investigated through the in vitro experiment.Results:A prognostic risk model consisting of 4 genes (PTPRN, OSMR, MYD88, and EFEMP2) was developed. GBM patients in the high-risk group had worse OS. The time-dependent ROC curves showed an area under the curve (AUC) of 0.782, 0.765, and 0.784 for 1-, 2-, and 3-year survival in TCGA cohort, while the AUC in the CGGA cohort was 0.589, 0.684, and 0.785 at 1, 2, and 3 years, respectively. The risk score, primary-recurrent-secondary (PRS) type, and isocitrate dehydrogenase (IDH) mutation could predict the prognosis of GBM patients well. The nomogram accurately predicted the 1-, 2-, and 3-year OS for each patient. Immune cell infiltration was associated with the risk score and the model could predict immunotherapy responsiveness. The expression of the prognostic gene was correlated with the sensitivity to antitumor drugs. Interference of OSMR inhibited proliferation and migration and promoted apoptosis of GBM cells.Conclusions:The prognostic model based on 4 inflammatory response-related genes had reliable predictive power to effectively predict clinical outcome in GBM patients and provided the guide for the personalized treatment.

Abstract Objective The primary objective of this study was to develop an advanced prognostic model centered on amino acid metabolism-related genes(AAMG), specifically in the context of colon adenocarcinoma (COAD). Another aim was to delve into the prognostic significance of these genes and uncover the intricate mechanisms governing COAD. Methods Leveraging comprehensive transcriptome gene expression data alongside detailed clinical records sourced from The Cancer Genome Atlas (TCGA) database, it was attempted to meticulously develop a prognostic model rooted in the realm of amino acid metabolism. Rigorous validation of this model was executed utilizing the Gene Expression Omnibus (GEO) dataset. The comprehensive analysis encompassed a multifaceted approach, involving Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment assessments, meticulous exploration of immunotherapy effectiveness, intricate single-gene bioinformatics examinations, and cellular assays. This broad spectrum of analytical methods was employed to shed light on both the model's prognostic capacity and the intricate mechanisms underpinning its predictive capability. Results The derived prognostic risk model, a meticulously curated composite of five amino acid metabolism-related genes (PTH1R, HEYL, GABRD, NAT1, CALB2), exhibited remarkable prowess in prognostic prediction, a commendable achievement subsequently confirmed in the GEO cohort. The analytical exploration via KEGG pathway enrichment revealed a fascinating enrichment of both tumor-related and immune-related pathways within the high-risk group, prominently featuring key pathways, such as MAPK and T cell receptor signaling pathways. This exhaustive analysis unveiled the intricate nexus among amino acid metabolism and immune-related functions. Furthermore, the high-risk cohort displayed regrettably limited benefits in the realm of immunotherapy. The rigorous suite of cellular experiments furnished conclusive evidence supporting CALB2's pivotal role in driving COAD cells’ proliferation, migration, and the epithelial-mesenchymal transition (EMT). Lastly, the results hinted at the likelihood of the P53 signaling pathway as a probable mediator of CALB2's profound impact on the progression of colon cancer. Conclusion The meticulously developed prognostic model, specializing in amino acid metabolism within the realm of COAD, stands as a robust predictor of COAD prognosis. It was successfully demonstrated that amino acid metabolism-related genes, especially within high-risk group, wield a significant influence over COAD's immune function, with evident consequences of diminished immunotherapy efficacy. Furthermore, the analysis spotlighted the intriguing enrichment of crucial tumor and immune-related pathways in high-risk group. On a granular level, CALB2, a foundational gene within the model, emerged as a key driver in facilitating COAD cell proliferation, migration, and the complex process of EMT. Moreover, findings suggested a tantalizing link between CALB2's mechanistic actions and P53 signaling pathway in the context of colon cancer progression.

