Identification of microenvironment-related genes with prognostic value in clear cell renal cell carcinoma

Association of the CD4+/CD8+ ratio with response to PD-1 inhibitor-based combination therapy and dermatological toxicities in patients with advanced gastric and esophageal cancer

The host immune system affects the treatment response to immune checkpoint inhibitors and can be reflected by circulating immune cells. This study aimed to evaluate whether circulating T cell subtypes are correlated with clinical response and dermatological toxicities in patients with advanced gastric and esophageal cancer receiving PD-1 inhibitor-based combination therapy (n = 203). In the training cohort, Eastern Cooperative Oncology Group performance status (ECOG PS), PD-L1 expression, antibiotic use, and CD4+/CD8+ ratio were identified as independent prognostic factors in these patients, using a Cox regression model. A nomogram to predict the overall survival (OS) and survival probabilities was constructed using these factors. The nomogram showed good discrimination ability (C-index, 0.767) and was externally confirmed in the validation and test cohorts. Kaplan-Meier analysis showed that median OS in patients with a CD4+/CD8+ ratio ≥1.10 was 6.2 months, which was significantly shorter than that in patients with a CD4+/CD8+ ratio <1.10 (P < 0.001). Patients with a CD4+/CD8+ ratio <1.10 had a superior objective response (43.8% vs. 23.1%) and disease control (72.9% vs. 59.0%) rate, relative to those with ratio ≥ 1.10. In addition, PD-L1 expression, corticosteroid use, and CD4+/CD8+ ratio can independently predict dermatological toxicities. In conclusion, baseline CD4+/CD8+ ratio is a potential prognostic factor for patients with advanced gastric and esophageal cancer treated with PD-1 inhibitor-based combination therapy, and can independently predict dermatological toxicities. In addition, a nomogram incorporating CD4+/CD8+ ratio, ECOG PS, PD-L1 expression, and antibiotic use can predict OS with considerable accuracy.

Correlation evaluation between cancer microenvironment related genes and prognosis based on intelligent medical internet of things

The study of tumor microenvironment plays an important role in the treatment of cancer patients. In this paper, intelligent medical Internet of Things technology was used to analyze cancer tumor microenvironment-related genes. Through experiments designed and analyzed cancer-related genes, this study concluded that in cervical cancer, patients with high expression of P16 gene had a shorter life cycle and a survival rate of 35%. In addition, through investigation and interview, it was found that patients with positive expression of P16 and Twist genes had a higher recurrence rate than patients with negative expression of both genes; high expression of FDFT1, AKR1C1, and ALOX12 in colon cancer is associated with short survival; high expressions of HMGCR and CARS1 is associated with longer survival; overexpression of NDUFA12, FD6, VEZT, GDF3, PDE5A, GALNTL6, OPMR1, and AOAH in thyroid cancer is associated with shortened survival; high expressions of NR2C1, FN1, IPCEF1, and ELMO1 is associated with prolonged survival. Among the genes associated with the prognosis of liver cancer, the genes associated with shorter survival period are AGO2, DCPS, IFIT5, LARP1, NCBP2, NUDT10, and NUDT16; the genes associated with longevity are EIF4E3, EIF4G3, METTL1, NCBP1, NSUN2, NUDT11, NUDT4, and WDR4. Depending on the prognostic role of genes in different cancers, they can influence patients to achieve the effect of reducing patients' symptoms. In the process of disease analysis of cancer patients, this paper uses bioinformation technology and Internet of things technology to promote the development of medical intelligence.

Identification of an immune subtype-related prognostic signature of clear cell renal cell carcinoma based on single-cell sequencing analysis

Background

There is growing evidence that immune cells are strongly associated with the prognosis and treatment of clear cell renal cell carcinoma (ccRCC). Our aim is to construct an immune subtype-related model to predict the prognosis of ccRCC patients and to provide guidance for finding appropriate treatment strategies.

Methods

Based on single-cell analysis of the GSE152938 dataset from the GEO database, we defined the immune subtype-related genes in ccRCC. Immediately afterwards, we used Cox regression and Lasso regression to build a prognostic model based on TCGA database. Then, we carried out a series of evaluation analyses around the model. Finally, we proved the role of VMP1 in ccRCC by cellular assays.

Result

Initially, based on TCGA ccRCC patient data and GEO ccRCC single-cell data, we successfully constructed a prognostic model consisting of five genes. Survival analysis showed that the higher the risk score, the worse the prognosis. We also found that the model had high predictive accuracy for patient prognosis through ROC analysis. In addition, we found that patients in the high-risk group had stronger immune cell infiltration and higher levels of immune checkpoint gene expression. Finally, cellular experiments demonstrated that when the VMP1 gene was knocked down, 786-O cells showed reduced proliferation, migration, and invasion ability and increased levels of apoptosis.

Conclusion

Our study can provide a reference for the diagnosis and treatment of patients with ccRCC.

