Integrated Analysis of the Prognosis-Associated RNA-Binding Protein Genes and Candidate Drugs in Renal Papillary Cell Carcinoma

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

BACKGROUND

Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis.

RESULTS

Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples.

CONCLUSION

A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

Comprehensive analysis of INTS family related to expression, prognosis, diagnosis and immune features in hepatocellular carcinoma

Purpose

The integrator subunit (INTS) family, a group exclusive to metazoans, participates in various biologic processes. However, their roles in hepatocellular carcinoma (HCC) remain largely unexplored.

Methods

Public databases were utilized to investigate the transcriptional and protein expression, and clinical relevance of the INTS family in HCC. Meanwhile, the effects of INTS13 knockdown and overexpression on cell proliferation and apoptosis were studied using HCC cell lines.

Results

The mRNA expression of most INTSs were higher in tumor than normal tissues. Higher expression of INTS1/2/3/4/7/8/9/11/12/13 were correlated with poorer overall survival (OS) in Kaplan-Meier Survival Analysis. Multivariate analysis revealed higher level of INTS13 was an independent prognostic factor for shorter OS. Furthermore, genetic alteration of INTS3/6/7/8/9/10 were found in HCC patients and was associated with shorter disease-free survival and progression-free survival. INTS1/2/3/5/7/11/13/14 were associated with activation of tumor-induced immune response and immune infiltration in HCC. Knockdown of INTS13 inhibited cell proliferation and induced apoptosis in HCC cell lines, while overexpression of INTS13 had the opposite effect.

Conclusion

Our results indicate that INTS13 is an independent prognostic biomarker in HCC. Furthermore, INTS13 enhances cell proliferation and decreases cell apoptosis in HCC cell lines leading to a poorer OS in HCC patients.

Integrated bioinformatics analysis of ferroptosis-related gene signature in inflammation and immunity in intervertebral disc degeneration

Ferroptosis has recently been shown to play a significant role in the progression of intervertebral disk degeneration (IDD), although the underlying mechanism is still unknown. The objective of this work was to use stringent bioinformatic techniques to clarify the crucial roles played by genes associated with ferroptosis in the emergence of IDD. For additional study, the microarray data pertinent to the IDD were acquired from the Gene Expression Omnibus database. The ferroptosis-related and IDD-related genes (FIDDRGs) were identified using a variety of bioinformatic techniques, which were also used to carry out function enrichment analysis, protein-protein correlation analysis, build the correlation regulatory network, and examine the potential connections between ferroptosis and immune abnormalities and inflammatory responses in IDD. A total of 16 FIDDRGs were eliminated for the further function enrichment analysis, and 10 hub FIDDRGs were chosen to build the correlation regulatory network. Hub FIDDRGs were shown to be highly associated with M2 macrophages and hub inflammatory response-related genes in IDD. When seen as a whole, our findings can give fresh perspectives on the mechanistic studies of ferroptosis in the emergence of IDD and new prospective targets for the therapeutic approaches.

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.

Construction of novel 7 integrin-related gene signatures in thyroid cancer construction of model based on integrin genes

Advanced and metastatic THCA patients usually have a poor prognosis. Thus, this study aimed to establish a risk model to discriminate the high risk population. The expression and clinical data were obtained from TCGA database. The cluster analysis, lasso, univariate and multivariate cox analyses were used to construct risk model. K-M, ROC and DCA were applied to validate the efficiency and stability of the model. GO, KEGG, and ssGSEA analysis were performed to identify the potential mechanism of signatures. The 7-gene prognosis model was constructed, including FAM27E3, FIGN, GSTM4, BEX5, RBPMS2, PHF13, and DCSTAMP. ROC and DCA results showed our model had a better prognosis prediction performance than other risk models. The high risk score was associated with the poor prognosis of THCA patients with different clinical characteristics. The risk score was closely related to cell cycle. Further, we found that the expressions of signatures were significantly dysregulated in THCA and associated with prognosis. These gene expressions were affected by some clinical characteristics, methylation and CNV. Some signatures played a role in drug sensitivity and pathway activation. We constructed a 7-gene signature model based on the integrin-related genes, which showed a great prognostic value in THCA.

Prognosis prediction based on methionine metabolism genes signature in gliomas

BACKGROUND

Glioma cells have increased intake and metabolism of methionine, which can be monitored with 11 C-L-methionine. However, a short half-life of 11 C (~ 20 min) limits its application in clinical practice. It is necessary to develop a methionine metabolism genes-based prediction model for a more convenient prediction of glioma survival.

METHODS

We evaluated the patterns of 29 methionine metabolism genes in glioma from the Cancer Genome Atlas (TCGA). A risk model was established using Lasso regression analysis and Cox regression. The reliability of the prognostic model was validated in derivation and validation cohorts (Chinese Glioma Genome Atlas; CGGA). GO, KEGG, GSEA and ESTIMATE analyses were performed for biological functions and immune characterization.

RESULTS

Our results showed that a majority of the methionine metabolism genes (25 genes) were involved in the overall survival of glioma (logrank p and Cox p < 0.05). A 7-methionine metabolism prognostic signature was significantly related to a poor clinical prognosis and overall survival of glioma patients (C-index = 0.83). Functional analysis revealed that the risk model was correlated with immune responses and with epithelial-mesenchymal transition. Furthermore, the nomogram integrating the signature of methionine metabolism genes manifested a strong prognostic ability in the training and validation groups.

CONCLUSIONS

The current model had the potential to improve the understanding of methionine metabolism in gliomas and contributed to the development of precise treatment for glioma patients, showing a promising application in clinical practice.

