Construction of a novel cancer-associated fibroblast-related signature to predict clinical outcome and immune response in cervical cancer.

This study developed a prognostic signature for cervical cancer using transcriptome profiling and clinical data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and TISCH database, focusing on cancer-associated fibroblasts (CAFs). Through LASSO Cox regression and integrated bioinformatics analyses, we identified 144 differentially expressed genes (DEGs) related to CAFs, from which an 11-gene CAF-related signature (CAFRSig) was constructed. The CAFRSig effectively stratified patients into high- and low-risk categories, demonstrating significant prognostic capability in predicting overall survival. Gene ontology (GO) and gene set variation analysis (GSVA) linked the DEGs to crucial pathways in tumor malignancy, immune response, and fatty acid metabolism. The immune landscape analysis, utilizing the TIMER platform and CIBERSORT algorithm, revealed a positive correlation between immune cell effector functions and CAFRSig scores, highlighting the model's potential to identify patients likely to respond to immune checkpoint blockade (ICB) therapies. Furthermore, neuropilin 1 (NRP1), a key gene in the CAFRSig, was upregulated in cervical cancer tissues and associated with disease progression and differentiation. The downregulation of NRP1 curbed cell proliferation and influenced the epithelial-mesenchymal transition (EMT), implicating the PI3K/AKT pathway and modulating PD-L1 expression. This comprehensive analysis establishes a robust prognostic signature based on CAF-related genes, offering valuable insights for optimizing therapeutic strategies in cervical cancer management.

Identification and Validation of a Cancer-Testis Antigen-Related Signature to Predict the Prognosis in Stomach Adenocarcinoma.

Background: Stomach adenocarcinoma (STAD) is the fifth most common cancer and the third leading cause of cancer-related deaths worldwide. Cancer-testis antigens (CTAs) participate in the pathogenesis and development of multiple cancers and are aberrantly overexpressed in various types of cancer. This study aimed to develop a CTA-related gene signature (CTARSig) to predict prognosis in STAD patients and explore its underlying mechanisms. Methods: We performed differential and prognostic analyses of CTA-related genes and constructed a CTA-related signature (CTARSig) along with a novel nomogram to predict the prognosis of patients with STAD based on the Cox and The Least Absolute Shrinkage and Selection Operator. CTARSig was further validated in an external cohort (GSE84437). Additionally, univariate and multivariate Cox regression, as well as receiver operating characteristic (ROC) analyses, were performed to assess the CTARSig systematically. Single-sample gene set enrichment analysis and ESTIMATE were used to characterise the Tumor Immune Microenvironment (TIME) in patients with STAD. Furthermore, Gene Set Variation Analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology analyses revealed the biological functions and signalling pathways associated with CTARSig. Finally, the human gastric cancer cell lines, HCG-27 and AGS, were used for in vitro and in vivo experiments, respectively, to further validate the role of ELOVL4. Results: Eleven CTA-related genes were identified to construct the CTARSig. Kaplan-Meier curves, independent prognostic analysis, and ROC curves revealed that CTARSig could better predict survival in patients with STAD. Moreover, in our study, we demonstrated that ELOVL4 is upregulated in gastric cancer tissues and that its high expression is associated with poor survival. Additionally, in vitro and in vivo experiments demonstrated that ELOVL4 promotes the metastatic and invasive potential of STAD cells, suggesting it may be a potential therapeutic target for STAD. Conclusion: In this study, a novel signature associated with CTAs was constructed for STAD, which may be a good predictor of patient prognosis. Thus, ELOVL4 may be a potential therapeutic target for gastric cancer. This study provides new insights into the potential roles of CTAs in gastric cancer.

Identification and verification of a PANoptosis-related long noncoding ribonucleic acid signature for predicting the clinical outcomes and immune landscape in lung adenocarcinoma.

