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Heat shock protein family A member 5 (HSPA5) plays a pivotal role in the endoplasmic reticulum (ER) stress response and unfolded protein response (UPR), both of which are crucial for protein folding, assembly, and quality control within cells. In response to ER stress, HSPA5 becomes overexpressed to preserve cellular homeostasis. A previous study revealed a robust association between HSPA5 expression and various cancers. However, the prognostic function of HSPA5 and its involvement in tumor formation remain largely unknown. In this study, we integrated HSPA5 expression data from databases such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) to conduct a comprehensive pan-cancer analysis of HSPA5. Our findings revealed that HSPA5 is overexpressed in various tumor types and is significantly associated with poor prognosis. Additionally, HSPA5 expression is significantly correlated with immune checkpoints, stromal infiltration, and consequent alterations in the immune landscape. Verification was conducted on samples from patients with various tumor types, including breast and liver cancers. Additionally, we also performed verification in vitro. In conclusion, HSPA5 may offer a potential target for cancer treatment.

Additional file 1: Table S1. Clinical characteristics of patients with osteosarcoma in each dataset. Table S5. Univariate and multivariate Cox regression analysis of overall survival, relapse free survival and metastasis free survival in each data set. Figure S1. The flow diagram of this study. Figure S2. ImmuneScore and StromalScore associate with clinical features and outcomes. Figure S3. Analysis of network topology for various soft-thresholding powers. Figure S4. OS prediction value of 4 genes in the TMEindex. Figure S5. RFS prediction value of 4 genes in the TMEindex. Figure S6. Differences in survival time and TMEindex genes expression between high and low TMEindex groups. Figure S7. Stratified analysis to further determine the prognostic value of the TMEindex based on the clinical characteristics of patients. Figure S8. Progonsis prediction value of the TMEindex in osteoblastic and chondroblastic osteosarcoma in the GSE21257 cohort. Figure S9. Validation of the prognostic value of the TMEindex in GSE33382. Figure S10. GSEA enrichment plots base on KEGG gene set showing the relatively enriched pathways in TMEindex-high (A) and TMEindex-low (B) group. Figure S11. GO enrichment and KEGG pathway analysis of DEGs identified between high and low TMEindex groups. Figure S12. Pan-cancer analysis of the prognostic predictive value of TMEindex. Figure S13. Univariate Cox regression analysis reveals the association of TMEindex with the PFS of 32 tumor types.

Fragestellung: Effektive Biomarker zur Prognosebeurteilung und zur individuellen Therapiefindung von Weichgewebssarkomen werden dringend benötigt. Die aktuellen Leitlinien fassen in ihren Therapieempfehlungen die über 100 Subtypen zu relativ wenigen unspezifischen Entitäten zusammen. [zum vollständigen Text gelangen Sie über die oben angegebene URL]

Objectives: The tumor microenvironment (TME) plays an important role in the development and progression of osteosarcoma. Immune checkpoint inhibitor (ICI) therapy could significantly improve the prognosis for patients with osteosarcoma. The aim of this study was to establish a TME-based prognostic index [for full text, please go to the a.m. URL]

Simple Summary Soft tissue sarcomas (STS) still lack effective clinical stratification and prognostic models. The aim of this study is to establish a reliable prognostic gene signature in STS. Using 189 STS samples from the TCGA database, a four-gene signature (including DHRS3, JRK, TARDBP and TTC3) and nomograms that can be used to predict the overall survival and relapse free survival of STS patients was developed. The predictive ability for metastasis free survival was externally verified in the GEO cohort. We demonstrated that the novel gene signature provides an attractive platform for risk stratification and prognosis prediction of STS patients, which is of great importance for individualized clinical treatment and long-term management of patients with this rare and severe disease. Abstract Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.

Fragestellung: Für Weichgewebssarkome (STS), eine heterogene Gruppe seltener maligner Tumoren, fehlen effektive klinische Prognosemodelle und molekulargenetische Angriffspunkte für neue zielgerichtete Medikamente. Methodik: Von 256 STS-Patienten aus der The Cancer Genome Atlas (TCGA)-Datenbank [zum vollständigen Text gelangen Sie über die oben angegebene URL]

The trophic structure the Lvsi Fishing Ground (LSFG) ecosystem in the East China Sea was analyzed by using two methods: mass-balance modeling (Ecopath) and stable isotope analysis (SIA). Trophic levels (TLs) of the main functional groups estimated from the two methods were compared. The Ecopath model was built based on the fishery resource survey in the Lvsi Fishing Ground in 2018. Specifically, data on species composition, biomass, diet composition and fishey catches around the region were obtained from the survey. The model consisted of 14 functional groups, including plankton, benthos, fish, cephalopods, shrimps and crabs. TLs of the main functional groups were estimated to be between 2.00 (zooplankton) and 3.22 (congers). The total transfer efficiencies from levels II to VI were 2.84%, 20.41%, 19.27%, 19.31% and 19.32% respectively. Shrimps and trawling fisheries have major trophic impacts on most functional groups in the LSFG ecosystem. The total system throughput was estimated to be 7060 t km−2 year−1. TLs derived from isotopic analysis were highly correlated with those estimated from Ecopath (y = 0.6484x+0.4715, R2 = 0.83, P