Bioinformatics identification of a T-cell-related signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a fatal disease with poor clinical outcomes. T cells play a vital role in the crosstalk between the tumour microenvironment and HCC. Single-cell RNA sequencing data were downloaded from the GSE149614 dataset. The T-cell-related prognostic signature (TRPS) was developed with the integrative procedure including 10 machine learning algorithms. The TRPS was established using 7 T-cell-related markers in the Cancer Genome Atlas cohort with 1-, 2- and 3-year area under curve values of 0.820, 0.725 and 0.678, respectively. TRPS acted as an independent risk factor for HCC patients. HCC patients with a high TRPS-based risk score had a higher Tumour Immune Dysfunction and Exclusion score, lower PD1 and CTLA4 immunophenoscore and lower level of immunoactivated cells, including CD8+ T cells and NK cells. The response rate was significantly higher in patients with low-risk scores in immunotherapy cohorts, including IMigor210 and GSE91061. The TRPS-based nomogram had a relatively good predictive value in evaluating the mortality risk at 1, 3 and 5 years in HCC. Overall, this study develops a TRPS by integrated bioinformatics analysis. This TRPS acted as an independent risk factor for the OS rate of HCC patients. It can screen for HCC patients who might benefit from immunotherapy, chemotherapy and targeted therapy.

AL360181.1 promotes proliferation and invasion in colon cancer and is one of ten m6A-related lncRNAs that predict overall survival

Background

N6-methyladenosine (m6A) exerted a pivotal role in colon cancer. Nevertheless, the long non-coding RNAs (lncRNAs) associated with this process have yet to be elucidated.

Methods

The open-access data used for analysis was downloaded from The Cancer Genome Atlas (TCGA) database for analysis, employing the R software for computational evaluations. The RNA level of specific molecules was assessed using the quantitative real-time PCR. CCK8, colony formation and transwell assay were used to evaluate the proliferation, invasion and migration ability of colon cancer cells.

Results

Here, we identified the m6A regulators from TCGA data and subsequently pinpointed lncRNAs with a -Cor- > 0.3 and P < 0.05, categorizing them as m6A-associated lncRNAs. Moreover, we formulated a prognosis signature rooted in ten m6A-related lncRNAs, consisting of AL360181.1, PCAT6, SNHG26, AC016876.1, AC104667.2, AL114730.3, LINC02257, AC147067.1, AP006621.3 and AC009237.14. This signature exhibited notable predictive accuracy in gauging patient survival. Immune-related evaluations revealed varied immune cell infiltration patterns across different risk groups, with our findings suggesting superior immunotherapy response in low-risk patients. Biological enrichment analysis indicated that the high-risk patients had a higher activity of multiple carcinogenic pathways, including glycolysis. The previously unreported lncRNA, AL360181.1, displayed a connection to glycolytic activity and diminished survival rates, warranting further investigation. The result indicated that AL360181.1 was correlated with more aggressive clinical characteristics. Immune infiltration assessments found AL360181.1 to have a positive correlation with Tcm infiltration, but an inverse relationship with entities like Th2 cells, T cells, neutrophils and macrophages. Biological enrichment analysis indicated that the pathways of WNT/β-catenin, pancreas beta cells, hedgehog signaling and some metabolism pathways were upregulated in high AL360181.1 patients. In vitro experiments showed that AL360181.1 was upregulated in the colon cancer cells. Moreover, AL360181.1 significantly promotes the proliferation, invasion and migration of colon cancer cells.

Conclusions

Our results can provide direction for future studies on m6A-related lncRNA in colon cancer.

Immune characteristics analysis and construction of a four-gene prognostic signature for lung adenocarcinoma based on estrogen reactivity

Lung adenocarcinoma (LUAD) is a common type of malignant tumor with poor prognosis and high mortality. In our previous studies, we found that estrogen is an important risk factor for LUAD, and different estrogen statuses can predict different prognoses. Therefore, in this study, we constructed a prognostic signature related to estrogen reactivity to determine the relationship between different estrogen reactivities and prognosis. We downloaded the LUAD dataset from The Cancer Genome Atlas (TCGA) database, calculated the estrogen reactivity of each sample, and divided them into a high-estrogen reactivity group and a low-estrogen reactivity group. The difference in overall survival between the groups was significant. We also analyzed the status of immune cell infiltration and immune checkpoint expression between the groups. We analyzed the differential gene expression between the groups and screened four key prognostic factors by the least absolute shrinkage and selection operator (LASSO) regression and univariable and multivariable Cox regression. Based on the four genes, a risk signature was established. To a certain extent, the receiver operating characteristic (ROC) curve showed the predictive ability of the risk signature, which was further verified using the GSE31210 dataset. We also determined the role of estrogen in LUAD using an orthotopic mouse model. Additionally, we developed a predictive nomogram combining the risk signature with other clinical characteristics. In conclusion, our four-gene prognostic signature based on estrogen reactivity had prognostic value and can provide new insights into the development of treatment strategies for LUAD.

Construction and validation of a metabolism-associated gene signature for predicting the prognosis, immune landscape, and drug sensitivity in bladder cancer

Tumor Metabolism is strongly correlated with prognosis. Nevertheless, the prognostic and therapeutic value of metabolic-associated genes in BCa patients has not been fully elucidated. First, in this study, metabolism-related differential expressed genes DEGs with prognostic value in BCa were determined. Through the consensus clustering algorithm, we identified two molecular clusters with significantly different clinicopathological features and survival prognosis. Next, a novel metabolism-related prognostic model was established. Its reliable predictive performance in BCa was verified by multiple external datasets. Multivariate Cox analysis exhibited that risk score were independent prognostic factors. Interestingly, GSEA enrichment analysis of GO, KEGG, and Hallmark gene sets showed that the biological processes and pathways associated with ECM and collagen binding in the high-risk group were significantly enriched. Notely, the model was also significantly correlated with drug sensitivity, immune cell infiltration, and immunotherapy efficacy prediction by the wilcox rank test and chi-square test. Based on the 7 immune infiltration algorithm, we found that Neutrophils, Myeloid dendritic cells, M2 macrophages, Cancer-associated fibroblasts, etc., were more concentrated in the high-risk group. Additionally, in the IMvigor210, GSE111636, GSE176307, or our Truce01 (registration number NCT04730219) cohorts, the expression levels of multiple model genes were significantly correlated with objective responses to anti-PD-1/anti-PD-L1 immunotherapy. Finally, the expression of interested model genes were verified in 10 pairs of BCa tissues and para-carcinoma tissues by the HPA and real-time fluorescent quantitative PCR. Altogether, the signature established and validated by us has high predictive power for the prognosis, immunotherapy responsiveness, and chemotherapy sensitivity of BCa.