PANoptosis is a type of programmed cell death (PCD) characterised by apoptosis, necroptosis and pyroptosis. Long non-coding ribonucleic acids (lncRNAs) are participating in the malignant behaviour of tumours regulated by PCD. Nevertheless, the function of PANoptosis-associated lncRNAs in lung adenocarcinoma remains to be investigated. In this work, a PANoptosis-related lncRNA signature (PRLSig) was developed based on the least absolute shrinkage and selection operator algorithm. The stability and fitness of PRLSig were confirmed by systematic evaluation of Kaplan-Meier, Cox analysis algorithm, receiver operating characteristic analysis, stratification analysis. In addition, ESTIMATE, single sample gene set enrichment analysis, immune checkpoints and the cancer immunome database confirmed the predictive value of the PRLSig in immune microenvironment and helped to identify populations for which immunotherapy is advantageous. The present research provides novel insights to facilitate risk stratification and optimise personalised treatment for LUAD.

PANoptosis-related long non-coding RNA signature to predict the prognosis and immune landscapes of pancreatic adenocarcinoma.

Background: Cancer growth is significantly influenced by processes such as pyroptosis, apoptosis, and necroptosis that underlie PANoptosis, a proinflammatory programmed cell death. Several studies have examined the long non-coding RNAs (lncRNAs) associated with pancreatic adenocarcinoma (PAAD). However, the predictive value of lncRNAs related to PANoptosis for PAAD has not been established. Methods: The Clinical Genome Atlas database was used to obtain the transcriptome 、clinical data and the corresponding mutation data of the patients with PAAD in this study. The least absolute shrinkage and selection operator regression analysis was employed to obtain prognosis-related lncRNAs for constructing a risk signature. According to the median risk score of the signature, patients with PAAD were grouped into low- and high-risk groups to further compare the survival prognosis of different risk groups. Time-dependent receiver operating characteristic curves, c-index analysis, nomograms, principal component analysis and univariate Cox and multivariate Cox regression were performed for the internal validation of the signature. In addition, enrichment analysis of different genes was performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Lastly, differences in tumor mutation burden (TMB), immune function, tumor immune dysfunction and rejection (TIDE), and drug response were determined for the two risk groups. Results: The signature was constructed with six PANoptosis-related lncRNAs (AC067817.2、LINC02004、AC243829.1、AC092171.5、AP005233.2、AC004687.1) that predicted the prognosis of the patients with PAAD. Survival curves showed that patients in the two risk groups had statistically significant differences in prognosis (P < 0.05), and multi-cox regression analysis identified risk score as an independent risk factor for PAAD prognosis, and internal validation of nomograms showed high confidence in the signature. GO and KEGG enrichment analysis showed functional and pathway differences between the high- and low-risk groups. TMB evaluation demonstrated that patients in the high-risk group had a higher frequency of mutations. The TIDE score indicated that the high-risk group had a lower risk of immunotherapy escape and better immunotherapy outcomes. Additionally, the two risk groups revealed significantly different responses to 11 anticancer drugs. Conclusion: We identified a novel risk signature for PANoptosis-related lncRNAs, which is a standalone prognostic indicator for PAAD. The PANoptosis-related lncRNA risk signature may be relevant for immunotherapy and a therapeutic target for PAAD.

Identification of a cancer associated fibroblasts-related index to predict prognosis and immune landscape in ovarian cancer.

