<i>Objective</i>. The purpose of our work was to explore the association of mutations in the androgen receptor gene and copy numbers of the androgen-receptor silk protein A complex with glutathione-S-transferases T1 and M1 in prostate cancer patients. <i>Materials and Methods</i>. Eighty-five patients with PC and 85 healthy controls were included in the study. Fasting peripheral venous blood was collected, whole blood genomic DNA was extracted, and AR gene-receptor genotype was detected by a high-resolution melting curve analysis detection technology. Expression levels of androgen receptor (AR) and filamin protein A (FlnA) were detected by Western blotting. RT-PCR was used to detect the copy number of T1 and M1 glutathione-S-transferases. <i>Results</i>. The wild-type androgen receptor gene rs5918762 is of TT type. The frequencies of CC and TC genes in the prostate cancer group were significantly higher than those in the normal control group <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="23.787pt" style="vertical-align:-2.26807pt" id="M1" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 23.787 11.5564"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-41" d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><path id="g113-81" d="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z"/></g><g transform="matrix(.013,0,0,-0.013,16.156,0)"><path id="g117-91" d="M512 -3V55L134 254V256L512 456V514L75 281V230L512 -3Z"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="26.45pt" style="vertical-align:-2.26807pt" height="11.5564pt" version="1.1" viewBox="27.369183800000002 -9.28833 26.45 11.5564"><g transform="matrix(.013,0,0,-0.013,27.419,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,33.659,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,36.623,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,42.863,0)"><path id="g113-54" d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"/></g><g transform="matrix(.013,0,0,-0.013,49.103,0)"><path id="g113-42" d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"/></g></svg>.</span></span> Compared with TT-type PC patients, PC patients with TC-type and CC-type had higher expression levels of sex hormone receptor silk protein A complex and higher copy numbers of GSTT1 and GSTM1 <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="23.787pt" style="vertical-align:-2.26807pt" id="M2" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 23.787 11.5564"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-41"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><use xlink:href="#g113-81"/></g><g transform="matrix(.013,0,0,-0.013,16.156,0)"><use xlink:href="#g117-91"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="26.45pt" style="vertical-align:-2.26807pt" height="11.5564pt" version="1.1" viewBox="27.369183800000002 -9.28833 26.45 11.5564"><g transform="matrix(.013,0,0,-0.013,27.419,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,33.659,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,36.623,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,42.863,0)"><use xlink:href="#g113-54"/></g><g transform="matrix(.013,0,0,-0.013,49.103,0)"><use xlink:href="#g113-42"/></g></svg>.</span></span> Androgen-receptor gene mutation (T ⟶ C) was significantly positively correlated with the expression level of androgen-receptor silk protein A complex and the copy number of GSTT1 and GSTM1. <i>Conclusion</i>. Androgen-receptor gene polymorphisms were significantly associated with expression levels of androgen receptor complex <i>A</i> and silk proteins, and copy numbers of T1 and M1 glutathione-S-transferases. A combination of four factors can be used to identify prostate cancer susceptibility and disease progression.

Yan Zhang

Xiangdi Meng

Zhaosen Ma

Zhou Sun

Zhixin Wang

Association of Androgen-Receptor Gene Mutations with the Copy Number of Androgen-Receptor Silk Protein A Complex and Glutathione-S-Transferases T1 and M1 in Prostate Cancer Patients