Multi-omics characteristics of tumor-associated macrophages in the tumor microenvironment of gastric cancer and their exploration of immunotherapy potential

The incidence and mortality rate of gastric cancer (GC) have remained high worldwide. Although some progress has been made in immunotargeted therapy, the treatment effect remains limited. With more attention has been paid to the immune potential of tumor-associated macrophages (TAMs), but the specific mechanisms of tumor immunity are still unclear. Thus, we screened marker genes in TAMs differentiation (MDMs) through single-cell RNA sequencing, and combined with GC transcriptome data from TCGA and GEO databases, the clinical and TME characteristics, prognostic differences, immune infiltration, and drug sensitivity among different subtypes of patients with GC in different data sets were analyzed. A prognostic model of GC was constructed to evaluate the prognosis and immunotherapy response of patients with GC. In this study, we extensively studied the mutations in MDMs such as CGN, S100A6, and C1QA, and found differences in the infiltration of immune cells and immune checkpoints including M2 TAMs, T cells, CD274, and CTLA4 in different GC subtypes. In the model, we constructed a predictive scoring system with high accuracy and screened out key MDMs-related genes associated with prognosis and M2 TAMs, among which VKORC1 may be involved in GC progression and iron death in tumor cells. Therefore, this study explores the therapeutic strategy of TAMs reprogramming in-depth, providing new ideas for the clinical diagnosis, treatment, and prognosis assessment of GC.

High expression of CCDC69 is correlated with immunotherapy response and protective effects on breast cancer

BACKGROUND

As a molecule controlling the assembly of central spindles and recruitment of midzone component, coiled-coil domain-containing protein 69 (CCDC69) plays an important role in multiple cancers. Currently, the relationships between CCDC69 and immune infiltration or immunotherapy in breast cancer remain unclear.

METHODS

The expression and prognostic significance of CCDC69 in breast cancer were comprehensively analyzed by quantitative real-time PCR, immunohistochemical staining and various databases. The data source of differentially expressed genes, gene set enrichment analysis, and immune cell infiltration analysis came from The Cancer Genome Atlas (TCGA) database. Single-cell analysis based on IMMUcan database was used. The protein-protein interaction network was developed applying STRING, Cytoscape, CytoHubba, and GeneMANIA. TISIDB was employed in analyzing the CCDC69 co-expressed immune related genes. The correlations between CCDC69 and immunotherapy or immune-related scores were analyzed by CAMOIP and TISMO. Ctr-db was also used to conduct drug sensitivity analysis.

RESULTS

The mRNA of CCDC69 was downregulated in breast cancer tissues compared with normal tissues. Higher CCDC69 expression was associated with a better breast cancer prognosis. Enrichment analysis showed that the co-expression genes of CCDC69 were mainly related to immune-related pathways. The expression of CCDC69 was found to be positively correlated with multiple tumor-suppression immune infiltration cells, especially T cells and dendritic cells. Meanwhile, high CCDC69 expression can predict better immunotherapy responses when compared with low CCDC69 expression. After the interferon-gamma treatment, the CCDC69 expression was elevated in vitro. CCDC69 expression was a reliable predictor for the response status of two therapeutic strategies in breast cancer.

CONCLUSIONS

Our research revealed the clinical significance of CCDC69 in breast cancer and validated the critical roles of CCDC69 in the tumor immune infiltration and immunotherapy responses.

Nomogram for predicting survival in patients with mucinous breast cancer undergoing chemotherapy and surgery: a population-based study

BACKGROUND

The prognosis of patients with mucinous breast cancer (MuBC) is affected by several factors, but the low incidence of MuBC makes it difficult to conduct extensive and in-depth studies. This study was designed to establish a prognostic model and verify its accuracy in patients with MuBC after chemotherapy and surgery to help develop personalized treatment strategies.

MATERIALS AND METHODS

Patients with MuBC who underwent chemotherapy and surgery from 2004 to 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic factors of patients with MuBC were investigated using a Cox proportional hazards regression analysis. Based on the identified factors, a nomogram was constructed to forecast the overall survival (OS) of patients at 3, 5, and 10 years. Internal (from SEER) and external (from Yunnan Cancer Center, YNCC) verification queues were used to verify the nomogram and demonstrate the predictive capacity of this model.

RESULTS

The study comprised 1668 MuBC patients from the SEER database and 107 from the YNCC. The nomogram included four characteristics: age, anatomical stage, surgical method, and radiotherapy. The concordance indices in the training, internal verification, and external verification queues were 0.680, 0.768, and 0.864, respectively. The calibration curves for the nomogram showed excellent agreement between the predictions and observations. This nomogram has good clinical application value according to the decision curve analysis.

CONCLUSIONS

The prognosis of patients with MuBC who have undergone chemotherapy and surgery can be forecasted using this nomogram, which would be beneficial to help create individualized treatment plans for the affected patients.

LncTUG1 promotes hepatocellular carcinoma immune evasion via upregulating PD-L1 expression

HCC is one of the most common malignant tumors worldwide. Although traditional treatment methods have been improved in recent years, the survival rate of HCC patients has not been significantly improved. Immunotherapy has shown extremely high clinical value in a variety of tumors. In this study, we found that TUG1 could regulate the expression of PD-L1 through JAK2/STAT3 to mediate immunosuppression. Here, The expression of TUG1 and PD-L1 in HCC tissues was evaluated through analysis of databases and verified in HCC tissue and HCC cancer cells by qRT-PCR. The effect of TUG1 on tumor immune escape was detected by coculture, and cell viability was detected with a CCK8 assay. The results demonstrated that TUG1 was closely associated with anticancer immunity. TUG1 and PD-L1 were highly expressed in HCC tissues and HCC cancer cells, and high expression of TUG1 and PD-L1 was related to the poor prognosis of HCC patients. In addition, knocking down TUG1 expression could reduce PD-L1 expression and enhance the cancer cell-killing capability of T cells. Downregulating TUG1 expression could also decrease the mRNA and protein expression of JAK2 and STAT3. To sum up, TUG1 and PD-L1 are overexpressed in patients with liver cancer and are related to the poor prognosis of these patients. Silencing TUG1 expression reduced the mRNA and protein expression of PD-L1 by affecting the JAK2/STAT3 pathway.

