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Abstract Purpose Although papillary renal cell carcinoma (PRCC) has a relatively favorable prognosis, a small number of patients with lymph node or distant metastasis have a poor prognosis. Owing to the complex typing and heterogeneity of PRCC, it remains difficult to provide risk stratification. The objective of our research was to identify potential markers of PRCC prognosis. Methods We performed proteomics and bioinformatics analyses on six pairs of formalin‐fixed paraffin‐embedded tumor and paired normal tissue samples. The Cancer Genome Atlas (TCGA) data were used to analyze the prognostic value of differentially expressed proteins (DEPs) in PRCC. We verified the expression of the major biomarker through immunohistochemistry (IHC) in 91 PRCC tumor specimens. Results Proteomic analysis revealed 1544 DEPs between tumor and paired normal tissues. PRCC transcriptomic data from the TCGA database revealed that compared to non‐tumor tissues, the expression of high‐mobility group protein A2 (HMGA2) was upregulated in tumor tissues, and patients with high HMGA2 expression exhibited shorter overall survival times. HMGA2 was associated with PRCC tissue subtype and higher cell pleomorphism. Both TCGA and IHC results showed that HMGA2 expression was associated with lymph node metastasis and clinical stage. Conclusion HMGA2 was positively correlated with malignant progression and could be a valuable novel prognostic biomarker for PRCC risk stratification.

Abstract Background As a nucleolar protein associated with ribosome biogenesis in multiple cancer types, PES1 has been reported to be overexpressed, promoting cancer cell proliferation and invasion. However, in head and neck squamous cell carcinoma (HNSCC), the role of PES1 on the prognosis and immune infiltration remains unknown. Methods Multiple databases and qRT‐PCR evaluated the expression of PES1 in HNSCC. The prognostic potential of PES1 in HNSCC patients was analyzed by Cox regression and Kaplan–Meier curves. Then, we used LASSO regression and stepwise multivariate Cox regression to construct the PES1‐related risk assessment model. In addition, the association between PES1 and tumor immune microenvironment and drug sensitivity was explored by R packages. Finally, we used cell function assays to explore in HNSCC if PES1 influences tumor growth and metastasis. Results PES1 was significantly up‐regulated in HNSCC and closely correlated with HPV status, tumor stage, clinical grade, and TP53 mutation status. Survival analysis suggested that PES1 is associated with worse survival outcomes, acting as an independent prognostic indicator for HNSCC. Our model also performed well in terms of prognosis prediction. Furthermore, tumor‐infiltrating immune cells and antitumor drug susceptibility were negatively related to PES1 expression. Functionally, as for HNSCC cell lines in vitro, the knockdown of PES1 could inhibit proliferation, migration, and invasion. Conclusion We have demonstrated that PES1 may be a promoter of tumor growth. PES1 holds excellent promise as a novel biomarker to assess the prognosis of patients with HNSCC and may guide immunotherapy.

Abstract Backgorund Colorectal cancer (CRC) is among the leading causes of cancer‐related deaths among Hispanics living in the United States (USH). Understanding the most common carcinogenic molecular pathways that affect Hispanics with CRC is crucial to guide research efforts in developing new therapeutic modalities incorporating genomically diverse populations. Tumor profiling techniques help identify actionable alternatives to recommend treatment and improve survival in cancer patients. Methods We conducted a secondary data analysis to evaluate the mutational profile of 218 CRC tumors in Hispanics living in Puerto Rico (PRH) who underwent next‐generation sequencing (NGS) testing from 2015 to 2020. We compared the prevalence of CRC tumor somatic mutations in PRHs with the mutational profiles reported for CRC from The Cancer Genome Atlas (TCGA) Pan‐Cancer Clinical Data, the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE)‐Non‐Hispanic, and GENIE‐Hispanic datasets. Results Among the top mutated genes in CRC tumors in PRHs were APC, TP53, and KRAS, which had significantly higher mutational frequencies in PRH compared to the examined datasets, including GENIE‐Hispanics. The most frequent gene amplifications for PRH were CDX2, CDKN1B, and HNRNPA2B1. Targetable biomarkers for CRC, such as microsatellite instability‐high (MSI), wild‐type KRAS, wild‐type NRAS, V600E BRAF, and ERBB2 gene amplifications were found in 2.0%, 43.8%, 97.8%, 3.9%, and 2.3%, respectively, of PRH patients. Conclusion This is the first study to report the mutational profile of CRC tumors in PRHs and make comparisons to other non‐Hispanic and USH populations.

Abstract Background This study aimed to investigate the validity of pathological diagnosis of early CRC (E‐CRC) from the genetic background by comparing data of E‐CRC to colorectal adenoma (CRA) and The Cancer Genome Atlas (TCGA) on advanced CRC (AD‐CRC). Methods TCGA data on AD‐CRC were studied in silico, whereas by next‐generation sequencer, DNA target sequences were performed for endoscopically obtained CRA and E‐CRC samples. Immunohistochemical staining of mismatch repair genes and methylation of MLH1 was also performed. The presence of oncogenic mutation according to OncoKB for the genes of the Wnt, MAPK, and cell‐cycle–signaling pathways was compared among CRA, E‐CRC, and AD‐CRC. Results The study included 22 CRA and 30 E‐CRC lesions from the Chiba University Hospital and 212 AD‐CRC lesions from TCGA data. Regarding the number of lesions with driver mutations in the Wnt and cell‐cycle–signaling pathways, E‐CRC was comparable to AD‐CRC, but was significantly greater than CRA. CRA had significantly more lesions with a driver mutation for the Wnt signaling pathway only, versus E‐CRC. Conclusions In conclusion, the definition of E‐CRC according to the Japanese criteria had a different genetic profile from CRA and was more similar to AD‐CRC. Based on the main pathway, it seemed reasonable to classify E‐CRC as adenocarcinoma. The pathological diagnosis of E‐CRC according to Japanese definition seemed to be valid from a genetic point of view.

Abstract Background Hepatocellular carcinoma (HCC) is one of the major causes of cancer‐related deaths globally. The tumor microenvironment (TME) plays a crucial role in the prognosis and treatment of HCC. Hence, it is important to exploit new biomarkers for survival surveillance and TME estimation of HCC. Methods HCC samples data was collected from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, and clinical samples were collected from our center. The TME of HCC were explored with ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data), ssGSEA (single sample Gene Sets Enrichment Analysis) and CIBERSORT algorithm. Differentially expressed genes were analyzed with functional enrichment analysis. Immunohistochemistry was implemented to validate the results. Results Based on TCGA database, we found that Neutrophil Cytosolic Factor 2 (NCF2) was significantly associated with the prognosis of HCC patients, involved in immune‐related biological processes of HCC and closely associated with some types of immunocompetent cells. The survival analysis based on NCF2 expression assessed by immunohistochemistry also confirmed that NCF2‐positive group had a shorter relapse free survival (RFS) and overall survival (OS) than NCF2‐negative group. Multivariate Cox regression revealed NCF2 expression level and lymphovascular space invasion (LVSI) were independent risk factors for HCC patients. Receiver operating characteristic curves showed that the combination of NCF2 and LVSI had higher predictive efficacy on the 1‐year RFS rate and 5‐year OS rate than each of them alone. Besides, the expression level of NCF2 was positively associated with M0 and M2 macrophages infiltration. Furthermore, NCF2 expression was positively correlated with CSF1, IL4, IL10, CD206, CD163, CSF1R and TGFβ1. Conclusion We proposed that higher NCF2 expression predicted an adverse prognosis and more M2 macrophages infiltration in HCC patients.