Cancer-associated fibroblasts (CAFs) play a role in ovarian cancer (OV) evolution, immunosuppression and promotion of drug resistance. Exploring the value of CAFs-related biomarker in OV is of great importance. In the present work, we developed a CAFs-related index (CAFRI) based on an integrated analysis of single-cell and bulk RNA-sequencing and highlighted the value of CAFRI in predicting clinical outcomes in individuals with OV, tumour immune microenvironment (TIME) and response to immune checkpoint inhibitors (ICIs). The GSE151214 cohort was used for cell subpopulation localization and analysis, the TCGA-OV patients as a training set. Moreover, the ICGC-OV, GSE26193, GSE26712 and GSE19829 cohorts were used for the validation of CAFRI. The TIMER 2.0, CIBERSORT and ssGSEA algorithms were used for analysis of TIME characteristics based on the CAFRI. The GSVA, GSEA, GO, KEGG and tumour mutation burden (TMB) analyses were used for mechanistic exploration. Additionally, the IMvigor210 cohort was conducted to validate the predictive value of CAFRI on the efficacy of ICIs. Finally, CAFRI-based antitumour drug sensitivity was analysed. The findings demonstrate that the CAFRI can served as an excellent predictor of prognosis for individuals with OV, as well as identifying patients with different TIME characteristics, differentiating between immune 'hot' and 'cold' tumour populations, and providing new insights into the selection of ICIs and personalised treatment regimens. CAFRI provides new perspectives for the development of novel prognostic and immunotherapy efficacy predictive biomarkers for OV.

The cancer/testis antigen HORMAD1 promotes gastric cancer progression by activating the NF-κB signaling pathway and inducing epithelial-mesenchymal transition.

OBJECTIVES: HORMAD1 is a cancer/testis antigen (CTAs) that regulates DNA homologous recombination, mismatch repair, and other tumor characteristics. However, its role and regulatory mechanisms in gastric cancer remain unclear. METHODS: We performed transcriptomic profiling on seven gastric cancers and paired tissues; HORMAD1 was significantly upregulated in gastric cancer samples and was related to poor prognosis survival. Furthermore, cancer pathway microarray, bioinformatic analysis, western blot, and immunochemistry assay demonstrated that HORMAD1 affected the NF-κB signaling pathway. RESULTS: In vitro and vivo studies confirmed that HORMAD1 knockdown inhibited cell growth and invasion, whereas overexpression reversed these effects. Mechanistically, HORMAD1 regulates the epithelial-mesenchymal transition process (EMT) via the NF-κB pathway by increasing the phosphorylation levels of NF-κB (p-65) and Iκκ-ß. Downstream target genes of the NF-κB signaling pathway, such as c-Myc, CyclinD1, may be involved in HORMAD1-induced tumorigenesis in gastric cancer (GC). CONCLUSIONS: HORMAD1 plays an important role in gastric cancer progression and could be a promising prognostic biomarker and therapeutic target.

Construction of a novel cancer-associated fibroblast-related signature to predict clinical outcome and immune response in colon adenocarcinoma.

The interaction between the tumour and the surrounding microenvironment determines the malignant biological behaviour of the tumour. Cancer-associated fibroblasts (CAFs) coordinate crosstalk between cancer cells in the tumour immune microenvironment (TIME) and are extensively involved in tumour malignant behaviours, such as immune evasion, invasion and drug resistance. Here, we performed differential and prognostic analyses of genes associated with CAFs and constructed CAF-related signatures (CAFRs) to predict clinical outcomes in individuals with colon adenocarcinoma (COAD) based on machine learning algorithms. The CAFRs were further validated in an external independent cohort, GSE17538. Additionally, Cox regression, receiver operating characteristic (ROC) and clinical correlation analysis were utilised to systematically assess the CAFRs. Moreover, CIBERSORT, single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) analysis were utilised to characterise the TIME in patients with COAD. Microsatellite instability (MSI) and tumour mutation burden were also analysed. Furthermore, Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) elucidated the biological functions and signalling pathways involved in the CAFRs. Consensus clustering analysis was used for the immunological analysis of patients with COAD. Finally, the pRRophic algorithm was used for sensitivity analysis of common drugs. The CAFRs constructed herein can better predict the prognosis in COAD. The cluster analysis based on the CAFRs can effectively differentiate between immune 'hot' and 'cold' tumours, determine the beneficiaries of immune checkpoint inhibitors (ICIs) and provide insight into individualised treatment for COAD.