Comprehensive analysis and validation of SNX7 as a novel biomarker for the diagnosis, prognosis, and prediction of chemotherapy and immunotherapy response in hepatocellular carcinoma

BACKGROUND

Studies have demonstrated that Sorting nexin 7 (SNX7) functions as an anti-apoptotic protein in liver tissue and plays a crucial role in the survival of hepatocytes during early embryonic development. However, its diagnostic and prognostic value as well as the predictive value of chemotherapy and immunotherapy have not been reported in hepatocellular carcinoma (HCC).

METHODS

SNX7 mRNA expression and its diagnostic efficacy were examined in GEO datasets, and the findings were further confirmed in TCGA, ICGC cohorts, and cell lines. The protein level of SNX7 was determined using CPTAC and HPA databases, and the results were validated through immunohistochemistry (IHC). Survival analyses were performed in TCGA and ICGC cohorts, and the results were subsequently validated via Kaplan-Meier Plotter. The response to chemotherapy and immunotherapy was predicted via GDSC dataset and TIDE algorithm, respectively. R packages were employed to explore the relationship between SNX7 expression and immune infiltration, m6A modification, as well as the functional enrichment of differentially expressed genes (DEGs).

RESULTS

The expression of SNX7 at both mRNA and protein levels was significantly upregulated in HCC tissues. SNX7 exhibited superior diagnostic efficacy compared to AFP alone for HCC detection, and combining it with AFP improved the diagnostic accuracy for HCC. High SNX7 was associated with unfavorable outcomes, including poor overall survival, disease-specific survival, progression-free survival, and advanced pathological stage, in patients with HCC, and SNX7 was identified as an independent risk factor for HCC. Moreover, elevated SNX7 expression was positively correlated with increased sensitivity to various chemotherapy drugs, including sorafenib, while it was associated with resistance to immunotherapy in HCC patients. Correlation analysis revealed a relationship between SNX7 and multiple m6A-related genes and various immune cells. Finally, enrichment analysis demonstrated strong associations of SNX7 with critical biological processes, such as cell cycle regulation, cellular senescence, cell adhesion, DNA replication, and mismatch repair pathway in HCC.

CONCLUSIONS

Our study highlights the association of SNX7 with the immune microenvironment and its potential influence on HCC progression. SNX7 emerges as a promising novel biomarker for the diagnosis, prognosis, and prediction of response to chemotherapy and immunotherapy in patients with HCC.

FAT4 expression in peripheral blood mononuclear cells is associated with prognosis and immune cell infiltration in hepatocellular carcinoma

Peripheral blood mononuclear cell (PBMC) genes reflect the host immune status and could be suitable for evaluating the prognosis of patients with hepatocellular carcinoma (HCC), for which a reliable biomarker is unavailable and the host immune responses to cancer cells. This study aimed to investigate prognostically relevant genes in HCC PBMCs and assessed whether their expression represents tumor immune infiltration. Gene expression in PBMCs from patients with advanced or terminal HCC who had survived or died was examined. Correlations among FAT atypical cadherin 4 (FAT4) expression, cancer immune characteristics, and infiltrated immune cell gene marker sets were analyzed. FAT4 expression was lower in the PBMCs of patients with advanced or terminal HCC who had died than that in patients who survived. Kaplan-Meier analysis indicated that FAT4 downregulation was associated with a relatively poor prognosis while overexpression was positively correlated with immune cell infiltration, several immune cell markers, and immune checkpoint expression. Hsa-miR-93-5p represented the most probable upstream microRNA of FAT4. Thus, upregulated FAT4 in PBMCs and HCC tissues might indicate a favorable prognosis and increased immune cell infiltration, while miRNA-93-5p could be a modulator of FAT4 expression. Collectively, these findings suggest novel immunotherapy targets for HCC.

Construction of a novel signature based on immune-related lncRNA to identify high and low risk pancreatic adenocarcinoma patients

BACKGROUND

Pancreatic adenocarcinoma is one of the most lethal tumors in the world with a poor prognosis. Thus, an accurate prediction model, which identify patients within high risk of pancreatic adenocarcinoma is needed to adjust the treatment and elevate the prognosis of these patients.

METHODS

We obtained RNAseq data of The Cancer Genome Atlas (TCGA) pancreatic adenocarcinoma (PAAD) from UCSC Xena database, identified immune-related lncRNAs (irlncRNAs) by correlation analysis, and identified differential expressed irlncRNAs (DEirlncRNAs) between pancreatic adenocarcinoma tissues from TCGA and normal pancreatic tissues from TCGA and Genotype-Tissue Expression (GTEx). Further univariate and lasso regression analysis were performed to construct prognostic signature model. Then, we calculated the areas under curve and identified the best cut-off value to identify high- and low-risk patients with pancreatic adenocarcinoma. The clinical characteristics, immune cell infiltration, immunosuppressive microenvironment, and chemoresistance were compared between high- and low-risk patients with pancreatic adenocarcinoma.

RESULTS

We identified 20 DEirlncRNA pairs and grouped the patients by the best cut-off value. We proved that our prognostic signature model possesses a remarkable efficiency to predict prognosis of PAAD patients. The AUC for ROC curve was 0.905 for 1-year prediction, 0.942 for 2-year prediction, and 0.966 for 3-year prediction. Patients in high-risk group have poor survival rate and worse clinical characteristics. We also proved that patients in high-risk groups were in immunosuppressive status and may be resistant to immunotherapy. Anti-cancer drug evaluation was performed based on in-silico predated tool, such as paclitaxel, sorafenib, and erlotinib, may be suitable for PAAD patients in high-risk group.

CONCLUSIONS

Overall, our study constructed a novel prognostic risk model based on pairing irlncRNAs, exhibited a promising prediction value in patients with pancreatic adenocarcinoma. Our prognostic risk model may help distinguish PAAD patients suitable for medical treatments.

Evaluation of ITGB1 expression as a predictor of the therapeutic effects of immune checkpoint inhibitors in gastric cancer

BACKGROUND

Gastric cancer (CC) is a disease with high incidence and mortality rate. Immunotherapy is an important method for gastric cancer while lack of effective predictor. Integrins play an important role in the development. We aimed to explore the predictive value of β1 integrin (ITGB1) as a predictor of immunnotherapy in gastric cancer.