<i>Background</i>. Cytochrome P450 complex plays a key role in drug metabolism. CYP2B6 has an essential part in Cytochrome P450 complex metabolism. This study aims to determine the allelic distribution of CYP2B6<sup>∗</sup>2 and CYP2B6<sup>∗</sup>3 in three main Iranian ethnicities: Fars, Turk, and Kurd. <i>Methods</i>. The study was conducted on 174 unrelated healthy volunteers from three main Iranian ethnicities. After DNA extraction from peripheral blood samples, genotyping of CYP2B6<sup>∗</sup>2 and <sup>∗</sup>3 was performed using tetra ARMS and ARMS PCR, respectively. <i>Results</i>. The average age of 174 cases was 40.69 <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.3499303pt" id="M1" height="7.35473pt" version="1.1" viewBox="-0.0498162 -7.0048 7.75925 7.35473" width="7.75925pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g117-37" d="M535 290V340H323V533H265V340H52V290H265V84H323V290H535ZM535 -22V28H52V-22H535Z"/></g></svg> 11.87 (mean <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.3499303pt" id="M2" height="7.35473pt" version="1.1" viewBox="-0.0498162 -7.0048 7.75925 7.35473" width="7.75925pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g117-37"/></g></svg> SD) and 39.06 <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.3499303pt" id="M3" height="7.35473pt" version="1.1" viewBox="-0.0498162 -7.0048 7.75925 7.35473" width="7.75925pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g117-37"/></g></svg> 11.63 (mean <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.3499303pt" id="M4" height="7.35473pt" version="1.1" viewBox="-0.0498162 -7.0048 7.75925 7.35473" width="7.75925pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g117-37"/></g></svg> SD) for males and females. In the CYP2B6<sup>∗</sup>2 variant, the genotyping frequency of wild type (C/C), heterozygous (C/T), and homozygous mutant (T/T) was 8.7%, 86%, and 5.2%, respectively. The CYP2B6<sup>∗</sup>2 (c.64C > T) allele frequency was 48.2% (95% CI: (37.8–58.6)). In the CYP2B6<sup>∗</sup>3 variant, the frequency of wild type (C/C), heterozygous (C/T), and homozygous mutant (T/T) was 75.3%, 11%, and 13.6%, respectively. The CYP2B6<sup>∗</sup>3 (c.777C > A) allelic frequency was 19.1% (95% CI: (17.5–20.7)). <i>Conclusion</i>. Allelic distribution in three main Iranian ethnicities, i.e., Turk, Kurd, and Fars, is remarkably higher than that in other populations, even that in Southern Iran. High frequencies of CYP2B6<sup>∗</sup>2 and <sup>∗</sup>3 in the Iranian population highly affect drug responsiveness. Understanding such variability could help to increase drug efficacy and reduce its toxicity.

Armin Khavandegar

Bahareh Tavakoli-Far

Sarina Ansari

Parisa Veis-Karami

Faezeh Ghasemi

Samira Sheibaninia

Roshanak Jazayeri

Massoud Houshmand

Allelic and Genotype Frequencies of CYP2B6<sup>∗</sup>2 (64C > T) and CYP2B6<sup>∗</sup>3 (777C > A) in Three Dominant Ethnicities of the Iranian Population

<i>Background</i>. Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD. <i>Methods</i>. RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465. <i>Results</i>. A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues. <i>Conclusion</i>. Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.

Jiao Yu

Gang Li

Yingxuan Tian

Shufen Huo

Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma

<i>Background</i>. Complex regulation exists between tumor metabolism and M2 macrophages. Long noncoding RNAs (lncRNAs) are famous for their wide regulatory role. This study aimed to identify the lncRNAs involved in the crosstalk between tumor metabolism and M2 macrophages. <i>Methods</i>. The Cancer Genome Atlas was responsible for the public data. R software was responsible for the analysis of public data. <i>Results</i>. Based on the input expression profile, we quantified the M2 macrophage infiltration using the CIBERSORT algorithm and found that M2 macrophages were a risk factor for lung cancer. Also, we found that M2 macrophages were correlated with multiple metabolism pathways. Then, 67 lncRNAs involved in both M2 macrophages and related metabolism pathways were identified. A prognosis signature based on AC027288.3, AP001189.3, FAM30A, GAPLINC, LINC00578, and LINC01936 was established, which had good prognosis prediction ability. The clinical parameters and risk score were combined into a nomogram plot for better prediction of the patient’s prognosis. A high fit of actual survival and nomogram-predicted survival was found using the calibration plot. Moreover, in low-risk patients, immunotherapy was more effective, while cisplatin and docetaxel were more effective in high-risk patients. Biological enrichment analysis indicated pathways of notch signaling, TGF-<i>β</i> signaling, interferon alpha response, and interferon-gamma response were activated in the high-risk group. Meanwhile, the risk score was associated with tumor metabolism and M2 macrophages. Also, we found that the promoting effect of CAPLINC on M2 macrophage polarization might act through multiple metabolism pathways. <i>Conclusions</i>. Our result can provide new insights into the interaction between M2 macrophages and tumor metabolism, as well as the involved lncRNAs, which can provide the direction for future studies.