Knowledge mapping of early-onset colorectal cancer from 2000 to 2022: A bibliometric analysis.

The incidence of early-onset colorectal cancer (EO-CRC), diagnosed in patients younger than 50 years, has increased in incidence alarmingly over the past few decades, while overall incidence and mortality of colorectal cancer are stabilizing or declining in many high-income countries. These unfavorable changes have raised significant concerns and led to extensive research, resulting in a surge in studies on EO-CRC. Our aim was to obtain a more comprehensive understanding of the current state of this field and to identify prospective research directions by performing a bibliometric analysis of EO-CRC. A total of 1952 papers on EO-CRC published from 2000 to 2022 were identified after a thorough search of the Web of Science Core Collection. The United States dominated this field, with Harvard University contributing the greatest number of papers, while the journal Familial Cancer (n = 52) published the most articles. Cooperation network analysis revealed close internal cooperation among countries, institutions and authors. Based on reference and keyword analysis, high-frequency keywords showed several popular research directions, including epidemiology (incidence, young patients, age of onset, etc.), risk factors (obesity, family history, lynch syndrome, etc.) and molecular characterization (germline mutation, genome wide association, MLH1, etc.). Overall, our research provides an overview of the current status in this field, which we hope will give researchers a comprehensive perspective on the present trends within this domain.

Identification and validation of a novel anoikis-related long non-coding RNA signature for pancreatic adenocarcinoma to predict the prognosis and immune response.

OBJECTIVE: To provide more precise treatment options for pancreatic adenocarcinoma (PAAD) patients and improve their prognosis,we established a novel anoikis-related long non-coding RNA signature (ARLSig) to predict the prognosis and immune response for PAAD patients. METHODS: We downloaded information on PAAD from The Cancer Genome Atlas (TCGA) database, and screened long non-coding RNA (lncRNA) linked with anoikis, and prognostic signatures with these lncRNAs. After that, ARLSig was verified using receiver operating characteristic (ROC) and C-index curves. To further investigate the role of ARLSig, we also performed enrichment analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Additionally, using immunological correlation analysis and single-sample genetic enrichment analysis, we investigated the effectiveness of PAAD immunotherapy. RESULTS: We screened 7 lncRNAs to construct a novel ARLSig and utilized it to predict the efficacy of immunotherapy and the prognosis of PAAD patients. CONCLUSION: ARLSig can identify patients who will benefit from immunotherapy and improve the prediction of PAAD patient prognosis.

N6AMT1 is a novel potential diagnostic, prognostic and immunotherapy response biomarker in pan-cancer.

BACKGROUND: The N-6-adenine-specific DNA methyltransferase 1 (N6AMT1) is the only writer responsible for DNA 6mA modifications. At present, its role in cancer is still unclear, and further systematic pan-cancer analysis is needed to explore its value in diagnosis, prognosis and immunological function. METHODS: The subcellular localization of N6AMT1 was explored by UniProt and HPA database. The expression data and prognosis data of N6AMT1 were downloaded from the UCSC (cohort: TCGA pan-cancer), and the diagnostic and prognostic value of N6AMT1 in pan-cancer was explored. The value of N6AMT1-guided immunotherapy was explored through three cohorts (GSE168204, GSE67501 and IMvigor210 cohort). The correlation between N6AMT1 expression and tumor immune microenvironment was explored using CIBERSORT and ESTIMATE calculation methods, combined with TISIDB database. The biological role of N6AMT1 in specific tumors was explored by GSEA method. Finally, we explored chemicals affecting N6AMT1 expression through the CTD. RESULTS: N6AMT1 is mainly localized in the nucleus and differentially expressed in 9 cancer types. In addition, N6AMT1 showed early diagnostic value in 7 cancers and showed potential prognostic value in multiple cancer types. We also demonstrated that N6AMT1 expression was significantly associated with immunomodulator-related molecules, infiltration of lymphocyte subsets, and biomarkers of immunotherapy response. Furthermore, we show that N6AMT1 is differentially expressed in the immunotherapy cohort. Finally, we explored 43 chemicals that can affect N6AMT1 expression. CONCLUSIONS: N6AMT1 has shown excellent diagnostic and prognostic capabilities in a variety of cancers, and it may reshape the tumor microenvironment and contribute to the ability to predict response to immunotherapy.