METHODS

Differential expression analysis was conducted using the Gene Expression Profiling Interactive Analysis (GEPIA) 2.0 and GEO databases. GEPIA data were used to evaluate the prognostic value of ITGB1 in gastric cancer (GC). Transcriptomic and clinical data of GC and normal tissues were downloaded from The Cancer Genome Atlas database, and the TIMER database was used to evaluate the association between ITGB1 and immune infiltration. Time-dependent receiver operating characteristic (ROC) curve analysis was used to determine the prognostic value of ITGB1. To verify ITGB1 expression at the protein level, immunohistochemical staining was conducted. In addition, to analyze the correlation of ITGB1 with PD-1 and PD-L1, we examined levels of PD-1 and PD-L1 by IHC and determined the predictive value of ITGB1 for anti-PD-1 therapy in GC by ROC curve analysis.

RESULTS

Compared with normal tissues, analysis of GEPIA and data at protein levels showed significantly higher expression of ITGB1 in GC. In addition, higher expression of ITGB1 was associated with worse pathological G-staging and tumor T-staging, which suggested that ITGB1 is a risk factor for poor prognosis in GC. The level of ITGB1 expression was positively correlated with CD8 + T cells, neutrophils, macrophages, and dendritic cells. ITGB1 expression was also correlated with PD-L1 expression, and this was further verified at the protein level by immunohistochemical analysis. The area under the ROC curve was 0.808.

CONCLUSION

ITGB1 may be a promising prognostic biomarker and effective predictor for anti-PD-1 therapy in GC.

TRIAL REGISTRATION

Retrospectively registered.

Lactate dehydrogenase D serves as a novel biomarker for prognosis and immune infiltration in lung adenocarcinoma

BACKGROUND

Lung cancer is reported to be the leading cause of death in males and females, globally. Increasing evidence highlights the paramount importance of Lactate dehydrogenase D (LDHD) in different types of cancers, though it's role in lung adenocarcinoma (LUAD) is still inadequately explored. In this study, we aimed to investigate and determine the relationship between LDHD and LUAD.

METHODS

The collection of the samples was guided by The Cancer Genome Atlas (TCGA) datasets and Gene Expression Omnibus (GEO). To ascertain various aspects around LDHD function, we analyzed different expression genes (DEGs), functional enrichment, and protein-protein interaction (PPI) networks. The predictive values for LDHD were collectively determined using the Kaplan-Meier method, Cox regression analysis, and a nomogram. Evaluation of the immune infiltration analysis was completed using Estimate and ssGSEA. The prediction of the immunotherapy response was based on TIDE and IPS. The LDHD expression levels in LUAD were validated through Western blot, qPCR, and immunohistochemistry methods. Wound healing and transwell assays were also performed to illustrate the aggressive features in LUAD cell lines.

RESULTS

The results showed that LDHD was generally downregulated in LUAD patients, with the low LDHD group presenting a decline in OS, DSS, and PFI. Enriched pathways, which include pyruvate metabolism, central carbon metabolism, and oxidative phosphorylation were observed through KEGG analysis. It was also noted that the expression of LDHD expression was inversely related to immune cell infiltration and typical checkpoints. The high LDHD group's response to immunotherapy was remarkable, particularly in CTAL4 + /PD1- therapy. In vitro studies revealed that the overexpression of LDHD caused tumor migration and invasion to be suppressed.

CONCLUSION

In conclusion, our study revealed that LDHD might be an effective predictor of prognosis and immune filtration, possibly leading to better choices for immunotherapy.

ADNP is associated with immune infiltration and radiosensitivity in hepatocellular carcinoma for predicting the prognosis

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most lethal diseases due to its high faculty of invasiveness and metastasis. Activity-dependent neuroprotective protein (ADNP) has been regarded as an oncogene in bladder cancer and ovarian cancer. However, the role of ADNP in the regulation of tumor immune response, development, and treatment resistance in HCC remains unknown and is worth exploring.

METHODS

The correlation between ADNP and prognosis, immune cell infiltration, immune checkpoints, chemokines, tumor mutation burden, microsatellite instability, and genomic mutation of pan-cancer cohorts in The Cancer Genome Atlas was analyzed. ADNP expression in HCC cell lines, HCC and the adjacent normal tissues was measured by western blotting and immunochemistry. Nomogram was constructed to predict the survival of patients with HCC based on the ADNP expression and significant clinical characteristics. The potential biological functions and impacts on radiotherapy of ADNP in HCC cell lines were verified by vitro experiments.

RESULTS

ADNP was upregulated in most cancers and patients with elevated ADNP expression were related to poor survival in several types of cancers including HCC. Functional enrichment analysis showed ADNP participated in the pathways correlated with coagulation cascades and DNA double strand break repair. Further, ADNP exhibited a negative correlation with the immune score, stromal score, estimated score, and chemokines, and a positive correlation with cancer-associated fibroblasts, myeloid-derived suppressor cells, neutrophils, regulatory T cells, and endothelial cells. Immunochemistry and western blotting results demonstrated ADNP was up-regulated in HCC. Vitro experiments verified that suppressing the ADNP expression significantly inhibited the proliferation, invasion and migration and elevated the radiosensitivity via decreasing DNA damage repair in HCC.

CONCLUSION

ADNP might play an oncogene and immunosuppression role in tumor immune infiltration and response, thus influencing the prognosis. Its downregulation could attenuate the proliferation, invasion, migration, radioresistance of HCC. Our results indicated the potential of ADNP as a promising biomarker to predict the survival of HCC patients, providing a theoretical basis for novel integrative strategies.

Second primary colorectal cancer in adults: a SEER analysis of incidence and outcomes

BACKGROUND

At present, there was no large epidemiological study exploring the actual incidence and survival of second primary colorectal cancer (spCRC). The different characteristics and survival of patients with spCRC and initial primary colorectal cancer (ipCRC) still need to be elucidated. In addition, the factors leading to different survival status of spCRC and ipCRC were still unclear. Our study plan to explore the annual incidence trend of spCRC as well as the factors influencing the occurrence and survival outcome of spCRC.