Fang Fang

Yuanshan Yao

Zhe Ma

Exploration of the Long Noncoding RNAs Involved in the Crosstalk between M2 Macrophages and Tumor Metabolism in Lung Cancer

Chinese herbal medicine (CHM), which includes herbal slices and proprietary
					products, is widely used in China. Shenqi Dihuang (SQDH) is a traditional
					Chinese medicine (TCM) formula with ingredients that affect tumor growth.
					Despite recent advances in prognosis, patients with renal cell carcinoma (RCC)
					cannot currently receive curative treatment. The present study aimed to explore
					the potential target genes closely associated with SQDH. The gene expression
					data for SQDH and RCC were obtained from the TCMSP and TCGA databases. The
					SQDH-based prognostic prediction model reveals a strong correlation between RCC
					and SQDH. In addition, the immune cell infiltration analysis indicated that SQDH
					might be associated with the immune response of RCC patients. Based on this, we
					successfully built the prognostic prediction model using SQDH-related genes. The
					results demonstrated that CCND1 and NR3C2 are closely associated with the
					prognosis of RCC patients. Finally, the pathways enrichment analysis revealed
					that response to oxidative stress, cyclin binding, programmed cell death, and
					immune response are the most enriched pathways in CCND1. Furthermore,
					transcription regulator activity, regulation of cell population proliferation,
					and cyclin binding are closely associated with the NR3C2.

Xinglin Chen

Tongtong Zhang

Xiangyang Zhan

Xinyue Zang

Xinyu Zhai

Zhong Wan

Minyao Ge

Mingyue Tan

Jianyi Gu

Dongliang Xu

Molecular Targets of Shenqi Dihuang, A Traditional Chinese Herbal
					Medicine, and Its Potential Role in Renal Cell Carcinoma Therapy

Methylenetetrahydrofolate reductase (MTHFR) plays a major role in the metabolism of folates and homocysteine, which in turn can affect gene expression and ultimately promote the development of breast cancer. Thus, mutations in the <i>MTHFR</i> gene could influence homocysteine, methionine, and S-adenosylmethionine levels and, indirectly, nucleotide levels. Imbalance in methionine and S-adenosylmethionine synthesis affects protein synthesis and methylation. These changes, which affect gene expression, may ultimately promote the development of breast cancer. We therefore hypothesized that such mutations could also play an important role in the occurrence and pathogenesis of breast cancer in a Malian population. In this study, we used the PCR-RFLP technique to identify the different genotypic profiles of the <i>C677T MTHFR</i> polymorphism in 127 breast cancer women and 160 healthy controls. The genotypic distribution of the <i>C677T</i> polymorphism in breast cancer cases was 88.2% for CC, 11.0% for CT, and 0.8% for TT. Healthy controls showed a similar distribution with 90.6% for CC, 8.8% for CT, and 0.6% for TT. We found no statistical association between the <i>C677T</i> polymorphism and breast cancer risk for the codominant models CT and TT <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="23.471pt" style="vertical-align:-3.42947pt" id="M1" height="12.7178pt" version="1.1" viewBox="-0.0498162 -9.28833 23.471 12.7178"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-41" d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><path id="g113-113" d="M570 304C570 398 525 448 414 448C385 448 343 445 312 434L329 511L321 518C297 504 262 482 244 460L233 411C195 397 159 381 128 358L135 332C160 347 189 360 224 373L111 -147C97 -210 84 -218 17 -231L13 -257L254 -247L259 -218L233 -216C183 -212 177 -202 189 -142L218 -1C238 -10 266 -12 283 -12C351 3 429 48 483 105C543 168 570 242 570 304ZM482 289C482 161 380 33 304 33C278 33 248 51 233 69L303 396C326 400 352 403 369 403C428 403 482 380 482 289Z"/></g><g transform="matrix(.013,0,0,-0.013,15.84,0)"><path id="g117-92" d="M512 230V281L75 514V456L453 256V254L75 55V-3L512 230Z"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="26.453pt" style="vertical-align:-3.42947pt" height="12.7178pt" version="1.1" viewBox="27.053183800000003 -9.28833 26.453 12.7178"><g transform="matrix(.013,0,0,-0.013,27.103,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,33.343,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,36.307,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,42.547,0)"><path id="g113-54" d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"/></g><g transform="matrix(.013,0,0,-0.013,48.787,0)"><path id="g113-42" d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"/></g></svg>.</span></span> The same trend was observed when the analysis was extended to other genetic models, including dominant (<i>p</i> = 0.50), recessive (<i>p</i> = 0.87), and additive (<i>p</i> = 0.50) models. The <i>C677T</i> polymorphism of <i>MTHFR</i> gene did not influence the risk of breast cancer in the Malian samples.