The progress on the relationship between gut microbiota and immune checkpoint blockade in tumors.

Immune checkpoint blockade (ICB) has emerged as a promising immunotherapeutic approach for the treatment of various tumors. However, the efficacy of this therapy is limited in a subset of patients, and it is important to develop strategies to enhance immune responses. Studies have demonstrated a critical role of gut microbiota in regulating the therapeutic response to ICB. Gut microbiota composition, diversity, and function are mediated by metabolites, such as short-chain fatty acids and secondary bile acids, that interact with host immune cells through specific receptors. In addition, gut bacteria may translocate to the tumor site and stimulate antitumor immune responses. Therefore, maintaining a healthy gut microbiota composition, for instance through avoiding the use of antibiotics or probiotic interventions, can be an effective approach to optimize ICB therapy. This review summarizes the current understanding of the microbiota-immunity interactions in the context of ICB therapy, and discusses potential clinical implications of these findings.

Prognostic analysis of the plasma fibrinogen combined with neutrophil-to-lymphocyte ratio in patients with non-small cell lung cancer after radical resection.

BACKGROUND: To investigate the correlation between the fibrinogen combined with neutrophil-to-lymphocyte ratio (F-NLR) and the clinicopathologic features of non-small cell lung cancer (NSCLC) patients who underwent radical resection. METHODS: This study reviewed the medical records of 289 patients with NSCLC who underwent radical resection. The patients were stratified into three groups based on F-NLR as follows: patients with low NLR and fibrinogen were group A, patients with high NLR or fibrinogen were group B, and patients with high NLR and fibrinogen were group C. Receiver operating characteristic curve and Youden index were used to determine the cutoff value of the NLR and fibrinogen. Survival curves were described by Kaplan-Meier method and compared by log-rank test. The univariate and multivariate analyses were performed with the Cox proportional hazard model to identify the prognostic factors. RESULTS: A value of 3.19 was taken as the optimal cutoff value of NLR in this study. A value of 309 was used as the optimal cutoff value of fibrinogen. Cox multivariate analysis showed that tumor, nodes, metastasis (TNM) stage and F-NLR were independent prognostic factors affecting the survival rate of patients. The first-, third-, and fifth-year survival rates in group A were 99.2%, 96.6%, and 95.0%, respectively. The first-, third-, and fifth-year survival rates in group B were 98.4%, 76.6%, and 63.2%, respectively. The first-, third-, and fifth-year survival rates in group C were 91.3%, 41.1%, and 22.8%, respectively. F-NLR was significantly correlated with overall survival in patients with NSCLC (p < 0.001). CONCLUSIONS: The F-NLR level is markedly related to the prognosis of patients with NSCLC undergoing radical surgery. Therefore, closer attention should be given to patients with NSCLC with a high F-NLR before surgery to provide postoperative adjuvant therapy.

Effectiveness and safety of mono-anlotinib mono therapy or in combination with chemotherapy in platinum-resistant recurrent ovarian cancer: a single-center retrospective study.