METHODS

This cohort study analyzed the data of 4680 spCRC patients and 330,937 initial primary colorectal cancer (ipCRC) patients. Whether patients had spCRC and whether spCRC patients survived or died were regarded as outcomes. The annual incidence of spCRC from 2004 to 2016 was analyzed by Jointpoint regression analysis. The truncation points were found, and the annual percentage change (APC) of each segment was calculated to explore the trend of spCRC change in the United States. Univariate and multivariable cox regression analyses were conducted to identify factors associated with the occurrence and prognosis of spCRC patients.

RESULTS

The total incidence of spCRC was decreased during 2000-2016 on the whole. The overall incidence of spCRC was lowered in both males and females despite 2013-2014, in the left colon, right colon, rectum and others. The incidence of spCRC was decreased in both 18-49 years' people and ≥ 50 years' people during 2000-2016, and the incidence of spCRC in the ≥ 50 years' people group was higher than those of 18-49 years. Insured (OR = 0.867 (0.778-0.966), initial primary site of other digestive (OR = 0.46, 95%CI: 0.42-0.50), rectum (OR = 0.74, 95%CI: 0.66-0.82), or right colon (OR = 0.73, 95%CI: 0.68-0.79), N 1 stage (OR = 0.87, 95%CI: 0.76-0.99), M 1 stage (OR = 0.49, 95%CI: 0.30-0.80), AJCC II stage (OR = 0.70, 95%CI: 0.60-0.82), AJCC III stage (OR = 0.69, 95%CI: 0.56-0.84), and radiation (OR = 0.69, 95%CI: 0.57-0.83) were associated with the risk of spCRC. At the end of follow-up, 2,246 spCRC patients were survived and 2,434 spCRC patients were dead. Patients with spCRC had poor survival probability than patients with ipCRC. Older age (HR = 1.02, 95%CI: 1.02-1.03), male (HR = 1.13, 95%CI: 1.04-1.23), Black (HR = 1.20, 95%CI: 1.06-1.35), uninsured (HR = 1.36, 95%CI: 1.16-1.59), Signet ring cell carcinoma (HR = 1.64, 95%CI: 1.19-2.25), T4 stage (HR = 1.63, 95%CI: 1.32-2.01), N2 stage (HR = 1.36, 95%CI: 1.08-1.72), M1 stage (HR = 4.51, 95%CI: 2.00-10.18), AJCC III (HR = 1.47, 95%CI: 1.08-1.98), and radiation (HR = 1.82, 95%CI: 1.43-2.33) were associated with increased risk of mortality in spCRC patients.

CONCLUSION

The incidence of spCRC was decreased except in people with initial primary tumor grade IV and those aged 15-39 years. The overall survival of spCRC patients was lower than ipCRC patients. Cancer patients with older age, high tumor grade, TNM stage, and AJCC stage should be caution to the occurrence of spCRC and timely interventions should be provided for spCRC patients to improve their outcomes.

Knockdown of RBM15 inhibits tumor progression and the JAK-STAT signaling pathway in cervical cancer

BACKGROUND

RNA binding motif protein 15 (RBM15), a writer of N6-methyladenosine (m6A) methylation, contributes significantly to the development of various tumors. However, the function of RBM15 in cervical cancer (CC) has not been determined.

METHODS

Based on the GSE9750, GSE63514, and m6A datasets, m6A-related differentially expressed genes (DEGs) were screened out. The hub genes were identified by generating a Protein-Protein Interaction (PPI) network. RT-qPCR was conducted to assess the mRNA expression of hub genes. CCK8, scratch wound healing, and transwell assays were utilized to examine the influence of RBM15 on HeLa and SiHa cells. Tumor xenograft models were used to assess the effects of RBM15 on tumorigenesis. A mechanistic analysis of RBM15 in CC tumors was conducted using the GeneCards and Coxpresdb databases, followed by a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and the pathway-related genes were subsequently validated using Western blotting.

RESULTS

Five DEGs were screened, including WTAP, RBM15, CBLL1, and YTHDC2. Among them, WTAP, RBM15, CBLL1, and YTHDC2 were hub genes and can be used as biomarkers for CC. RBM15 expression was considerably increased, while WTAP, CBLL1, and YTHDC2 were significantly downregulated. Knockdown of RBM15 significantly suppressed the proliferation, invasion, and migration of CC cells and tumorigenesis. Moreover, knockdown of RBM15 significantly reduced the expression levels of proteins related to the JAK-STAT pathway.

CONCLUSIONS

Knockdown of RBM15 inhibited the progression of CC cells, which probably by inhibiting the JAK-STAT pathway pathway.

A pan-cancer analysis for the oncogenic role of cyclin-dependent kinase inhibitor 1B in human cancers

BACKGROUND

Human health and life are threatened by cancer with high morbidity and mortality worldwide. In many experiments, CDKN1B level is associated with cancer risk, Nevertheless, no pan-cancer analysis has been conducted on CDKN1B in human cancers.

METHODS

With the help of bioinformatics, a pan-cancer analysis was conducted on the expression levels of CDKN1B in cancer tissues and adjacent tissues from the TCGA, CPTAC and GEO databases. The CDKN1B expression levels in tumor patients was further validated using immunohistochemistry (IHC) and quantitative real-time PCR.

RESULTS

In the study, we first investigated the cancer-related roles of CDKN1B's in 40 tumors with malignancy. The CDKN1B gene encodes the p27^Kip1 protein, which can block the production cyclin-dependent kinase (CDK), which is obviously related to the function and survival of cancer cells and alters the prognosis of cancer patients. Furthermore, CDKN1B function requires both protein processing and RNA metabolism. Additionally, the elevated expression of the CDKN1B gene and protein was validated in several cancer tissues from the patients.

CONCLUSIONS

These results showed that the levels of CDKN1B were considerably different in a number of cancer tissues, offering a potential future target for cancer therapy.

An integrative bioinformatics investigation and experimental validation of chromobox family in diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is one of the most aggressive malignant tumors. Chromobox (CBX) family plays the role of oncogenes in various malignancies.