Purpose: Although papillary renal cell carcinoma (PRCC) has a relatively favorable prognosis, a small number of patients with lymph node or distant metastasis have a poor prognosis. Owing to the complex typing and heterogeneity of PRCC, it remains difficult to provide risk stratification. The objective of our research was to identify potential markers of PRCC prognosis. Methods: We performed proteomics and bioinformatics analyses on six pairs of formalin‐fixed paraffin‐embedded tumor and paired normal tissue samples. The Cancer Genome Atlas (TCGA) data were used to analyze the prognostic value of differentially expressed proteins (DEPs) in PRCC. We verified the expression of the major biomarker through immunohistochemistry (IHC) in 91 PRCC tumor specimens. Results: Proteomic analysis revealed 1544 DEPs between tumor and paired normal tissues. PRCC transcriptomic data from the TCGA database revealed that compared to non‐tumor tissues, the expression of high‐mobility group protein A2 (HMGA2) was upregulated in tumor tissues, and patients with high HMGA2 expression exhibited shorter overall survival times. HMGA2 was associated with PRCC tissue subtype and higher cell pleomorphism. Both TCGA and IHC results showed that HMGA2 expression was associated with lymph node metastasis and clinical stage. Conclusion: HMGA2 was positively correlated with malignant progression and could be a valuable novel prognostic biomarker for PRCC risk stratification. [ABSTRACT FROM AUTHOR]

Abstract Background Head and neck squamous cell carcinoma (HNSCC) is one of the most common and highly heterogeneous malignancies worldwide. Increasing studies have proven that hypoxia and related long non‐coding RNA (lncRNA) are involved in the occurrence and prognosis of HNSCC. The goal of this work is to construct a risk assessment model using hypoxia‐related lncRNAs (hrlncRNAs) for HNSCC prognosis prediction and personalized treatment. Methods Transcriptome expression matrix, clinical follow‐up data, and somatic mutation data of HNSCC patients were obtained from The Cancer Genome Atlas (TCGA). We used co‐expression analysis to identify hrlncRNAs, then screened for differentially expressed lncRNAs (DEhrlncRNAs), and paired these DEhrlncRNAs. The risk model was established through univariate, least absolute shrinkage and selection operator (LASSO), and stepwise multivariate Cox regression. Finally, we assessed the model from multiple perspectives of tumor mutation burden (TMB), tumor immune infiltration, chemotherapeutic sensitivity, immune checkpoint inhibitor (ICI), and functional enrichment. Results The risk assessment model included 14 hrlncRNA pairs. The risk score was observed to be a reliable prognostic factor. The high‐risk patients had an unfavorable prognosis and significant differences from the low‐risk group in TMB and tumor immune infiltration. In the high‐risk patients, the common immune checkpoints were down‐regulated, including CTLA4 and PDCD1, and the sensibility to paclitaxel and docetaxel was higher. The functional enrichment analysis suggested that the low‐risk group was accompanied by activated immune function. Conclusions The risk assessment model of 14‐hrlncRNA‐pairs demonstrated a promising prognostic prediction for HNSCC patients and can guide personalized clinical treatment.

Background: As a nucleolar protein associated with ribosome biogenesis in multiple cancer types, PES1 has been reported to be overexpressed, promoting cancer cell proliferation and invasion. However, in head and neck squamous cell carcinoma (HNSCC), the role of PES1 on the prognosis and immune infiltration remains unknown. Methods: Multiple databases and qRT‐PCR evaluated the expression of PES1 in HNSCC. The prognostic potential of PES1 in HNSCC patients was analyzed by Cox regression and Kaplan–Meier curves. Then, we used LASSO regression and stepwise multivariate Cox regression to construct the PES1‐related risk assessment model. In addition, the association between PES1 and tumor immune microenvironment and drug sensitivity was explored by R packages. Finally, we used cell function assays to explore in HNSCC if PES1 influences tumor growth and metastasis. Results: PES1 was significantly up‐regulated in HNSCC and closely correlated with HPV status, tumor stage, clinical grade, and TP53 mutation status. Survival analysis suggested that PES1 is associated with worse survival outcomes, acting as an independent prognostic indicator for HNSCC. Our model also performed well in terms of prognosis prediction. Furthermore, tumor‐infiltrating immune cells and antitumor drug susceptibility were negatively related to PES1 expression. Functionally, as for HNSCC cell lines in vitro, the knockdown of PES1 could inhibit proliferation, migration, and invasion. Conclusion: We have demonstrated that PES1 may be a promoter of tumor growth. PES1 holds excellent promise as a novel biomarker to assess the prognosis of patients with HNSCC and may guide immunotherapy. [ABSTRACT FROM AUTHOR]

Backgorund: Colorectal cancer (CRC) is among the leading causes of cancer-related deaths among Hispanics living in the United States (USH). Understanding the most common carcinogenic molecular pathways that affect Hispanics with CRC is crucial to guide research efforts in developing new therapeutic modalities incorporating genomically diverse populations. Tumor profiling techniques help identify actionable alternatives to recommend treatment and improve survival in cancer patients. Methods: We conducted a secondary data analysis to evaluate the mutational profile of 218 CRC tumors in Hispanics living in Puerto Rico (PRH) who underwent next-generation sequencing (NGS) testing from 2015 to 2020. We compared the prevalence of CRC tumor somatic mutations in PRHs with the mutational profiles reported for CRC from The Cancer Genome Atlas (TCGA) Pan-Cancer Clinical Data, the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE)-Non-Hispanic, and GENIE-Hispanic datasets. Results: Among the top mutated genes in CRC tumors in PRHs were APC, TP53, and KRAS, which had significantly higher mutational frequencies in PRH compared to the examined datasets, including GENIE-Hispanics. The most frequent gene amplifications for PRH were CDX2, CDKN1B, and HNRNPA2B1. Targetable biomarkers for CRC, such as microsatellite instability-high (MSI), wild-type KRAS, wild-type NRAS, V600E BRAF, and ERBB2 gene amplifications were found in 2.0%, 43.8%, 97.8%, 3.9%, and 2.3%, respectively, of PRH patients. Conclusion: This is the first study to report the mutational profile of CRC tumors in PRHs and make comparisons to other non-Hispanic and USH populations. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is one of the major causes of cancer‐related deaths globally. The tumor microenvironment (TME) plays a crucial role in the prognosis and treatment of HCC. Hence, it is important to exploit new biomarkers for survival surveillance and TME estimation of HCC. Methods: HCC samples data was collected from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, and clinical samples were collected from our center. The TME of HCC were explored with ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data), ssGSEA (single sample Gene Sets Enrichment Analysis) and CIBERSORT algorithm. Differentially expressed genes were analyzed with functional enrichment analysis. Immunohistochemistry was implemented to validate the results. Results: Based on TCGA database, we found that Neutrophil Cytosolic Factor 2 (NCF2) was significantly associated with the prognosis of HCC patients, involved in immune‐related biological processes of HCC and closely associated with some types of immunocompetent cells. The survival analysis based on NCF2 expression assessed by immunohistochemistry also confirmed that NCF2‐positive group had a shorter relapse free survival (RFS) and overall survival (OS) than NCF2‐negative group. Multivariate Cox regression revealed NCF2 expression level and lymphovascular space invasion (LVSI) were independent risk factors for HCC patients. Receiver operating characteristic curves showed that the combination of NCF2 and LVSI had higher predictive efficacy on the 1‐year RFS rate and 5‐year OS rate than each of them alone. Besides, the expression level of NCF2 was positively associated with M0 and M2 macrophages infiltration. Furthermore, NCF2 expression was positively correlated with CSF1, IL4, IL10, CD206, CD163, CSF1R and TGFβ1. Conclusion: We proposed that higher NCF2 expression predicted an adverse prognosis and more M2 macrophages infiltration in HCC patients. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the validity of pathological diagnosis of early CRC (E‐CRC) from the genetic background by comparing data of E‐CRC to colorectal adenoma (CRA) and The Cancer Genome Atlas (TCGA) on advanced CRC (AD‐CRC). Methods: TCGA data on AD‐CRC were studied in silico, whereas by next‐generation sequencer, DNA target sequences were performed for endoscopically obtained CRA and E‐CRC samples. Immunohistochemical staining of mismatch repair genes and methylation of MLH1 was also performed. The presence of oncogenic mutation according to OncoKB for the genes of the Wnt, MAPK, and cell‐cycle–signaling pathways was compared among CRA, E‐CRC, and AD‐CRC. Results: The study included 22 CRA and 30 E‐CRC lesions from the Chiba University Hospital and 212 AD‐CRC lesions from TCGA data. Regarding the number of lesions with driver mutations in the Wnt and cell‐cycle–signaling pathways, E‐CRC was comparable to AD‐CRC, but was significantly greater than CRA. CRA had significantly more lesions with a driver mutation for the Wnt signaling pathway only, versus E‐CRC. Conclusions: In conclusion, the definition of E‐CRC according to the Japanese criteria had a different genetic profile from CRA and was more similar to AD‐CRC. Based on the main pathway, it seemed reasonable to classify E‐CRC as adenocarcinoma. The pathological diagnosis of E‐CRC according to Japanese definition seemed to be valid from a genetic point of view. [ABSTRACT FROM AUTHOR]