Brehima Diakite

Yaya Kassogue

Mamoudou Maiga

Guimogo Dolo

Oumar Kassogue

Jane L. Holl

Brian Joyce

Jun Wang

Kadidiatou Cisse

Fousseyni Diarra

Mamadou L. Keita

Cheick B. Traore

Bakarou Kamate

Sidi B. Sissoko

Bourama Coulibaly

Adama S. Sissoko

Drissa Traore

Fatoumata M. Sidibe

Sekou Bah

Ibrahim Teguete

Madani Ly

Sellama Nadifi

Hind Dehbi

Kyeezu Kim

Robert Murphy

Lifang Hou

Lack of Association of <i>C677T</i> Methylenetetrahydrofolate Reductase Polymorphism with Breast Cancer Risk in Mali

<i>Background</i>. In Ethiopia, livestock contributes 45% of agricultural GDP. Despite the economic role played by the sector, there have been little efforts to genetically improve the indigenous cattle. Morphological characterization of selected Ethiopian indigenous cattle has been made for (Bonga, Jimma, and Kerayu) cattle types. But, the selected indigenous cattle were not characterized at molecular level (genetic diversity information). Hence, this work was initiated to detect and determine the genetic diversity and population structure of selected Ethiopian indigenous cattle ecotypes using microsatellite markers. <i>Results</i>. Different alleles were identified (131) and thirty-three of these alleles were unique to specific ecotypes. All loci used were informative with PIC values ranging from 0.5 (TGLA126) to 0.84 (ETH10) with a mean of 0.70 per locus. The Shannon information index ranged from (I = 1.02) ILST006 to (I = 1.63) ETH10 with an average of 1.28 revealing there is genetic diversity. Moreover, analysis of molecular variance (AMOVA) revealed 84% genetic variation within a population and 13% variation among populations. The value of <i>F</i>-statistics (Fst) (0.129 = 13%) indicated that there was moderate genetic differentiation among ecotypes. The (UPGMA) revealed, Bonga and Jimma clustered together while Kerayu cattle were relatively distinct, Principal coordinates analysis (PCOA) and structure analysis grouped the individual into different clusters confirming the presence of ecotype admixture due to geographical origins and uncontrolled mating. <i>Conclusion</i>. In general, this study has successfully characterized the genetic diversity and population structure of Bonga, Jimma, and Kerayu cattle ecotypes using high polymorphic/informative microsatellite markers. According to this study, Kerayu cattle have high AR and PA when compared to Bonga and Jimma cattle populations. So, the Kerayu population is more diverse than others and it is the hotspot for genetic diversity study. The generated information is very relevant for breeder and genetic conservation.

Shelema Kelbessa Bora

Tesfaye Sisay Tessema

Gebrerufael Girmay

Genetic Diversity and Population Structure of Selected Ethiopian Indigenous Cattle Breeds Using Microsatellite Markers