METHODS

The transcriptional and protein levels of CBX family were confirmed by GEPIA, Oncomine, CCLE, and HPA database. Screening of co-expressed genes and gene function enrichment analysis were performed by GeneMANIA and DAVID 6.8. The prognostic value, immune cell infiltration and drug sensitivity analysis of CBX family in DLBCL were performed by Genomicscape, TIMER2.0, and GSCALite database. Confirmatory Tests of CBX family protein expression in DLBCL were performed by immunohistochemistry.

RESULTS

The mRNA and protein expressions of CBX1/2/3/5/6 were higher in DLBCL tissues than control groups. Enrichment analysis showed that the functions of CBX family were mainly related to chromatin remodeling, methylation-dependent protein binding, and VEGF signaling pathway. The high mRNA expressions of CBX2/3/5/6 were identified to be associated with short overall survival (OS) in DLBCL patients. Multivariate COX regression indicated that CBX3 was independent prognostic marker. Immune infiltration analysis revealed that the mRNA expressions of CBX family (especially CBX1, CBX5, and CBX6) in DLBCL were significantly correlated with the infiltration of most immune cells (including B cells, CD8 + T cells, CD4 + T cells, neutrophils, monocytes, macrophages, and Treg cells). Meanwhile, there was a strong correlation between the expression levels of CBX1/5/6 and surface markers of immune cells, such as the widely studied PVR-like protein receptor/ligand and PDL-1 immune checkpoint. Notably, our study found that DLBCL cells with CBX1 over-expression were resistant to the common anti-tumor drugs, but CBX2/5 had two polarities. Finally, we confirmed the higher expressions of CBX1/2/3/5/6 in DLBCL tissues compared with control groups by immunohistochemistry.

CONCLUSION

We provided a detailed analysis of the relationship between the CBX family and the prognosis of DLBCL. Distinguished from other studies, We found that high mRNA expressions of CBX2/3/5/6 were associated with poor prognosis in DLBCL patients, and Multivariate COX regression indicated that CBX3 was independent prognostic marker. Besides, our study also found an association between the CBX family and anti-tumour drug resistance, and provided a relationship between CBX family expression and immune cell infiltration.

Development and validation of prognostic nomograms based on De Ritis ratio and clinicopathological features for patients with stage II/III colorectal cancer

BACKGROUND

Metabolic derangements and systemic inflammation are related to the progression of colorectal cancer (CRC) and the prognoses of these patients. The survival of stage II and III CRC patients existed considerable heterogeneity highlighting the urgent need for new prediction models. This study aimed to develop and validate prognostic nomograms based on preoperative serum liver enzyme as well as evaluate the clinical utility.

METHODS

A total of 4014 stage II/III primary CRC patients pathologically diagnosed from January 2007 to December 2013 were included in this study. These patients were randomly divided into a training set (n = 2409) and a testing set (n = 1605). Univariate and multivariate Cox analyses were used to select the independent factors for predicting overall survival (OS) and disease-free survival (DFS) of stage II/III CRC patients. Next, nomograms were constructed and validated to predict the OS and DFS of individual CRC patients. The clinical utility of nomograms, tumor-node-metastasis (TNM), and the American Joint Committee on Cancer (AJCC) system was evaluated using time-dependent ROC and decision curve analyses.

RESULTS

Among seven preoperative serum liver enzyme markers, aspartate aminotransferase-to-alanine aminotransferase ratio (De Ritis ratio) was identified as an independent factor for predicting both OS and DFS of stage II/III CRC patients. The nomograms incorporated De Ritis ratio and significant clinicopathological features achieved good accuracy in terms of OS and DFS prediction, with C-index of 0.715 and 0.692, respectively. The calibration curve showed good agreement between prediction by nomogram and actual observation. The results of time-dependent ROC and decision curve analyses suggested that the nomograms had improved discrimination and greater clinical benefits compared with TNM and AJCC staging.

CONCLUSIONS

De Ritis ratio was an independent predictor in predicting both the OS and DFS of patients with stage II/III CRC. Nomograms based on De Ritis ratio and clinicopathological features showed better clinical utility, which is expected to help clinicians develop appropriate individual treatment strategies for patients with stage II /III CRC.

Identification of STEAP3-based molecular subtype and risk model in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane epithelial antigen of prostate family member 3 (STEAP3) as a prognostic predictor and therapeutic target in OC using bioinformatics analysis.

METHODS

STEAP3 expression and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Unsupervised clustering was used to identify molecular subtypes. Prognosis, tumor immune microenvironment (TIME), stemness indexes, and functional enrichment analysis were compared between two definite clusters. Through the least absolute shrinkage and selection operator (LASSO) regression analysis, a STEAP3-based risk model was developed, and the predictive effectiveness of this signature was confirmed using GEO datasets. A nomogram was used to predict the survival possibility of patients. Additionally, TIME, tumor immune dysfunction and exclusion (TIDE), stemness indexes, somatic mutations, and drug sensitivity were evaluated in different risk groups with OC. STEAP3 protein expression was detected using immunohistochemistry (IHC).

RESULTS

STEAP3 displayed marked overexpression in OC. STEAP3 is an independent risk factor for OC. Based on the mRNA levels of STEAP3-related genes (SRGs), two distinct clusters were identified. Patients in the cluster 2 (C2) subgroup had a considerably worse prognosis, higher immune cell infiltration, and lower stemness scores. Pathways involved in tumorigenesis and immunity were highly enriched in the C2 subgroup. A prognostic model based on 13 SRGs was further developed. Kaplan-Meier analysis indicated that the overall survival (OS) of high-risk patients was poor. The risk score was significantly associated with TIME, TIDE, stemness indexes, tumor mutation burden (TMB), immunotherapy response, and drug sensitivity. Finally, IHC revealed that STEAP3 protein expression was noticeably elevated in OC, and overexpression of STEAP3 predicted poor OS and relapse-free survival (RFS) of patients.

CONCLUSION

In summary, this study revealed that STEAP3 reliably predicts patient prognosis and provides novel ideas for OC immunotherapy.

Analyzing molecular typing and clinical application of immunogenic cell death-related genes in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is considered one of the most common cancers, characterized by low early detection and high mortality rates, and is a global health challenge. Immunogenic cell death (ICD) is defined as a specific type of regulated cell death (RCD) capable of reshaping the tumor immune microenvironment by releasing danger signals that trigger immune responses, which would contribute to immunotherapy.