Sarcomatoid-associated gene risk index for clear cell renal cell carcinoma

Sarcomatoid renal cell carcinoma is a de-differentiated form of kidney cancer with an extremely poor prognosis. Genes associated with sarcomatoid differentiation may be closely related to the prognosis of renal cell carcinoma. The prognosis of renal cell carcinoma itself is extremely variable, and a new prognostic model is needed to stratify patients and guide treatment. Data on clear cell renal cell carcinoma with or without sarcomatoid differentiation were obtained from TCGA database, and a sarcomatoid-associated gene risk index (SAGRI) and column line graphs were constructed using sarcomatoid-associated genes. The predictive power of the SAGRI and column line graphs was validated using an internal validation set and an independent validation set (E-MTAB-1980). The SAGRI was constructed using four sarcoma-like differentiation-related genes, COL7A1, LCTL, NPR3, ZFHX4, and had a 1-year AUC value of 0.725 in the training set, 0.712 in the internal validation set, and 0.770 in the independent validation set for TCGA training cohort, with high model reliability. The molecular characteristics among the SAGRI subgroups were analyzed by multiple methods, and results suggested that the SAGRI-HIGH subgroup may benefit more from immunotherapy to improve prognosis. SAGRI satisfactorily predicted the prognosis of patients with clear cell renal cell carcinoma with or without sarcomatoid differentiation.

Characterization of Subtypes of BRAF-Mutant Papillary Thyroid Cancer Defined by Their Thyroid Differentiation Score

CONTEXT

The BRAFV600E mutation has been associated with more advanced clinical stage in papillary thyroid cancer (PTC) and decreased responsiveness to radioiodine (RAI). However, some BRAF mutant PTCs respond to RAI and have an indolent clinical behavior suggesting the presence of different subtypes of BRAF mutant tumors with distinct prognosis.

OBJECTIVE

To characterize the molecular and clinical features of 2 subtypes of BRAF-mutant PTCs defined by their degree of expression of iodine metabolism genes.

DESIGN

227 BRAF-mutant PTCs from the Cancer Genome Atlas Thyroid Cancer study were divided into 2 subgroups based on their thyroid differentiation score (TDS): BRAF-TDShi and BRAF-TDSlo. Demographic, clinico-pathological, and molecular characteristics of the 2 subgroups were compared.

RESULTS

Compared to BRAF-TDShi tumors (17%), BRAF-TDSlo tumors (83%) were more frequent in blacks and Hispanics (6% vs 0%, P = 0.035 and 12% vs 0%, P = 0.05, respectively), they were larger (2.95 ± 1.7 vs 2.03 ± 1.5, P = 0.002), with more tumor-involved lymph nodes (3.9 ± 5.8 vs 2.0 ± 4.2, P = 0.042), and a higher frequency of distant metastases (3% vs 0%, P = 0.043). Gene set enrichment analysis showed positive enrichment for RAS signatures in the BRAF-TDShi cohort, with corresponding reciprocal changes in the BRAF-TDSlo group. Several microRNAs (miRs) targeting nodes in the transforming growth factor β (TGFβ)-SMAD pathway, miR-204, miR-205, and miR-144, were overexpressed in the BRAF-TDShi group. In the subset with follow-up data, BRAF-TDShi tumors had higher complete responses to therapy (94% vs 57%, P < 0.01) than BRAF-TDSlo tumors.

CONCLUSION

Enrichment for RAS signatures, key genes involved in cell polarity and specific miRs targeting the TGFβ-SMAD pathway define 2 subtypes of BRAF-mutant PTCs with distinct clinical characteristics and prognosis.

Establishment of Immune-related Gene Pair Signature to Predict Lung Adenocarcinoma Prognosis

Tumor microenvironment (TME) has critical impacts on the pathogenesis of lung adenocarcinoma (LUAD). However, the molecular mechanism of TME effects on the prognosis of LUAD patients remains unclear. Our study aimed to establish an immune-related gene pair (IRGP) model for prognosis prediction and internal mechanism investigation. Based on 702 TME-related differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) training cohort using the ESTIMATE algorithm, a 10-IRGP signature was established to predict LUAD patient prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that DEGs were significantly associated with tumor immune response. In both TCGA training and Gene Expression Omnibus validation datasets, the risk score was an independent prognostic factor for LUAD patients using Lasso-Cox analysis, and patients in the high-risk group had poorer prognosis than those in the low-risk one. In the high-risk group, M2 macrophage and neutrophil infiltrations were higher, while the levels of T cell follicular helpers were significantly lower. The gene set enrichment analysis results showed that DNA repair signaling pathways were involved. In summary, we established an IRGP signature as a potential biomarker to predict the prognosis of LUAD patients.

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Ovarian cancer (OC) is the deadliest gynecological cancer in women. Immune cell infiltration has a critical role in regulating carcinogenesis and prognosis in OC. To identify prognostic genes relevant to the tumor microenvironment in OC, we investigated the association between OC and gene expression profiles. Results obtained with the ESTIMATE R tool showed that immune score and stromal score were correlated with lymphatic invasion, and high immune score predicted a favorable prognosis. A total of 342 common differentially expressed genes were identified according to the two scores; these genes were mainly involved in immune response, extracellular region, and serine-type endopeptidase activity. Three immune-related prognostic genes were selected by univariate and multivariate Cox regression analysis. We further established a prognostic model and validated the prognostic value of three hub genes in different databases; our results showed that this model could accurately predict survival and evaluate prognosis independent of clinical characteristics. Three hub genes have prognostic value in OC. TIMER analysis revealed that the three genes were correlated with different immune cells. Low levels of macrophage infiltration and high levels of CD4+ T cell infiltration were associated with favorable survival outcomes. Arm-level gain of GYPC was correlated with neutrophils and dendritic cells. These findings indicate that CXCR4, GYPC, and MMP12 modulate prognosis via effects on the infiltration of immune cells. Thus, these genes represent potential targets for immune therapy in OC.