Abstract Background Head and neck squamous cell carcinoma (HNSCC) is a life‐threatening disease with poor prognosis. Pyroptosis has been recently disclosed as a programmed cell death triggered by invasive infection, involved in cancer development. However, the prognosis role of pyroptosis‐related genes in HNSCC has not been discussed. Methods The RNA sequence data of pyroptosis‐related genes were obtained from The Cancer Genome Atlas (TCGA) database. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were performed to screen the HNSCC survival‐related signature genes. We established a HNSCC risk model with the identified prognostic genes, then divided the HNSCC patients into low‐ and high‐risk subgroups according to median risk score. Moreover, we utilized Gene Expression Omnibus (GEO) dataset to validate the risk model. Go and KEGG analyses were conducted to reveal the potential function of differential expression of genes that identified between low‐ and high‐risk subgroups. ESTIMATE algorithm was performed to investigate the immune infiltration of tumors. Correlation between signature gene expression and drug‐sensitivity was disclosed by Spearman's analysis. Results We constructed a HNSCC risk model with identified seven pyroptosis‐related genes (CASP1, GSDME, IL6, NLRP1, NLRP2, NLRP6, and NOD2) as prognostic signature genes. High‐risk subgroup of HNSCC patients in TCGA cohort correlated with lower survival probability than patients from low‐risk subgroup (p

Abstract Background Open chromatin is associated with gene transcription. Previous studies have shown that the density of mutations in open chromatin regions is lower than that in flanking regions because of the higher accessibility of DNA repair machinery. However, in several cancer types, open chromatin regions show an increased local density of mutations in activated regulatory regions. Although the mutation distribution within open chromatin regions in cancer cells has been investigated, only few studies have focused on their functional implications in cancer. To reveal the impact of highly mutated open chromatin regions on cancer, we investigated the association between mutations in open chromatin regions and their possible functions. Methods Whole‐genome sequencing data of 18 cancer types were downloaded from the PanCancer Analysis of Whole Genomes and Catalog of Somatic Mutations in Cancer. We quantified the mutations located in open chromatin regions defined by The Cancer Genome Atlas and classified open chromatin regions into three categories based on the number of mutations. Then, we investigated the chromatin state, amplification, and possible target genes of the open chromatin regions with a high number of mutations. We also analyzed the association between the number of mutations in open chromatin regions and patient prognosis. Results In some cancer types, the proportion of promoter or enhancer chromatin state in open chromatin regions with a high number of mutations was significantly higher than that in the regions with a low number of mutations. The possible target genes of open chromatin regions with a high number of mutations were more strongly associated with cancer than those of other open chromatin regions. Moreover, a high number of mutations in open chromatin regions was significantly associated with a poor prognosis in some cancer types. Conclusions These results suggest that highly mutated open chromatin regions play an important role in cancer pathogenesis and can be effectively used to predict patient prognosis.

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most common and highly heterogeneous malignancies worldwide. Increasing studies have proven that hypoxia and related long non‐coding RNA (lncRNA) are involved in the occurrence and prognosis of HNSCC. The goal of this work is to construct a risk assessment model using hypoxia‐related lncRNAs (hrlncRNAs) for HNSCC prognosis prediction and personalized treatment. Methods: Transcriptome expression matrix, clinical follow‐up data, and somatic mutation data of HNSCC patients were obtained from The Cancer Genome Atlas (TCGA). We used co‐expression analysis to identify hrlncRNAs, then screened for differentially expressed lncRNAs (DEhrlncRNAs), and paired these DEhrlncRNAs. The risk model was established through univariate, least absolute shrinkage and selection operator (LASSO), and stepwise multivariate Cox regression. Finally, we assessed the model from multiple perspectives of tumor mutation burden (TMB), tumor immune infiltration, chemotherapeutic sensitivity, immune checkpoint inhibitor (ICI), and functional enrichment. Results: The risk assessment model included 14 hrlncRNA pairs. The risk score was observed to be a reliable prognostic factor. The high‐risk patients had an unfavorable prognosis and significant differences from the low‐risk group in TMB and tumor immune infiltration. In the high‐risk patients, the common immune checkpoints were down‐regulated, including CTLA4 and PDCD1, and the sensibility to paclitaxel and docetaxel was higher. The functional enrichment analysis suggested that the low‐risk group was accompanied by activated immune function. Conclusions: The risk assessment model of 14‐hrlncRNA‐pairs demonstrated a promising prognostic prediction for HNSCC patients and can guide personalized clinical treatment. [ABSTRACT FROM AUTHOR]

Background: Open chromatin is associated with gene transcription. Previous studies have shown that the density of mutations in open chromatin regions is lower than that in flanking regions because of the higher accessibility of DNA repair machinery. However, in several cancer types, open chromatin regions show an increased local density of mutations in activated regulatory regions. Although the mutation distribution within open chromatin regions in cancer cells has been investigated, only few studies have focused on their functional implications in cancer. To reveal the impact of highly mutated open chromatin regions on cancer, we investigated the association between mutations in open chromatin regions and their possible functions. Methods: Whole‐genome sequencing data of 18 cancer types were downloaded from the PanCancer Analysis of Whole Genomes and Catalog of Somatic Mutations in Cancer. We quantified the mutations located in open chromatin regions defined by The Cancer Genome Atlas and classified open chromatin regions into three categories based on the number of mutations. Then, we investigated the chromatin state, amplification, and possible target genes of the open chromatin regions with a high number of mutations. We also analyzed the association between the number of mutations in open chromatin regions and patient prognosis. Results: In some cancer types, the proportion of promoter or enhancer chromatin state in open chromatin regions with a high number of mutations was significantly higher than that in the regions with a low number of mutations. The possible target genes of open chromatin regions with a high number of mutations were more strongly associated with cancer than those of other open chromatin regions. Moreover, a high number of mutations in open chromatin regions was significantly associated with a poor prognosis in some cancer types. Conclusions: These results suggest that highly mutated open chromatin regions play an important role in cancer pathogenesis and can be effectively used to predict patient prognosis. [ABSTRACT FROM AUTHOR]

Abstract Background Ovarian cancer (OC) is the most lethal gynecological malignancy. The objective of this study was to establish and validate an individual aging‐related gene signature and a clinical nomogram that can powerfully predict independently the overall survival rate of patients with ovarian cancer. Methods Data on transcriptomic profile and relevant clinical information were retrieved from The Cancer Genome Atlas (TCGA) database as a training group, and the same data from three public Gene Expression Omnibus (GEO) databases as validation groups. Univariate Cox regression analysis, lasso regression analysis, and multiple multivariate Cox analysis were analyzed sequentially to select the genes to be included in the aging‐associated signature. A risk scoring model was established and verified, the predictive value of the model was evaluated, and a clinical nomogram was established. Results We found eight genes that were most relevant to prognosis and constructed an eight‐mRNA signature. Based on the model, each OC patient's risk score was able to be calculated and patients were split into groups of low and high risks with a distinct outcome. Survival analysis confirmed that the outcome of patients in the high‐risk group was dramatically shorter than that of those in the low‐risk group, and the eight‐mRNA signature can be considered as a powerful and independent predictor that could predict the outcome of OC patient. Additionally, the risk score and age can be used to construct a clinical nomogram as a simpler tool for predicting prognosis. We also explored the association between the risk score and immunity and drug sensitivity. Conclusion This study suggested that the aging‐related gene signature could be used as an intervention point and latent prognostic predictor in OC, which may provide new perceptions for postoperative treatment strategies.