METHODS

The ICD gene sets were collected from the literature. We collected expression data and clinical information from public databases for the HCC samples in our study. Data processing and mapping were performed using R software to analyze the differences in biological characteristics between different subgroups. The expression of the ICD representative gene in clinical specimens was assessed by immunohistochemistry, and the role of the representative gene in HCC was evaluated by various in vitro assays, including qRT-PCR, colony formation, and CCK8 assay. Lasso-Cox regression was used to screen prognosis-related genes, and an ICD-related risk model (ICDRM) was constructed. To improve the clinical value of ICDRM, Nomograms and calibration curves were created to predict survival probabilities. Finally, the critical gene of ICDRM was further investigated through pan-cancer analysis and single-cell analysis.

RESULTS

We identified two ICD clusters that differed significantly in terms of survival, biological function, and immune infiltration. As well as assessing the immune microenvironment of tumors in HCC patients, we demonstrate that ICDRM can differentiate ICD clusters and predict the prognosis and effectiveness of therapy. High-risk subpopulations are characterized by high TMB, suppressed immunity, and poor survival and response to immunotherapy, whereas the opposite is true for low-risk subpopulations.

CONCLUSIONS

This study reveals the potential impact of ICDRM on the tumor microenvironment (TME), immune infiltration, and prognosis of HCC patients, but also a potential tool for predicting prognosis.

Exploring the prognostic function of TMB-related prognostic signature in patients with colon cancer

Tumor mutation burden (TMB) level is identified as a useful predictor in multiple tumors including colon adenocarcinoma (COAD). However, the function of TMB related genes has not been explored previously. In this study, we obtained patients' expression and clinical data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI). TMB genes were screened and subjected to differential expression analysis. Univariate Cox and LASSO analyses were utilized to construct the prognostic signature. The efficiency of the signature was tested by using a receiver operating characteristic (ROC) curve. A nomogram was further plotted to assess the overall survival (OS) time of patients with COAD. In addition, we compared the predictive performance of our signature with other four published signatures. Functional analyses indicated that patients in the low-risk group have obviously different enrichment of tumor related pathways and tumor infiltrating immune cells from that of high-risk patients. Our findings suggested that the ten genes' prognostic signature could exert undeniable prognostic functions in patients with COAD, which might provide significant clues for the development of personalized management of these patients.

A novel PD-1/PD-L1 pathway molecular typing-related signature for predicting prognosis and the tumor microenvironment in breast cancer

BACKGROUND

Currently, the development of breast cancer immunotherapy based on the PD-1/PD-L1 pathway is relatively slow, and the specific mechanism affecting the immunotherapy efficacy in breast cancer is still unclear.

METHODS

Weighted correlation network analysis (WGCNA) and the negative matrix factorization (NMF) were used to distinguish subtypes related to the PD-1/PD-L1 pathway in breast cancer. Then univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were used to construct the prognostic signature. A nomogram was established based on the signature. The relationship between the signature gene IFNG and breast cancer tumor microenvironment was analyzed.

RESULTS

Four PD-1/PD-L1 pathway-related subtypes were distinguished. A prognostic signature related to PD-1/PD-L1 pathway typing was constructed to evaluate breast cancer's clinical characteristics and tumor microenvironment. The nomogram based on the RiskScore could be used to accurately predict breast cancer patients' 1-year, 3-year, and 5-year survival probability. The expression of IFNG was positively correlated with CD8+ T cell infiltration in the breast cancer tumor microenvironment.

CONCLUSION

A prognostic signature is constructed based on the PD-1/PD-L1 pathway typing in breast cancer, which can guide the precise treatment of breast cancer. The signature gene IFNG is positively related to CD8+ T cell infiltration in breast cancer.

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis

Colon cancer (CC) has a poor 5-year survival rate though the treatment techniques and strategies have been improved. Succinylation and long noncoding RNAs (lncRNAs) have prognostic value for CC patients. We analyzed and obtained succinylation-related lncRNA by co-expression in CC. A novel succinylation-related lncRNA model was developed by univariate and Least absolute shrinkage and selection operator (Lasso) regression analysis and we used principal component analysis (PCA), functional enrichment annotation, tumor immune environment, drug sensitivity and nomogram to verify the model, respectively. Six succinylation-related lncRNAs in our model were finally confirmed to distinguish the survival status of CC and showed statistically significant differences in training set, testing set, and entire set. The prognosis of with this model was associated with age, gender, M0 stage, N2 stage, T3 + T4 stage and Stage III + IV. The high-risk group showed a higher mutation rate than the low-risk group. We constructed a model to predict overall survival for 1-, 3-, and 5-year with AUCs of 0.694, 0.729, and 0.802, respectively. The high-risk group was sensitive to Cisplatin and Temozolomide compounds. Our study provided novel insights into the value of the succinylation-related lncRNA signature as a predictor of prognosis, which had high clinical application value in the future.

Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Purpose

Since the prognosis of renal cell carcinoma (RCC) patients with bone metastasis (BM) is poor, this study is aimed at using big data to build a machine learning (ML) model to predict the risk of BM in RCC patients.

Methods

A retrospective study was conducted on 40,355 RCC patients in the SEER database from 2010 to 2017. LASSO regression and multivariate logistic regression analysis was performed to determine independent risk factors of RCC-BM. Six ML algorithm models, including LR, GBM, XGB, RF, DT, and NBC, were used to establish risk models for predicting RCC-BM. The prediction performance of ML models was weighed by 10-fold cross-validation.

Results

The study investigated 40,355 patients diagnosed with RCC in the SEER database, where 1,811 (4.5%) were BM patients. Independent risk factors for BM were tumor grade, T stage, N stage, liver metastasis, lung metastasis, and brain metastasis. Among the RCC-BM risk prediction models established by six ML algorithms, the XGB model showed the best prediction performance (AUC = 0.891). Therefore, a network calculator based on the XGB model was established to individually assess the risk of BM in patients with RCC.

Conclusion

The XGB risk prediction model based on the ML algorithm performed a good prediction effect on BM in RCC patients.