<i>Background</i>. Non-small cell lung cancer (NSCLC) is the most prevalent malignant tumor of the lung cancer, for which the molecular mechanisms remain unknown. In this study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC by bioinformatics analysis. <i>Methods</i>. From the Gene Expression Omnibus database, GSE118370 and GSE10072 microarray datasets were obtained. Identifying the differentially expressed genes (DEGs) between lung adenocarcinoma and normal samples was done. By using bioinformatics tools, a protein-protein interaction (PPI) network was constructed, modules were analyzed, and enrichment analyses were performed. The expression and prognostic values of 14 hub genes were validated by the GEPIA database, and the correlation between hub genes and survival in lung adenocarcinoma was assessed by UALCAN, <i>cBioPortal</i>, String and Cytoscape, and Timer tools. <i>Results</i>. We found three genes (PIK3R1, SPP1, and PECAM1) that have a clear correlation with OS in the lung adenocarcinoma patient. It has been found that lung adenocarcinoma exhibits high expression of SPP1 and that this has been associated with poor prognosis, while low expression of PECAM1 and PIK3R1 is associated with poor prognosis <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="23.787pt" style="vertical-align:-2.26807pt" id="M1" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 23.787 11.5564"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-41" d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><path id="g113-81" d="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z"/></g><g transform="matrix(.013,0,0,-0.013,16.156,0)"><path id="g117-91" d="M512 -3V55L134 254V256L512 456V514L75 281V230L512 -3Z"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="26.45pt" style="vertical-align:-2.26807pt" height="11.5564pt" version="1.1" viewBox="27.369183800000002 -9.28833 26.45 11.5564"><g transform="matrix(.013,0,0,-0.013,27.419,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,33.659,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,36.623,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,42.863,0)"><path id="g113-54" d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"/></g><g transform="matrix(.013,0,0,-0.013,49.103,0)"><path id="g113-42" d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"/></g></svg>.</span></span> We also found that the expression of SPP1 was associated with miR-146a-5p, while the high expression of miR-146a-5p was related to good prognosis <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="23.787pt" style="vertical-align:-2.26807pt" id="M2" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 23.787 11.5564"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-41"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><use xlink:href="#g113-81"/></g><g transform="matrix(.013,0,0,-0.013,16.156,0)"><use xlink:href="#g117-91"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="26.45pt" style="vertical-align:-2.26807pt" height="11.5564pt" version="1.1" viewBox="27.369183800000002 -9.28833 26.45 11.5564"><g transform="matrix(.013,0,0,-0.013,27.419,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,33.659,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,36.623,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,42.863,0)"><use xlink:href="#g113-54"/></g><g transform="matrix(.013,0,0,-0.013,49.103,0)"><use xlink:href="#g113-42"/></g></svg>.</span></span> On the contrary, the lower miR-21-5p on upstream of PIK3R1 is associated with a higher surviving rate in cancer patients <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="23.787pt" style="vertical-align:-2.26807pt" id="M3" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 23.787 11.5564"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-41"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><use xlink:href="#g113-81"/></g><g transform="matrix(.013,0,0,-0.013,16.156,0)"><use xlink:href="#g117-91"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="26.45pt" style="vertical-align:-2.26807pt" height="11.5564pt" version="1.1" viewBox="27.369183800000002 -9.28833 26.45 11.5564"><g transform="matrix(.013,0,0,-0.013,27.419,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,33.659,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,36.623,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,42.863,0)"><use xlink:href="#g113-54"/></g><g transform="matrix(.013,0,0,-0.013,49.103,0)"><use xlink:href="#g113-42"/></g></svg>.</span></span> Finally, we found that the immune checkpoint genes CD274(PD-L1) and PDCD1LG2(PD-1) were also related to SPP1 in lung adenocarcinoma. <i>Conclusions</i>. The results indicated that SPP1 is a cancer promoter (oncogene), while PECAM1 and PIK3R1 are cancer suppressor genes. These genes take part in the regulation of biological activities in lung adenocarcinoma, which provides a basis for improving detection and immunotherapeutic targets for lung adenocarcinoma.

Liyan Yu

Xuemei Liang

Jianwei Wang

Guangxiang Ding

Jinhai Tang

Juan Xue

Xin He

Jingxuan Ge

Xianzhang Jin

Zhiyi Yang

Xianwei Li

Hehuan Yao

Hongtao Yin

Wu Liu

Shengchen Yin

Bing Sun

Junxiu Sheng

Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis

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<p><em>Genetics Research </em>is a fully open access journal providing a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations, developmental, evolutionary, and population genetics as well as ethical, legal and social aspects.</p>


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Genetics Research - Table of contents 2023

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Genetics Research is a fully open access journal providing a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations, developmental, evolutionary, and population genetics as well as ethical, legal and social aspects.

Genetics Research