Cuprotosis, an emerging mode of cell death, has recently caught the attention of researchers worldwide. However, its impact on low-grade glioma (LGG) patients has not been fully explored. To gain a deeper insight into the relationship between cuprotosis and LGG patients' prognosis, we conducted this study in which LGG patients were divided into two clusters based on the expression of 18 cuprotosis-related genes. We found that LGG patients in cluster A had better prognosis than those in cluster B. The two clusters also differed in terms of immune cell infiltration and biological functions. Moreover, we identified differentially expressed genes (DEGs) between the two clusters and developed a cuprotosis-related prognostic signature through the least absolute shrinkage and selection operator (LASSO) analysis in the TCGA training cohort. This signature divided LGG patients into high- and low-risk groups, with the high-risk group having significantly shorter overall survival (OS) time than the low-risk group. Its predictive reliability for prognosis in LGG patients was confirmed by the TCGA internal validation cohort, CGGA325 cohort and CGGA693 cohort. Additionally, a nomogram was used to predict the 1-, 3-, and 5-year OS rates of each patient. The analysis of immune checkpoints and tumor mutation burden (TMB) has revealed that individuals belonging to high-risk groups have a greater chance of benefiting from immunotherapy. Functional experiments confirmed that interfering with the signature gene TNFRSF11B inhibited LGG cell proliferation and migration. Overall, this study shed light on the importance of cuprotosis in LGG patient prognosis. The cuprotosis-related prognostic signature is a reliable predictor for patient outcomes and immunotherapeutic response and can help to develop new therapies for LGG.

Abstract Stomach adenocarcinoma (STAD) is a major contributor to cancer mortality worldwide. Alterations in amino acid metabolism have been reported in various tumors. However, the prognostic value of amino acid metabolism-related genes in STAD deserves to be further elucidated. In this study, we constructed a prognostic risk model consisting of 3 amino acid metabolism-related genes (SERPINE1, NRP1, MATN3) in STAD. Based on the median risk score, STAD patients were divided into high- and low-risk groups. The patients with high-risk scores had a worse prognosis. A nomogram consisting of risk score and various clinical characteristics accurately predicted the 1-, 3-, and 5-year survival time of STAD patients. Notably, KEGG pathway enrichment analysis indicated immune-related pathways enriched in the high-risk group. High-risk scores were significantly related to C6 (TGF-β dominant type), while low-risk scores were significantly related to C4 (lymphocyte-depleted type). The higher risk score was associated with higher immune infiltration, immune-related function, lower tumor purity and worse response to immunotherapy. In addition, the model genes were correlated with antitumor drug sensitivity. Finally, functional assays confirmed that interference of model gene MATN3 inhibited the proliferation and migration of STAD cells. In conclusion, the amino acid metabolism-related prognostic model might be used as a biomarker to predict the prognosis and guide immunotherapy for STAD patients.

Background: Stomach adenocarcinoma (STAD) is one of the most common tumors. Tumor mutation burden (TMB) has been linked to immunotherapy response. We wanted to see if there was any link between TMB and cancer prognosis. Methods: The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to obtain mutation data, gene expression profiles, and clinical data. We looked at the differences in gene expression and immune markers between low and high TMB groups, built an immune prognostic model, and created a dynamic nomograph App that may be used in the clinic. Simultaneously, We ran the immunotherapy prediction and model comparison at the same time. Finally, model gene mutation and copy number variation (CNV) were displayed. The cellular functional experiments were used to investigate the potential role of GLP2R in gastric cancer. Results: Firstly, basic mutation information and differences in immune infiltration in STAD are revealed. Secondly, the prognostic model developed by us has good accuracy, and the corresponding dynamic nomograph Apps online and immunotherapy prediction facilitate clinical transformation. Furthermore, GLP2R knockdown significantly inhibited the proliferation, migration of gastric cancer cells in vitro. Conclusion: Our findings imply that TMB plays a significant role in the prognosis of STAD patients from a biological perspective. GLP2R may serve as a potential target for gastric cancer.

Radiation-induced brain injury is a common complication of brain irradiation that eventually leads to irreversible cognitive impairment. Evidence has shown that the gut microbiome may play an important role in radiation-induced cognitive function. However, the effects of gut microbiota on radiation-induced brain injury (RIBI) remain poorly understood. Here we studied the link between intestinal microbes and radiation-induced brain injury to further investigate the effects of intestinal bacteria on neuroinflammation and cognitive function. We first verified the differences in gut microbes between male and female mice and administered antibiotics to C57BL/6 male mice to deplete the gut flora and then expose mice to radiation. We found that depletion of intestinal flora after irradiation may act as a protective modulator against radiation-induced brain injury. Moreover, we found that pretreatment with depleted gut microbes in RIBI mice suppressed brain pro-inflammatory factor production, and high-throughput sequencing analysis of mouse feces at 1-month postirradiation revealed microbial differences. Interestingly, a proportion of Verrucomicrobia Akkermansia showed partial recovery. Additionally, short-chain fatty acid treatments increased neuroinflammation in the radiation-induced brain injury model. Although a further increase in cognitive function was not observed, brain injury was aggravated in whole-brain irradiated mice to some extent. The protective effects of depleted intestinal flora and the utilization of the brain-gut axis open new avenues for development of innovative therapeutic strategies for radiation-induced brain injury.