OBJECTIVE: To evaluate the clinical efficacy of mono-anlotinib therapy by itself or in combination with chemotherapy in platinum-resistant recurrent ovarian cancer (PROC). METHODS: The clinical data of 35 patients with platinum-resistant recurrent ovarian cancer admitted to the First Affiliated Hospital of Anhui Medical University from March 2019 to July 2020 were retrospectively analyzed. All the patients received anlotinib mono- or combined chemotherapy. The effectiveness and adverse events (AEs) were analyzed by RECIST1.1 and CTCAE5.0. RESULTS: In the 35 patients, the median follow-up was 9.80 (95% CI: 3.83-15.77) months. The median progression free survival (mPFS) achieved 6.50 (95% CI: 2.02-10.98) months, the objective response rate (ORR) achieved 17.14%, and disease control rate (DCR) achieved 60.00%. ORR and DCR were 12.50% and 25.0% for monotherapy, 18.52% and 70.37% for combined chemotherapy. The PFS of combined chemotherapy was longer than that of monotherapy (log-rank P = 0.003). thirty-four patients (97.14%) were in a third-line therapy or above, and their ORR and DCR were 14.71% and 58.82%, respectively. Two patients discontinued treatment because of intolerable AEs. No cases of grade 4-5 AEs have been reported. CONCLUSION: Anlotinib had promising effectiveness and tolerable safety in patients with PROC, even in patients who accepted anlotinib as a third-line or above therapy or with a history of other antiangiogenic drugs.

An angiogenesis-related three-long non-coding ribonucleic acid signature predicts the immune landscape and prognosis in hepatocellular carcinoma.

The tumour microenvironment is a key determinant of the efficacy of immunotherapy. Angiogenesis is closely linked to tumour immunity. We aimed to screen long non-coding ribonucleic acids (lncRNAs) associated with angiogenesis to predict the prognosis of individuals with hepatocellular carcinoma (HCC) and characterise the tumour immune microenvironment (TIME). Patient data, including transcriptome and clinicopathological parameters, were retrieved from The Cancer Genome Atlas database. Moreover, co-expression algorithm was utilized to obtain angiogenesis-related lncRNAs. Additionally, survival-related lncRNAs were identified using Cox regression and the least absolute shrinkage and selection operator algorithm, which aided in constructing an angiogenesis-related lncRNA signature (ARLs). The ARLs was validated using Kaplan-Meier method, time-dependent receiver operating characteristic analyses, and Cox regression. Additionally, an independent external HCC dataset was used for further validation. Then, gene set enrichment analysis, immune landscape, and drug sensitivity analyses were implemented to explore the role of the ARLs. Finally, cluster analysis divided the entire HCC dataset into two clusters to distinguish different subtypes of TIME. This study provides insight into the involvement of angiogenesis-associated lncRNAs in predicting the TIME characteristics and prognosis for individuals with HCC. Furthermore, the developed ARLs and clusters can predict the prognosis and TIME characteristics in HCC, thereby aiding in selecting the appropriate therapeutic strategies involving immune checkpoint inhibitors and targeted drugs.

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma.