Abstract Background Recently, the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) have achieved remarkable survival benefits in non‐small cell lung cancer (NSCLC) treatment. However, epidermal growth factor receptor (EGFR) wild type and low expression of programmed death‐ligand 1 (PD‐L1) NSCLC remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a hypoxia‐related lncRNAs (hypolncRNAs) classifier that could successfully identify the high‐risk patients and reveal its underlying molecular immunology characteristics. Methods By identifying the bottom 25% PD‐L1 expression level as low expression of PD‐L1 and removing EGFR mutant samples, a total of 222 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) samples and 93 adjacent non‐tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). A 0 or 1 matrix was constructed by cyclically pairing hypoxia‐related long non‐coding RNAs (hypolncRNAs) and divided into the train set and test set. The univariate Cox regression analysis determined the prognostic hypolncRNAs pairs. Then, the prognostic classifier contained nine hypolncRNAs pairs which were generated by Lasso regression and multivariate Cox analysis. It successfully stratified EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC (double‐negative LUAD and LUSC) patients into the high‐ and low‐risk groups, whose accuracy was proved by the time‐dependent receiver operating characteristic (ROC) curve. Furthermore, diverse acknowledged immunology methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT‐ABS, CIBERSORT, and the single‐sample gene set enrichment analysis (ssGSEA) revealed its underlying antitumor immunosuppressive status in the high‐risk patients. Conclusions It is noteworthy that hypolncRNAs are associated with the survival of double‐negative LUAD and LUSC patients, for which the possible mechanism is inhibiting the antitumor immune process.

Abstract Background Bladder cancer is the second most common malignant tumor in the urogenital system. The research investigated the prognostic role of immune‐related long non‐coding RNA (lncRNA) in bladder cancer. Methods We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single‐sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis. Multivariate Cox analysis was used to establish the risk score model. Then we established and evaluated the nomogram. Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis. Results Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune‐related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high‐ and low‐risk cohorts. Patients in the low‐risk cohort had better survival outcomes than those in the high‐risk cohort. The nomogram was established based on the indicators including age, gender, tumor‐node‐metastases stage, and risk score. The model's predictive performance was confirmed by the receiver operating characteristic curve analysis, concordance index method, calibration curve, and decision curve analysis. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9‐lncRNA signature was involved in the modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway. Conclusions Our study uncovered the prognostic value of immune‐related lncRNAs for bladder cancer and showed that they may regulate tumor pathogenesis in various ways.

Abstract Background The composition of the population of immune‐related long non‐coding ribonucleic acid (irlncRNA) generates a signature, irrespective of expression level, with potential value in predicting the survival status of patients with invasive breast carcinoma. Methods The current study uses univariate analysis to identify differentially expressed irlncRNA (DEirlncRNA) pairs from RNA‐Seq data from The Cancer Genome Atlas (TCGA). 36 pairs of DEirlncRNA pairs were identified. Using various algorithms to construct a model, we have compared the area under the curve and calculated the 5‐year curve of Akaike information criterion (AIC) values, which allows determination of the threshold indicating the maximum value for differentiation. Through cut‐off point to establish the optimal model for distinguishing high‐risk or low‐risk groups among breast cancer patients. We assigned individual patients with invasive breast cancer to either high risk or low risk groups depending on the cut‐off point, re‐evaluated the tumor immune cell infiltration, the effectiveness of chemotherapy, immunosuppressive biomarkers, and immunotherapy. Results After re‐assessing patients according to the threshold, we demonstrated an effective means of distinguish the severity of the disease, and identified patients with different clinicopathological characteristics, specific tumor immune infiltration states, high sensitivity to chemotherapy,wellpredicted response to immunotherapy and thus a more favorable survival outcome. Conclusions The current study presents novel findings regarding the use of irlncRNA without the need to predict precise expression levels in the prognosis of breast cancer patients and to indicate their suitability for anti‐tumor immunotherapy.

Abstract Background Osteosarcoma is a tumour of malignant origin in children and adolescents. Recent progression indicates that it is necessary to develop new therapies to improve the patient's prognosis rather than strengthen anti‐tumour chemotherapy. Researchers recently realised that cancer is a kind of disease with a metabolic disorder, and metabolic reprogramming is becoming a new cancer hallmark. Hence, our study's primary purpose is to explore the value of genes related to osteosarcoma metabolism. Methods From public databases, three osteosarcoma datasets with adequate clinical information were obtained. Besides, the IMvigor dataset through the ‘IMvigor’ package as a supplement was downloaded, the metabolic‐related genes were identified, and these genes were used to construct the metabolic‐related gene pairs (MRGP). Based on the prognosis‐related MRGP, two molecular subtypes were identified. There are significant differences in the metabolic characteristics between the two molecular subtypes. Subsequently, the MRGP signature is constructed using the least absolute shrinkage and selection operator regression method. Finally, use SubMap analysis to evaluate the response of patients in the MRPG signature group to immunotherapy. Results The MRGP signature can reliably predict overall survival in patients with osteosarcoma. The MRGP signature is also associated with osteosarcoma patients’ metastatic status and can be used for subsequent risk classification of metastatic patients. The immunotherapy is more likely to benefit the patients in the MRGP low‐risk group. Conclusion Metabolic‐related gene pairs signature can assess the prognosis of patients with osteosarcoma.

Abstract Background Prognostic indicators in lung adenocarcinoma (LUAD) have been seeking under database analysis, and remarkable advance is on the way. Methods This study calculated the scores of stromal and immune components of the tumor microenvironment (TME) in 551 LUAD samples using the ESTIMATE algorithm on The Cancer Genome Atlas (TCGA) database. R package ''limma'' was used to selected differentially expressed genes (DEG). We have analyzed the DEGs by means of Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments. The protein‐protein network, univariate Cox analysis, and Lasso regression analysis were performed to selected survival‐related genes. Gene Set Enrichment Analysis (GSEA) represented the enriched pathway of CC chemokine receptor 2 (CCR2). The ratios of immune cells in the TME of each LUAD sample were obtained using the R package "limma" and CIBERSORT algorithm in R 4.0.2. Results The ImmuneScore was positively correlated with prognosis regarding survival rate, T classification of TNM stages, and clinicopathological staging characteristics. GO and KEGG enrichments showed DEGs were associated with immune‐related activities. Three genes of LUAD were selected from the PPI network and Cox proportional hazards regression analysis. CCR2 was the most survival correlated gene by Lasso regression analysis. GSEA results showed that C2 kegg gene sets in the CCR2 high‐expression group were mainly enriched in the B cell or T cell receptor signaling pathway and natural killer cell‐mediated cytotoxicity. Correlation of CCR2 expression with prognosis was conducted, implicating a positive correlation with the prognosis of survival rate and M classification, negative correlation with the prognosis of T and N classifications. The correlation between CCR2 and tumor‐infiltrating immune cells (TICs) was analyzed, and 14 kinds of TICs were found closely correlated with CCR2 expression through difference analysis. Conclusion Therefore, CCR2 has prognostic value as an immune indicator in LUAD.