N6-methylandrostenedione (m6A) methylation plays a very important role in the development of malignant tumors. The immune system is the key point in the progression of tumors, particularly in terms of tumor treatment and drug resistance. Tumor immunotherapy has now become a hot spot and a new approach for tumor treatment. However, as far as the stomach adenocarcinoma (STAD) is concerned, the in-depth research is still a gap in the m6A-associated immune markers. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases is extremely important for our research, where we obtained gene mutation, gene expression data and relevant clinical information of STAD patients. Firstly, the samples from GEO were used as external validation groups, while the TCGA samples were divided into a training group and an internal validation group randomly. Using the way of Single factor COX-LASSO- and multi-factor Cox to construct the prognostic model. Then, all samples were subjected to cluster analysis to generate high and low expression groups of immune gene. Meanwhile, we also collected the correlation between these types and tumor microenvironment. On this basis, a web version of the dynamic nomogram APP was developed. In addition, we performed microenvironmental correlation, copy number variation and mutation analyses for model genes. The prognostic model for STAD developed here demonstrated a very strong predictive ability. The results of cluster analysis manifested that the immune gene low expression group had lower survival rate and higher degree of immune infiltration. Therefore, the immune gene low expression group was associated with lower survival rates and a higher degree of immune infiltration. Gene set enrichment analysis suggested that the potential mechanism might be related to the activation of immunosuppressive functions and multiple signaling pathways. Correspondingly, the web version of the dynamic nomogram APP produced by the DynNom package has successfully achieved rapid and accurate calculation of patient survival rates. Finally, the multi-omics analysis of model genes further enriched the research content. Interference of RAB19 was confirmed to facilitate migration of STAD cells in vitro, while its overexpression inhibited these features. The prognostic model for STAD constructed in this study is accurate and efficient based on multi-omics analysis and experimental validation. Additionally, the results of the correlation analysis between the tumor microenvironment and m6Ascore are the basics of further exploration of the pathophysiological mechanism in STAD.

Background Accumulating evidence shows that m6A regulates oncogene and tumor suppressor gene expression, thus playing a dual role in cancer. Likewise, there is a close relationship between the immune system and tumor development and progression. However, for glioblastoma, m6A-associated immunological markers remain to be identified. Methods We obtained gene expression, mutation, and clinical data on glioblastoma from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Next, we performed univariate COX–least absolute shrinkage and selection operator (LASSO)–multivariate COX regression analyses to establish a prognostic gene signature and develop a corresponding dynamic nomogram application. We then carried out a clustering analysis twice to categorize all samples according to their m6A-regulating and m6A-associated immune gene expression levels (high, medium, and low) and calculated their m6A score. Finally, we performed quantitative reverse transcription-polymerase chain reaction, cell counting kit-8, cell stemness detection, cell migration, and apoptosis detection in vitro assays to determine the biological role of CD81 in glioblastoma cells. Results Our glioblastoma risk score model had extremely high prediction efficacy, with the area under the receiver operating characteristic curve reaching 0.9. The web version of the dynamic nomogram application allows rapid and accurate calculation of patients’ survival odds. Survival curves and Sankey diagrams indicated that the high-m6A score group corresponded to the groups expressing medium and low m6A-regulating gene levels and high m6A-associated prognostic immune gene levels. Moreover, these groups displayed lower survival rates and higher immune infiltration. Based on the gene set enrichment analysis, the pathophysiological mechanism may be related to the activation of the immunosuppressive function and related signaling pathways. Moreover, the risk score model allowed us to perform immunotherapy benefit assessment. Finally, silencing CD81 in vitro significantly suppressed proliferation, stemness, and migration and facilitated apoptosis in glioblastoma cells. Conclusion We developed an accurate and efficient prognostic model. Furthermore, the correlation analysis of different stratification methods with tumor microenvironment provided a basis for further pathophysiological mechanism exploration. Finally, CD81 may serve as a diagnostic and prognostic biomarker in glioblastoma.