Background: Cancer-associated fibroblasts (CAFs) are an essential cell population in the pancreatic cancer tumor microenvironment and are extensively involved in drug resistance and immune evasion mechanisms. Long non-coding RNAs (lncRNAs) are involved in pancreatic cancer evolution and regulate the biological behavior mediated by CAFs. However, there is a lack of understanding of the prognostic signatures of CAFs-associated lncRNAs in pancreatic cancer patients. Methods: Transcriptomic and clinical data for pancreatic adenocarcinoma (PAAD) and the corresponding mutation data were obtained from The Cancer Genome Atlas database. lncRNAs associated with CAFs were obtained using co-expression analysis. lncRNAs were screened by Cox regression analysis using least absolute shrinkage and selection operator (LASSO) algorithm for constructing predictive signature. According to the prognostic model, PAAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used for survival validation of the model in the training and validation groups. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. The gene set variation analysis (GSVA) and gene ontology (GO) analyses were used to explore differences in the biological behavior of the risk groups. Furthermore, single-sample gene set enrichment analysis (ssGSEA), tumor mutation burden (TMB), ESTIMATE algorithm, and a series of immune correlation analyses were performed to investigate the relationship between predictive signature and the tumor immune microenvironment and screen for potential responders to immune checkpoint inhibitors. Finally, drug sensitivity analyses were used to explore potentially effective drugs in high- and low-risk groups. Results: The signature was constructed with seven CAFs-related lncRNAs (AP005233.2, AC090114.2, DCST1-AS1, AC092171.5, AC002401.4, AC025048.4, and CASC8) that independently predicted the prognosis of PAAD patients. Additionally, the high-risk group of the model had higher TMB levels than the low-risk group. Immune correlation analysis showed that most immune cells, including CD8+ T cells, were negatively correlated with the model risk scores. ssGSEA and ESTIMATE analyses further indicated that the low-risk group had a higher status of immune cell infiltration. Meanwhile, the mRNA of most immune checkpoint genes, including PD1 and CTLA4, were highly expressed in the low-risk group, suggesting that this population may be "hot immune tumors" and have a higher sensitivity to immune checkpoint inhibitors (ICIs). Finally, the predicted half-maximal inhibitory concentrations of some chemical and targeted drugs differ between high- and low-risk groups, providing a basis for treatment selection. Conclusion: Our findings provide promising insights into lncRNAs associated with CAFs in PAAD and provide a personalized tool for predicting patient prognosis and immune microenvironmental landscape.

Role of ARRB1 in prognosis and immunotherapy: A Pan-Cancer analysis.

Background: ß-arrestin1 (ARRB1), was originally identified as a multifunctional adaptor protein. Although ARRB1 has recently been shown to also play an important role in tumor growth, metastasis, inflammation, and immunity, its relationship with distinct tumor types and the tumor immune microenvironment remains unclear. Methods: We analyzed the ARRB1 expression profile and clinical characteristics in 33 cancer types using datasets from The Cancer Genome Atlas (TCGA) database. Clinical parameters such as patient survival, tumor stage, age, and gender were used to assess the prognostic value of ARRB1. The Human Protein Atlas (HPA) database was used to explore ARRB1 protein expression data. ESTIMATE and CIBERSORT algorithms were performed to assess immune infiltration. Furthermore, putative correlations between ARRB1 and tumor-infiltrating immune cells, the signatures of T-cell subtypes, immunomodulators, the tumor mutation burden (TMB), Programmed cell death ligand 1 (PD-L1), and microsatellite instability (MSI) were also explored. Gene functional enrichment was determined using GSEA. GSE40435 and GSE13213 cohorts were used to validate the correlation of ARRB1 with KIRC and LUAD clinicopathological parameters. Finally, the relationship between ARRB1 and immunotherapeutic responses was assessed using three independent immunotherapy cohorts, namely, GSE67501, GSE168204, and IMvigor210. Results: We found that ARRB1 expression levels were lower in 17 tumor tissues than in the corresponding normal tissues. We further found that ARRB1 expression was significantly correlated with tumor stage in BRCA, ESCA, KIRC, TGCT, and THCA, while in some tumors, particularly KIRC and LUAD, ARRB1 expression was associated with better prognosis. ARRB1 expression was also positively correlated with the stromal score or the immune score in some tumors. Regarding immune cell infiltration, ARRB1 expression in DLBC was positively correlated with M1 macrophage content and negatively correlated with B-cell infiltration. Additionally, there was a broad correlation between ARRB1 expression and three classes of immunomodulators. Furthermore, high ARRB1 expression levels were significantly correlated with some tumor immune-related pathways. Finally, ARRB1 expression was significantly associated with MSI, PD-L1, and TMB in some tumors and with the efficacy of immune checkpoint inhibitors (ICIs) in melanoma. Conclusion: ARRB1 has prognostic value in malignant tumors, especially in KIRC and LUAD. At the same time, ARRB1 was closely correlated with the tumor immune microenvironment and indicators of immunotherapy efficacy, indicating its great potential as a reliable marker for predicting the efficacy of immunotherapy.