Abstract Background A simple measure of immune cytolytic activity (CYT) base on mRNA expression levels of two genes, GZMA and PRF1, was recently reported. Here, we aimed to evaluate the CYT score's potential as a measure of antitumor immunity and predictor of clinical outcome in gastric cancer (GC) patients. Materials and Methods We evaluated the correlations between tumor‐infiltrating immune cells and the CYT score in 238 GC samples from The Cancer Genome Atlas (TCGA). Next, we investigated CYT score associations with molecular subtypes, somatic mutation load, and immune checkpoint molecules in GC samples from TCGA and Asian Cancer Research Group (ACRG). Moreover, we evaluated the clinical significance of the CYT score calculated by reverse transcription (RT)‐quantitative PCR (qPCR) data in 123 GC samples and the association of the CYT score with the response to anti‐PD‐1 therapy in 7 GC samples from Kyushu University Hospital. Results The CYT score positively correlated with the proportions of tumor‐infiltrating CD8+ T cells and macrophages and negatively correlated with the proportion of regulatory T cells in GC tissues. A high CYT score was associated with common immune checkpoint molecules, a high mutation, the Epstein–Barr virus subtype, and the microsatellite instability subtype in GC. Moreover, a low CYT score was a poor prognosis factor in patients with GC. Finally, the CYT score was higher in a responder to anti‐PD‐1 therapy compared to nonresponders. Conclusion The CYT score reflects antitumor immunity and predicts clinical outcome in GC patients.

Abstract A primary factor in tumor morbidity and mortality, lung adenocarcinoma (LUAD) is known to be a major subtype of lung cancer, having the lowest survival rate among all other cancers. Using The Cancer Genome Atlas (TCGA) database the relationship between the immune infiltrate and the NUP62CL was explored and the value of the NUP62CL expression in the prognosis and diagnosis LUAD was examined. Using the logistic regression and the Wilcoxon signed‐rank test the relationship between the NUP62CL and the clinico‐pathological features was analyzed. There was a significant correlation between the clinical stage (p = 0.005), the N (p = 0.004), and the decreased expression of NUP62CL. The prognosis of LUAD with high NUP62CL expression was revealed to be worse than that with low NUP62CL expression (p

Abstract Purpose Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients. Methods The RNA‐seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value. Results Based on the immune score, we found that the high‐score group showed better clinical outcomes than the low‐score group. Twenty‐five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low‐risk group had longer overall survival than the high‐risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25‐gene prognostic signatures and several clinicopathological prognostic factors was constructed. Conclusions This study revealed the significance of immune infiltration and constructed a 25‐gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer.

Abstract Background The tumor microenvironment (TME) plays a critical role in tumorigenesis, development, and therapeutic efficacy. Major advances have been achieved in the treatment of various cancers through immunotherapy. Nevertheless, only a minority of patients have positive responses to immunotherapy, which is partly due to conditions of the immunosuppressive microenvironment. Therefore, it is essential to identify prognostic biomarkers that reflect heterogeneous landscapes of the TME. Methods and materials Based upon the ESTIMATE algorithm, we evaluated the infiltrating levels of immune and stromal components derived from patients afflicted by various types of cancer from The Cancer Genome Atlas database (TCGA). According to respective patient immune and stromal scores, we categorized cases into high‐ and low‐scoring subgroups for each cancer type to explore associations between TME and patient prognosis. Gene Set Enrichment Analyses (GSEA) were conducted and genes enriched in IFNγ response signaling pathway were selected to facilitate establishment of a risk model for predicting overall survival (OS). Furthermore, we investigated the associations between the prognostic signature and tumor immune infiltration landscape by using CIBERSORT algorithm and TIMER database. Results Among the cancers assessed, the immune scores for skin cutaneous melanoma (SKCM) were the most significantly correlated with patients' survival time (P

Abstract Long noncoding RNAs (lncRNAs) dysregulation leads to malignant progression of lung cancer. Our study profiled differentially expressed lncRNA and mRNA in tumor and normal tissues of lung adenocarcinoma (LUAD). Further, analysis of the gene expression profiles of LUAD tissues (n = 533) and normal tissues (n = 59) from The Cancer Genome Atlas (TCGA). A total of 138 lncRNAs were differentially expressed between LUAD tissues and normal tissues (false discovery rate [FDR] q

Abstract Based on accumulating evidence, long noncoding RNAs (lncRNAs) are potential biomarkers and therapeutic targets for many diseases, including tumors. In this study, we consulted The Cancer Genome Atlas (TCGA) database to explore the functions and modulatory mechanisms of lncRNAs as competing endogenous RNAs (ceRNAs) in hepatocellular carcinoma (HCC) in Asian patients and constructed a risk scoring system composed of four lncRNAs (SNHG1, STEAP3‐AS1, RUSC1‐AS1, and SNHG3) to predict the outcomes of Asian patients with HCC. The prognostic value of this risk model was validated in the internal validation cohort (n = 157). The stratified survival analysis revealed good performance for the risk model in stratifying clinical features. According to the Cox proportional hazard regression analysis, the four‐lncRNA risk model is an independent prognostic model for Asian patients with HCC. Finally, we developed a nomogram that integrates prognostic signals and other clinical information to predict 1‐, 3‐, and 5‐year overall survival rates. In conclusion, the prognostic lncRNAs identified in our study exerted potential biological effects on the development of HCC. The risk scoring model based on four lncRNAs may be an effective classification tool for assessing the prognosis of Asian patients with HCC.

Abstract Previous studies have demonstrated the role of abnormal alternative splicing (AS) in tumor progression. This study examines the prognostic index (PI) of alternative splices (ASs) in patients with hepatocellular carcinoma (HCC). The clinical features and splicing events of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed AS (DEAS) were compared between HCC and adjacent normal samples. Univariate Cox regression analysis was used to determine changes in DEAS associated with overall survival (OS). A PI was generated from OS‐associated DEASs using Kaplan‐Meier curves, receiver operating characteristic (ROC) curves, multivariate Cox regression, and cluster analysis. Then, the correlation between DEASs and splicing factors was assessed, followed by functional and pathway enrichment analysis. We identified 34 163 ASs of 8985 genes in HCC, and 153 OS‐ASs were identified using univariate Cox regression analysis. Low‐ and high‐PI groups were determined based on the median “PI‐ALL” value according to significantly different survival (P = 2.2e − 16). The ROC curve of all PI (PI‐ALL) had an area under the curve (AUC) of 0.993 for survival status in patients with HCC. A potential regulatory network associated with prognosis of patients with HCC was established. Enrichment analysis also resulted in the identification of several pathways potentially associated with carcinogenesis and progression of HCC. Four clusters were identified that were associated with clinical features and prognosis. Our study generated comprehensive profiles of ASs in HCC. The interaction network and functional connections were used to elucidate the underlying mechanisms of AS in HCC.

Abstract Background Increasing evidence has validated the crucial role of alternative splicing (AS) in tumors. However, comprehensive investigations on the entirety of AS and their clinical value in glioblastoma (GBM) are lacking. Methods The AS profiles and clinical survival data related to GBM were obtained from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses were performed to identify survival‐associated AS events. A risk score was calculated, and prognostic signatures were constructed using seven different types of independent prognostic AS events, respectively. The Kaplan‐Meier estimator was used to display the survival of GBM patients. The receiver operating characteristic curve was applied to compare the predictive efficacy of each prognostic signature. Enrichment analysis and protein interactive networks were conducted using the gene symbols of the AS events to investigate important processes in GBM. A splicing network between splicing factors and AS events was constructed to display the potential regulatory mechanism in GBM. Results A total of 2355 survival‐associated AS events were identified. The splicing prognostic model revealed that patients in the high‐risk group have worse survival rates than those in the low‐risk group. The predictive efficacy of each prognostic model showed satisfactory performance; among these, the Alternate Terminator (AT) model showed the best performance at an area under the curve (AUC) of 0.906. Enrichment analysis uncovered that autophagy was the most enriched process of prognostic AS gene symbols in GBM. The protein network revealed that UBC, VHL, KCTD7, FBXL19, RNF7, and UBE2N were the core genes in GBM. The splicing network showed complex regulatory correlations, among which ELAVL2 and SYNE1_AT_78181 were the most correlated (r = −.506). Conclusions Applying the prognostic signatures constructed by independent AS events shows promise for predicting the survival of GBM patients. A splicing regulatory network might be the potential splicing mechanism in GBM.