Construction of a Necroptosis-Associated Long Non-Coding RNA Signature to Predict Prognosis and Immune Response in Hepatocellular Carcinoma.

Background: Necroptosis is a form of programmed cell death, and studies have shown that long non-coding RNA molecules (lncRNAs) can regulate the process of necroptosis in various cancers. We sought to screen lncRNAs associated with necroptosis to predict prognosis and tumor immune infiltration status in patients with hepatocellular carcinoma (HCC). Methods: Transcriptomic data from HCC tumor samples and normal tissues were extracted from The Cancer Genome Atlas database. Necroptosis-associated lncRNAs were obtained by co-expression analysis. Necroptosis-associated lncRNAs were then screened by Cox regression and least absolute shrinkage and selection operator methods to construct a risk model for HCC. The models were also validated and evaluated by Kaplan-Meier analysis, univariate and multivariate Cox regression, and time-dependent receiver operating characteristic (ROC) curves. In addition, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment, gene set enrichment, principal component, immune correlation, and drug sensitivity analyses were applied to assess model risk groups. To further differentiate the immune microenvironment of different HCC subtypes, the entire dataset was divided into three clusters, based on necroptosis-associated lncRNAs, and a series of analyses performed. Results: We constructed a model comprising four necroptosis-associated lncRNAs: POLH-AS1, DUXAP8, AC131009.1, and TMCC1-AS1. Overall survival (OS) duration was significantly longer in patients classified as low-risk than those who were high-risk, according to our model. Univariate and multivariate Cox regression analyses further confirmed risk score stability. The analyzed models had area under the ROC curve values of 0.786, 0.713, and 0.639 for prediction of 1-, 3-, and 5-year OS, respectively, and risk score was significantly associated with immune cell infiltration and ESTIMATE score. In addition, differences between high and low-risk groups in predicted half-maximal inhibitory concentration values for some targeted and chemical drugs, providing a potential basis for selection of treatment approach. Finally, cluster analysis facilitated more refined differentiation of the immune microenvironment in patients with HCC and may allow prediction of the effectiveness of immune checkpoint inhibitors. Conclusions: This study contributes to understanding of the function of necroptosis-related lncRNAs in predicting the prognosis and immune infiltration status of HCC. The risk model constructed and cluster analysis provide a basis for predicting the prognosis of patients with HCC and to inform the selection of immunotherapeutic strategies.

Copy Number Aberrations of Multiple Genes Identified as Prognostic Markers for Extrahepatic Metastasis-free Survival of Patients with Hepatocellular Carcinoma.

Extrahepatic metastasis confers unfavorable patient prognosis in patients with hepatocellular carcinoma (HCC), however, reliable markers allowing prediction of extrahepatic metastasis at the time of initial diagnosis are still lacking. This study was to identify gene-level copy number aberrations (CNAs) related to extrahepatic metastasis-free survival of HCC patients, and further examine the associations between CNAs and gene expression. Array comparative genomic hybridization (aCGH) and expression array were used to analyze gene CNAs and expression levels, respectively. The associations between CNAs of a panel of 20 genes and extrahepatic metastasis-free survival were analyzed in 66 patients with follow-up period of 1.6-90.5 months. The gene expression levels between HCCs with and without gene CNA were compared in 109 patients with HCC. We observed that gains at MDM4 and BCL2L1, and losses at APC and FBXW7 were independent prognostic markers for extrahepatic metastasis-free survival of HCC patients. Integration analysis of aCGH and expression data showed that MDM4 and BCL2L1 were significantly upregulated in HCCs with gene gain, while APC and FBXW7 were significantly downregulated in HCCs with gene loss. We concluded that gene gains at MDM4 and BCL2L1, and losses at APC and FBXW7, with concordant expression changes, were associated with extrahepatic metastasis-free survival of HCC patients and have potential to act as novel prognostic markers.