Abstract Owing to the development of next‐generation sequencing (NGS) technologies, a large number of somatic variants have been identified in various types of cancer. However, the functional significance of most somatic variants remains unknown. Somatic variants that occur in exonic splicing enhancer (ESE) regions are thought to prevent serine and arginine‐rich (SR) proteins from binding to ESE sequence motifs, which leads to exon skipping. We computationally identified somatic variants in ESEs by compiling numerous open‐access datasets from The Cancer Genome Atlas (TCGA). Using somatic variants and RNA‐seq data from 9635 patients across 32 TCGA projects, we identified 646 ESE‐disrupting variants. The false positive rate of our method, estimated using a permutation test, was approximately 1%. Of these ESE‐disrupting variants, approximately 71% were located in the binding motifs of four classical SR proteins. ESE‐disrupting variants occurred in proportion to the number of somatic variants, but not necessarily in the specific genes associated with the biological processes of cancer. Existing bioinformatics tools could not predict the pathogenicity of ESE‐disrupting variants identified in this study, although these variants could cause exon skipping. We demonstrated that ESE‐disrupting nonsense variants tended to escape nonsense‐mediated decay surveillance. Using integrated analyses of open access data, we could specifically identify ESE‐disrupting variants. We have generated a powerful tool, which can handle datasets without normal samples or raw data, and thus contribute to reducing variants of uncertain significance because our statistical approach only uses the exon‐junction read counts from the tumor samples.

Background: The composition of the population of immune‐related long non‐coding ribonucleic acid (irlncRNA) generates a signature, irrespective of expression level, with potential value in predicting the survival status of patients with invasive breast carcinoma. Methods: The current study uses univariate analysis to identify differentially expressed irlncRNA (DEirlncRNA) pairs from RNA‐Seq data from The Cancer Genome Atlas (TCGA). 36 pairs of DEirlncRNA pairs were identified. Using various algorithms to construct a model, we have compared the area under the curve and calculated the 5‐year curve of Akaike information criterion (AIC) values, which allows determination of the threshold indicating the maximum value for differentiation. Through cut‐off point to establish the optimal model for distinguishing high‐risk or low‐risk groups among breast cancer patients. We assigned individual patients with invasive breast cancer to either high risk or low risk groups depending on the cut‐off point, re‐evaluated the tumor immune cell infiltration, the effectiveness of chemotherapy, immunosuppressive biomarkers, and immunotherapy. Results: After re‐assessing patients according to the threshold, we demonstrated an effective means of distinguish the severity of the disease, and identified patients with different clinicopathological characteristics, specific tumor immune infiltration states, high sensitivity to chemotherapy,wellpredicted response to immunotherapy and thus a more favorable survival outcome. Conclusions: The current study presents novel findings regarding the use of irlncRNA without the need to predict precise expression levels in the prognosis of breast cancer patients and to indicate their suitability for anti‐tumor immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Bladder cancer is the second most common malignant tumor in the urogenital system. The research investigated the prognostic role of immune‐related long non‐coding RNA (lncRNA) in bladder cancer. Methods: We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single‐sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis. Multivariate Cox analysis was used to establish the risk score model. Then we established and evaluated the nomogram. Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis. Results: Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune‐related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high‐ and low‐risk cohorts. Patients in the low‐risk cohort had better survival outcomes than those in the high‐risk cohort. The nomogram was established based on the indicators including age, gender, tumor‐node‐metastases stage, and risk score. The model's predictive performance was confirmed by the receiver operating characteristic curve analysis, concordance index method, calibration curve, and decision curve analysis. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9‐lncRNA signature was involved in the modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway. Conclusions: Our study uncovered the prognostic value of immune‐related lncRNAs for bladder cancer and showed that they may regulate tumor pathogenesis in various ways. [ABSTRACT FROM AUTHOR]

Background: Recently, the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) have achieved remarkable survival benefits in non‐small cell lung cancer (NSCLC) treatment. However, epidermal growth factor receptor (EGFR) wild type and low expression of programmed death‐ligand 1 (PD‐L1) NSCLC remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a hypoxia‐related lncRNAs (hypolncRNAs) classifier that could successfully identify the high‐risk patients and reveal its underlying molecular immunology characteristics. Methods: By identifying the bottom 25% PD‐L1 expression level as low expression of PD‐L1 and removing EGFR mutant samples, a total of 222 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) samples and 93 adjacent non‐tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). A 0 or 1 matrix was constructed by cyclically pairing hypoxia‐related long non‐coding RNAs (hypolncRNAs) and divided into the train set and test set. The univariate Cox regression analysis determined the prognostic hypolncRNAs pairs. Then, the prognostic classifier contained nine hypolncRNAs pairs which were generated by Lasso regression and multivariate Cox analysis. It successfully stratified EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC (double‐negative LUAD and LUSC) patients into the high‐ and low‐risk groups, whose accuracy was proved by the time‐dependent receiver operating characteristic (ROC) curve. Furthermore, diverse acknowledged immunology methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT‐ABS, CIBERSORT, and the single‐sample gene set enrichment analysis (ssGSEA) revealed its underlying antitumor immunosuppressive status in the high‐risk patients. Conclusions: It is noteworthy that hypolncRNAs are associated with the survival of double‐negative LUAD and LUSC patients, for which the possible mechanism is inhibiting the antitumor immune process. [ABSTRACT FROM AUTHOR]

Background: Osteosarcoma is a tumour of malignant origin in children and adolescents. Recent progression indicates that it is necessary to develop new therapies to improve the patient's prognosis rather than strengthen anti‐tumour chemotherapy. Researchers recently realised that cancer is a kind of disease with a metabolic disorder, and metabolic reprogramming is becoming a new cancer hallmark. Hence, our study's primary purpose is to explore the value of genes related to osteosarcoma metabolism. Methods: From public databases, three osteosarcoma datasets with adequate clinical information were obtained. Besides, the IMvigor dataset through the 'IMvigor' package as a supplement was downloaded, the metabolic‐related genes were identified, and these genes were used to construct the metabolic‐related gene pairs (MRGP). Based on the prognosis‐related MRGP, two molecular subtypes were identified. There are significant differences in the metabolic characteristics between the two molecular subtypes. Subsequently, the MRGP signature is constructed using the least absolute shrinkage and selection operator regression method. Finally, use SubMap analysis to evaluate the response of patients in the MRPG signature group to immunotherapy. Results: The MRGP signature can reliably predict overall survival in patients with osteosarcoma. The MRGP signature is also associated with osteosarcoma patients' metastatic status and can be used for subsequent risk classification of metastatic patients. The immunotherapy is more likely to benefit the patients in the MRGP low‐risk group. Conclusion: Metabolic‐related gene pairs signature can assess the prognosis of patients with osteosarcoma. [ABSTRACT FROM AUTHOR]

A primary factor in tumor morbidity and mortality, lung adenocarcinoma (LUAD) is known to be a major subtype of lung cancer, having the lowest survival rate among all other cancers. Using The Cancer Genome Atlas (TCGA) database the relationship between the immune infiltrate and the NUP62CL was explored and the value of the NUP62CL expression in the prognosis and diagnosis LUAD was examined. Using the logistic regression and the Wilcoxon signed‐rank test the relationship between the NUP62CL and the clinico‐pathological features was analyzed. There was a significant correlation between the clinical stage (p = 0.005), the N (p = 0.004), and the decreased expression of NUP62CL. The prognosis of LUAD with high NUP62CL expression was revealed to be worse than that with low NUP62CL expression (p < 0.001) by the Kaplan‐Meier survival analysis. The potentiality of NUP62CL to be a significant factor of prognosis for LUAD was indicated by the analyses of the multivariate and the univariate Cox regression models. In LUAD, the crucial role of recombination and maintenance of telomere as a significant pathway for NUP62CL was suggested by the Gene Set Enrichment Analysis (GSEA). To analyze the correlation between the genes and the tumor infiltrating immune cells the CIBERSORT was used. Moreover the positive correlation with the NUP62CL expression in LUAD of the infiltration level of the tumor infiltrating B lymphocytes and memory CD4+ T cells was exhibited by CIBERSORT. Therefore, NUP62CL may be a new valuable prognostic indicator for LUAD. [ABSTRACT FROM AUTHOR]

Background: A simple measure of immune cytolytic activity (CYT) base on mRNA expression levels of two genes, GZMA and PRF1, was recently reported. Here, we aimed to evaluate the CYT score's potential as a measure of antitumor immunity and predictor of clinical outcome in gastric cancer (GC) patients. Materials and Methods: We evaluated the correlations between tumor‐infiltrating immune cells and the CYT score in 238 GC samples from The Cancer Genome Atlas (TCGA). Next, we investigated CYT score associations with molecular subtypes, somatic mutation load, and immune checkpoint molecules in GC samples from TCGA and Asian Cancer Research Group (ACRG). Moreover, we evaluated the clinical significance of the CYT score calculated by reverse transcription (RT)‐quantitative PCR (qPCR) data in 123 GC samples and the association of the CYT score with the response to anti‐PD‐1 therapy in 7 GC samples from Kyushu University Hospital. Results: The CYT score positively correlated with the proportions of tumor‐infiltrating CD8+ T cells and macrophages and negatively correlated with the proportion of regulatory T cells in GC tissues. A high CYT score was associated with common immune checkpoint molecules, a high mutation, the Epstein–Barr virus subtype, and the microsatellite instability subtype in GC. Moreover, a low CYT score was a poor prognosis factor in patients with GC. Finally, the CYT score was higher in a responder to anti‐PD‐1 therapy compared to nonresponders. Conclusion: The CYT score reflects antitumor immunity and predicts clinical outcome in GC patients. [ABSTRACT FROM AUTHOR]

Abstract Colon cancer is one of the most malignant cancers worldwide. Nearly 20% of all colon cancer patients are diagnosed at stage IV (metastasis). However, further study of colon cancer is difficult due to a lack of understanding of its pathogenesis. In this study, we acquired high–throughput sequence data from TCGA datasets and performed integrated bioinformatic analysis including differential gene expression analysis, gene ontology and KEGG pathways analysis, protein–protein analysis, survival analysis, and multivariate Cox proportional hazards regression analysis in order to identify a panel of key candidate genes involved in the metastasis of colon cancer. We then constructed a prognostic signature based on the expression of REG1B, TGM6, NTF4, PNMA5, and HOXC13 which could provide significant prognostic value for colon cancer.

Purpose: Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients. Methods: The RNA‐seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value. Results: Based on the immune score, we found that the high‐score group showed better clinical outcomes than the low‐score group. Twenty‐five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low‐risk group had longer overall survival than the high‐risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25‐gene prognostic signatures and several clinicopathological prognostic factors was constructed. Conclusions: This study revealed the significance of immune infiltration and constructed a 25‐gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer. [ABSTRACT FROM AUTHOR]

Background: The tumor microenvironment (TME) plays a critical role in tumorigenesis, development, and therapeutic efficacy. Major advances have been achieved in the treatment of various cancers through immunotherapy. Nevertheless, only a minority of patients have positive responses to immunotherapy, which is partly due to conditions of the immunosuppressive microenvironment. Therefore, it is essential to identify prognostic biomarkers that reflect heterogeneous landscapes of the TME. Methods and materials: Based upon the ESTIMATE algorithm, we evaluated the infiltrating levels of immune and stromal components derived from patients afflicted by various types of cancer from The Cancer Genome Atlas database (TCGA). According to respective patient immune and stromal scores, we categorized cases into high‐ and low‐scoring subgroups for each cancer type to explore associations between TME and patient prognosis. Gene Set Enrichment Analyses (GSEA) were conducted and genes enriched in IFNγ response signaling pathway were selected to facilitate establishment of a risk model for predicting overall survival (OS). Furthermore, we investigated the associations between the prognostic signature and tumor immune infiltration landscape by using CIBERSORT algorithm and TIMER database. Results: Among the cancers assessed, the immune scores for skin cutaneous melanoma (SKCM) were the most significantly correlated with patients' survival time (P <.0001). We identified and validated a five‐IFNγ response‐related gene signature (UBE2L6, PARP14, IFIH1, IRF2, and GBP4), which was closely correlated with the prognosis for SKCM afflicted patients. Multivariate Cox regression analysis indicated that this risk model was an independent prognostic factor for SKCM. Tumor‐infiltrating lymphocytes and specific immune checkpoint molecules had notably differential levels of expression in high‐ compared to low‐risk samples. Conclusion: In this study, we established a novel five‐IFNγ response‐related gene signature that provided a better and increasingly comprehensive understanding of tumor immune landscape, and which demonstrated good performance in predicting outcomes for patients afflicted by SKCM. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) dysregulation leads to malignant progression of lung cancer. Our study profiled differentially expressed lncRNA and mRNA in tumor and normal tissues of lung adenocarcinoma (LUAD). Further, analysis of the gene expression profiles of LUAD tissues (n = 533) and normal tissues (n = 59) from The Cancer Genome Atlas (TCGA). A total of 138 lncRNAs were differentially expressed between LUAD tissues and normal tissues (false discovery rate [FDR] q < 0.05, fold change (FC) ≥ 2), a number of which are key regulators of multiple cancer and biological processes in humans. For example, lncRNA A2M‐AS1 displayed the highest correlation with the co‐expressed mRNAs, indicating that it might play a key role in regulating differential gene expression in LUAD. The data from the current study of the comprehensive lncRNA expression profile in LUAD tissues provided useful information to guide the identification of potential LUAD biomarkers. [ABSTRACT FROM AUTHOR]

Abstract Increasing studies showed that kinesin family member 20A (KIF20A) was overexpessed in several types of cancer, and its overexpression correlated with the oncogenesis and prognosis of cancers. However, little is known about the role of KIF20A in lung adenocarcinoma (LUAD). In this study, we employed the bioinformatics analysis to identify the upregulation of KIF20A in LUAD, then verified the results in human tumor specimens and LUAD cell lines. Compared with normal lung tissues, a ubiquitous upregulation of KIF20A was observed in LUAD tissues by immunohistochemistry (IHC) as well as TCGA analysis. Higher expression of KIF20A was significantly associated with more advanced clinicopathological features and shorter overall survival (OS). Moreover, multivariate Cox regression analysis revealed that KIF20A was an independent prognostic factor for OS. The expression of KIF20A was significantly elevated in LUAD cell lines. After silencing KIF20A, lung cancer cell cycle arrested in G1 phase and apoptosis increased. The same results were observed in vivo. Thus, our study demonstrated that KIF20A might confer malignant phenotype to LUAD by regulating cell proliferation and apoptosis, providing a new potential biomarker for clinical treatment of LUAD.

Based on accumulating evidence, long noncoding RNAs (lncRNAs) are potential biomarkers and therapeutic targets for many diseases, including tumors. In this study, we consulted The Cancer Genome Atlas (TCGA) database to explore the functions and modulatory mechanisms of lncRNAs as competing endogenous RNAs (ceRNAs) in hepatocellular carcinoma (HCC) in Asian patients and constructed a risk scoring system composed of four lncRNAs (SNHG1, STEAP3‐AS1, RUSC1‐AS1, and SNHG3) to predict the outcomes of Asian patients with HCC. The prognostic value of this risk model was validated in the internal validation cohort (n = 157). The stratified survival analysis revealed good performance for the risk model in stratifying clinical features. According to the Cox proportional hazard regression analysis, the four‐lncRNA risk model is an independent prognostic model for Asian patients with HCC. Finally, we developed a nomogram that integrates prognostic signals and other clinical information to predict 1‐, 3‐, and 5‐year overall survival rates. In conclusion, the prognostic lncRNAs identified in our study exerted potential biological effects on the development of HCC. The risk scoring model based on four lncRNAs may be an effective classification tool for assessing the prognosis of Asian patients with HCC. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated the role of abnormal alternative splicing (AS) in tumor progression. This study examines the prognostic index (PI) of alternative splices (ASs) in patients with hepatocellular carcinoma (HCC). The clinical features and splicing events of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed AS (DEAS) were compared between HCC and adjacent normal samples. Univariate Cox regression analysis was used to determine changes in DEAS associated with overall survival (OS). A PI was generated from OS‐associated DEASs using Kaplan‐Meier curves, receiver operating characteristic (ROC) curves, multivariate Cox regression, and cluster analysis. Then, the correlation between DEASs and splicing factors was assessed, followed by functional and pathway enrichment analysis. We identified 34 163 ASs of 8985 genes in HCC, and 153 OS‐ASs were identified using univariate Cox regression analysis. Low‐ and high‐PI groups were determined based on the median "PI‐ALL" value according to significantly different survival (P = 2.2e − 16). The ROC curve of all PI (PI‐ALL) had an area under the curve (AUC) of 0.993 for survival status in patients with HCC. A potential regulatory network associated with prognosis of patients with HCC was established. Enrichment analysis also resulted in the identification of several pathways potentially associated with carcinogenesis and progression of HCC. Four clusters were identified that were associated with clinical features and prognosis. Our study generated comprehensive profiles of ASs in HCC. The interaction network and functional connections were used to elucidate the underlying mechanisms of AS in HCC. [ABSTRACT FROM AUTHOR]

Background: Increasing evidence has validated the crucial role of alternative splicing (AS) in tumors. However, comprehensive investigations on the entirety of AS and their clinical value in glioblastoma (GBM) are lacking. Methods: The AS profiles and clinical survival data related to GBM were obtained from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses were performed to identify survival‐associated AS events. A risk score was calculated, and prognostic signatures were constructed using seven different types of independent prognostic AS events, respectively. The Kaplan‐Meier estimator was used to display the survival of GBM patients. The receiver operating characteristic curve was applied to compare the predictive efficacy of each prognostic signature. Enrichment analysis and protein interactive networks were conducted using the gene symbols of the AS events to investigate important processes in GBM. A splicing network between splicing factors and AS events was constructed to display the potential regulatory mechanism in GBM. Results: A total of 2355 survival‐associated AS events were identified. The splicing prognostic model revealed that patients in the high‐risk group have worse survival rates than those in the low‐risk group. The predictive efficacy of each prognostic model showed satisfactory performance; among these, the Alternate Terminator (AT) model showed the best performance at an area under the curve (AUC) of 0.906. Enrichment analysis uncovered that autophagy was the most enriched process of prognostic AS gene symbols in GBM. The protein network revealed that UBC, VHL, KCTD7, FBXL19, RNF7, and UBE2N were the core genes in GBM. The splicing network showed complex regulatory correlations, among which ELAVL2 and SYNE1_AT_78181 were the most correlated (r = −.506). Conclusions: Applying the prognostic signatures constructed by independent AS events shows promise for predicting the survival of GBM patients. A splicing regulatory network might be the potential splicing mechanism in GBM. [ABSTRACT FROM AUTHOR]

Owing to the development of next‐generation sequencing (NGS) technologies, a large number of somatic variants have been identified in various types of cancer. However, the functional significance of most somatic variants remains unknown. Somatic variants that occur in exonic splicing enhancer (ESE) regions are thought to prevent serine and arginine‐rich (SR) proteins from binding to ESE sequence motifs, which leads to exon skipping. We computationally identified somatic variants in ESEs by compiling numerous open‐access datasets from The Cancer Genome Atlas (TCGA). Using somatic variants and RNA‐seq data from 9635 patients across 32 TCGA projects, we identified 646 ESE‐disrupting variants. The false positive rate of our method, estimated using a permutation test, was approximately 1%. Of these ESE‐disrupting variants, approximately 71% were located in the binding motifs of four classical SR proteins. ESE‐disrupting variants occurred in proportion to the number of somatic variants, but not necessarily in the specific genes associated with the biological processes of cancer. Existing bioinformatics tools could not predict the pathogenicity of ESE‐disrupting variants identified in this study, although these variants could cause exon skipping. We demonstrated that ESE‐disrupting nonsense variants tended to escape nonsense‐mediated decay surveillance. Using integrated analyses of open access data, we could specifically identify ESE‐disrupting variants. We have generated a powerful tool, which can handle datasets without normal samples or raw data, and thus contribute to reducing variants of uncertain significance because our statistical approach only uses the exon‐junction read counts from the tumor samples. [ABSTRACT FROM AUTHOR]

Liver cancer is the most common cancer and is the epitome of a recalcitrant cancer. Increasing evidence shown that long noncoding RNAs (lncRNA) were associated with cancer‐related death and could function as a competing endogenous RNA (ceRNA). To explore regulatory roles and potential prognostic biomarkers of lncRNA for liver cancer, RNA‐sequencing expression data were downloaded from the TCGA database and GEO database. A total of 357 patients were randomly divided into a discovery group and a validation group, of which 313 patients can obtain clinical data. In discovery phrase, 58 lncRNAs, 16 miRNAs, and 34 mRNAs were screened to construct the ceRNA network based on 252 patients employed from discovery group. Univariate and multivariate Cox hazard regression analysis model revealed that five lncRNAs (AATK‐AS1, C10orf91, LINC00162, LINC00200, and LINC00501) from 58 lncRNAs were formulated to predict the overall survival (OS). We used the value of gene expression and regression coefficients to construct a risk score based on the five lncRNAs. Next, we validated our model in the GSE116174 dataset (n = 64) and the validation group (n = 94) from TCGA database. Subgroup analysis suggest that the five lncRNAs played critical parts in early stage in cancer from both discovery and validation groups. The five lncRNAs were also found to be associated with immune cells infiltration including CD4+ memory activated, NK cells activated and mast cells activated, then the results were also validated according to the validation group. Furthermore, KEGG pathway enrichment analysis showed that these nine coexpressed modules using the method of WGCNA, and many of these pathways are associated with the development and progression of disease. At last, the transcription factor binding sites (TFBS) of the five lncRNAs were predicted, which help us to understand the potential mechanism that the TFBS adjusted the ceRNA network. In summary, the ceRNA regulatory network may contribute to a better understanding of liver cancer mechanism and provide potential prognostic biomarkers and therapeutic targets. [ABSTRACT FROM AUTHOR]

Colon cancer is one of the most malignant cancers worldwide. Nearly 20% of all colon cancer patients are diagnosed at stage IV (metastasis). However, further study of colon cancer is difficult due to a lack of understanding of its pathogenesis. In this study, we acquired high–throughput sequence data from TCGA datasets and performed integrated bioinformatic analysis including differential gene expression analysis, gene ontology and KEGG pathways analysis, protein–protein analysis, survival analysis, and multivariate Cox proportional hazards regression analysis in order to identify a panel of key candidate genes involved in the metastasis of colon cancer. We then constructed a prognostic signature based on the expression of REG1B, TGM6, NTF4, PNMA5, and HOXC13 which could provide significant prognostic value for colon cancer. [ABSTRACT FROM AUTHOR]

Abstract: Increasing studies showed that kinesin family member 20A (KIF20A) was overexpessed in several types of cancer, and its overexpression correlated with the oncogenesis and prognosis of cancers. However, little is known about the role of KIF20A in lung adenocarcinoma (LUAD). In this study, we employed the bioinformatics analysis to identify the upregulation of KIF20A in LUAD, then verified the results in human tumor specimens and LUAD cell lines. Compared with normal lung tissues, a ubiquitous upregulation of KIF20A was observed in LUAD tissues by immunohistochemistry (IHC) as well as TCGA analysis. Higher expression of KIF20A was significantly associated with more advanced clinicopathological features and shorter overall survival (OS). Moreover, multivariate Cox regression analysis revealed that KIF20A was an independent prognostic factor for OS. The expression of KIF20A was significantly elevated in LUAD cell lines. After silencing KIF20A, lung cancer cell cycle arrested in G1 phase and apoptosis increased. The same results were observed in vivo. Thus, our study demonstrated that KIF20A might confer malignant phenotype to LUAD by regulating cell proliferation and apoptosis, providing a new potential biomarker for clinical treatment of LUAD. [ABSTRACT FROM AUTHOR]